diff --git a/Makefile b/Makefile index 816bfcdc..320b75e0 100644 --- a/Makefile +++ b/Makefile @@ -23,12 +23,11 @@ install-generator: install ## Install dependencies for SDK code generator npm install generate: install-generator ## Generate the SDK from our public openapi spec - node_modules/.bin/openapi-generator-cli generate -i spec/public-api.yaml \ - -g python \ + node_modules/.bin/openapi-generator-cli generate \ + -i spec/public-api.yaml \ + -g python-pydantic-v1 \ -o ./generated \ --additional-properties=packageName=groundlight_openapi_client -# strict-nullable makes nullable fields Optional in the generated Pydantic classes: https://github.com/koxudaxi/datamodel-code-generator/issues/327 - poetry run datamodel-codegen --input spec/public-api.yaml --output generated/model.py --strict-nullable --use-schema-description --output-model-type pydantic_v2.BaseModel --use-subclass-enum poetry run black . PYTEST=poetry run pytest -v @@ -68,7 +67,8 @@ test-docs-integ: install-extras ## Run the example code and tests in our docs a GROUNDLIGHT_ENDPOINT="https://api.integ.groundlight.ai/" ${PYTEST} --markdown-docs ${TEST_ARGS} docs README.md # Adjust which paths we lint -LINT_PATHS="src test bin samples" +# LINT_PATHS="src test bin samples" +LINT_PATHS="src test" lint: install-lint ## Run linter to check formatting and style ./code-quality/lint ${LINT_PATHS} diff --git a/bin/simple_example.py b/bin/simple_example.py deleted file mode 100644 index 3382b971..00000000 --- a/bin/simple_example.py +++ /dev/null @@ -1,5 +0,0 @@ -from groundlight import Groundlight - -gl = Groundlight() -detectors = gl.list_detectors() -print(f"Found {detectors.count} detectors") diff --git a/docs/.gitignore b/docs/.gitignore deleted file mode 100644 index b58c623c..00000000 --- a/docs/.gitignore +++ /dev/null @@ -1,23 +0,0 @@ -# Dependencies -/node_modules - -# Production -/build - -# Generated files -.docusaurus -.cache-loader -.package-lock.json -# api-reference-docs are generated by sphinx and placed here. -/static/api-reference-docs/ - -# Misc -.DS_Store -.env.local -.env.development.local -.env.test.local -.env.production.local - -npm-debug.log* -yarn-debug.log* -yarn-error.log* diff --git a/docs/README.md b/docs/README.md deleted file mode 100644 index 38fe9281..00000000 --- a/docs/README.md +++ /dev/null @@ -1,30 +0,0 @@ -# Documentation: code.groundlight.ai - -This part of the SDK repo contains all the code for the website at [code.groundlight.ai](https://code.groundlight.ai/) It is built with [Docusaurus 2](https://docusaurus.io/). - -The docs are included with the SDK so that we can automate testing of the code samples in the documentation. This way we can ensure that the code samples in our docs always work with the current SDK. - -## Previewing doc changes - -Doc changes are published automatically when they're merged to main. To preview changes, build and host the site locally. You'll need a reasonably modern version of `npm` and then: - -``` -cd docs -npm install -cd .. -make develop-docs-comprehensive -``` - -and then open [http://localhost:3000/python-sdk](http://localhost:3000/python-sdk). - -Or if you're feeling luck, you can try `./start_docs_server.sh` which also rebuilds the sphinx docs on every change. -This script is tested on MacOS with [homebrew](https://brew.sh/) installed, but could work elsewhere too. - -## Running docs tests - -You'll need `python poetry` and `make` installed. And you'll need an API Token configured. Then you can just run: - -``` -make test-docs -``` - diff --git a/docs/babel.config.js b/docs/babel.config.js deleted file mode 100644 index e00595da..00000000 --- a/docs/babel.config.js +++ /dev/null @@ -1,3 +0,0 @@ -module.exports = { - presets: [require.resolve('@docusaurus/core/lib/babel/preset')], -}; diff --git a/docs/blog/2024-01-16-binomial-tails.md b/docs/blog/2024-01-16-binomial-tails.md deleted file mode 100644 index 4b37c709..00000000 --- a/docs/blog/2024-01-16-binomial-tails.md +++ /dev/null @@ -1,390 +0,0 @@ ---- -title: "Tales from the Binomial Tail: Confidence intervals for balanced accuracy" -description: How we assess and report detector accuracy -slug: confidence-intervals-for-balanced-accuracy -authors: - - name: Ted Sandler - title: Senior Applied Scientist at Groundlight - image_url: https://a-us.storyblok.com/f/1015187/1000x1000/efc35da152/sandlert.jpg - - name: Leo Dirac - title: CTO and Co-founder at Groundlight - image_url: https://a-us.storyblok.com/f/1015187/284x281/602a9c95c5/diracl.png -tags: [metrics, math, deep dive] -image: ./images/binomial-tails/binomial_confidence_intervals__muted_theme__social.png -hide_table_of_contents: false ---- - -At Groundlight, we put careful thought into measuring the correctness of our machine learning detectors. In the simplest case, this means measuring detector accuracy. But our customers have vastly different performance needs since our platform allows them to train an ML model for nearly any Yes/No visual question-answering task. A single metric like accuracy is unlikely to provide adequate resolution for all such problems. Some customers might care more about false positive mistakes (precision) whereas others might care more about false negatives (recall). - - - -To provide insight for an endless variety of use cases yet still summarize performance with a single number, Groundlight's **accuracy details** view displays each detector's [balanced accuracy](https://scikit-learn.org/stable/modules/model_evaluation.html#balanced-accuracy-score). Balanced accuracy is the average of recall for all classes and is Groundlight's preferred summary metric. For binary problems, this is just the mean of accuracy on the should-be-YES images and accuracy on the should-be-NOs. We prefer balanced accuracy because it is easier to understand than metrics like the F1 score or AUROC. And since many commercially interesting problems are highly imbalanced - that is the answer is almost always YES or always NO - standard accuracy is not a useful performance measure because always predicting the most common class will yield high accuracy but be useless in practice. - -
- -
- - Figure 1: the detector accuracy details view shows balanced accuracy and per-class accuracy with exact 95% confidence intervals - -
-
- -However, we've found that just displaying the balanced accuracy is not informative enough, as we do not always have an ample supply of **ground truth** labeled images to estimate it from. Ground truth labels are answers to image queries that have been provided by a customer, or customer representative, and are therefore trusted to be correct. With only a few ground truth labels, the estimate of a detector's balanced accuracy may itself be inaccurate. As such, we find it helpful to quantify and display the degree of possible inaccuracy by constructing confidence intervals for balanced accuracy, which brings us to the subject of this blog post! - -At Groundlight, we compute and display **exact** confidence intervals in order to upper and lower bound each detector's balanced accuracy, and thereby convey the amount of precision in the reported metric. The detector's accuracy details view displays these intervals as colored bars surrounding the reported accuracy numbers (see figure 1, above). This blog post describes the mathematics behind how we compute the intervals using the tails of the binomial distribution, and it also strives to provide a healthy amount of intuition for the math. - -Unlike the approximate confidence intervals based on the Gaussian distribution, which you may be familiar with, confidence intervals based on the binomial tails are exact, regardless of the number of ground truth labels we have available. Our exposition largely follows [Langford, 2005](https://jmlr.org/papers/v6/langford05a.html) and we use his "program bound" as a primitive to construct confidence intervals for the balanced accuracy metric. - -## Background - -To estimate and construct confidence intervals for balanced accuracy, we first need to understand how to construct confidence intervals for standard "plain old" accuracy. So we'll start here. - -Recall that standard accuracy is just the fraction of predictions a classifier makes which happen to be correct. This sounds simple enough, but to define this fraction rigorously, we actually need to make assumptions. To see why, consider the case that our classifier performs well on daytime images but poorly on nighttime ones. If the stream of images consists mainly of daytime photos, then our classifier's accuracy will be high, but if it's mainly nighttime images, our classifier's accuracy will be low. Or if the stream of images drifts slowly over time from day to nighttime images, our classifier won't even have a single accuracy. Its accuracy will be time-period dependent. - -Therefore, a classifier's "true accuracy" is inherently a function of the distribution of examples it's applied to. In practice, we almost never know what this distribution is. In fact, it's something of a mathematical fiction. But it happens to be a useful fiction in so far as it reflects reality, in that it lets us do things like bound the Platonic true accuracy of a classifier and otherwise reason about out-of-sample performance. Consequently, we make the assumption that there exists a distribution over the set of examples that our classifier sees, and that this distribution remains fixed over time. - -Let's call the distribution over images that our classifier sees, $D$. Each example in $D$ consists of an image, $x \in \mathcal{X}$, and an associated binary label, $y \in$ \{ YES, NO \}, which is the answer to the query. Let $(x,y) \sim D$ denote the action of sampling an example from $D$. We conceptualize our machine learning classifier as a function, $h$, which maps from the set of images, $\mathcal{X}$, to the set of labels, $\mathcal{Y}$. We say that $h$ correctly classifies an example $(x,y)$ if $h(x) = y$, and that $h$ misclassifies it otherwise. - -For now, our goal is to construct a confidence inverval for the true, but unknown, accuracy of $h$. We define this true accuracy as the probability that $h$ correctly classifies an example drawn from $D$: -$$ - \text{acc}_{D}(h) = \Pr_{(x,y) \sim D}[ \,h(x) = y\, ]. -$$ - -The true accuracy is impossible to compute exactly because $D$ is unknown and the universe of images is impossibly large. However, we can estimate it by evaluating $h$ on a finite set of test examples, $S$, which have been [drawn i.i.d.](https://en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables) from $D$. That is, -$$ -S = \{ (x_1, y_1), (x_2, y_2), ..., (x_{n}, y_{n}) \} -$$ -where each $(x_i, y_i) \sim D$ for $i=1,\ldots,n$. - -The fraction of images in $S$ that $h$ correctly classifies is called $h$'s empirical accuracy on $S$, and this fraction is computed as -$$ -\widehat{\text{acc}}_{S}(h) = \frac{1}{n} \sum_{i=1}^n \mathbf{1}[\, h(x_i) = y_i \,]. -$$ - -The notation $\mathbf{1}[\, \texttt{condition} \,]$ is shorthand for the indicator function which equals 1 when the $\texttt{condition}$ is true and 0 otherwise. So the formula above just sums the number of examples in $S$ that are correctly classified and then multiplies by 1/n. - -The egg-shaped infographic below depicts the scenario of estimating $h$'s true accuracy from its performance on a finite test set. The gray ellipse represents the full distribution of examples, $D$. Each dot corresponds to a single example image, $x$, whose true label, $y$, is represented by the dot's color - red for YES and blue for NO. The classifier, $h$, is represented by the dotted black line. Here, $h$ is the decision rule that classifies all points to the left of the line as should-be YES and all points to the right as should-be-NO. The points with light gray circles around them are the ones that have been sampled to form the test set, $S$. - -
- -
- - Figure 2: true accuracy can only be estimated from performance on a finite test set. The gray shaded region - represents the full distribution. The lightly circled points are examples sampled for the test set. - -
-
- -In this case, our choice of test set, $S$, was unlucky because $h$'s empirical accuracy on $S$ looks great, appearing to be 9/9 = 100%. But evaluating $h$ on the full distribution of examples, $D$, reveals that its true accuracy is much lower, only 24/27 = 89%. If our goal is to rarely be fooled into thinking that $h$'s performance is much better than it really is, then this particular test set was unfortunate in the sense that $h$ performs misleadingly well. - - -## Test Set Accuracy and Coin Flips - -It turns out that the problem of determining a classifier's true accuracy from its performance on a finite test set exactly mirrors the problem of determining the bias of a possibly unfair coin after observing some number of flips. In this analogy, the act of classifying an example corresponds to flipping the coin, and the coin landing heads corresponds to the classifier's prediction being correct. - -Usefully, the binomial distribution completely characterizes the probability of observing $k$ heads in $N$ independent tosses of a biased coin whose bias, or propensity to land heads, is known to be the probability, $p$, through its probability mass function (PMF), defined as -$$ -f_{N,p}(k) = {N \choose k} p^k (1 - p)^{N-k}. -$$ - -The cumulative density function (CDF) is the associated function that sums up the PMF probabilities over all outcomes (i.e., number of heads) from 0 through $k$. It tells us the probability of observing $k$ or fewer heads in $N$ independent tosses when the coin's bias is the probability $p$. The CDF is defined as -$$ -F_{N,p}(k) = \sum_{j = 0}^k f_{N,p}(k). -$$ - -Below we've plotted the PMF (left) and CDF (right) functions for a binomial distribution whose parameters are *N*=30 and *p*=0.3. - - - - - -
- -The PMF looks like a symmetric "bell curve". Its x-axis is the number of tosses that are heads, $k$. And its y-axis is the probability of observing $k$ heads in $N$ tosses. The CDF plot shows the cumulative sum of the PMF probabilities up through $k$ on its y-axis. The CDF is a monotonically increasing function of $k$. Its value is 1.0 on the right side of the plot since the sum of all PMF probabilities must equal one. - -The binomial PMF doesn't always resemble a bell-shaped curve. This is true of the binomial distributions in the two plots below, whose respective bias parameters are p=0.15 and p=0.96. - - - - - -
- -## Upper Bounding the True Accuracy from Test Set Performance - -Now that we've examined the probability of coin tossing and seen how the number of heads from tosses of a biased coin mirrors the number of correctly classified examples in a randomly sampled test set, let's consider the problem of determining an upper bound for the true accuracy of a classifier given its performance on a test set. - -Imagine that we've sampled a test set, $S$, from $D$ with 100 examples, and that our classifier, $h$, correctly classified 80 of them. We would like to upper bound $h$'s true accuracy, $\text{acc}_D(h)$, having observed its empirical accuracy, $\widehat{\text{acc}}_S(h)$ = 80/100 = 80%. - -Let's start by considering a very naive choice for the upper bound, taking it to equal the empirical accuracy of 80%. - -The figure below plots the PMF of a binomial distribution with parameters *N*=100 and *p*=0.80. Here, *N* is the test set size and *p* corresponds to the true, but unknown, classifier accuracy. The plot shows that if our classifier's true accuracy were in fact 80%, there would be a very good chance of observing an even lower empirical accuracy than what we actually observed. This is reflected in the substantial amount of probability mass lying to the left of the purple vertical line, which is placed at the empirical accuracy point of 80/100 = 80%. - -
- - -
- - Figure 3: Binomial PMF (top) and CDF (bottom) for N=100 and true accuracy 80.0%. The CDF shows there is a 54% chance of seeing an empirical accuracy of 80% or less. - -
-
- -In fact, the CDF of the binomial tells us that there is a 54% chance of seeing an empirical accuracy of 80% ***or less*** when the true accuracy is 80%. And since 54% is fairly good odds, our naive choice of 80% as an upper bound doesn't appear very safe. It would therefore be wise to increase our upper bound if we want it to be an upper bound! - -In contrast, the plot below shows that if the true accuracy were a bit higher, say 83%, we would only have a 1 in 4 chance of observing an empirical accuracy less than or equal to our observed accuracy of 80%. Or put differently, roughly a quarter of the test sets we could sample from $D$ would yield an empirical accuracy of 80% or lower if $h$'s true accuracy was 83%. This is shown by the 24.8% probability mass located to the left of the purple line at the 80% empirical accuracy point. The red line is positioned at the hypothesized true accuracy of 83%. -
- - -
- - Figure 4: Binomial PMF (top) and CDF (bottom) for N=100 and true accuracy 83.0%. The CDF shows there is a 24.8% chance of seeing an empirical accuracy of 80% or less. - -
-
- -Still, events with one in four odds are quite common, so hypothesizing an even larger true accuracy would be wise if we want to ensure it's not less than the actual true accuracy. - -The next plot shows that if the true accuracy were higher still, at 86.3%, the empirical accuracy of 80% or less would be observed on only 5% of sampled test sets. This is evidenced by the even smaller amount of probability mass to the left of the purple line located at the empirical accuracy of 80%. Again, the red line is positioned at the hypothesized true accuracy of 86.3%. - -
- - -
- - Figure 5: Binomial PMF (top) and CDF (bottom) for N=100 and true accuracy 86.3%. The CDF shows there is a 5% chance of seeing an empirical accuracy of 80% or less. - -
-
- -In other words, if $h$'s true accuracy were 86.3% or greater, we'd observe an empirical accuracy of 80% or lower on just 1 in 20 test sets. Consequently, the hypothesized true accuracy of 86.3% seems like a pretty safe choice for an upper bound. - - -### Constructing a 95% Upper Confidence Bound - -The procedure we just outlined, of increasing the hypothesized true accuracy starting from the observed empirical accuracy until exactly 5% of the binomial's probability mass lies to the left of the empirical accuracy, is how we construct an exact 95% upper confidence bound for the true accuracy. - -Remarkably, if we apply this procedure many times to find 95% accuracy upper confidence bounds for different ML classifiers at Groundlight, the computed upper bounds will in fact be larger than the respective classifiers' true accuracies in 95% of these encountered cases. This last statement is worth mulling over because it is exactly the right way to think about the guarantees associated with upper confidence bounds. - -Restated, a 95% upper confidence bound procedure for the true accuracy is one that produces a quantity greater than the true accuracy 95% of the time. - -## Exact Upper Confidence Bounds based on the Binomial CDF - -So now that we've intuitively described the procedure used to derive exact upper confidence bounds, we give a more formal treatment that will be useful in discussing confidence intervals for balanced accuracy. - -First, recall that the binomial's CDF function, $F_{N,p}(k)$, gives the probability of observing $k$ or fewer heads in $N$ tosses of a biased coin whose bias is $p$. - -Also, recall in the previous section that we decided to put exactly 5% of the probability mass in the lower tail of the PMF, and this yielded a 95% upper confidence bound. But we could have placed 1% in the lower tail, and doing so would have yielded a 99% upper confidence bound. A 99% upper confidence bound is looser than a 95% upper bound, but it upper bounds the true accuracy on 99% of test sets sampled as opposed to just 95%. - -The tightness of the bound versus the fraction of test sets it holds for is a trade off that we get to make referred to as the *coverage*. We control the coverage through a parameter named $\delta$. Above we had set $\delta$ to 5% which gave us a 1 - $\delta$ = 95% upper confidence bound. But we could have picked some other value for $\delta$. - -With $\delta$ understood, we are now ready to give our formal definition of upper confidence bounds. Let $\delta$ be given, $N$ be the number of examples in the test set, $k$ be the number of correctly classified test examples, and $p$ be the true accuracy. - -**Definition:** the 100(1 - $\delta$)% binomial upper confidence bound for $p$ is defined as -$$ -\bar{p}(N, k, \delta) = \max \{ \, p \,:\, F_{N,p}(k) \ge \delta \,\, \}. -$$ -In words, $\bar{p}$ is the maximum accuracy for which there exists at least $\delta$ probability mass in the lower tail lying to the left of the observed number of correct classifications for the test set. And this definition exactly mirrors the procedure we used above to find the 95% upper confidence bound. We picked $\bar{p}$ to be the max $p$ such that the CDF $F_{N=100,p}(k=80)$ was at least $\delta$ = 5%. - -We can easily implement this definition in code. The binomial CDF is available in python through the **[scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html)** module as **[binom.cdf](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.binom.html#scipy.stats.binom)**. And we can use it to find the largest value of $p$ for which $F_{N,p}(k) \ge \delta$. However the CDF isn't directly invertible, so we can't just plug in $\delta$ and get $\bar{p}$ out. Instead we need to search over possible values of $p$ until we find the largest one that satisfies the inequality. This can be done efficiently using the interval bisection method which we implement below. - -```python notest -from scipy.stats import binom - -def binomial_upper_bound(N, k, delta): - """ - Returns a 100*(1 - delta)% upper confidence bound on the accuracy - of a classifier that correctly classifies k out of N examples. - """ - def cdf(p): - return binom.cdf(k, N, p) - - def search(low, high): - if high - low < 1e-6: - return low # we have converged close enough - mid = (low + high) / 2 - if cdf(mid) >= delta: - return search(mid, high) - else: - return search(low, mid) - - return search(low=k/N, high=1.0) -``` - -## Lower Confidence Bounds - -Referring back to our discussion of coin flips makes it clear how to construct lower bounds for true accuracy. We had likened a correct classification to a biased coin landing heads and we upper bounded the probability of heads based on the observed number of heads. - -But we could have used the same math to upper bound the probability of tails. And likening tails to misclassifications lets us upper bound the true error rate. Moreover, the error rate equals one minus the accuracy. And so we immediately get a lower bound on the accuracy by computing an upper bound on the error rate and subtracting it from one. - -Again, let $\delta$ be given, $N$ be the number of test examples, $k$ be the number of correctly classified test examples, and let $p$ be the true, but unknown, accuracy. - -**Definition:** the 100(1 - $\delta$)% binomial lower confidence bound for $p$ is defined as -$$ -\underline{p}(N, k, \delta) = 1 - \max \{ \, p \,:\, F_{N,p}(N - k) \ge \delta \,\, \}. -$$ -Here $N - k$ is the number of misclassified examples observed in the test set. - - -## Central Confidence Intervals - -Now that we know how to derive upper and lower bounds which hold individually at a given confidence level, we can use our understanding to derive upper and lower bounds which hold simultaneously at the given confidence level. To do so, we compute what is called a *central confidence interval*. A 100$\times$(1 - $\delta$)% central confidence interval is computed by running the upper and lower bound procedures with the adjusted confidence level of 100$\times$(1 - $\delta$/2)%. - -For example, if we want to compute a 95% central confidence interval, we compute 97.5% lower and upper confidence bounds. This places $\delta$/2 = 2.5% probability mass in each tail, thereby providing 95% coverage in the central region. - -Pictorially below, you can see that the 95% central confidence interval (top row) produces wider bounds than just using the 95% lower and upper confidence bounds separately (bottom row). The looser bounds are unfortunate. But naively computing the lower and upper bounds at the original confidence level of 95% sacrifices coverage due to [multiple testing](https://en.wikipedia.org/wiki/Multiple_comparisons_problem). - -
- -
- - Figure 6: central confidence intervals produce wider bounds to correct for multiple testing - -
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- -In the next section, where we compute central confidence intervals for balanced accuracy, we will have to do even more to correct for multiple testing. - -## Confidence Bounds for Balanced Accuracy - -Recall that the balanced accuracy for a binary classifier is the mean of its accuracy on examples from the positive class and its accuracy on examples from the negative class. - -To define what we mean by the "true balanced accuracy", we need to define appropriate distributions over examples from each class. To do so, we decompose $D$ into separate class conditional distributions, $D^+$ and $D^-$, where -$$ -\Pr\left\{ (x,y) \sim D^+ \right\} = \Pr\left\{ (x,y) \sim D \mid y = +1 \right\}, -$$ -$$ -\Pr\left\{ (x,y) \sim D^- \right\} = \Pr\left\{ (x,y) \sim D \mid y = -1 \right\}. -$$ - -The positive and negative true accuracies are defined with respect to each of these class specific distributions: -$$ -\text{acc}^+(h) = E_{(x,y) \sim D^+} \, \mathbf{1}[ h(x_i) = y_i ], -$$ -$$ -\text{acc}^-(h) = E_{(x,y) \sim D^-} \, \mathbf{1}[ h(x_i) = y_i ]. -$$ - -The true balanced accuracy is then defined as the average of these, -$$ -\text{acc}_\text{bal}(h) = \frac{\text{acc}^+(h) + \text{acc}^-(h)}{2}. -$$ - - -### Constructing the Bound for Balanced Accuracy - -With the above definitions in hand, we can now bound the balanced accuracy of our classifier based on its performance on a test set. Let $S$ be the test set, and let -* $N^+$ denote the number of positive examples in $S$ -* $N^-$ denote the number of negative examples in $S$ -* $k^+$ denote the number of positive examples in $S$ that $h$ correctly classified -* $k^-$ denote the number of negative examples in $S$ that $h$ correctly classified - -From these quantities, we can find lower and upper bounds for the positive and negative accuracies based on the binomial CDF. - -Denote these lower and upper bounds on positive and negative accuracy as -$$ - \underline{\text{acc}^+}(h) -,~~ \overline{\text{acc}^+}(h) -,~~ \underline{\text{acc}^-}(h) -,~~ \overline{\text{acc}^-}(h). -$$ - -To find a 100(1 - $\delta$)% confidence interval for the $\text{acc}_\text{bal}(h)$, we first compute the quantities -$$ -\underline{\text{acc}^+}(h) = \underline{p}(N^+, k^+, \delta/4) -~~ \text{ and } ~~ -\overline{\text{acc}^+}(h) = \overline{p}(N^+, k^+, \delta/4) -$$ -$$ -\underline{\text{acc}^-}(h) = \underline{p}(N^-, k^-, \delta/4) -~~ \text{ and } ~~ -\overline{\text{acc}^-}(h) = \overline{p}(N^-, k^-, \delta/4) -$$ - -Importantly, we've used an adjusted delta value of $\delta/4$ to account for mulitple testing. That is, if we desire our overall coverage to be (1 - $\delta$) = 95%, we run our individual bounding procedures with the substituted delta value of $\delta/4 = 1.25\%$. - -The reason why is as follows. By construction, each of the four bounds will fail to hold with probability $\delta/4$. The union bound in **appendix A** tells us that the probability of at least one of these four bounds failing is no greater than the sum of the probabilities that each fails. Summing up the failure probabilities for all four bounds, the probability that at least one bound fails is therefore no greater than $4\cdot(\delta/4) = \delta$. Thus the probability that none of the bounds fails is at least 1 - $\delta$, giving us the desired level of coverage. - -Last, we obtain our exact lower and upper bounds for balanced accuracy by averaging the respective lower and upper bounds for the positive and negative class accuracies: -$$ -\underline{\text{acc}_\text{bal}}(h) = (1/2) \left( -\underline{\text{acc}^+}(h) + \underline{\text{acc}^-}(h) -\right) -$$ -$$ -\overline{\text{acc}_\text{bal}}(h) = (1/2) \left( -\overline{\text{acc}^+}(h) + \overline{\text{acc}^-}(h) -\right) -$$ - -Pictorially below, we can see how the averaged lower and upper bounds contain the true balanced accuracy. - -
- -
- - Figure 7: the balanced accuracy is bounded by the respective averages of the lower and upper bounds - -
-
- - -## Comparison with intervals based on the Normal approximation - -The main benefit of using bounds derived from the binomial CDF is that they are exact and always contain the true accuracy the desired fraction of the time. - -Let's compare this with the commonly used bound obtained by approximating the binomial PMF with a normal distribution. The motivation for the normal approximation comes from the central limit theorem, which states that for a binomial distribution with parameters $N$ and $p$, the distribution of the empirical accuracy, $\hat{p} = k/N$, converges to a normal distribution as the sample size, $N$, goes to infinity, -$$ -\hat{p} \stackrel{d}{\longrightarrow} \mathcal{N}\left(p, \frac{p(1-p)}{N}\right). -$$ - -This motivates the use of the traditional two-standard deviation confidence interval in which one reports -$$ -\Pr\left\{ | p - \hat{p} | \le 1.96 \,\hat{\sigma} \right\} \ge 95\% -~ ~ ~ \text{where} ~ ~ ~ -\hat{\sigma} = \sqrt{ - \frac{ \hat{p}(1-\hat{p}) }{N} -}. -$$ - -But it's well known that the normal distribution poorly approximates the sampling distribution of $\hat{p}$ when $p$ is close to zero or one. For instance, if we observe zero errors on the test set, then $\hat{p}$ will equal 1.0 (i.e., 100% empirical accuracy), and the sample standard deviation, $\hat{\sigma}$, will equal zero. The estimated lower bound will therefore be equal to the empirical accuracy of 100%, which is clearly unbelievable. - -And since we train classifiers to have as close to 100% accuracy as possible, the regime in which $p$ is close to one is of major interest. Thus, exact confidence intervals based on the binomial CDF are both more accurate and practically useful than those based on the normal approximation. - - -## Conclusion - -At Groundlight, we've put a lot of thought and effort into assessing the performance of our customers' ML models so they can easily understand how their detectors are performing. This includes the use of balanced accuracy as the summary performance metric and exact confidence intervals to convey the precision of the reported metric. - -Here we've provided a detailed tour of the methods we use to estimate confidence intervals around balanced accuracy. The estimated intervals are exact in that they possess the stated coverage, no matter how many ground truth labeled examples are available for testing. Our aim in this post has been to provide a better understanding of the metrics we display, how to interpret them, and how they're derived. We hope we've succeeded! If you are interested in reading more about these topics, see the references and brief appendices below. - - -## References - -[\[Langford, 2005\]](https://jmlr.org/papers/v6/langford05a.html) *Tutorial on Practical Prediction Theory for Classification*. Journal of Machine Learning Research 6 (2005) 273–306. - -[\[Brodersen et al., 2010\]](https://ieeexplore.ieee.org/document/5597285) *The balanced accuracy and its posterior distribution*. Proceedings of the 20th International Conference on Pattern Recognition, 3121-24. - - -### Appendix A - the union bound - -Recall that the union bound states that for a collection of events, $A_1, A_2, \ldots, A_n$, the probability that at least one of them occurs is less than the sum of the probabilities that each of them occurs: -$$\Pr\left\{ \cup_{i=1}^n A_i \right\} \le \sum_{i=1}^n \Pr(A_i).$$ - -Pictorially, the union bound is understood from the image below which shows that area of the union of the regions is no greater than the sum of the regions' areas. - -
- -
- - Figure 8: Visualizing the union bound. The area of each region $A_i$ corresponds to the probability that event $A_i$ occurs. - The sum of the total covered area must be less than the sum of the individual areas. - -
-
- -### Appendix B - interpretation of confidence intervals - -The semantics around frequentist confidence intervals is subtle and confusing. The construction of a 95% upper confidence bound does **NOT** imply there is a 95% probability that the true accuracy is less than the bound. It only guarantees that the true accuracy is less than the upper bound in at least 95% of the cases that we run the the upper confidence bounding procedure (assuming we run the procedure many many times). For each individual case, however, the true accuracy is either greater than or less than the bound. And thus, for each case, the probability that the true accuracy is less than the bound equals either 0 or 1, we just don't know which. - -If you instead desire more conditional semantics, you need to use Bayesian credible intervals. See [Brodersen et al., 2010](https://ieeexplore.ieee.org/document/5597285) for a nice derivation of credible intervals for balanced accuracy. diff --git a/docs/blog/2024-02-01-grime-guardian.md b/docs/blog/2024-02-01-grime-guardian.md deleted file mode 100644 index c880d3e4..00000000 --- a/docs/blog/2024-02-01-grime-guardian.md +++ /dev/null @@ -1,150 +0,0 @@ ---- -title: "The Grime Guardian: Building Stateful Multi-camera applications with Groundlight" -slug: grime-guardian -authors: - - name: Sunil Kumar - title: Machine Learning Engineer - image_url: https://a-us.storyblok.com/f/1015187/1000x1000/a265e322bd/kumars.jpg -tags: [Groundlight Python SDK, Raspberry Pi, Multithreading, State Management] -image: ./images/grime_guardian/gg_pfp.png -hide_table_of_contents: false ---- -## Groundlight has a Problem -Here at the Groundlight office we have a bit of a problem - sometimes we leave dirty dishes in the office sink. They pile up, and as the pile grows it becomes more and more tempting to simply add to the pile instead of cleaning it up. It was clear that the Groundlight office needed a “grime guardian” to save us from our messy selves. One day, I realized that this was the perfect problem to solve using Groundlight’s computer vision SDK. I could focus on developing the complex embedded application logic while Groundlight handled the computer vision. My design provided me with an opportunity to test out a handful of interesting design patterns, including deployment on a Raspberry Pi, multi-camera and multi-detector usage, a microservice-like architecture achieved via multithreading, and complex state handling. - - -
- -
- - The Groundlight office sink, where dishes accumulate faster than git commits. - -
-
- - - -## Overview of the Application - The Grime Guardian -The application I developed, the **Grime Guardian**, is designed to make it fun for the Groundlight team to clean up dishes that have been abandoned in the sink ([source code](https://github.com/sunildkumar/GrimeGuardian)). Using two cameras, the application monitors the state of the office sink and the overall kitchen scene. If it recognizes that dirty dishes were left in the sink for over a minute, it posts a funny yet inspiring message and photo to a Discord server that alerts the Groundlight team and encourages someone to help. Then, while the dishes remain unattended it surveys the kitchen until it sees someone. Once someone comes to help, it posts a message and photo, celebrating them as a hero, giving everyone in the Discord server a chance to recognize them. While this is cheesy, it has made it a bit more fun for us to do the dishes! - -
- -
- - The Grime Guardian alerting the Groundlight Team through Discord - -
-
- -## Architecture of a Sophisticated Groundlight Application -The Grime Guardian demonstrates how to build an advanced Groundlight application in a handful of ways: -1. **Raspberry Pi Deployment** - The Grime Guardian leverages our custom [Raspberry Pi Image](https://github.com/groundlight/groundlight-pi-gen), which makes it easy to deploy Groundlight applications on Raspberry Pi. -2. **Multiple Cameras** - The Grime Guardian actively uses more than one camera to solve a problem (it has one camera pointed at the sink and one pointed at the general kitchen scene). -3. **Multiple Detectors** - The Grime Guardian combines multiple Groundlight detectors to solve a problem. -4. **Microservice-like architecture via Multithreading** - The Grime Guardian’s architecture is broken down into a handful of microservice-like processes - each running in a different thread on the same machine. This improves the app’s robustness and allows for more flexibility and scalability. -5. **Complex State** - As described in the previous section, the state of the world this app is tracking is somewhat complex. In addition to knowing the state of the sink and kitchen, the app tracks how recently the state was updated and how recently it has sent a notification to the Groundlight team. -6. **Discord Bot Integration/Notifications** - The Grime Guardian uses the Discord Bot API to send notifications to a Discord server. Discord can be an extremely powerful and flexible tool for building applications (e.g. Midjourney). -7. **Robustness** - In practice, the Grime Guardian has been extremely robust, with only one or two incorrect (false positive) notifications over many weeks of deployment and hundreds of thousands of Groundlight queries. - -## Microservice-like Architecture -The Grime Guardian leverages a microservice-like architecture via multithreading to enhance its performance and robustness. Each microservice within the application runs in its own thread on a single Raspberry Pi, allowing for simultaneous execution of tasks. This architecture is particularly beneficial in this context as it allows the application to monitor the sink and the kitchen scene concurrently using two cameras, and to process the data from these cameras independently. Furthermore, it enables the application to manage complex state tracking and Discord notifications without blocking or slowing down the image processing tasks. - -The application is broken into six microservices: -1. **Sink Image Capturer**: This microservice captures images from a camera pointed at the sink and submits them as queries to a Groundlight detector via the `ask_async` SDK method (this method is useful for times in which the thread submitting image queries is not the same thread that will be retrieving and using the results). I set the detector's query to "Is there at least one dish in the sink? Cleaning supplies like a sponge, brush, soap, etc. are not considered dishes. If you cannot see into the sink, consider it empty and answer NO" and set the confidence threshold to 75%. After Groundlight replies with a query ID, the service passes the query ID to the **Query Processor** service. -2. **Kitchen Image Capturer**: This microservice is identical to the **Sink Image Capturer** except it uses the camera that can view the whole kitchen and submits images to a detector with the query "Is there at least one person in this image?" and set the confidence threshold to 75% as well. -3. **Query Processor**: This microservice processes the queries passed to it by the two **Capturer** services, waiting for confident answers from Groundlight and filtering out queries that do not become confident within a reasonable time (I chose a 10 second timeout as that was how frequently each **Capturer** service submitted a query to Groundlight). Queries that become confident are passed to the **State Updater** service. -4. **State Updater**: This microservice updates a complex model of the application's state based on Groundlight's responses. It tracks the status and last update time of the sink and kitchen, the image query IDs that led to the current state, and the timestamps of the last clean sink and notifications sent. -5. **Notification Publisher**: This microservice listens for updates to the state of the application (written by the **State Updater**) and decides whether it is appropriate to send one of two possible notifications. If a notification is needed, it adds it to a queue of notifications to be processed by the **Discord Bot**. Importantly, the **Notification Publisher** only determines if a notification should be sent. It does not handle the mechanics of what data to send or how and where to send it. -6. **Discord Bot**: This microservice runs a Discord bot, which listens for requests from the **Notification Publisher**. When a request arrives, the bot collects the relevant data and sends notifications to a Discord server. - - -
- -
- - Diagram created by Jared Randall
Architecture diagram for the application - -
-
- -## State Management and Notification Logic -The Grime Guardian's ability to track and manage a complex state is a cornerstone of its functionality. The application not only needs to know the current state of the sink and kitchen but also when these states were last updated and when the last notifications were sent. In total, the application needs nine separate variables to function properly (a combination of binary-encoded state fields, timestamps, and image query IDs). This level of detail is crucial for avoiding redundant alerts and ensuring timely and accurate updates. - -As seen in the architecture diagram in the previous section, multiple services read and write to the state simultaneously. To handle this complexity, I implemented a wrapper around the state to handle reads and writes in a thread safe manner. This wrapper ensures the state can be accessed and modified safely across many services. It uses a lock to prevent race conditions, ensuring that only one thread can modify the state at a time. - -```python -import threading -import copy - -# simplified version of how the Grime Guardian manages state safely -class SimpleThreadSafeState: - def __init__(self): - self.state = False - self.lock = threading.Lock() - - def update_state(self, new_state: bool): - with self.lock: - self.state = new_state - - def get_state(self) -> bool: - with self.lock: - return copy.copy(self.state) -``` - -The application uses this state to determine when to send notifications. I've tried to break down this logic into a few of flowcharts. At a high level, the logic is pretty simple. Whenever the the application's state is updated, the application performs a check to determine if the new state justifies sending each type of notification. - -
- -
- - Diagram created by Jared Randall
High level flow for determining if a notification should be sent - -
-
- -The logic for determining if each notification should be sent is a bit more complex. It first checks for the last time a notification was sent. If the last notification was sent in the last 5 minutes, no notification is sent. This is important as it prevents the application from spamming the Discord server with notifications. Next, the application checks if the sink currently has dirty dishes in it, and how long it has been since the sink was empty. We only send the notification if dirty dishes have been present for more than a minute. This approach ensures that the Grime Guardian does not send a notification every time someone puts a dirty dish in the sink, but only when dishes have been abandoned for a while. This ensures that the app only notifies the team when it is actually needed. - -
- -
- - Diagram created by Jared Randall
Flow for determining if the dirty dishes notification should be sent - -
-
- -The logic for determining if someone has arrived to help is similar. We have a check that ensures we do not spam the Discord server. Then, we only send a notification if there are currently dishes in the sink and someone is present in the kitchen. This ensures that the Grime Guardian does not send a notification every time someone walks into the kitchen, but only when dishes are in the sink. - -
- -
- - Diagram created by Jared Randall
Flow for determining if the help arrived notification should be sent - -
-
- -In retrospect, getting the notification logic to work properly was one of the more challenging parts of this project. The version I presented here is the result of many iterations and tweaks based on real world usage and results. I think this is because this logic is an expression of the application's core value proposition. If this "business logic" is not correct, the application will not be fun or useful. Fortunately, Groundlight enabled me to focus on this logic and not worry about the computer vision. - -## Discord Bot Notifications -The Grime Guardian uses the [Discord Bot API](https://discord.com/developers/docs/intro) to send notifications to a Discord server I set up. At startup, Discord requires some boilerplate to handle authentication. After this is done, the bot listens for new notification requests from the **Notification Publisher**. Based on the type of request, the bot collects the relevant information (e.g. the image of the dirty sink, or the person doing the dishes) and sends the message. The Discord Bot API makes this incredibly simple, after handling authentication, a new message and an attached image can be sent in a single line. - -```python -await channel.send("message", file=discord.File(fpath)) -``` -While I did not have time to add more complexity to the bot, Discord’s strong documentation gives me confidence it would not be that hard to add more features. For example, it would have been nice if the bot could listen for replies or emote reactions to its notifications - if the bot reported that the sink was full of dishes when really it was not, I could react to the notification with an emote that indicates the correct label for the image, and then the bot could automatically send this information to Groundlight, improving ML performance. - -## Future Improvements and Enhancements -Extending the functionality of the application, I can imagine adding motion detection to limit the frequency of image submissions to Groundlight. Currently, the application sends images to Groundlight at a fixed interval (every 10 seconds), regardless of whether there has been any significant change in the scene. This approach, while simple, could be optimized to become more cost effective. As it is now, it can lead to unnecessary image submissions when the scene is static. By incorporating motion detection, the application could intelligently decide when to send images to Groundlight. Fortunately, some of my excellent colleagues have built [`framegrab`](https://github.com/groundlight/framegrab/), an open source tool that automatically handles this. - -## Build Your Own Grime Guardian -Thank you for taking the time to read my post! As I reflect back, I’m very proud of how Groundlight enabled me to very quickly and effortlessly stand up an ML solution to solve a simple office problem in a fun and engaging way! If you are particularly interested or inspired, I encourage you to check out the [source code](https://github.com/sunildkumar/GrimeGuardian). Feel free to open a GitHub issue with questions or submit a PR with improvements! - -
- -
- - The Grime Guardian celebrates Tom, my colleague, for his heroic cleaning effort. The grime is no match for his dish-defeating determination! - -
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a/docs/docs/answer-modes/1-binary-detectors.md b/docs/docs/answer-modes/1-binary-detectors.md deleted file mode 100644 index 2d7c7f38..00000000 --- a/docs/docs/answer-modes/1-binary-detectors.md +++ /dev/null @@ -1,55 +0,0 @@ -# Binary Classification Detectors - -Binary classification detectors are used to answer yes/no questions about images. Most of Groundlight's documentation examples are for binary classification detectors, as they are the simplest type of detector. - -In order to create a binary classification detector, you need to provide a query that asks a yes/no question. For example, "Is there an eagle visible?" or "Is the door fully closed?". - -```python notest -from groundlight import Groundlight -gl = Groundlight() - -# highlight-start -detector = gl.create_detector( - name="eagle-detector", - query="Is there an eagle visible?", - confidence_threshold=0.9, -) -# highlight-end -``` - -## Submit an Image Query to a Binary Classification Detector - -Now that you have created a binary classification detector, you can submit an image query to it. - -```python notest -from groundlight import Groundlight -gl = Groundlight() - -detector = gl.get_detector_by_name("eagle-detector") - -# highlight-start -# Check if an eagle is visible in an image -image_query = gl.submit_image_query(detector, "path/to/image.jpg") -# highlight-end - -print(f"Result: {image_query.result.label}") -print(f"Confidence: {image_query.result.confidence}") -``` - -Binary classification detectors return a `label` attribute in the result object, which will be either `"YES"` or `"NO"`. If a query is ambiguous, it is also possible for the detector to return an `"UNCLEAR"` label. - -The `confidence` attribute represents the confidence level in the predicted label, which (for a binary classification detector) is a value between 0.5 and 1. A higher confidence score indicates that the model is more certain about its prediction. - -## Add a label to a Binary Classification Detector - -To provide ground truth labels for binary classification detectors, you can specify the label as either `"YES"`, `"NO"`, or `"UNCLEAR"`. This helps improve the accuracy of your detector over time. - -```python notest -from groundlight import Groundlight -gl = Groundlight() - -# highlight-start -# Add a binary label to the image query from the previous example -gl.add_label(image_query, label="YES") -# highlight-end -``` diff --git a/docs/docs/answer-modes/2-multi-choice-detectors.md b/docs/docs/answer-modes/2-multi-choice-detectors.md deleted file mode 100644 index 2318302b..00000000 --- a/docs/docs/answer-modes/2-multi-choice-detectors.md +++ /dev/null @@ -1,60 +0,0 @@ -# Multiple Choice (Choose One) Detectors - -If you want to classify images into multiple categories, you can create a multi-class detector. - -```python notest -from groundlight import ExperimentalApi -gl_exp = ExperimentalApi() - -# highlight-start -class_names = ["Golden Retriever", "Labrador Retriever", "German Shepherd", "Other"] -detector = gl_exp.create_multiclass_detector( - name="dog-breed-detector", - query="What kind of dog is this?", - class_names=class_names, -) -# highlight-end -``` - -:::tip -We recommend adding an "Other" class to your multi-class detector to handle cases where the image does not belong to any of the pre-defined classes. -::: - -:::note -Multi-Class Detectors are available on [Business and Enterprise plans](https://www.groundlight.ai/pricing). -::: - -## Submit an Image Query to a Multi-Class Detector - -Now that you have created a multi-class detector, you can submit an image query to it. - -```python notest -from groundlight import ExperimentalApi -gl_exp = ExperimentalApi() - -detector = gl_exp.get_detector_by_name("dog-breed-detector") - -# highlight-start -# Classify the breed of a dog in an image -image_query = gl_exp.submit_image_query(detector, "path/to/image.jpg") -# highlight-end - -print(f"Result: {image_query.result.label}") -print(f"Confidence: {image_query.result.confidence}") -``` - -Multi-class detectors return a `label` attribute in the result object, which contains the predicted class label. The `label` attribute will be one of the class names provided when creating the detector. The `confidence` attribute represents the confidence level in the predicted class, which is a value between `1/len(class_names)` and 1. - -## Add a label to a Multi-Class Detector - -To provide ground truth labels for multi-class detectors, you can specify the label of the correct class. - -```python notest -from groundlight import ExperimentalApi -gl_exp = ExperimentalApi() - -# highlight-start -# Add a multi-class label to the image query from the previous example -gl_exp.add_label(image_query, label="German Shepherd") -# highlight-end -``` diff --git a/docs/docs/answer-modes/3-counting-detectors.md b/docs/docs/answer-modes/3-counting-detectors.md deleted file mode 100644 index 7cc1b7bb..00000000 --- a/docs/docs/answer-modes/3-counting-detectors.md +++ /dev/null @@ -1,115 +0,0 @@ -# Count Detectors - -Counting detectors are used to count the number of objects in an image. Groundlight's counting detectors also return bounding boxes around the objects they count. - -```python notest -from groundlight import Groundlight -gl = Groundlight() - -# highlight-start -detector = gl.create_counting_detector( - name="car-counter", - query="How many cars are in the parking lot?", - class_name="car", - max_count=20, - confidence_threshold=0.2, -) -# highlight-end -``` - -Counting detectors should be provided with a query that asks "how many" objects are in the image. - -A maximum count (of 25 or fewer) must be specified when creating a counting detector. This is the maximum number of objects that the detector will count in an image. Groundlight's ML models are optimized for counting up to 20 objects, but you can increase the maximum count to 25 if needed. If you have an application that requires counting more than 25 objects, please [contact us](mailto:support@groundlight.ai). - -The `confidence_threshold` parameter sets the minimum confidence level required for the ML model's predictions. If the model's confidence falls below this threshold, the query will be sent for human review. Count detectors can have a `confidence_threshold` set to any value between `1/(max_count + 2)` and 1. - -:::note -Counting Detectors are available on [Business and Enterprise plans](https://www.groundlight.ai/pricing). -::: - -## Submit an Image Query to a Counting Detector - -Now that you have created a counting detector, you can submit an image query to it. - -```python notest -from groundlight import Groundlight -gl = Groundlight() - -detector = gl.get_detector_by_name("car-counter") - -# highlight-start -# Count the number of cars in an image -image_query = gl.submit_image_query(detector, "path/to/image.jpg") -# highlight-end - -print(f"Counted {image_query.result.count} cars") -print(f"Confidence: {image_query.result.confidence}") -print(f"Bounding Boxes: {image_query.rois}") -``` - -In the case of counting detectors, the `count` attribute of the result object will contain the number of objects counted in the image. The `confidence` attribute represents the confidence level in the specific count. Note that this implies that confidences may be lower (on average) for counting detectors with a higher maximum count. - - - -:::tip Drawing Bounding Boxes -You can visualize the bounding boxes returned by counting detectors using a library like OpenCV. Here's an example of how to draw bounding boxes on an image: - -```python notest -import cv2 -import numpy as np - -def draw_bounding_boxes(image_path, rois): - """ - Draw bounding boxes on an image based on ROIs returned from a counting detector. - - Args: - image_path: Path to the image file - rois: List of ROI objects returned from image_query.rois - """ - image = cv2.imread(image_path) - if image is None: - raise ValueError(f"Could not read image from {image_path}") - height, width = image.shape[:2] - - # Draw bounding boxes - for roi in rois: - x1 = int(roi.geometry.left * width) - y1 = int(roi.geometry.top * height) - x2 = int(roi.geometry.right * width) - y2 = int(roi.geometry.bottom * height) - cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2) - label_text = f"{roi.label}: {roi.score:.2f}" - cv2.putText(image, label_text, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) - - # Display the image - cv2.imshow("Image with Bounding Boxes", image) - cv2.waitKey(0) - cv2.destroyAllWindows() - -# Example usage: -# image_query = gl.submit_image_query(detector, "path/to/image.jpg") -# draw_bounding_boxes("path/to/image.jpg", image_query.rois) -``` -::: - -## Add a label to a Counting Detector - -The Groundlight API allows you to add labels to image queries, including Region of Interest (ROI) data. -When adding a label to a counting detector, if you include ROIs, the number of ROIs should match -the count you are labeling. - -```python notest -from groundlight import Groundlight -gl = Groundlight() - -# highlight-start -# Add a count label with corresponding ROIs to the image query from the previous example. -# ROIs are specified as (left, top) and (right, bottom) coordinates, with values -# between 0 and 1 representing the percentage of the image width and height. -roi1 = gl.create_roi("car", (0.1, 0.2), (0.2, 0.3)) -roi2 = gl.create_roi("car", (0.4, 0.4), (0.5, 0.6)) -roi3 = gl.create_roi("car", (0.6, 0.5), (0.8, 0.9)) -rois = [roi1, roi2, roi3] -gl.add_label(image_query, label=len(rois), rois=rois) -# highlight-end -``` \ No newline at end of file diff --git a/docs/docs/answer-modes/_category_.json b/docs/docs/answer-modes/_category_.json deleted file mode 100644 index d1e461da..00000000 --- a/docs/docs/answer-modes/_category_.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "label": "Answer Modes", - "position": 3 -} diff --git a/docs/docs/answer-modes/answer-modes.md b/docs/docs/answer-modes/answer-modes.md deleted file mode 100644 index 48cdb173..00000000 --- a/docs/docs/answer-modes/answer-modes.md +++ /dev/null @@ -1,11 +0,0 @@ -# Detector Answer Modes - -Groundlight offers several detector modalities to suit different computer vision tasks. While previous examples have focused on binary classification, this guide will walk you through using counting and multi-class detectors. Let's explore how these different modes can be used via the Groundlight SDK. -![Examples of Computer Vision Answer Modes](https://cdn.prod.website-files.com/664b7cc2ac49aeb2da6ef0f4/67f99ecc0c0e8843cb706dd0_Examples_of_Computer_Vision_Answer_Modes.png) - -- **[Binary Detectors](1-binary-detectors.md)**: Learn how to create detectors that answer yes/no questions about images. -- **[Multiple Choice (Choose One) Detectors](2-multi-choice-detectors.md)**: Create detectors that select one answer from a predefined list of options. -- **[Count Detectors](3-counting-detectors.md)**: Use detectors to count the number of objects present in an image - and return bounding boxes around the counted objects. - - - diff --git a/docs/docs/api-reference/_category_.json b/docs/docs/api-reference/_category_.json deleted file mode 100644 index a820f610..00000000 --- a/docs/docs/api-reference/_category_.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "label": "API Reference", - "position": 6, - "collapsed": false -} \ No newline at end of file diff --git a/docs/docs/api-reference/api-reference.md b/docs/docs/api-reference/api-reference.md deleted file mode 100644 index 170b80f9..00000000 --- a/docs/docs/api-reference/api-reference.md +++ /dev/null @@ -1,7 +0,0 @@ ---- -id: redirect -title: API Reference -hide_title: true ---- - - \ No newline at end of file diff --git a/docs/docs/getting-started/4-writing-queries.md b/docs/docs/getting-started/4-writing-queries.md deleted file mode 100644 index 83235b7c..00000000 --- a/docs/docs/getting-started/4-writing-queries.md +++ /dev/null @@ -1,49 +0,0 @@ -# Writing Queries - -## Introduction - -Groundlight allows you to ask a variety of questions about images. The most common type of question is a binary question that can be answered with a simple "YES" or "NO". For example, "Is there a car in the leftmost parking space?" or "Is the door open?". Ambiguity in the question can lead to "UNCLEAR" responses. - -```python notest -detector = gl.get_or_create_detector( - name="Conveyor belt boxes", - query="Are there any cardboard boxes on the conveyor belt?" -) -image_query = gl.submit_image_query(detector=detector, image=some_image) - -# The SDK can return "YES" or "NO" (or "UNCLEAR") -print(f"The answer is {image_query.result.label}") -``` - -So, what makes a good question for a binary-mode detector? Let's look at a few good ✅, moderate 🟡, and bad ❌ examples! - -## Examples - -### ✅ Are there any cardboard boxes on the conveyor belt? - -This question is binary and can be answered unambiguously with a simple "YES" or "NO" based on the image content. - -### 🟡 Is the trash can full? - -This question is okay, but it could be rephrased to be more specific. For example, "Is the black trash can more than 80% full?" - -:::tip -With Groundlight, your questions may be routed to a machine learning model or a human reviewer. One way to improve your questions is to think, "If I saw this question for the first time, would I know precisely what the person was trying to convey?" -::: - -### ✅ Is the garage door completely closed? - -The query is very specific about what "YES" means. According to this query, any slight / partial opening would be considered "NO". - -### 🟡 Is the weather nice out? - -This question is somewhat ambiguous. Different people may have different opinions on what -is nice weather. Instead, you might ask "Can you see any clouds in the sky?" - -### ❌ Where is the thing? - -This is not a binary question — "YES" and "NO" don't make sense in this context. Also, it's not clear what the "thing" refers to. - -### 🟡 Is the factory floor clean and organized? - -While this question is binary, "cleanliness" can be somewhat subjective. An improved version could be: "Are there any visible spills or clutter on the factory floor?" diff --git a/docs/docs/getting-started/5-api-tokens.md b/docs/docs/getting-started/5-api-tokens.md deleted file mode 100644 index 4f8780dc..00000000 --- a/docs/docs/getting-started/5-api-tokens.md +++ /dev/null @@ -1,45 +0,0 @@ -# Using API Tokens - -API tokens authenticate your code to access Groundlight services. They look like `api_2GdXMflhJ...` and should be treated as sensitive credentials. - -The SDK can access your token in two ways: - -1. **Environment Variable (Recommended)** -```python -from groundlight import Groundlight - -# Automatically uses GROUNDLIGHT_API_TOKEN environment variable -gl = Groundlight() -``` - -2. **Direct Configuration** -```python notest -from groundlight import Groundlight - -token = get_token_from_secure_location() -gl = Groundlight(api_token=token) -``` - -## Security Best Practices - -- Store tokens in environment variables or secure vaults -- Never commit tokens to code repositories -- Limit token access to necessary personnel -- Rotate tokens periodically -- Revoke unused tokens promptly - -## Managing Tokens - -Access token management at [dashboard.groundlight.ai/reef/my-account/api-tokens](https://dashboard.groundlight.ai/reef/my-account/api-tokens) - -### Create a Token -1. Navigate to the [API tokens page](https://dashboard.groundlight.ai/reef/my-account/api-tokens) -2. Enter a token name and click "Create API Token" -3. Save the generated token securely - it won't be shown again! - -### Revoke a Token -1. Find the token in your dashboard by name -2. Click "Delete" -3. Confirm revocation - -> **Important**: Update your applications with a new token before revoking an old one to prevent service interruption. \ No newline at end of file diff --git a/docs/docs/getting-started/_category_.json b/docs/docs/getting-started/_category_.json deleted file mode 100644 index 3562d433..00000000 --- a/docs/docs/getting-started/_category_.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "label": "Getting Started", - "position": 1 -} diff --git a/docs/docs/getting-started/getting-started.mdx b/docs/docs/getting-started/getting-started.mdx deleted file mode 100644 index 0b718915..00000000 --- a/docs/docs/getting-started/getting-started.mdx +++ /dev/null @@ -1,169 +0,0 @@ -# Getting Started with Groundlight - -## Build Powerful Computer Vision Applications in Minutes - -Welcome to Groundlight AI! This guide will walk you through creating powerful computer vision applications in minutes using our Python SDK. -No machine learning expertise required! Groundlight empowers businesses across industries - -from [revolutionizing industrial quality control](https://www.groundlight.ai/blog/lkq-corporation-uses-groundlight-ai-to-revolutionize-quality-control-and-inspection) -and [monitoring workplace safety compliance](https://www.groundlight.ai/use-cases/ppe-detection-in-the-workplace) -to [optimizing inventory management](https://www.groundlight.ai/use-cases/inventory-monitoring-using-vision-ai). -Our human-in-the-loop technology delivers accurate results while continuously improving over time, making -sophisticated computer vision accessible to everyone. - -Don't code? No problem! [Contact our team](mailto:support@groundlight.ai) and we'll build a custom solution tailored to your needs. - -In this example, we'll build a simple eagle detector that can analyze live video streams and tell us if there are baby eagles being fed. -![Eagle Detector](/img/eagle_detector.png) - -### Prerequisites -Before diving in, you'll need: -1. A [Groundlight account](https://dashboard.groundlight.ai/) (sign up is quick and easy!) -2. An API token from your [Groundlight dashboard](https://dashboard.groundlight.ai/reef/my-account/api-tokens). Check out our [API Tokens guide](/docs/getting-started/api-tokens) for details. -3. Python 3.9 or newer installed on your system. - -### Setting Up Your Environment - -Let's set up a clean Python environment for your Groundlight project! The Groundlight SDK is available on PyPI and can be installed with [pip](https://packaging.python.org/en/latest/tutorials/installing-packages/#use-pip-for-installing). - -First, let's create a virtual environment to keep your Groundlight dependencies isolated from other Python projects: -```bash -python3 -m venv groundlight-env -``` -Now, activate your virtual environment: -```bash -# MacOS / Linux -source groundlight-env/bin/activate -``` -``` -# Windows -.\groundlight-env\Scripts\activate -``` - -With your environment ready, install the Groundlight SDK with a simple pip command: -```bash -pip install groundlight -``` - -Let's also install [framegrab](https://github.com/groundlight/framegrab) with YouTube support - -this useful library will let us capture frames from YouTube livestreams, webcams, and other video -sources, making it easy to get started! -```bash -pip install "framegrab[youtube]" -``` -:::tip Camera Support -Framegrab is versatile! It works with: -- Webcams and USB cameras -- RTSP streams (security cameras) -- Professional cameras (Basler USB/GigE) -- Depth cameras (Intel RealSense) -- Video files and streams (mp4, mov, mjpeg, avi) -- YouTube livestreams - -This makes it perfect for quickly prototyping your computer vision applications! -::: - -Need more options? Check out our detailed [installation guide](/docs/installation/) for advanced setup instructions. - -### Authentication - -Now let's set up your credentials so you can start making API calls. Groundlight uses API tokens to securely authenticate your requests. - -If you don't have an API token yet, refer to our [API Tokens guide](/docs/getting-started/api-tokens) to create one. - -The SDK will automatically look for your token in the `GROUNDLIGHT_API_TOKEN` environment variable. Set it up with: -```bash -# MacOS / Linux -export GROUNDLIGHT_API_TOKEN='your-api-token' -``` -```powershell -# Windows -setx GROUNDLIGHT_API_TOKEN "your-api-token" -``` -:::important API Tokens -Keep your API token secure! Anyone who has access to it can impersonate you and can access to your Groundlight data. -::: - -### Call the Groundlight API - -Call the Groundlight API by creating a `Detector` and submitting an `ImageQuery`. A `Detector` represents a specific -visual question you want to answer, while an `ImageQuery` is a request to analyze an image with that question. - -The Groundlight system is designed to provide consistent, highly confident answers for similar images -(such as frames from the same camera) when asked the same question repeatedly. This makes it ideal for -scenarios where you need reliable visual detection. - -Let's see how to use Groundlight to analyze an image: -```python title="ask.py" notest -from framegrab import FrameGrabber -from groundlight import Groundlight, Detector, ImageQuery - -gl = Groundlight() -detector: Detector = gl.get_or_create_detector( - name="eagle-detector", - query="Are the baby eagles being fed??", -) - -# Big Bear Bald Eagle Nest livestream -youtube_live_url = 'https://www.youtube.com/watch?v=B4-L2nfGcuE' - -framegrab_config = { - 'input_type': 'youtube_live', - 'id': {'youtube_url': youtube_live_url}, -} - -with FrameGrabber.create_grabber(framegrab_config) as grabber: - frame = grabber.grab() - if frame is None: - raise RuntimeError("No frame captured") - -iq: ImageQuery = gl.submit_image_query(detector=detector, image=frame) - -print(f"{detector.query} -- Answer: {iq.result.label} with confidence={iq.result.confidence:.3f}\n") -print(iq) -``` - -Run the code using `python ask.py`. The code will submit an image from the live-stream to the Groundlight API and print the result: -``` -Is there an eagle visible? -- Answer: YES with confidence=0.988 - -ImageQuery( - id='iq_2pL5wwlefaOnFNQx1X6awTOd119', - query="Is there an eagle visible?, - detector_id='det_2owcsT7XCsfFlu7diAKgPKR4BXY', - result=BinaryClassificationResult( - confidence=0.9884857543478209, - label= - ), - created_at=datetime.datetime(2025, 2, 25, 11, 5, 57, 38627, tzinfo=tzutc()), - patience_time=30.0, - confidence_threshold=0.9, - type=, - result_type=, - metadata=None -) -``` -## What's Next? - -**Amazing job!** You've just built your first computer vision application with Groundlight. -In just a few lines of code, you've created an eagle detector that can analyze live video streams! - -### Supercharge Your Application - -Take your application to the next level: - -- **Monitor in real-time** through the [Groundlight Dashboard](https://dashboard.groundlight.ai/) - see your detections, review results, and track performance -- **Get instant alerts** when important events happen - [set up text and email notifications](/docs/guide/alerts) for critical detections -- **Improve continuously** with Groundlight's human-in-the-loop technology that learns from your feedback - -### Next Steps - -| What You Want To Do | Resource | -|---|---| -| 📝 Create better detectors | [Writing effective queries](/docs/getting-started/writing-queries) | -| 📷 Connect to cameras, RTSP, or other sources | [Grabbing images from various sources](/docs/guide/grabbing-images) | -| 🎯 Fine-tune detection accuracy | [Managing confidence thresholds](/docs/guide/managing-confidence) | -| 📚 Explore the full API | [SDK Reference](/docs/api-reference/) | - -Ready to explore more possibilities? Visit our [Guides](https://www.groundlight.ai/guides) to discover sample -applications built with Groundlight AI — from [industrial inspection workflows](https://www.groundlight.ai/blog/lkq-corporation-uses-groundlight-ai-to-revolutionize-quality-control-and-inspection) -to [hummingbird detection systems](https://www.groundlight.ai/guides/detecting-hummingbirds-with-groundlight-ai). \ No newline at end of file diff --git a/docs/docs/guide/2-grabbing-images.md b/docs/docs/guide/2-grabbing-images.md deleted file mode 100644 index f9c9e8f0..00000000 --- a/docs/docs/guide/2-grabbing-images.md +++ /dev/null @@ -1,131 +0,0 @@ -# Grabbing Images -In order to analyze images with Groundlight, you first need to capture images from a camera or other image source. This guide will show you how to capture images from different sources and formats. - -## Framegrab - -For a unified interface to many different kinds of image sources, see [framegrab](https://pypi.org/project/framegrab/), an [open-source](https://github.com/groundlight/framegrab) python library maintained by Groundlight. - -### Capturing Images -Framegrab has many useful features for working with cameras and other image sources. It provides a single interface for extracting images from many different image sources, including generic USB cameras (such as webcams), RTSP streams, HTTP live streams, YouTube live streams, Basler USB cameras, Basler GigE cameras, and Intel RealSense depth cameras. - -Installation is straightforward: -```bash -pip install framegrab[all] -``` - -To capture frames, first configure a `FrameGrabber` object, specifying the image source. Then call the `grab()` method to capture a frame: -```python notest -from framegrab import FrameGrabber - -# Create a FrameGrabber for a generic USB camera (e.g., a webcam) -config = { - 'input_type': 'generic_usb', -} -grabber = FrameGrabber.create_grabber(config) - -frame = grabber.grab() -``` - -Framegrab returns images as numpy arrays in BGR format, which is the standard format for OpenCV. This makes it easy to use the images with other image processing libraries, such as OpenCV. - -See the [framegrab documentation](https://github.com/groundlight/framegrab/blob/main/README.md) for more information on configuring different image sources. - -### Motion Detection -Framegrab also includes a motion detection module, which can be used to detect motion in a video stream. This can be useful for detecting when something changes in a scene, such as when a person enters a room or a car pulls into a parking space. - -To use the built-in motion detection functionality, first create a `MotionDetector` object, specifying the percentage threshold for motion detection. Then, use the motion_detected() method with every captured frame to check if motion has been detected: -```python notest -from framegrab import FrameGrabber, MotionDetector - -config = {'input_type': 'generic_usb'} -grabber = FrameGrabber.create_grabber(config) - -motion_threshold = 1.0 -mdet = MotionDetector(pct_threshold=motion_threshold) - -while True: - frame = grabber.grab() - if frame is None: - print("No frame captured!") - continue - - if mdet.motion_detected(frame): - print("Motion detected!") -``` - -In this example, `motion_threshold` specifies the sensitivity level for detecting motion based on the percentage of pixels that have changed. By default, this is set to 1.0, indicating a 1% change. To increase the sensitivity, set the threshold to a lower value, such as 0.5%. Likewise, to decrease the sensitivity, set the threshold to a higher value, such as 2%. - - -## Image Formats -Groundlight's SDK accepts images in many popular formats, including PIL, OpenCV, and numpy arrays. -### PIL - -The Groundlight SDK can accept PIL images directly in `submit_image_query`. Here's an example: - -```python -from groundlight import Groundlight -from PIL import Image - -gl = Groundlight() -det = gl.get_or_create_detector(name="path-clear", query="Is the path clear?") -pil_img = Image.open("./docs/static/img/doorway.jpg") -gl.submit_image_query(det, pil_img) -``` - -### OpenCV - -OpenCV is a popular image processing library, with many utilities for working with images. -OpenCV images are stored as numpy arrays. (Note they are stored in BGR order, not RGB order, but as of Groundlight SDK v0.8 this is the expected order.) -OpenCV's images can be send directly to `submit_image_query` as follows: - -```python notest -import cv2 - -cam = cv2.VideoCapture(0) # Initialize camera (0 is the default index) - -_, frame = cam.read() # Capture one frame -gl.submit_image_query(detector, frame) # Send the frame to Groundlight -cam.release() # Release the camera -``` - - -### Numpy - -The Groundlight SDK can accept images as `numpy` arrays. They should be in the standard HWN format in BGR color order, matching OpenCV standards. -Pixel values should be from 0-255 (not 0.0-1.0 as floats). So `uint8` data type is preferable since it saves memory. - -Here's sample code to create an 800x600 random image in numpy: - - - -```python notest -import numpy as np - -np_img = np.random.uniform(low=0, high=255, size=(600, 800, 3)).astype(np.uint8) -# Note: channel order is interpretted as BGR not RGB -gl.submit_image_query(detector, np_img) -``` - -#### Channel order: BGR vs RGB - -Groundlight expects images in BGR order, because this is standard for OpenCV, which uses numpy arrays as image storage. -(OpenCV uses BGR because it was originally developed decades ago for compatibility with the BGR color format used by many cameras and image processing hardware at the time of its creation.) -Most other image libraries use RGB order, so if you are using images as numpy arrays which did not originate from OpenCV you likely need to reverse the channel order before sending the images to Groundlight. -Note this change was made in v0.8 of the Groundlight SDK - in previous versions, RGB order was expected. - -If you have an RGB array, you must reverse the channel order before sending it to Groundlight, like: - -```python notest -# Convert numpy image in RGB channel order to BGR order -bgr_img = rgb_img[:, :, ::-1] -``` - -The difference can be surprisingly subtle when red and blue get swapped. Often images just look a little off, but sometimes they look very wrong. - -Here's an example of a natural-scene image where you might think the color balance is just off: -![Correct color order](/img/michonne.jpg) -![Swapped color channels](/img/michonne-bgr.jpg) - -In industrial settings, the difference can be almost impossible to detect without prior knowledge of the scene: -![Correct color order](/img/cnc-gripper.jpg) -![Swapped color channels](/img/cnc-gripper-bgr.jpg) diff --git a/docs/docs/guide/3-working-with-detectors.md b/docs/docs/guide/3-working-with-detectors.md deleted file mode 100644 index 1ad2cddd..00000000 --- a/docs/docs/guide/3-working-with-detectors.md +++ /dev/null @@ -1,64 +0,0 @@ -# Working with Detectors - -This guide will walk you through creating, retrieving, and managing detectors in Groundlight. Groundlight supports several detector modalities to suit different computer vision tasks - for more information on these modes, see the [Detector Answer Modes](../answer-modes/answer-modes.md) guide. - - -## Explicitly create a new detector - -Typically you'll use the `get_or_create_detector(name: str, query: str)` method to find an existing detector you've already created with the same name, or create a new one if it doesn't exists. But if you'd like to force creating a new detector you can also use the `create_detector(name: str, query: str)` method - - - -```python notest -from groundlight import Groundlight - -gl = Groundlight() - -# highlight-start -detector = gl.create_detector(name="your_detector_name", query="is there a hummingbird near the feeder?") -# highlight-end -``` - -## Retrieve an existing detector -To work with a detector that you've previously created, you need to retrieve it using its unique identifier. This is typical in Groundlight applications where you want to continue to use a detector you've already created. - - - -```python notest -from groundlight import Groundlight - -gl = Groundlight() - -# highlight-start -detector = gl.get_detector(id="your_detector_id") -# highlight-end -``` - -Alternatively, you can retrieve a detector by its name: - -```python notest -from groundlight import Groundlight - -gl = Groundlight() - -# highlight-start -detector = gl.get_detector_by_name(name="your_detector_name") -# highlight-end -``` - -## List your detectors -To manage and interact with your detectors, you might need to list them. Groundlight provides a straightforward way to retrieve a list of detectors you've created. By default, the list is paginated to show 10 results per page, but you can customize this to suit your needs. - -```python -from groundlight import Groundlight - -gl = Groundlight() - -# highlight-start -# Defaults to 10 results per page -detectors = gl.list_detectors() - -# Pagination: 1st page of 5 results per page -detectors = gl.list_detectors(page=1, page_size=5) -# highlight-end -``` diff --git a/docs/docs/guide/4-submitting-image-queries.md b/docs/docs/guide/4-submitting-image-queries.md deleted file mode 100644 index 64692759..00000000 --- a/docs/docs/guide/4-submitting-image-queries.md +++ /dev/null @@ -1,165 +0,0 @@ -# Submitting Image Queries -Once you have [created a `Detector`](./3-working-with-detectors.md) and [captured an image](./2-grabbing-images.md), you can submit your image to Groundlight for analysis. - -The Groundlight SDK provides the `submit_image_query` method which gives you full control over how you'd like your image to be processed. For convenience while getting started, there are a few aliases that capture common use cases. - -| Method | Usage | Examples | -| --- | --- | --- | -| `ask_confident` | [For users who want correct answers, waiting for humans to review if necessary](#get-the-first-confident-answer) | Visual verification in safety applications | -| `ask_ml` | [For users who need fast ML answers and cannot wait for human review](#get-the-first-available-answer-regardless-of-confidence) | Live facilities monitoring, machine tending | -| `ask_async` | [For users who want to bulk process images with no time constraint](#submit-an-imagequery-asynchronously) | Warming up a detector with data from another ML platform, Collecting data in the field with the intent to analyze it tomorrow | -| `submit_image_query` | [For advanced users who want more control than any of the aliased functions provide](#submit-an-image-query) | Submitting image queries with security requirements or using image query tags (available in our enterprise tier)| - -## Submit an Image Query -The primary method for submitting an image query is `submit_image_query(detector: Detector, image: Any)`. This method takes a `Detector` object and an image as input and returns an `ImageQuery` object. -```python notest -from groundlight import Groundlight - -gl = Groundlight() -detector = gl.get_detector(id="det_abcdef...") - -# highlight-start -image_query = gl.submit_image_query(detector=detector, image="path/to/image.jpg") -# highlight-end -``` - -`submit_image_query` provides fine-grained control over how the `ImageQuery` is processed. For example, a per-query confidence threshold can be set (defaults to the `Detector`'s confidence threshold), and the query can be set to wait for up to a certain amount of time for a confident response (defaults to 30s). For example: -```python notest -from groundlight import Groundlight - -gl = Groundlight() -detector = gl.get_detector(id="det_abcdef...") - -# highlight-start -image_query = gl.submit_image_query( - detector=detector, - image="path/to/image.jpg", - confidence_threshold=0.95, - wait=10.0, # seconds -) -# highlight-end -``` - -See the [API Reference](../api-reference/api-reference.md) for more information on the `submit_image_query` method. - -## Aliases for `submit_image_query` -For convenience, the `submit_image_query` method has aliases for the different patterns of usage. These aliases are `ask_confident`, `ask_ml`, and `ask_async`. - -### Get the first confident answer -`ask_confident` evaluates an image with Groundlight waiting until an answer above the confidence threshold - of the detector is reached or the wait period has passed. -```python notest -from groundlight import Groundlight - -gl = Groundlight() -detector = gl.get_detector(id="det_abcdef...") - -# highlight-start -image_query = gl.ask_confident(detector=detector, image="path/to/image.jpg") -# highlight-end -``` - -### Submit an ImageQuery asynchronously -`ask_async` is a convenience method for submitting an `ImageQuery` asynchronously. This is equivalent to calling `submit_image_query` with `want_async=True` and `wait=0`. Use `get_image_query` to retrieve the `result` of the ImageQuery. - -```python notest -from groundlight import Groundlight - -gl = Groundlight() -detector = gl.get_detector(id="det_abcdef...") - -# highlight-start -# Submit ImageQuery asynchronously -image_query = gl.ask_async(detector=detector, image="path/to/image.jpg") - -# Do other work while waiting for the result -sleep(1.0) - -# Retrieve the result of the ImageQuery. Note that the provided -# result can change over time - as the query is escalated through -# our ladder - until a confident answer is reached. -image_query = gl.get_image_query(id=image_query.id) -# highlight-end -``` - -See this [guide](./7-async-queries.md) for more information on ImageQueries submitted asynchronously. - -### Get the first available answer, regardless of confidence -`ask_ml` evaluates an image with Groundlight and returns the first answer Groundlight can provide, agnostic of confidence. There is no wait period when using this method. It is called `ask_ml` because our machine learning models are earliest on our escalation ladder and thus always the fastest to respond. - -:::note - -We recommend using the `ask_confident` or the `ask_async` methods whenever possible for best results. - -::: - -```python notest -from groundlight import Groundlight - -gl = Groundlight() -detector = gl.get_detector(id="det_abcdef...") - -# highlight-start -image_query = gl.ask_ml(detector=detector, image="path/to/image.jpg") -# highlight-end -``` - -When using this method, low-confidence Image Queries will still be escalated to human review - this allows our models to continue to improve over time. - -## Working with Image Queries Post-Submission - -### Retrieve an Image Query result - -In practice, you may want to check for a new result on your query. For example, after a cloud reviewer labels your query. For example, you can use the `image_query.id` after the above `submit_image_query()` call. - - - -```python notest -from groundlight import Groundlight - -gl = Groundlight() - -# highlight-start -image_query = gl.get_image_query(id="iq_YOUR_IMAGE_QUERY_ID") -# highlight-end -``` - -### List your previous Image Queries - -```python -from groundlight import Groundlight - -gl = Groundlight() - -# highlight-start -# Defaults to 10 results per page -image_queries = gl.list_image_queries() - -# Pagination: 1st page of 5 results per page -image_queries = gl.list_image_queries(page=1, page_size=5) -# highlight-end -``` - -### Add a label to an Image Query - -Groundlight lets you start using models by making queries against your very first image, but there are a few situations where you might either have an existing dataset, or you'd like to handle the escalation response programatically in your own code but still include the label to get better responses in the future. - -With your `ImageQuery` from either `submit_image_query()` or `get_image_query()` you can add the label directly. Note that if the query is already in the escalation queue due to low ML confidence or audit thresholds, it may also receive labels from another source. However, user-provided labels are always treated as the most authoritative. - -```python -import requests -from PIL import Image -from groundlight import Groundlight - -gl = Groundlight() - -d = gl.get_or_create_detector(name="doorway", query="Is the doorway open?") - -image_url= "https://images.selfstorage.com/large-compress/2174925f24362c479b2.jpg" -image = Image.open(requests.get(image_url, stream=True).raw) -image_query = gl.submit_image_query(detector=d, image=image) - -# highlight-start -gl.add_label(image_query, 'YES') # or 'NO' -# highlight-end -``` diff --git a/docs/docs/guide/5-managing-confidence.md b/docs/docs/guide/5-managing-confidence.md deleted file mode 100644 index 00cfda31..00000000 --- a/docs/docs/guide/5-managing-confidence.md +++ /dev/null @@ -1,61 +0,0 @@ -# Setting Confidence Thresholds - -## Introduction to Confidence Thresholds -When creating a Detector or submitting an ImageQuery, you can set the necessary confidence level for your use case. We call this the `confidence_threshold`. Tuning this value allows you to balance the trade-offs between accuracy and latency / cost. - -Confidence scores represent the model's internal assessment of its prediction reliability. Groundlight models provide *calibrated* confidence scores, which means that, when a model makes a prediction with a confidence of 0.95, we expect that (under typical conditions) 95% of the time that prediction will be correct. In other words, a prediction with a confidence of 0.95 is expected to be correct 19 out of 20 times. Confidence calibration kicks in after a sufficient number of labeled images have been collected. - -Confidence thresholds represent a minimum confidence that must be achieved for Groundlight to return an answer. If a confidence above the confidence threshold is not achieved, Groundlight will escalate your query up our heirarchy to stronger models and human reviewers. Confidence thresholds should be determined based on your application's acceptable error rate and the potential impact of those errors. - -Higher confidence thresholds result in predictions that are more accurate but may take longer to process (because they are escalated to more complex/expensive models or human review). Lower confidence thresholds result in faster responses but may be less accurate. Over time, and as more human-provided labels are collected, the ML models will improve, and our fastest models will be able to provide higher confidence predictions more quickly. - -## Configuring Timeouts - -In some cases, challenging queries that require human review can take a number of seconds, so we provide both client-side and server-side timeouts to ensure that your application can continue to function even if the query takes longer than expected. - -Set a client-side timeout by configuring the `wait` parameter in the `submit_image_query` method. This simply stops the client from waiting for a response after a certain amount of time. - -Set a server-side timeout by configuring the `patience_time` parameter in the `submit_image_query` method. This tells Groundlight to deprioritize the query after a certain amount of time, which can be useful if the result of a query becomes less relevant over time. For example, if you are monitoring a live video feed, you may want to deprioritize queries that are more than a few seconds old so that our human reviewers can focus on the most recent data. - - - -```python notest -from groundlight import Groundlight -from PIL import Image -import requests - -gl = Groundlight() -image_url = "https://www.photos-public-domain.com/wp-content/uploads/2010/11/over_flowing_garbage_can.jpg" -image = Image.open(requests.get(image_url, stream=True).raw) - -d = gl.get_or_create_detector( - name="trash", - query="Is the trash can full?", -# highlight-start - confidence_threshold=0.95, # Set the confidence threshold to 0.95 -# highlight-end -) - -# This will wait until either 60 seconds have passed or the confidence reaches 0.95 -image_query = gl.submit_image_query( - detector=d, - image=image, -# highlight-start - wait=10, # tell the client to stop waiting after 10 seconds - patience_time=20, # tell Groundlight to deprioritize the query after 20 seconds -# highlight-end -) - -print(f"The answer is {image_query.result.label}") -print(f"The confidence is {image_query.result.confidence}") -``` - -:::tip - -Tuning the `confidence_threshold` allows you to balance accuracy with response time. - -Higher confidence thresholds result in more accurate predictions but can increase latency. Achieving these higher confidence levels often requires more labels, which can increase labor costs. - -As our models improve over time, they will become more confident, enabling you to receive higher-confidence answers more quickly and at a lower cost. - -::: diff --git a/docs/docs/guide/6-handling-errors.md b/docs/docs/guide/6-handling-errors.md deleted file mode 100644 index e618c651..00000000 --- a/docs/docs/guide/6-handling-errors.md +++ /dev/null @@ -1,67 +0,0 @@ -# Handling Errors - -When building applications with the Groundlight SDK, you may encounter errors during API calls. This page covers how to handle such errors and build robust code that can gracefully handle exceptions. - -## Handling `ApiException` - -In the event of an HTTP error during an API call, the Groundlight SDK raises an `ApiException`. This exception provides access to various metadata: - -```python notest -import traceback -from groundlight import ApiException, Groundlight - -gl = Groundlight() -try: - d = gl.get_or_create_detector( - name="Road Checker", - query="Is the site access road blocked?", - ) - iq = gl.submit_image_query(d, get_image(), wait=60) -except ApiException as e: - # Print a traceback for debugging - traceback.print_exc() - - # e.reason contains a textual description of the error - print(f"Error reason: {e.reason}") - - # e.status contains the HTTP status code - print(f"HTTP status code: {e.status}") - - # Common HTTP status codes: - # 400 Bad Request: The request was invalid or malformed - # 401 Unauthorized: Your GROUNDLIGHT_API_TOKEN is missing or invalid - # 403 Forbidden: The request is not allowed due to insufficient permissions - # 404 Not Found: The requested resource was not found - # 429 Too Many Requests: The rate limit for the API has been exceeded - # 500 Internal Server Error: An error occurred on the server side -``` - -## Best Practices for Handling Exceptions - -When working with the Groundlight SDK, follow these best practices to handle exceptions and build robust code: - -### Catch Specific Exceptions - -Catch only the specific exceptions that you expect to be raised, such as `ApiException`. Avoid catching broad exceptions like `Exception`, as it may make debugging difficult and obscure other unrelated issues. - -### Use Custom Exception Classes - -Consider creating custom exception classes for your application-specific errors. This can help you differentiate between errors originating from the Groundlight SDK and those from your application. - -### Log Exceptions - -Log exceptions using appropriate log levels (e.g., error, warning) and include relevant context. This practice aids in effective debugging and monitoring application health. - -### Implement Retry Logic - -Incorporate retry logic with exponential backoff for transient errors, such as network issues or rate limits. This strategy allows your application to recover from temporary problems automatically. - -### Handle Exceptions Gracefully - -Ensure your application remains functional despite errors by handling exceptions gracefully. This might involve displaying user-friendly error messages or reverting to default behaviors. - -### Test Your Error Handling - -Write tests to ensure that your error handling works as expected. This can help you catch issues early and ensure that your application can handle errors gracefully in production. - -By following these best practices, you can create robust and resilient applications that can handle server errors and other exceptions when using the Groundlight SDK. diff --git a/docs/docs/guide/7-async-queries.md b/docs/docs/guide/7-async-queries.md deleted file mode 100644 index 2bbf8baa..00000000 --- a/docs/docs/guide/7-async-queries.md +++ /dev/null @@ -1,82 +0,0 @@ -# Using Asynchronous Queries - -Groundlight provides a simple interface for submitting asynchronous queries. This is useful for situations in which the thread or process or machine submitting image queries is not the same thread or machine that will be retrieving and using the results. - -For example, you might have a forward deployed robot or camera that submits image queries to Groundlight, and a separate server that retrieves the results and takes action based on them. We will refer to these two machines as the **submitting machine** and the **retrieving machine**. - -## Setup Submitting Machine -On the **submitting machine**, you will need to install the Groundlight Python SDK. Then you can submit image queries asynchronously using the `ask_async` interface (read the full documentation [here](pathname:///python-sdk/api-reference-docs/#groundlight.client.Groundlight.ask_async)). - -`ask_async` submits your query and immediately returns, without waiting for an answer. This minimizes the time your program spends interacting with Groundlight. Consequently, the `ImageQuery` object returned by `ask_async` does not contain a `result` (the `result` field will be `None`). This is suitable for scenarios where the **submitting machine** does not need the result. Instead, the **submitting machine** only needs to share the `ImageQuery.id` with the **retrieving machine**. This can be done through a database, message queue, or another method. In this example, we assume you are using a database to save the `ImageQuery.id` with `db.save(image_query.id)`. - -```python notest -from time import sleep -from framegrab import FrameGrabber -from groundlight import Groundlight - -# Create a FrameGrabber for a generic USB camera (e.g., a webcam) -config = {'input_type': 'generic_usb'} -grabber = FrameGrabber.create_grabber(config) - -detector = gl.get_or_create_detector(name="your_detector_name", query="your_query") - -while True: - image = grabber.grab() - - # highlight-start - image_query = gl.ask_async(detector=detector, image=image) - db.save(image_query.id) # Save the image_query.id to a database for the retrieving machine to use - # highlight-end - - sleep(10) # Sleep for 10 seconds before grabbing the next image - -grabber.release() -``` - -## Setup Retrieving Machine -On the **retrieving machine**, ensure the Groundlight Python SDK is installed. You can then use the `get_image_query` method to fetch results of image queries submitted by the **submitting machine**. The **retrieving machine** can utilize the `ImageQuery.result` to perform actions based on the application's requirements. In this example, we assume your application retrieves the next image query ID to process from a database using `db.get_next_image_query_id()`. This function should return `None` when all `ImageQuery` entries have been processed. - -```python notest -from groundlight import Groundlight - -detector = gl.get_or_create_detector(name="your_detector_name", query="your_query") - -image_query_id = db.get_next_image_query_id() - -while image_query_id is not None: - # highlight-start - image_query = gl.get_image_query(id=image_query_id) # retrieve the image query from Groundlight - # highlight-end - result = image_query.result - - # take action based on the result of the image query - if result.label == 'YES': - pass # TODO: do something based on your application - elif result.label == 'NO': - pass # TODO: do something based on your application - elif result.label == 'UNCLEAR': - pass # TODO: do something based on your application - - # update image_query_id for next iteration of the loop - image_query_id = db.get_next_image_query_id() -``` - -## Important Considerations -When you submit an image query asynchronously, ML prediction on your query is **not** instant. So attempting to retrieve the result immediately after submitting an async query will likely result in an `UNCLEAR` result as Groundlight is still processing your query. Instead, if your code needs a `result` synchronously we recommend using one of our methods with a polling mechanism to retrieve the result (e.g. `ask_confident`). You can see all of the interfaces available in the documentation [here](pathname:///python-sdk/api-reference-docs/#groundlight.client.Groundlight). - -```python notest -from PIL import Image -from groundlight import Groundlight - -detector = gl.get_or_create_detector(name="your_detector_name", query="your_query") -image = Image.open("/path/to/your/image.jpg") - -image_query = gl.ask_async(detector=detector, image=image) # Submit async query to Groundlight -assert image_query.result is None # IQs returned from `ask_async` will not have a result - -image_query = gl.get_image_query(id=image_query.id) # Immediately retrieve the image query from Groundlight -result = image_query.result # This may be 'UNCLEAR' as Groundlight continues to process the query - -image_query = gl.wait_for_confident_result(id=image_query.id) # Poll for a confident result from Groundlight -result = image_query.result -``` \ No newline at end of file diff --git a/docs/docs/guide/8-edge.md b/docs/docs/guide/8-edge.md deleted file mode 100644 index 07d4f043..00000000 --- a/docs/docs/guide/8-edge.md +++ /dev/null @@ -1,52 +0,0 @@ -# Processing Images on the Edge - -If your account includes access to edge models, you can download and install them on your edge devices. This allows you to run Groundlight's ML models locally on your edge devices, reducing latency and increasing throughput. Additionally, inference requests handled on the edge are not counted towards your account's usage limits. - -This is achieved through a proxy service called the `edge-endpoint`, a lightweight, open-source service that runs on your edge devices. The `edge-endpoint` is responsible for downloading and running models and communicating with the Groundlight cloud service. You can find the source code and documentation for the `edge-endpoint` [on GitHub](https://github.com/groundlight/edge-endpoint). - -## How the Edge Endpoint Works - -The `edge-endpoint` is a [proxy service](https://en.wikipedia.org/wiki/Proxy_server) that runs on your edge devices. It intercepts requests and responses between your application and the Groundlight cloud service, enabling you to run Groundlight's ML models locally on your edge devices. - -When your application sends an image query to the Groundlight cloud service, the `edge-endpoint` intercepts the request and downloads the relevant edge-sized model from the cloud. It then runs the model locally on the edge device and returns the result to your application. By default, it will return answers without escalating to the cloud if the edge model answers above the specified confidence threshold. Otherwise, it will escalate to the cloud for a more confident answer. This process also allows Groundlight to learn from examples that are challenging for the edge model. Once a new edge model is trained to handle such examples, it will automatically be downloaded to the edge device for future queries. - -The `edge-endpoint` operates as a set of [containers](https://en.wikipedia.org/wiki/Containerization_(computing)) on an "edge device," which can be an NVIDIA Jetson device, a rack-mounted server, or even a Raspberry Pi. The main container is the `edge-endpoint` proxy service, which handles requests and manages other containers, such as the `inferencemodel` containers responsible for loading and running the ML models. - -## Installing and Running the Edge Endpoint -To set up an `edge-endpoint` manually, please refer to [the deploy README](https://github.com/groundlight/edge-endpoint/blob/main/deploy/README.md). - -Groundlight also provides managed `edge-endpoint` servers. Management is performed via [Balena](https://www.balena.io/). To received a managed `edge-endpoint`, please [contact us](mailto:info@groundlight.ai). - -## Using the Edge Endpoint -To utilize the `edge-endpoint`, set the Groundlight SDK to use the `edge-endpoint`'s URL instead of the cloud endpoint. Your application logic can remain unchanged and will work seamlessly with the Groundlight `edge-endpoint`. This setup allows some ML responses to be returned much faster, locally. - -Note that image queries processed at the `edge-endpoint` will not appear on the Groundlight cloud dashboard unless specifically configured. In such cases, the edge prediction will not be reflected in the cloud image query. Additional documentation and configuration options are available in the [edge-endpoint repository](https://github.com/groundlight/edge-endpoint#running-the-edge-endpoint). - -To set the Groundlight Python SDK to submit requests to your `edge-endpoint` proxy server, you can either pass the endpoint URL to the Groundlight constructor like this: -```python notest -from groundlight import Groundlight -gl = Groundlight(endpoint="http://localhost:30101") -``` - -or set the `GROUNDLIGHT_ENDPOINT` environment variable like: -```bash -export GROUNDLIGHT_ENDPOINT=http://localhost:30101 -python your_app.py -``` - -:::tip -In the above example, the `edge-endpoint` is running on the same machine as the application, so the endpoint URL is `http://localhost:30101`. If the `edge-endpoint` is running on a different machine, you should replace `localhost` with the IP address or hostname of the machine running the `edge-endpoint`. -::: - -## Edge Endpoint performance - -We have benchmarked the `edge-endpoint` handling 500 requests/sec at a latency of less than 50ms on an off-the-shelf [Katana 15 B13VGK-1007US](https://us.msi.com/Laptop/Katana-15-B13VX/Specification) laptop (Intel® Core™ i9-13900H CPU, NVIDIA® GeForce RTX™ 4070 Laptop GPU, 32GB DDR5 5200MHz RAM) running Ubuntu 20.04. - -The following graphs show the throughput and latency of the `edge-endpoint` running on the Katana 15 laptop. As time progresses along the x-axis, the benchmark script ramps up the number of requests per second from 1 to 500 (and the number of clients submitting requests from 1 to 60). The y-axes shows the throughput in requests per second and the latency in seconds. - -![edge-endpoint throughput](../../static/img/edge-endpoint-throughput.png) - -![edge-endpoint latency](../../static/img/edge-endpoint-latency.png) - - -The `edge-endpoint` is designed to be lightweight and efficient, and can be run on a variety of edge devices, including NVIDIA Jetson devices, Raspberry Pi, and other ARM- and x86-based devices. \ No newline at end of file diff --git a/docs/docs/guide/9-alerts.md b/docs/docs/guide/9-alerts.md deleted file mode 100644 index 427a8ee3..00000000 --- a/docs/docs/guide/9-alerts.md +++ /dev/null @@ -1,71 +0,0 @@ -# Configuring Alerts - -Groundlight supports triggering alerts based on the results of image queries. Alerts can be configured to notify you when a specific condition is met. - -To configure an alert, navigate to [the **Alerts** tab on the Groundlight dashboard](https://dashboard.groundlight.ai/reef/alerts). Here, you can create a new alert by clicking the **Create New Alert** button. - -## Alert Configuration - -When creating a new alert, you can configure alerts for the following conditions: -1. A specific answer is returned N times in a row. -2. The answer changes from one value to another. -3. There are no changes in the answer for a specified period of time. -4. There are no queries submitted for a specified period of time. - -A snooze period can be configured to prevent the alert from triggering multiple times in quick succession. - -Optionally, you can configure the alert to include the triggering image in the alert message. - -:::tip - -Consider configuring a "no queries submitted" alert to monitor system health. If your application is expected to submit queries regularly (e.g., monitoring a camera feed), setting an alert for when no queries are received for a few minutes can help quickly identify if your system has gone offline or is experiencing connectivity issues. - -::: - -## Alert Mediums - -Groundlight supports alerts via Email, Text Message (SMS), and Webhooks. - -### Webhooks - -By setting up a webhook alert for your Groundlight detector, you can integrate Groundlight's computer vision technology with your existing messaging platform, other tech stack, or even a custom application. - -You can either set up an alert using one of our default templates (currently available for Slack, more platforms to come) or build your own. - -#### Custom Templates - -We use Jinja2 to manage and render templates. See the ([Jinja template documentation](https://jinja.palletsprojects.com/en/stable/templates/)) for information on how to construct your template. -The template will need to be a valid Jinja template which renders to valid JSON to be used as a payload for your webhook alert. - -We provide a set of variables which you can use to put information about your detector and alert into your template. -The available variables are: - -- `detector_name`: The name of the detector that the alert was triggered on -- `detector_query`: The detector's query. -- `detector_id`: The detector's unique ID -- `confidence_threshold`: The current confidence threshold for the detector. -- `detector_mode`: The detector's mode (binary, count, multiclass, etc). Currently, alerts are only available for binary detectors. -- `image_query_id`: The id of the image query which triggered the alert -- `time_repr`: A human readable string of the time the alert was triggered in UTC. Does not include the date. -- `activation_time`: The time and date the alert was triggered in UTC. -- `condition_repr`: The condition the alert is configured with, put into a human-readable string (eg. " returned a YES answer at "). -- `image_url`: An image URL to access the image which triggered the alert. Only available if the alert was configured with `include_image` set to True. - -For example, the template below could be used as a custom payload to configure a Slack alert which includes a basic message and the triggering image. -``` -"""{ - "text": "Alert activated on Groundlight detector {{ detector_name }} (Query: {{ detector_query }}). Detector {{ condition_repr }} {{ time_repr }}", - "attachments": [ - { - "fallback": "Optional image attachment for the detector alert.", - "image_url": "{{ image_url }}", - "title": "Related Image" - } - ] -}""" -``` - -#### Headers - -Optionally, you can also configure the headers for your webhook alert POST request. This is particularly useful if your application requires a specific security token to be present to accept incoming POST requests. -If your application requires headers, you can provide them as a JSON dictionary. If not, you can configure your template and leave the headers blank. \ No newline at end of file diff --git a/docs/docs/guide/_category_.json b/docs/docs/guide/_category_.json deleted file mode 100644 index f4658fde..00000000 --- a/docs/docs/guide/_category_.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "label": "Guide", - "position": 4 -} diff --git a/docs/docs/guide/guide.md b/docs/docs/guide/guide.md deleted file mode 100644 index bb8c9b99..00000000 --- a/docs/docs/guide/guide.md +++ /dev/null @@ -1,16 +0,0 @@ -# Guide - -Groundlight provides a powerful "computer vision powered by natural language" system that enables you to build visual applications with minimal code. With Groundlight, you can quickly create applications for various use cases, from simple object detection to complex visual analysis. - -On the following pages, we'll guide you through the process of building applications with Groundlight. -- **[Grabbing images](2-grabbing-images.md)**: Understand the intricacies of how to submit images from various input sources to Groundlight. -- **[Working with detectors](3-working-with-detectors.md)**: Learn how to create, configure, and use detectors in your Groundlight-powered applications. -- **[Submitting image queries](4-submitting-image-queries.md)**: Submit images to Groundlight for analysis and retrieve the results. -- **[Confidence levels](5-managing-confidence.md)**: Master how to control the trade-off of latency against accuracy by configuring the desired confidence level for your detectors. -- **[Handling errors](6-handling-errors.md)**: Understand how to handle and troubleshoot HTTP errors (`ApiException`) that may occur while using Groundlight. -- **[Asynchronous queries](7-async-queries.md)**: Groundlight makes it easy to submit asynchronous queries. Learn how to submit queries asynchronously and retrieve the results later. -- **[Using Groundlight on the edge](8-edge.md)**: Discover how to deploy Groundlight in edge computing environments for improved performance and reduced latency. -- **[Alerts](9-alerts.md)**: Learn how to set up alerts to notify you via text (SMS) or email when specific conditions are met in your visual applications. - - -By exploring these resources and sample applications, you'll be well on your way to building powerful visual applications using Groundlight's computer vision and natural language capabilities. diff --git a/docs/docs/installation/1-linux.md b/docs/docs/installation/1-linux.md deleted file mode 100644 index 1a240a98..00000000 --- a/docs/docs/installation/1-linux.md +++ /dev/null @@ -1,121 +0,0 @@ -# Installing on Linux - -This guide will help you install the Groundlight SDK on Linux. The Groundlight SDK requires Python 3.9 or higher. - -## Prerequisites - -Ensure that you have the following installed on your system: - -- Python 3.9 or higher -- pip (Python package installer) - -## Basic Installation - -Assuming you have Python 3.9 or higher installed on your system, you can proceed with the following steps to install or upgrade the Groundlight SDK: - -### Installing Groundlight SDK - -To install the Groundlight SDK using pip, run the following command in your terminal: - -```bash -pip install groundlight -``` - -If you're also using `python2` on your system, you might need to use `pip3` instead: - -```bash -pip3 install groundlight -``` - -The Groundlight SDK is now installed and ready for use. - -### Checking Groundlight SDK Version - -To check if the Groundlight SDK is installed and to display its version, you can use the following Python one-liner: - -```bash -python -c "import groundlight; print(groundlight.__version__)" -``` - -or the groundlight command line tool that comes with the SDK: - -```bash -groundlight --help -``` - -### Upgrading Groundlight SDK - -If you need to upgrade the Groundlight SDK to the latest version, use the following pip command: - -```bash -pip install --upgrade groundlight -``` - -Or, if you're using `pip3`: - -```bash -pip3 install --upgrade groundlight -``` - -After upgrading, you can use the Python one-liner mentioned in the "Checking Groundlight SDK Version" section to verify that the latest version is now installed. - -## Getting the right Python Version - -To check your installed Python version, open a terminal and run: - -```bash -python --version -``` - -If you see a version number starting with "3.9" or higher (e.g., "3.9.5" or "3.9.0"), you're good to go. If not, you might need to upgrade Python on your system. - -### Upgrading Python on Linux - -Use your distribution's package manager to install the latest Python version: - -- For Ubuntu or Debian-based systems: - - ```bash - sudo apt update - sudo apt install python3 - ``` - - (For Ubuntu 18.04 see note below.) - -- For Fedora-based systems: - - ```bash - sudo dnf install python3 - ``` - -- For Arch Linux: - - ```bash - sudo pacman -S python - ``` - -After upgrading, verify the Python version by running `python --version` or `python3 --version`, as described earlier. - -### Special note about Ubuntu 18.04 - -Ubuntu 18.04 still uses python 3.6 by default, which is end-of-life. We generally recommend using python 3.10. If you know how to install py3.10, please go ahead. But the easiest version of python 3 to use with Ubuntu 18.04 is python 3.9, which can be installed as follows without adding any extra repositories: - -```shell -# Prepare Ubuntu to install things -sudo apt-get update -# Install the basics -sudo apt-get install -y python3.9 python3.9-distutils curl -# Configure `python3` to run python3.9 by default -sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 10 -# Download and install pip3.9 -curl https://bootstrap.pypa.io/get-pip.py > /tmp/get-pip.py -sudo python3.9 /tmp/get-pip.py -# Configure `pip3` to run pip3.9 -sudo update-alternatives --install /usr/bin/pip3 pip3 $(which pip3.9) 10 -# Now we can install Groundlight! -pip3 install groundlight -``` - -## Ready to go! - -You're now ready to start using the Groundlight SDK in your projects. For more information on using the SDK, refer to the [API Tokens](../getting-started/5-api-tokens.md) documentation and the [Building Applications Guide](../guide/). diff --git a/docs/docs/installation/2-macos.md b/docs/docs/installation/2-macos.md deleted file mode 100644 index 4e95b110..00000000 --- a/docs/docs/installation/2-macos.md +++ /dev/null @@ -1,83 +0,0 @@ -# Installing on macOS - -This guide will help you install the Groundlight SDK on macOS. The Groundlight SDK requires Python 3.9 or higher. - -## Prerequisites - -Ensure that you have the following installed on your system: - -- Python 3.9 or higher -- pip (Python package installer) - -## Basic Installation - -Assuming you have Python 3.9 or higher installed on your system, you can proceed with the following steps to install or upgrade the Groundlight SDK: - -### Installing Groundlight SDK - -To install the Groundlight SDK using pip, run the following command in your terminal: - -```bash -pip install groundlight -``` - -If you're also using `python2` on your system, you might need to use `pip3` instead: - -```bash -pip3 install groundlight -``` - -The Groundlight SDK is now installed and ready for use. - -### Checking Groundlight SDK Version - -To check if the Groundlight SDK is installed and to display its version, you can use the following Python one-liner: - -```bash -python -c "import groundlight; print(groundlight.__version__)" -``` - -or the groundlight command line tool that comes with the SDK: - -```bash -groundlight --help -``` -### Upgrading Groundlight SDK - -If you need to upgrade the Groundlight SDK to the latest version, use the following pip command: - -```bash -pip install --upgrade groundlight -``` - -Or, if you're using `pip3`: - -```bash -pip3 install --upgrade groundlight -``` - -After upgrading, you can use the Python one-liner mentioned in the "Checking Groundlight SDK Version" section to verify that the latest version is now installed. - -## Getting the right Python Version - -To check your installed Python version, open a terminal and run: - -```bash -python --version -``` - -If you see a version number starting with "3.9" or higher (e.g., "3.9.5" or "3.9.0"), you're good to go. If not, you might need to upgrade Python on your system. - -### Upgrading Python on MacOS - -Download the latest Python installer from the [official Python website](https://www.python.org/downloads/mac-osx/) and run it, or use [Homebrew](https://brew.sh/) to install Python: - - ```bash - brew install python - ``` - -After upgrading, verify the Python version by running `python --version` or `python3 --version`, as described earlier. - -## Ready to go! - -You're now ready to start using the Groundlight SDK in your projects. For more information on using the SDK, refer to the [API Tokens](../getting-started/5-api-tokens.md) documentation and the [Building Applications Guide](../guide/). diff --git a/docs/docs/installation/3-windows.md b/docs/docs/installation/3-windows.md deleted file mode 100644 index e71c0c5d..00000000 --- a/docs/docs/installation/3-windows.md +++ /dev/null @@ -1,74 +0,0 @@ -# Installing on Windows - -This guide will help you install the Groundlight SDK on Windows. The Groundlight SDK requires Python 3.9 or higher. - -## Prerequisites - -Ensure that you have the following installed on your system: - -- Python 3.9 or higher -- pip (Python package installer) - -## Basic Installation - -Assuming you have Python 3.9 or higher installed on your system, you can proceed with the following steps to install or upgrade the Groundlight SDK: - -### Installing Groundlight SDK - -To install the Groundlight SDK using pip, run the following command in your Command Prompt: - -```bash -pip install groundlight -``` - -If you're also using `python2` on your system, you might need to use `pip3` instead: - -```bash -pip3 install groundlight -``` - -The Groundlight SDK is now installed and ready for use. - -### Checking Groundlight SDK Version - -To check if the Groundlight SDK is installed and to display its version, you can use the following Python one-liner: - -```bash -python -c "import groundlight; print(groundlight.__version__)" -``` - -### Upgrading Groundlight SDK - -If you need to upgrade the Groundlight SDK to the latest version, use the following pip command: - -```bash -pip install --upgrade groundlight -``` - -Or, if you're using `pip3`: - -```bash -pip3 install --upgrade groundlight -``` - -After upgrading, you can use the Python one-liner mentioned in the "Checking Groundlight SDK Version" section to verify that the latest version is now installed. - -## Getting the right Python Version - -To check your installed Python version, open a Command Prompt and run: - -```bash -python --version -``` - -If you see a version number starting with "3.9" or higher (e.g., "3.9.5" or "3.9.0"), you're good to go. If not, you might need to upgrade Python on your system. - -### Upgrading Python on Windows - -Download the latest Python installer from the [official Python website](https://www.python.org/downloads/windows/) and run it. - -After upgrading, verify the Python version by running `python --version` or `python3 --version`, as described earlier. - -## Ready to go! - -You're now ready to start using the Groundlight SDK in your projects. For more information on using the SDK, refer to the [API Tokens](../getting-started/5-api-tokens.md) documentation and the [Building Applications Guide](../guide/). diff --git a/docs/docs/installation/4-optional-libraries.md b/docs/docs/installation/4-optional-libraries.md deleted file mode 100644 index 92d1f13d..00000000 --- a/docs/docs/installation/4-optional-libraries.md +++ /dev/null @@ -1,31 +0,0 @@ -# Optional libraries - -## Smaller is better! - -The Groundlight Python SDK is optimized to run on small edge devices. As such, you can use the Groundlight SDK without -installing large libraries like `numpy` or `OpenCV`. - -But if you're already installing them, we'll use them. Our SDK detects if these libraries are installed -and will make use of them if they're present. If not, we'll gracefully degrade, and tell you what's -wrong if you try to use these features. - -## PIL - optional but default installed - -The `PIL` library offers a bunch of standard utilities for working with images in python. The Groundlight SDK can work without `PIL`. - -Because `PIL` is not very large, and is quite useful, we install it by default with the normal build of the Groundlight SDK. So when you - -```shell -pip3 install groundlight -``` - -it comes with the `pillow` version of the `PIL` library already installed. - -### Working without PIL - -If you are extremely space constrained, you can install the Groundlight SDK from source without `PIL` and it will work properly, but with reduced functionality. -Specifically, you will need to convert your images into `JPEG` format yourself. The SDK normally relies on `PIL` to do JPEG compression (which is a non-trivial algorithm), and the API requires images to be in JPEG format. However on space-constrained platforms, sometimes this conversion is done in hardware, and so we don't want to force you to install `PIL` if you don't need it. - -## Numpy, OpenCV - fully optional - -These commonly-used libraries are not installed by default, because they are quite large, and their installation can often cause conflicts with other dependent libraries. If you want to use them, install them directly. diff --git a/docs/docs/installation/5-raspberry-pi.md b/docs/docs/installation/5-raspberry-pi.md deleted file mode 100644 index 3f178059..00000000 --- a/docs/docs/installation/5-raspberry-pi.md +++ /dev/null @@ -1,42 +0,0 @@ -# Usage on Raspberry Pi - -This guide will help you install the Groundlight SDK on Raspberry Pi. The Groundlight SDK requires Python 3.9 or higher. - -## Prerequisites - -Ensure that you have the following installed on your Raspberry Pi: - -- Python 3.9 or higher -- pip (Python package installer) - -## Basic Installation - -Assuming you have Python 3.9 or higher installed on your Raspberry Pi, you can proceed with the following steps to install or upgrade the Groundlight SDK: - -### Installing Groundlight SDK - -To install the Groundlight SDK using pip, run the following command in your terminal: - -```bash -pip3 install groundlight -``` - -An ARM-compatible version will automatically get installed. The Groundlight SDK is now installed and ready for use. - -## Using RTSP Streams - -If you have `docker` installed on your Raspberry Pi, you can even just run - -```bash -docker run groundlight/stream -``` - -as we publish an ARM version of our streaming application to Docker Hub. - -## Sample application - -For a complete end-to-end example of running on a Raspberry Pi, see [this GitHub repo](https://github.com/groundlight/raspberry-pi-door-lock). - -## Ready to go! - -You're now ready to start using the Groundlight SDK in your projects. For more information on using the SDK, refer to the [API Tokens](../getting-started/5-api-tokens.md) documentation and the [Building Applications Guide](../guide/). \ No newline at end of file diff --git a/docs/docs/installation/6-nvidia-jetson.md b/docs/docs/installation/6-nvidia-jetson.md deleted file mode 100644 index 7478811b..00000000 --- a/docs/docs/installation/6-nvidia-jetson.md +++ /dev/null @@ -1,42 +0,0 @@ -# Usage on NVIDIA Jetson - -This guide will help you install the Groundlight SDK on NVIDIA Jetson devices. The Groundlight SDK requires Python 3.9 or higher. - -## Prerequisites - -Ensure that you have the following installed on your NVIDIA Jetson: - -- Python 3.9 or higher -- pip (Python package installer) - -## Basic Installation - -Assuming you have Python 3.9 or higher installed on your NVIDIA Jetson, you can proceed with the following steps to install or upgrade the Groundlight SDK: - -### Installing Groundlight SDK - -To install the Groundlight SDK using pip, run the following command in your terminal: - -```bash -pip3 install groundlight -``` - -An ARM-compatible version will automatically get installed. The Groundlight SDK is now installed and ready for use. - -## Using RTSP Streams - -If you have `docker` installed on your NVIDIA Jetson, you can even just run - -```bash -docker run groundlight/stream -``` - -as we publish an ARM version of our streaming application to Docker Hub. - -## Sample application - -For a complete end-to-end example of running on an NVIDIA Jetson, see [this GitHub repo](https://github.com/groundlight/raspberry-pi-door-lock). - -## Ready to go! - -You're now ready to start using the Groundlight SDK in your projects. For more information on using the SDK, refer to the [API Tokens](../getting-started/5-api-tokens.md) documentation and the [Building Applications Guide](../guide/). \ No newline at end of file diff --git a/docs/docs/installation/_category_.json b/docs/docs/installation/_category_.json deleted file mode 100644 index e1112892..00000000 --- a/docs/docs/installation/_category_.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "label": "Installation", - "position": 2 -} diff --git a/docs/docs/installation/installation.md b/docs/docs/installation/installation.md deleted file mode 100644 index 0c9f3287..00000000 --- a/docs/docs/installation/installation.md +++ /dev/null @@ -1,28 +0,0 @@ -# Installation Guide for Groundlight Python SDK - -The Groundlight Python SDK requires Python 3.9 or higher and can be installed on all major platforms. Follow the installation guide for your specific operating system or device below. - -## Operating System Installation Guides - -For desktop and server installations: - -- [Linux Installation Guide](1-linux.md) - For Ubuntu, Debian, Fedora and other Linux distributions -- [macOS Installation Guide](2-macos.md) - For Intel and Apple Silicon Macs -- [Windows Installation Guide](3-windows.md) - For Windows 10 and 11 - -## Edge Device Installation Guides - -For IoT and edge computing devices: - -- [Raspberry Pi Installation Guide](5-raspberry-pi.md) - For Raspberry Pi 4 and 5 devices -- [NVIDIA Jetson Installation Guide](6-nvidia-jetson.md) - For Jetson Nano, Xavier and Orin devices - -## Alternative Groundlight Usage Options - -Explore different ways to utilize Groundlight: - -- [Streaming Processor with Docker](../other-ways-to-use/1-stream-processor.md) -- [ESP32 Camera Integration](../other-ways-to-use/2-iot.md) -- [Linux with Monitoring and Notification Server](../other-ways-to-use/3-monitoring-notification-server.md) - -Once you've completed the installation for your platform, you can begin [developing visual applications](/docs/guide/) using the Groundlight SDK. diff --git a/docs/docs/other-ways-to-use/1-stream-processor.md b/docs/docs/other-ways-to-use/1-stream-processor.md deleted file mode 100644 index 5842713b..00000000 --- a/docs/docs/other-ways-to-use/1-stream-processor.md +++ /dev/null @@ -1,82 +0,0 @@ -# Low-Code Stream Processor - -The [Groundlight Stream Processor](https://github.com/groundlight/stream) is a simple containerized application for processing video streams and submitting frames to Groundlight. - -It supports a variety of input sources, including: -- Video devices (webcams) -- Video files (MP4, etc) -- RTSP streams -- HLS streams -- YouTube videos -- Image directories -- Image URLs - -The Stream Processor can be combined with [Groundlight Alerts](../guide/9-alerts.md) to create a simple video analytics system. For example, you could use the Stream Processor to process a video stream from a security camera and send an alert when a person is detected. - -## Prerequisites: -You will need: -1. A [Groundlight account](https://dashboard.groundlight.ai/) -2. An API token from the [Groundlight dashboard](https://dashboard.groundlight.ai/reef/my-account/api-tokens) -3. [Docker](https://www.docker.com/) installed on your system - -Set your Groundlight API token as an environment variable: -```bash -export GROUNDLIGHT_API_TOKEN="" -``` - -## Create a Detector via the Groundlight Dashboard -Once signed in to [the Groundlight dashboard](https://dashboard.groundlight.ai/), create a new detector by clicking the "Create New" button. Give your detector a name, a question, and a confidence threshold, then click "Save." - -You will be redirected to the detector's page, where you can find the detector ID under the Setup tab. Note this ID for later use. - -## Processing a Stream -Processing a stream is as easy as running a Docker container. For example, the following command will process a video file: -```bash -docker run -v /path/to/video:/videos groundlight/stream \ - -t "$GROUNDLIGHT_API_TOKEN" \ - -d “” \ - -s /videos/video.mp4 \ - -f 1 -``` -This will begin submitting frames from the video file to Groundlight. The `-f` flag specifies the frame rate in terms of frames per second. The container can be stopped by pressing `Ctrl+C`. - -A variety of input sources are supported, including RTSP streams. To process an RTSP stream, run the following command: -```bash -docker run groundlight/stream \ - -t "$GROUNDLIGHT_API_TOKEN" \ - -d “” \ - -s "" \ - -f 0.5 \ - -v -``` - -This will begin submitting frames from the RTSP stream to Groundlight. The `-v` flag enables verbose logging. - -If you only wish to submit frames to Groundlight when there is motion detected in the video stream, you can add the `-m` flag: -```bash -docker run groundlight/stream \ - -t "$GROUNDLIGHT_API_TOKEN" \ - -d “” \ - -s "" \ - -f 2 \ - -m -``` - -You may want the container to run in the background. To do this, add the `--detach` flag to the `docker run` command: -```bash -docker run --detach groundlight/stream \ - -t "$GROUNDLIGHT_API_TOKEN" \ - -d “” \ - -s "" \ - -f 2 \ - -m -``` - -:::tip -The Groundlight Stream Processor is lightweight and can be run on a Raspberry Pi or other low-power devices. -::: - -## Combining with Groundlight Alerts -The Stream Processor submits frames to Groundlight, but it does not do anything with the results. - -In order to build a useful alerting system, you can combine the Stream Processor with [Groundlight Alerts](../guide/9-alerts.md). diff --git a/docs/docs/other-ways-to-use/2-iot.md b/docs/docs/other-ways-to-use/2-iot.md deleted file mode 100644 index 7e25f9e3..00000000 --- a/docs/docs/other-ways-to-use/2-iot.md +++ /dev/null @@ -1,70 +0,0 @@ ---- -id: esp32cam -title: "No-Code deployment on an ESP32 Camera Board" -sidebar_position: 1 ---- - -# No-Code IoT Deployment - -Groundlight supplies a tool for no-code deployment of a detector to an ESP32 Camera board. You can find it at [https://iot.groundlight.ai/espcam](https://iot.groundlight.ai/espcam). - -## Easy Deployment - -This tool is designed to make it as easy as possible to deploy your Groundlight detector on an ESP32 Camera Board. You can deploy your detector in just a few clicks. - -1. Go to [https://iot.groundlight.ai/espcam](https://iot.groundlight.ai/espcam) -2. Plug your ESP32 Camera Board into your computer with a USB cable. -3. Click through the steps to upload your detector to your ESP32 Camera Board. -4. When prompted, allow your browser access to the serial port, so that it can program the device. - - - -If you don't see a prompt like this, try using a current version of Chrome or another browser that supports Web Serial. - -## Notification Options - -The tool supports the following notification options for your deployed detector: - -- Email -- SMS (With Twilio) -- Slack - -## Multiple Supported Boards - -Tested with the following boards. Many other ESP32 boards should work as well, but may require building the firmware from source and changing the IO pin definitions. - -- M5Stack ESP32 PSRAM Timer Camera [[purchase here](https://shop.m5stack.com/products/esp32-psram-timer-camera-ov3660)] -- M5Stack ESP32 PSRAM Timer Camera X [[purchase here](https://shop.m5stack.com/products/esp32-psram-timer-camera-x-ov3660)] -- ESP32-CAM [[purchase here](https://www.amazon.com/s?k=ESP32-CAM&i=electronics)] -- SeeedStudio ESP32S3 Sense [[purchase here](https://www.seeedstudio.com/XIAO-ESP32S3-Sense-p-5639.html)] - - - - - - -## Source Code - -The source code is written as an Arduino-based PlatformIO project for ESP32, and is available on GitHub at https://github.com/groundlight/esp32cam - -If you need assistance or have questions about integrating Groundlight with Arduino, please consider opening an issue on the GitHub repository or reaching out to our [support team](mailto:support@groundlight.ai). diff --git a/docs/docs/other-ways-to-use/3-monitoring-notification-server.md b/docs/docs/other-ways-to-use/3-monitoring-notification-server.md deleted file mode 100644 index 3a337ed6..00000000 --- a/docs/docs/other-ways-to-use/3-monitoring-notification-server.md +++ /dev/null @@ -1,88 +0,0 @@ -# Low-Code Monitoring Notification Server - -Groundlight's Monitoring Notification Server (MNS) is the easiest way to deploy your Groundlight detectors on a linux computer. All configuration is done through a web user interface, and no code development is required. - -## Prerequisites - -1. Internet-connected Linux computer -2. Video source (USB camera or RTSP stream) -3. Groundlight API Key (available from [groundlight.ai](https://www.groundlight.ai/)) - -## Using the Application - -The Monitoring Notification Server is a versatile tool that can be deployed on any server to facilitate the creation and management of Groundlight Detectors. It allows you to configure detectors to retrieve images from custom sources and send notifications. - -Featuring an intuitive web interface, the Monitoring Notification Server enables easy configuration of detectors. The server operates on your device, capturing images from your camera and sending notifications as needed. - -### MNS Home Page - -![Intro Page](/img/docker-img-frontpage.png) - -### Detector Dashboard - -![Detector Dashboard](/img/docker-img-dashboard.png) - -## Running the server - -To begin, clone the GitHub repository: https://github.com/groundlight/monitoring-notification-server - -```bash -git clone https://github.com/groundlight/monitoring-notification-server.git -cd monitoring-notification-server -``` - -Deployment options include Docker Compose, AWS Greengrass, and Kubernetes. - -### Running with Docker Compose - -1. Locate the [`docker-compose.yml`](https://github.com/groundlight/monitoring-notification-server/blob/main/deploy/docker-compose.yml) file. - -2. Run `docker-compose up` in the directory containing the `docker-compose.yml` file (the root of the repository). - -:::tip -If you're using Docker Compose v2, replace `docker-compose` with `docker compose`. -::: - -### Running from Docker Compose on 32-bit ARM (armv7) - -32-bit arm requires different binary images. - -1. Use the slightly different [`docker-compose-armv7.yml`](https://github.com/groundlight/monitoring-notification-server/blob/main/deploy/docker-compose-armv7.yml). - -2. Run `docker-compose -f docker-compose-armv7.yml up`. - -### Running with AWS Greengrass - -Before creating the component, run `sudo usermod -aG docker ggc_user` on your Greengrass device to allow the Greengrass service to access the host's Docker daemon. - -1. Create a new Greengrass Component -2. Select "Enter recipe as YAML" -3. Paste the YAML from [greengrass-recipe.yaml](https://github.com/groundlight/monitoring-notification-server/blob/main/deploy/greengrass-recipe.yaml) into the text box -4. Click "Create component" -5. Click "Deploy" to deploy the component to your Greengrass group - -### Running with Kubernetes - -For a minimal Kubernetes setup, we recommend using [k3s](https://k3s.io/). - -1. Set up a Kubernetes cluster and install `kubectl` on your machine. -2. Apply the Kubernetes configuration by running: - ```bash - kubectl apply -f kubernetes.yaml - ``` - Ensure you are in the directory containing the [`kubernetes.yaml`](https://github.com/groundlight/monitoring-notification-server/blob/main/deploy/kubernetes.yaml) file. - -## Building from Source - -1. Install [Node.js](https://nodejs.org/en/download/) and [Python 3.9+](https://www.python.org/downloads/). - -```bash -git clone https://github.com/groundlight/monitoring-notification-server -cd monitoring-notification-server -npm install -npm run dev -``` - -Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. - -The FastApi server will be running on [http://0.0.0.0:8000](http://0.0.0.0:8000) – feel free to change the port in `package.json` (you'll also need to update it in `next.config.js`). diff --git a/docs/docs/other-ways-to-use/_category_.json b/docs/docs/other-ways-to-use/_category_.json deleted file mode 100644 index 16f37a0d..00000000 --- a/docs/docs/other-ways-to-use/_category_.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "label": "Alternative Deployment Options", - "position": 6, - "collapsed": false -} \ No newline at end of file diff --git a/docs/docs/sample-applications/1-dog-on-couch.md b/docs/docs/sample-applications/1-dog-on-couch.md deleted file mode 100644 index 28e9272d..00000000 --- a/docs/docs/sample-applications/1-dog-on-couch.md +++ /dev/null @@ -1,108 +0,0 @@ -# A Fun Example: Dog-on-Couch Detector - -Here is a whimsical example of how you could use Groundlight in your home to keep your dog off the couch. This document will guide you through creating a complete application. If the dog is detected on the couch, the application will play a pre-recorded sound over the computer's speakers, instructing the dog to get off the couch. Be sure to record your own voice so that your dog pays attention to you. - -## Requirements - -- [Groundlight SDK](/docs/installation/) with Python 3.9 or higher -- A supported USB or network-connected camera -- A pre-recorded sound file (e.g., `get_off_couch.mp3`) -- A couch and a dog are recommended for proper end-to-end testing. - -## Installation - -Ensure you have Python 3.9 or higher installed, and then install the Groundlight SDK, OpenCV library, and other required libraries: - -```bash -pip install groundlight opencv-python pillow pyaudio -``` - -## Creating the Application - -1. First, log in to the [Groundlight dashboard](https://dashboard.groundlight.ai) and create an [API Token](https://dashboard.groundlight.ai/reef/my-account/api-tokens). - -2. Next, we'll write the Python script for the application. Import the required libraries: - -```python notest -import cv2 -import pyaudio -import time -import wave -from PIL import Image -from groundlight import Groundlight, ApiException -``` - -3. Define a function to capture an image from the camera using OpenCV: - -```python -def capture_image(): - cap = cv2.VideoCapture(0) - - ret, frame = cap.read() - cap.release() - - if ret: - # Convert to PIL image - return Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) - else: - return None -``` - -4. Define a function to play the pre-recorded sound: - -```python -def play_sound(file_path): - CHUNK = 1024 - wf = wave.open(file_path, 'rb') - - p = pyaudio.PyAudio() - - stream = p.open(format=p.get_format_from_width(wf.getsampwidth()), - channels=wf.getnchannels(), - rate=wf.getframerate(), - output=True) - - data = wf.readframes(CHUNK) - - while data: - stream.write(data) - data = wf.readframes(CHUNK) - - stream.stop_stream() - stream.close() - - p.terminate() -``` - -5. Write the main application loop: - -```python notest -gl = Groundlight() - -detector = gl.get_or_create_detector("Dog on Couch Detector") - -while True: - image = capture_image() - if image: - try: - iq = gl.submit_image_query(image=image, detector=detector, wait=60) - answer = iq.result.label - if answer == "YES": - print("Dog detected on the couch!") - play_sound("get_off_couch.mp3") - except ApiException as e: - print(f"Error submitting image query: {e}") - else: - print("Failed to capture image") - - # Sleep for a minute before checking again - time.sleep(60) -``` - -This application captures an image using the `capture_image` function, then submits it to the Groundlight API for analysis. If the dog is detected on the couch, it plays the pre-recorded sound using the `play_sound` function. - -Save the script as `dog_on_couch_detector.py` and run it: - -```bash -python dog_on_couch_detector.py -``` diff --git a/docs/docs/sample-applications/2-retail-analytics.md b/docs/docs/sample-applications/2-retail-analytics.md deleted file mode 100644 index e3be7a23..00000000 --- a/docs/docs/sample-applications/2-retail-analytics.md +++ /dev/null @@ -1,170 +0,0 @@ -# A Serious Example: Retail Analytics - -## Tracking utilization of a customer service counter - -This example demonstrates the application of Groundlight to a retail analytics solution, which monitors the usage of a service counter by customers throughout the day. The application creates a detector to identify when the service desk is being utilized by a customer. It checks the detector every minute, and once an hour, it prints out a summary of the percentage of time that the service counter is in use. At the end of the day, it emails the daily log. - -This retail analytics application can be beneficial in various ways: - -1. **Staff allocation and scheduling:** By analyzing the usage patterns of the service counter, store managers can optimize staff allocation and scheduling, ensuring that enough employees are available during peak hours and reducing wait times for customers. - -1. **Identifying trends:** The application can help identify trends in customer behavior, such as busier times of the day or specific days of the week with higher traffic. This information can be used to plan targeted marketing campaigns or promotions to increase sales and customer engagement. - -1. **Improving store layout:** Understanding when and how often customers use the service counter can provide insights into the effectiveness of the store's layout. Retailers can use this information to make data-driven decisions about rearranging the store layout to encourage customers to visit the service counter or explore other areas of the store. - -1. **Customer satisfaction:** By monitoring the usage of the service counter and proactively addressing long wait times or crowded areas, retailers can improve customer satisfaction and loyalty. A positive customer experience can lead to increased sales and return visits. - -To implement this retail analytics solution, a store would need to install a supported camera near the service counter, ensuring a clear view of the area. The camera would then be connected to a computer running the Groundlight-based application. Store managers would receive hourly summaries of the service counter usage and a daily log via email, enabling them to make informed decisions to improve store operations and customer experience. - -## Requirements - -- [Groundlight SDK](/docs/installation/) with Python 3.9 or higher -- A supported USB or network-connected camera -- An email account with SMTP access to send the daily log - -## Installation - -Ensure you have Python 3.9 or higher installed, and then install the Groundlight SDK, OpenCV library, and other required libraries: - -```bash -pip install groundlight opencv-python pillow -``` - -## Creating the Application - -1. First, log in to the [Groundlight dashboard](https://dashboard.groundlight.ai) and create an [API Token](https://dashboard.groundlight.ai/reef/my-account/api-tokens). - -2. Next, we'll write the Python script for the application. Import the required libraries: - -```python notest -import smtplib -import time -from datetime import datetime, timedelta -from email.mime.multipart import MIMEMultipart -from email.mime.text import MIMEText - -import cv2 -from PIL import Image - -from groundlight import Groundlight -``` - -3. Define a function to capture an image from the camera using OpenCV: - -```python -def capture_image(): - cap = cv2.VideoCapture(0) - - ret, frame = cap.read() - cap.release() - - if ret: - # Convert to PIL image - return Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) - else: - return None -``` - -4. Define a function to send the daily log via email. You will need to customize this for your particular network environment. - -```python -def send_email(sender, receiver, subject, body): - msg = MIMEMultipart() - msg['From'] = sender - msg['To'] = receiver - msg['Subject'] = subject - msg.attach(MIMEText(body, 'plain')) - - server = smtplib.SMTP('smtp.example.com', 587) - server.starttls() - server.login(sender, "your-password") - text = msg.as_string() - server.sendmail(sender, receiver, text) - server.quit() -``` - -5. Define when your business's operating hours are: - -```python notest -START_OF_BUSINESS = 9 # e.g. 9am -END_OF_BUSINESS = 17 # e.g. 5pm - -def is_within_business_hours(): - current_hour = datetime.now().hour - return START_OF_BUSINESS <= current_hour < END_OF_BUSINESS - -``` - - -6. Write the main application loop: - -```python notest -gl = Groundlight() - -detector = gl.get_or_create_detector( - name="counter-in-use", - query="Is there a customer at the service counter?", - # We can get away with relatively low confidence since we're aggregating across images - confidence_threshold=0.8) - -DELAY = 60 - -log = [] -daily_log = [] -next_hourly_start = datetime.now().replace(minute=0, second=0, microsecond=0) + timedelta(hours=1) - -while True: - if not is_within_business_hours(): - time.sleep(DELAY) - continue - - image = capture_image() - if not image: - print("Failed to capture image") - time.sleep(DELAY) - continue - - try: - iq = gl.submit_image_query(image=image, detector=detector, wait=60) - except Exception as e: - print(f"Error submitting image query: {e}") - time.sleep(DELAY) - continue - - answer = iq.result.label - log.append(answer) - - if datetime.now() >= next_hourly_start: - next_hourly_start += timedelta(hours=1) - - percent_in_use = (log.count("YES") / len(log)) * 100 - current_time = datetime.now().replace(hour=START_OF_BUSINESS, minute=0, second=0) - formatted_time = current_time.strftime("%I%p") # like 3pm - msg = f"Hourly summary for {formatted_time}: {percent_in_use:.0f}% counter in use" - print(msg) - daily_log.append(msg) - log = [] - - current_hour = datetime.now().hour - if current_hour == END_OF_BUSINESS and not daily_log == []: - daily_summary = "Daily summary:\n" - for msg in daily_log: - daily_summary += f"{msg}\n" - - print(daily_summary) - send_email(sender="counterbot@example.com", - receiver="manager@example.com", - subject="Daily Service Counter Usage Log", - body=daily_summary) - daily_log = [] - - time.sleep(DELAY) -``` - -This application captures an image using the `capture_image` function, then submits it to the Groundlight API for analysis. If a customer is detected at the counter, it logs the event. Every hour, it prints a summary of the counter's usage percentage, and at the end of the day, it emails the daily log using the `send_email` function. - -Save the entire script as `service_counter_monitor.py` and run it: - -```bash -python service_counter_monitor.py -``` diff --git a/docs/docs/sample-applications/3-streaming-with-alerts.md b/docs/docs/sample-applications/3-streaming-with-alerts.md deleted file mode 100644 index f6a30b7a..00000000 --- a/docs/docs/sample-applications/3-streaming-with-alerts.md +++ /dev/null @@ -1,94 +0,0 @@ -# A Quick Example: Live Stream Monitor - -A quick example to help you get started with monitoring live streams using the [groundlight/stream](../other-ways-to-use/1-stream-processor.md) container. - -In this example, we will set up a monitor on a live stream of a bird feeder and configure Groundlight to alert us when a bird is present at the feeder. - -## Requirements - -- [Docker](https://www.docker.com/) installed on your system -- A YouTube live stream URL or video ID you'd like to monitor. For example, this live stream of a Bird Feeder in Panama hosted by the Cornell Lab of Ornithology: [`https://www.youtube.com/watch?v=WtoxxHADnGk`](https://www.youtube.com/watch?v=WtoxxHADnGk) -- A [Groundlight account](https://dashboard.groundlight.ai) - - - -## Installation - -Pull the Groundlight Stream container: - -```bash -docker pull groundlight/stream -``` - -## Creating the Monitor - -The Groundlight Stream container makes it easy to monitor video streams. Here's how to use it: - -1. First, get (or create) your API token from the [Groundlight dashboard](https://dashboard.groundlight.ai/reef/my-account/api-tokens). Set your Groundlight API token as an environment variable: - -```bash -export GROUNDLIGHT_API_TOKEN="" -``` - -2. Create a Binary-mode detector in the [dashboard](https://dashboard.groundlight.ai). Set the question to "Is there a bird at the feeder?" and the confidence threshold to 0.75. Note the detector ID for later use. - -:::tip - -We use a relatively low confidence threshold in this example because birdwatching is a fun and casual activity. For more critical applications, you may want to set a higher confidence threshold. - -::: - -3. Now, run the Groundlight Stream container to process the live stream. For example, to monitor the Cornell Lab of Ornithology's bird feeder live stream, you could use the following command: - -```bash -docker run groundlight/stream \ - -t "$GROUNDLIGHT_API_TOKEN" \ - -d "" \ - -s "https://www.youtube.com/watch?v=WtoxxHADnGk" \ - -f 0.25 \ # 1 frame every 4 seconds - -m \ # enable motion detection to only process frames when movement occurs - -v # enable verbose logging -``` -You should see Image Queries being submitted to Groundlight as the container processes the live stream. Once you have confirmed that the container is working as expected, you can remove the `-v` flag to reduce the amount of logging, and you can also run the container in the background by adding the `--detach` flag. -```bash -docker run --detach groundlight/stream \ - -t "$GROUNDLIGHT_API_TOKEN" \ - -d "" \ - -s "https://www.youtube.com/watch?v=WtoxxHADnGk" \ - -f 0.25 \ # 1 frame every 4 seconds - -m # enable motion detection to only process frames when movement occurs -``` - -4. Finally, let's set up an Alert to notify you when a bird visits the feeder. In the Groundlight dashboard: - - 1. Navigate to [the Alerts tab](https://dashboard.groundlight.ai/reef/alerts) and click "Create New Alert" - 2. Enter a descriptive name like for the alert, such as "Bird at feeder" - 3. Select your bird detector by name - 4. Set the alert condition to `Gives answer 'Yes' For 1 Consecutive answer(s)` - 5. Choose "Text" as the Alert Type and enter your phone number - 6. Enable "Include image in message" to receive a photo of the bird with each alert (optional) - 7. Enable a 5-minute snooze period to prevent alert fatigue (optional) - 8. Click "Create" to activate your alert - -5. Now, sit back and relax! The container will begin submitting frames from the live stream to Groundlight. You will receive alerts when a bird is detected at the feeder. - -## Additional Options - -You can also use `groundlight/stream` to process local video files, RTSP streams, and more. Here are some examples: -- Process local video files by mounting them: -```bash -docker run -v /path/to/video:/videos groundlight/stream \ - -t "$GROUNDLIGHT_API_TOKEN" \ - -d \ - -s /videos/video.mp4 -``` - -- Connect to RTSP cameras: -```bash -docker run groundlight/stream \ - -t "$GROUNDLIGHT_API_TOKEN" \ - -d \ - -s "rtsp://username:password@camera_ip:554/stream" -``` - -See the complete documentation at https://github.com/groundlight/stream \ No newline at end of file diff --git a/docs/docs/sample-applications/4-industrial.md b/docs/docs/sample-applications/4-industrial.md deleted file mode 100644 index 48dd0eef..00000000 --- a/docs/docs/sample-applications/4-industrial.md +++ /dev/null @@ -1,23 +0,0 @@ -# Industrial and Manufacturing Applications - -Modern natural language-based computer vision is transforming industrial and manufacturing applications by enabling more intuitive interaction with automation systems. Groundlight offers cutting-edge computer vision technology that can be seamlessly integrated into various industrial processes, enhancing efficiency, productivity, and quality control. - -## Machine Tending - -Groundlight's computer vision technology can assist in automating machine-tending tasks, such as loading and unloading materials in CNC machines, milling centers, or injection molding equipment. By enabling robots to recognize parts and tools using natural language, complex machine-tending tasks become more accessible and efficient. - -## Process Automation - -Integrating Groundlight's computer vision into your process automation systems can help identify bottlenecks, optimize workflows, and reduce manual intervention. Our technology can work hand-in-hand with robotic systems to perform tasks like sorting, assembly, all while interpreting natural language commands to streamline operations. - -## Quality Control - -Groundlight's computer vision technology can play a vital role in ensuring the highest quality standards in your manufacturing processes. By identifying defects or irregularities in products, our computer vision system can help maintain strict quality control, reducing the need for manual inspections and increasing overall product quality. - -## Integration with Cobots and CNC Machines - -Groundlight's computer vision technology can be easily integrated with popular cobot robotic arms, such as Universal Robots, to enhance their capabilities and improve collaboration between humans and robots. Additionally, our technology can be integrated into existing CNC machines or other devices using the Modbus interface, allowing for seamless communication and control within your manufacturing environment. - -# Contact Sales - -To learn more about how Groundlight's natural language computer vision technology can revolutionize your industrial and manufacturing processes, please reach out to us at [info@groundlight.ai](mailto:info@groundlight.ai). \ No newline at end of file diff --git a/docs/docs/sample-applications/_category_.json b/docs/docs/sample-applications/_category_.json deleted file mode 100644 index 138a71ea..00000000 --- a/docs/docs/sample-applications/_category_.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "label": "Sample Applications", - "position": 5 - } diff --git a/docs/docs/sample-applications/sample-applications.md b/docs/docs/sample-applications/sample-applications.md deleted file mode 100644 index 8c5d09d4..00000000 --- a/docs/docs/sample-applications/sample-applications.md +++ /dev/null @@ -1,45 +0,0 @@ -# Sample Applications - -Explore these example applications to see Groundlight's computer vision capabilities in action: - -## Industrial and Manufacturing Applications - -Groundlight's natural language-based computer vision technology transforms industrial processes in several key areas: - -- **Machine Tending**: Automate loading/unloading of CNC machines and manufacturing equipment -- **Process Automation**: Optimize workflows and reduce manual intervention through intelligent vision systems -- **Quality Control**: Identify defects and maintain strict quality standards -- **Cobot Integration**: Enhance capabilities of collaborative robots and CNC machines - -[Learn more about industrial applications →](/docs/sample-applications/industrial) - -## Retail Analytics Solution - -Monitor customer service counter utilization with this practical retail application. Features include: - -- Real-time tracking of service counter usage -- Hourly utilization summaries -- Automated daily reports via email -- Data-driven insights for staff scheduling and store layout optimization - -[View the retail analytics implementation →](/docs/sample-applications/retail-analytics) - -## Fun Project: Dog-on-Couch Detector - -Create a playful home automation system that detects when your dog is on the couch and plays a pre-recorded message. This example demonstrates: - -- Real-time image capture and analysis -- Audio playback integration -- Continuous monitoring capabilities - -[Build your own dog detector →](/docs/sample-applications/dog-on-couch) - -## Live Stream Monitor: Bird Feeder Edition - -Monitor live streams with automated alerts using Groundlight's vision API. Features include: - -- Frame capture from live streams -- Alert system integration -- Simple command-line interface - -[Create a monitor for birds at your feeder →](/docs/sample-applications/streaming-with-alerts) \ No newline at end of file diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js deleted file mode 100644 index 58be19f4..00000000 --- a/docs/docusaurus.config.js +++ /dev/null @@ -1,259 +0,0 @@ -// @ts-check -// Note: type annotations allow type checking and IDEs autocompletion - -// Libraries that support mathematics in documentation -import rehypeKatex from "rehype-katex"; -import remarkMath from "remark-math"; - -// Options: https://github.com/FormidableLabs/prism-react-renderer/tree/master/src/themes -const lightCodeTheme = require("prism-react-renderer").themes.github; -const darkCodeTheme = require("prism-react-renderer").themes.vsDark; - -/** @type {import('@docusaurus/types').Config} */ -const config = { - title: "Groundlight", - tagline: "Computer Vision powered by Natural Language", - favicon: "img/favicon.ico", - - // Set the production url of your site here - url: "https://code.groundlight.ai", - // Set the // pathname under which your site is served - // For GitHub pages deployment, it is often '//' - baseUrl: "/python-sdk/", - - // GitHub pages deployment config. - // If you aren't using GitHub pages, you don't need these. - organizationName: "groundlight", // Usually your GitHub org/user name. - projectName: "python-sdk", // Usually your repo name. - deploymentBranch: "gh-pages", // Branch that GitHub pages will deploy from. - trailingSlash: false, - - onBrokenLinks: "throw", - onBrokenMarkdownLinks: "throw", - - // Even if you don't use internalization, you can use this field to set useful - // metadata like html lang. 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"integrity": "sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==", - "license": "MIT", - "funding": { - "type": "github", - "url": "https://github.com/sponsors/wooorm" - } - } - } -} diff --git a/docs/package.json b/docs/package.json deleted file mode 100644 index e8323f8b..00000000 --- a/docs/package.json +++ /dev/null @@ -1,51 +0,0 @@ -{ - "name": "code-groundlight-ai", - "version": "0.0.0", - "private": true, - "scripts": { - "docusaurus": "docusaurus", - "start": "docusaurus start", - "build": "docusaurus build", - "swizzle": "docusaurus swizzle", - "deploy": "docusaurus deploy", - "clear": "docusaurus clear", - "serve": "docusaurus serve", - "write-translations": "docusaurus write-translations", - "write-heading-ids": "docusaurus write-heading-ids", - "typecheck": "tsc" - }, - "dependencies": { - "@docusaurus/core": "^3.6.3", - "@docusaurus/plugin-client-redirects": "^3.6.3", - "@docusaurus/preset-classic": "^3.6.3", - "@easyops-cn/docusaurus-search-local": "^0.46.1", - "@mdx-js/react": "^3.1.0", - "clsx": "^2.1.1", - "prism-react-renderer": "^2.4.0", - "react": "^18.0.0", - "react-dom": "^18.0.0", - "rehype-katex": "^7.0.1", - "remark-math": "^6.0.0" - }, - "devDependencies": { - "@docusaurus/module-type-aliases": "^3.6.3", - "@docusaurus/tsconfig": "^3.6.3", - "@docusaurus/types": "^3.6.3", - "typescript": "~5.2.2" - }, - "browserslist": { - "production": [ - ">0.5%", - "not dead", - "not op_mini all" - ], - "development": [ - "last 3 chrome version", - "last 3 firefox version", - "last 5 safari version" - ] - }, - "engines": { - "node": ">=21.0" - } -} diff --git a/docs/sidebars.js b/docs/sidebars.js deleted file mode 100644 index 8f1e3da2..00000000 --- a/docs/sidebars.js +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Creating a sidebar enables you to: - - create an ordered group of docs - - render a sidebar for each doc of that group - - provide next/previous navigation - - The sidebars can be generated from the filesystem, or explicitly defined here. - - Create as many sidebars as you want. - */ - -// @ts-check - -/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */ -const sidebars = { - // By default, Docusaurus generates a sidebar from the docs folder structure - tutorialSidebar: [{ type: "autogenerated", dirName: "." }], - - // But you can create a sidebar manually - /* - tutorialSidebar: [ - 'intro', - 'hello', - { - type: 'category', - label: 'Tutorial', - items: ['tutorial-basics/create-a-document'], - }, - ], - */ -}; - -module.exports = sidebars; diff --git a/docs/src/components/HomepageFeatures/index.tsx b/docs/src/components/HomepageFeatures/index.tsx deleted file mode 100644 index 08903cdd..00000000 --- a/docs/src/components/HomepageFeatures/index.tsx +++ /dev/null @@ -1,81 +0,0 @@ -import clsx from "clsx"; -import styles from "./styles.module.css"; -// There should be a line here that says -// import React from "react"; -// VSCode might try to delete it, but that will break the site. -import React from "react"; - -type FeatureItem = { - title: string; - imgsrc: string; - description: JSX.Element; -}; - -const FeatureList: FeatureItem[] = [ - { - title: "Instant Models", - imgsrc: "img/1-models.png", - description: ( - <> - Groundlight's Visual LLM creates - computer vision models from English instructions instead of a dataset. - This reduces the time to get an AI-driven solution off the ground. - Did we mention you don't need a dataset? - - ), - }, - { - title: "Human Reliability", - - imgsrc: "img/2-reliability.png", - description: ( - <> - Groundlight's models are allowed to say they're "Unsure" and can - escalate to a larger model or human expert for assistance. - By knowing what they know, Groundlight's models act more robust, - combining the speed of AI with the reliability of human oversight. - - ), - }, - { - title: "Seamless MLOps", - imgsrc: "img/3-mlops.png", - description: ( - <> - Because Groundlight starts with humans-in-the-loop (HITL), - continuous monitoring and auditing are automatic. - Any data drift is automatically detected and corrected for. - So you know your visual applications won't fall behind as the world - around them inevitably changes. - - ), - }, -]; - -function Feature({ title, imgsrc, description }: FeatureItem) { - return ( -
-
- -
-
-

{title}

-

{description}

-
-
- ); -} - -export default function HomepageFeatures(): JSX.Element { - return ( -
-
-
- {FeatureList.map((props, idx) => ( - - ))} -
-
-
- ); -} diff --git a/docs/src/components/HomepageFeatures/styles.module.css b/docs/src/components/HomepageFeatures/styles.module.css deleted file mode 100644 index b248eb2e..00000000 --- a/docs/src/components/HomepageFeatures/styles.module.css +++ /dev/null @@ -1,11 +0,0 @@ -.features { - display: flex; - align-items: center; - padding: 2rem 0; - width: 100%; -} - -.featureSvg { - height: 200px; - width: 200px; -} diff --git a/docs/src/css/custom.css b/docs/src/css/custom.css deleted file mode 100644 index 5555a2f0..00000000 --- a/docs/src/css/custom.css +++ /dev/null @@ -1,40 +0,0 @@ -/** - * Any CSS included here will be global. The classic template - * bundles Infima by default. Infima is a CSS framework designed to - * work well for content-centric websites. - */ - -/* -You can override the default Infima variables here. -See: https://docusaurus.io/docs/styling-layout#styling-your-site-with-infima -*/ -:root { - --ifm-color-primary: #2e4b85; - --ifm-color-primary-dark: #294378; - --ifm-color-primary-darker: #274071; - --ifm-color-primary-darkest: #20345d; - --ifm-color-primary-light: #335292; - --ifm-color-primary-lighter: #355699; - --ifm-color-primary-lightest: #3c61ad; - --ifm-code-font-size: 95%; - --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.1); -} - -/* -For readability concerns, you should choose a lighter palette in dark mode. -See: https://docusaurus.io/docs/styling-layout#styling-your-site-with-infima -*/ -[data-theme='dark'] { - --ifm-color-primary: #71a3e5; - --ifm-color-primary-dark: #5490e0; - --ifm-color-primary-darker: #4687dd; - --ifm-color-primary-darkest: #256cca; - --ifm-color-primary-light: #8eb6ea; - --ifm-color-primary-lighter: #9cbfed; - --ifm-color-primary-lightest: #c8dbf5; - --docusaurus-highlighted-code-line-bg: rgba(68, 105, 165, 0.3); -} - -.page-content[data-id='redirect'] { - display: none; -} \ No newline at end of file diff --git a/docs/src/css/style.module.css b/docs/src/css/style.module.css deleted file mode 100644 index 54fe0a89..00000000 --- a/docs/src/css/style.module.css +++ /dev/null @@ -1,640 +0,0 @@ -@import url("https://fonts.googleapis.com/css2?family=Chivo:ital,wght@0,100..900;1,100..900&display=swap"); -* { - box-sizing: border-box; -} - -a { - color: #3498db; - display: inline-block; -} - -a:hover { - color: #2980b9; -} - -h1, -h2, -h3 { - line-height: 1.25; - font-weight: 500; -} - -.container { - width: 100%; - margin-inline: auto; - padding-inline: 12px; -} - -.landing-page-container { - margin: 0; - padding: 0; - color: #ffffff; - background-color: #121212; - font-family: "Chivo", sans-serif; - line-height: 1.5; -} - -@media (min-width: 576px) { - .container { - max-width: 540px; - } -} -/* Small */ -@media (min-width: 768px) { - .container { - max-width: 720px; - } -} -/* Medium */ -@media (min-width: 992px) { - .container { - max-width: 960px; - } -} -/* Large */ -@media (min-width: 1200px) { - .container { - max-width: 1140px; - } -} -/* Extra large */ -@media (min-width: 1400px) { - .container { - max-width: 1254px; - } -} -/* Fluid container */ -.container-fluid { - width: 100%; - padding-right: var(--bs-gutter-x, 0.75rem); - padding-left: var(--bs-gutter-x, 0.75rem); - margin-right: auto; - margin-left: auto; -} - -.cmnButton { - display: inline-block; - padding: 4px 35px 6px; - color: #000000 !important; - background-color: #eacc8b; - border: 1px solid #eacc8b; - font-size: 20px; - text-decoration: none; - transition: all 0.3s linear; -} -.cmnButton:hover { - color: #eacc8b !important; - background-color: transparent; -} -.cmnButton.outline { - color: #eacc8b !important; - background-color: transparent; -} -.cmnButton.outline:hover { - color: #000000 !important; - background-color: #eacc8b; - border: 1px solid #eacc8b; -} - -.header { - padding: 30px 0; - border-bottom: 1px solid #4d4d4d; -} -@media (max-width: 991px) { - .header { - padding: 20px 0; - } -} -.header .headerwrapper { - display: flex; - justify-content: space-between; - align-items: center; -} -.header .headerwrapper .logo { - display: inline-block; - height: -moz-maxcontent; - height: maxcontent; -} -.header .headerwrapper .logo img { - width: 246px; -} -@media (max-width: 1399px) { - .header .headerwrapper .logo img { - width: 220px; - } -} -.header .menutoggle { - display: flex; - align-items: center; - justify-content: center; - background: #1c1c1c; - border: 1px solid #4d4d4d; - border-radius: 4px; - padding-top: 4px; - padding-bottom: 4px; -} -@media (min-width: 992px) { - .header .menutoggle { - display: none; - } -} -.header .menutoggle .closee { - display: none; -} -.header .menutoggle.active .closee { - display: block; -} -.header .menutoggle.active .toggle { - display: none; -} -.menu { - display: flex; - align-items: center; - gap: 10px 50px; - list-style: none; - margin: 0; - padding: 0; -} -@media (max-width: 1399px) { - .menu { - gap: 10px; - } -} -@media (min-width: 992px) { - .menu .containlogo { - display: none; - } -} -@media (max-width: 991px) { - .menu { - position: fixed; - top: 0; - left: 0; - height: 100dvh; - flex-direction: column; - padding: 40px; - text-align: left; - align-items: flex-start; - background-color: #1c1c1c; - min-width: 300px; - gap: 15px; - transform: translateX(-100%); - transition: all 0.3s linear; - } - .menu.active { - transform: translateX(0); - } -} -.header .menu .menuitem .menulink { - transition: all 0.3s linear; - text-decoration: none; - font-size: 24px; - padding: 5px 20px; -} -@media (max-width: 1399px) { - .header .menu .menuitem .menulink { - font-size: 22px; - } -} -@media (max-width: 1199px) { - .header .menu .menuitem .menulink { - font-size: 18px; - padding-inline: 15px; - } -} -@media (max-width: 991px) { - .header .menu .menuitem .menulink { - font-size: 16px; - padding-inline: 8px; - } -} -.header .menu .menuitem .menulink:hover { - color: #eacc8b; -} -.header .menu .menuitem .menulink.active { - color: #eacc8b; -} - -.bannersection { - padding-top: 120px; - padding-bottom: 120px; -} -@media (max-width: 991px) { - .bannersection { - padding-top: 60px; - padding-bottom: 60px; - } -} -.bannersection .bannerwrapper { - display: flex; - gap: 40px; -} -@media (max-width: 991px) { - .bannersection .bannerwrapper { - flex-direction: column; - gap: 25px; - } -} -.bannersection .bannerwrapper .title { - font-size: 64px; - font-weight: 500; - max-width: 700px; - width: 100%; - line-height: 1.3; - margin-top: 0; - margin-bottom: 0; - letter-spacing: -0.5px; -} -@media (max-width: 1199px) { - .bannersection .bannerwrapper .title { - font-size: 44px; - } -} -.bannersection .bannerwrapper .content { - max-width: 495px; -} -@media (max-width: 1199px) { - .bannersection .bannerwrapper .content { - max-width: 420px; - } -} -.bannersection .bannerwrapper .content p { - margin-top: 0; - font-size: 20px; -} -@media (max-width: 1199px) { - .bannersection .bannerwrapper .content p { - font-size: 18px; - } -} -.bannersection .bannerwrapper .buttonwrapper { - display: flex; - flex-wrap: wrap; - gap: 12px 16px; -} -@media (max-width: 991px) { - .bannersection .bannerwrapper .buttonwrapper { - margin-top: 35px; - } -} - -.featuresection { - padding-bottom: 120px; -} -@media (max-width: 575px) { - .featuresection { - padding-bottom: 60px; - } -} -.featuresection .featurewrapper { - display: grid; - grid-template-columns: repeat(3, 1fr); - gap: 80px; - justify-content: center; -} -@media (max-width: 1199px) { - .featuresection .featurewrapper { - gap: 50px 30px; - } -} -@media (max-width: 991px) { - .featuresection .featurewrapper { - grid-template-columns: repeat(2, 1fr); - } -} -@media (max-width: 575px) { - .featuresection .featurewrapper { - grid-template-columns: repeat(1, 1fr); - } -} -.featuresection .featureitem { - text-align: center; - margin-inline: auto; -} -.featuresection .featureitem .icon { - height: 246px; -} -.featuresection .featureitem .title { - font-size: 24px; - margin-top: 0; - margin-bottom: 20px; -} -.featuresection .featureitem p { - font-size: 18px; - margin-bottom: 0; -} - -.integrationcompatibility { - padding-bottom: 120px; -} -@media (max-width: 575px) { - .integrationcompatibility { - padding-bottom: 60px; - } -} - -.sectiontitle { - font-size: 64px; - margin-bottom: 30px; - margin-top: 0; -} -@media (min-width: 1200px) { - .sectiontitle { - letter-spacing: -0.3px; - } -} -@media (max-width: 1199px) { - .sectiontitle { - font-size: 48px; - } -} -@media (max-width: 575px) { - .sectiontitle { - font-size: 36px; - } -} - -.integrationcompatibilitywrapper { - display: flex; - gap: 40px; -} -@media (max-width: 991px) { - .integrationcompatibilitywrapper { - flex-direction: column; - } -} -.integrationcompatibilitywrapper .leftcontent { - max-width: 500px; - width: 100%; -} -@media (max-width: 1199px) { - .integrationcompatibilitywrapper .leftcontent { - max-width: 400px; - } -} -@media (max-width: 991px) { - .integrationcompatibilitywrapper .leftcontent { - max-width: 100%; - } -} -.integrationcompatibilitywrapper .leftcontent p { - margin-top: 0; -} -.integrationcompatibilitywrapper .pipe { - background-color: white; - width: 1px; -} -.integrationcompatibilitywrapper .rightcontent { - max-width: calc(100% - 580px); -} -@media (max-width: 1199px) { - .integrationcompatibilitywrapper .rightcontent { - max-width: calc(100% - 400px); - } -} -@media (max-width: 991px) { - .integrationcompatibilitywrapper .rightcontent { - max-width: 100%; - } -} -.integrationcompatibilitywrapper .logoswrapper { - display: grid; - grid-template-columns: repeat(4, 1fr); - gap: 30px 42px; - margin-top: 45px; -} -@media (max-width: 575px) { - .integrationcompatibilitywrapper .logoswrapper { - gap: 20px 15px; - } -} -.integrationcompatibilitywrapper .logoswrapper img { - max-width: 70px; - max-height: 54px; - -o-object-fit: contain; - object-fit: contain; -} - -.integrationcompatibilitygrid { - display: grid; - grid-template-columns: repeat(2, 1fr); - gap: 42px; -} -@media (max-width: 575px) { - .integrationcompatibilitygrid { - grid-template-columns: repeat(1, 1fr); - } -} - -.integrationcompatibilityitem .title { - margin-top: 0; - margin-bottom: 20px; - font-size: 24px; -} -.integrationcompatibilityitem p { - margin: 0; -} -.integrationcompatibilityitem .cmnButton { - margin-top: 20px; -} - -.codesection { - padding-bottom: 120px; -} -@media (max-width: 575px) { - .codesection { - padding-bottom: 60px; - } -} - -.supportsection { - padding-bottom: 90px; -} -@media (max-width: 991px) { - .supportsection { - padding-bottom: 60px; - } -} - -.supportgrid { - display: grid; - grid-template-columns: repeat(3, 1fr); - gap: 28px; -} -@media (max-width: 991px) { - .supportgrid { - grid-template-columns: repeat(2, 1fr); - } -} -@media (max-width: 767px) { - .supportgrid { - grid-template-columns: repeat(1, 1fr); - } -} - -.supportitem { - padding: 24px 24px 34px; - border-radius: 26px; - background-color: #313131; - position: relative; - display: flex; - gap: 28px; -} -@media (max-width: 1199px) { - .supportitem .icon { - max-width: 42px; - flex-shrink: 0; - } - .supportitem .icon img { - width: 100%; - } -} -.supportitem .title { - margin-top: 0; - font-size: 34px; - margin-bottom: 10px; -} -.supportitem p { - line-height: 2; -} -.supportitem .cmnButton { - align-self: flex-start; - font-size: 12px; - padding-top: 0; - padding-bottom: 2px; - padding-inline: 20px; -} -.supportitem::before { - position: absolute; - content: ""; - left: -1px; - top: -1px; - right: -1px; - bottom: -1px; - background: linear-gradient(135deg, #fff 0%, rgba(255, 255, 255, 0.1) 120%); - z-index: -1; - border-radius: 26px; -} - -.supportitem .content { - display: flex; - flex-direction: column; - justify-content: space-between; -} - -.footer { - background-color: #1c1c1c; - border-top: 1px solid #4d4d4d; - padding-top: 50px; - padding-bottom: 40px; -} - -.footerwrapper { - display: grid; - grid-template-columns: repeat(3, 1fr); - gap: 40px 0; -} -@media (max-width: 991px) { - .footerwrapper { - grid-template-columns: repeat(2, 1fr); - } -} -@media (max-width: 575px) { - .footerwrapper { - grid-template-columns: repeat(1, 1fr); - } -} - -.footerwidget { - max-width: -moz-fitcontent; - max-width: fitcontent; -} -.footerwidget:nth-child(2) { - margin-inline: auto; -} -@media (max-width: 991px) { - .footerwidget:nth-child(2) { - margin: 0; - } -} -.footerwidget:nth-child(3) { - margin-inline: auto; -} -@media (max-width: 991px) { - .footerwidget:nth-child(3) { - margin: 0; - } -} -.footerwidget .title { - font-size: 24px; - margin-top: 0; -} -.footerwidget .footerlinks { - margin: 0; - padding: 0; - list-style-type: none; - display: flex; - flex-direction: column; - gap: 24px; -} -.footerwidget .footerlinks li a { - text-decoration: none; -} - -.copyright { - text-align: center; - margin-top: 40px; -} - -.codeheader { - display: flex; - align-items: center; - justify-content: space-between; - font-size: 12px; - padding: 8px 16px; - background-color: #262626; - border-radius: 4px 4px 0 0; -} -.codeheader .title { - font-weight: 500; -} - -.codecontainer { - display: flex; - background-color: #343434; - color: #dcdcdc; - border-radius: 0 0 4px 4px; - overflow: auto; - font-family: "Courier New", monospace; - font-size: 14px; - line-height: 1.5; - box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); -} -.codecontainer .linenumbers { - text-align: right; - margin-right: 10px; - color: #888; - -webkit-user-select: none; - -moz-user-select: none; - user-select: none; - padding-right: 10px; - padding-left: 8px; - border-right: 1px solid #444; - padding-top: 15px; -} -.codecontainer .linenumbers div { - line-height: 1.385; -} - -.code { - white-space: pre; - padding: 10px; -} - -.codecontainer .codecontent { - background-color: transparent; -} - -.codecontent .codeline { - min-height: 19.28px; -} diff --git a/docs/src/hooks/useIntercom.tsx b/docs/src/hooks/useIntercom.tsx deleted file mode 100644 index 0b19b996..00000000 --- a/docs/src/hooks/useIntercom.tsx +++ /dev/null @@ -1,52 +0,0 @@ -import React, { useEffect } from "react"; - -const useIntercom = () => { - useEffect(() => { - const loadIntercom = () => { - const w = window; - const ic = w.Intercom; - if (typeof ic === "function") { - ic("reattach_activator"); - ic("update", w.intercomSettings); - } else { - const i = function () { - i.c(arguments); - }; - i.q = []; - i.c = function (args) { - i.q.push(args); - }; - w.Intercom = i; - - const s = document.createElement("script"); - s.type = "text/javascript"; - s.async = true; - s.src = "https://widget.intercom.io/widget/fu9pkks5"; - document.body.appendChild(s); - } - }; - - if (document.readyState === "complete") { - loadIntercom(); - } else { - window.addEventListener("load", loadIntercom); - } - - return () => window.removeEventListener("load", loadIntercom); - }, []); - - useEffect(() => { - if (typeof window.Intercom === "function") { - window.Intercom("boot", { - api_base: "https://api-iam.intercom.io", - app_id: "fu9pkks5", - }); - } - - if (typeof window.Intercom === "function") { - window.Intercom("update", window.intercomSettings); - } - }, []); -}; - -export default useIntercom; diff --git a/docs/src/pages/index.module.css b/docs/src/pages/index.module.css deleted file mode 100644 index 9f71a5da..00000000 --- a/docs/src/pages/index.module.css +++ /dev/null @@ -1,23 +0,0 @@ -/** - * CSS files with the .module.css suffix will be treated as CSS modules - * and scoped locally. - */ - -.heroBanner { - padding: 4rem 0; - text-align: center; - position: relative; - overflow: hidden; -} - -@media screen and (max-width: 996px) { - .heroBanner { - padding: 2rem; - } -} - -.buttons { - display: flex; - align-items: center; - justify-content: center; -} diff --git a/docs/src/pages/index.tsx b/docs/src/pages/index.tsx deleted file mode 100644 index b4d08374..00000000 --- a/docs/src/pages/index.tsx +++ /dev/null @@ -1,264 +0,0 @@ -import { useState } from "react"; -import baseStyles from '../css/style.module.css'; - -function HomepageHeader() { - const [isActive, setIsActive] = useState(false); - const toggleMenu = () => { - setIsActive((prevState) => !prevState); - }; - - - return ( -
-
-
- logo - - -
-
-
- ); -} - -export default function Home(): JSX.Element { - -const code = `import groundlight -from framegrab import FrameGrabber - -# Initialize Groundlight client and create a Detector -gl = groundlight.Groundlight() -detector = gl.get_or_create_detector(name="doorway", query="Is the doorway open?") - -# Grab an image from a camera or video stream -grabber = list(FrameGrabber.autodiscover().values())[0] -image = grabber.grab() - -# Process image and get a confident answer to the Detector's query -image_query = gl.ask_confident(detector, image) -print(image_query)`; -// Split the code into lines - const codeLines = code.split("\n"); - - return ( -
- -
-
-
-
-

Build custom computer vision apps - faster & more reliably

-
-

With Groundlight’s Python SDK, you don’t need to be a machine learning scientist to develop your own - computer vision application. Groundlight’s fully managed computer vision solution takes care of the ML so - you can focus on building.

- -
-
-
-
- -
-
-
-
-
- img -
-
-

AutoML

-

Aspects such as selecting the model architecture, choosing hyperparameters, determining the training - dataset, or managing dataset labels are automated

-
-
-
-
- img -
-
-

24/7 Human Annotation

-

No need to label all your images yourself, Groundlight system provides annotation by humans, 24/7

-
-
-
-
- img -
-
-

Fast Edge Inference

-

On-premises deployment, so you can have real-time predictions without having to rely on the cloud

-
-
-
-
-
- -
-
-

Groundlight integrations and compatibility

-
-
-

Groundlight is compatible across major development platforms and available through a REST API or Python - SDK. Enjoy easy deployments using Arduino, Raspberry Pi, or any number of hardware kits.

-
- icon - icon - icon - icon - icon - icon - icon - icon -
-
- -
-
-
-

Python SDK

-

With only a few lines of code, you can have custom computer vision inside your application.

- Learn More -
-
-

API

-

API to let you access your models in the cloud - no need to run your own models or hardware.

- Learn More -
-
-

Fast Edge Inference

-

We offer specialized hardware for local inference. Reduce latency, cost, network bandwidth, and energy. -

- Learn More -
-
-

ROS

-

Seamlessly integrate AI-driven perception into ROS2 projects, enabling natural language queries and - real-time decision-making for smarter, more adaptable robotic systems.

- Learn More -
-
-
-
-
-
- -
-
-

Build a working computer vision application in just - a few lines of code:

-
-
Code Block
-
PYTHON
-
-
- {/* Line numbers container */} -
- {codeLines.map((_, index) => ( -
{index + 1}
- ))} -
- {/* Code container */} - 
-        {codeLines.map((line, index) => (
-          
{line === "" ? "" : line}

-        ))}
-
-
-
-
- -
-
-

Connect with us, we’re here to support you:

-
-
-
- youtube -
-
-
-

YouTube

-

Watch our tutorials and learn how computer vision can be applied to various industries. 

-
- Go to YouTube -
-
-
-
- x -
-
-
-

X

-

Follow us at @GroundlightAI - we post about the latest in machine learning and more.

-
- Follow us on X -
-
-
-
- support -
-
-
-

Support

-

Reach out to us for questions and get an answer from a real human being.

-
- Contact us -
-
-
-
-
- - -
-
- ); -} \ No newline at end of file diff --git a/docs/src/pages/markdown-page.md b/docs/src/pages/markdown-page.md deleted file mode 100644 index 9756c5b6..00000000 --- a/docs/src/pages/markdown-page.md +++ /dev/null @@ -1,7 +0,0 @@ ---- -title: Markdown page example ---- - -# Markdown page example - -You don't need React to write simple standalone pages. diff --git a/docs/src/theme/Layout.tsx b/docs/src/theme/Layout.tsx deleted file mode 100644 index ee539f55..00000000 --- a/docs/src/theme/Layout.tsx +++ /dev/null @@ -1,11 +0,0 @@ -import React from 'react'; -import OriginalLayout from '@theme-original/Layout'; -import useIntercom from '@site/src/hooks/useIntercom'; - -function Layout(props) { - useIntercom(); - - return ; -} - -export default Layout; \ No newline at end of file diff --git a/docs/static/img/1-models.png b/docs/static/img/1-models.png deleted file mode 100644 index ada81827..00000000 Binary files a/docs/static/img/1-models.png and /dev/null differ diff --git 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It is here just for a nice editor experience. - "extends": "@tsconfig/docusaurus/tsconfig.json", - "compilerOptions": { - "baseUrl": "." - } -} diff --git a/generated/.github/workflows/python.yml b/generated/.github/workflows/python.yml new file mode 100644 index 00000000..418c66dc --- /dev/null +++ b/generated/.github/workflows/python.yml @@ -0,0 +1,38 @@ +# NOTE: This file is auto generated by OpenAPI Generator. +# URL: https://openapi-generator.tech +# +# ref: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python + +name: groundlight_openapi_client Python package + +on: [push, pull_request] + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] + + steps: + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install flake8 pytest + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + if [ -f test-requirements.txt ]; then pip install -r test-requirements.txt; fi + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + pytest diff --git a/generated/.openapi-generator/FILES b/generated/.openapi-generator/FILES index 07eb726b..89b440ae 100644 --- a/generated/.openapi-generator/FILES +++ b/generated/.openapi-generator/FILES @@ -1,7 +1,7 @@ +.github/workflows/python.yml .gitignore README.md docs/Action.md -docs/ActionList.md docs/ActionsApi.md docs/AllNotes.md docs/AnnotationsRequestedEnum.md @@ -18,24 +18,26 @@ docs/CountModeConfiguration.md docs/CountingResult.md docs/Detector.md docs/DetectorCreationInputRequest.md +docs/DetectorCreationInputRequestModeConfiguration.md docs/DetectorGroup.md docs/DetectorGroupRequest.md docs/DetectorGroupsApi.md docs/DetectorResetApi.md +docs/DetectorStatus.md docs/DetectorTypeEnum.md docs/DetectorsApi.md docs/EdgeApi.md docs/EdgeModelInfo.md docs/EscalationTypeEnum.md +docs/GetDetectorEvaluation200Response.md +docs/GetDetectorEvaluation200ResponseEvaluationResults.md +docs/GetDetectorMetrics200Response.md +docs/GetDetectorMetrics200ResponseSummary.md +docs/GetDetectorMetrics200ResponseSummaryClassCounts.md docs/ImageQueriesApi.md docs/ImageQuery.md +docs/ImageQueryResult.md docs/ImageQueryTypeEnum.md -docs/InlineResponse200.md -docs/InlineResponse2001.md -docs/InlineResponse2001EvaluationResults.md -docs/InlineResponse2002.md -docs/InlineResponse200Summary.md -docs/InlineResponse200SummaryClassCounts.md docs/Label.md docs/LabelValue.md docs/LabelValueRequest.md @@ -56,6 +58,7 @@ docs/ROI.md docs/ROIRequest.md docs/ResultTypeEnum.md docs/Rule.md +docs/RuleAction.md docs/RuleRequest.md docs/SnoozeTimeUnitEnum.md docs/Source.md @@ -67,6 +70,7 @@ docs/UserApi.md docs/VerbEnum.md docs/WebhookAction.md docs/WebhookActionRequest.md +docs/WhoAmI200Response.md git_push.sh groundlight_openapi_client/__init__.py groundlight_openapi_client/api/__init__.py @@ -80,76 +84,77 @@ groundlight_openapi_client/api/labels_api.py groundlight_openapi_client/api/notes_api.py groundlight_openapi_client/api/user_api.py groundlight_openapi_client/api_client.py -groundlight_openapi_client/apis/__init__.py +groundlight_openapi_client/api_response.py groundlight_openapi_client/configuration.py groundlight_openapi_client/exceptions.py -groundlight_openapi_client/model/__init__.py -groundlight_openapi_client/model/action.py -groundlight_openapi_client/model/action_list.py -groundlight_openapi_client/model/all_notes.py -groundlight_openapi_client/model/annotations_requested_enum.py -groundlight_openapi_client/model/b_box_geometry.py -groundlight_openapi_client/model/b_box_geometry_request.py -groundlight_openapi_client/model/binary_classification_result.py -groundlight_openapi_client/model/blank_enum.py -groundlight_openapi_client/model/bounding_box_mode_configuration.py -groundlight_openapi_client/model/bounding_box_result.py -groundlight_openapi_client/model/channel_enum.py -groundlight_openapi_client/model/condition.py -groundlight_openapi_client/model/condition_request.py -groundlight_openapi_client/model/count_mode_configuration.py -groundlight_openapi_client/model/counting_result.py -groundlight_openapi_client/model/detector.py -groundlight_openapi_client/model/detector_creation_input_request.py -groundlight_openapi_client/model/detector_group.py -groundlight_openapi_client/model/detector_group_request.py -groundlight_openapi_client/model/detector_type_enum.py -groundlight_openapi_client/model/edge_model_info.py -groundlight_openapi_client/model/escalation_type_enum.py -groundlight_openapi_client/model/image_query.py -groundlight_openapi_client/model/image_query_type_enum.py -groundlight_openapi_client/model/inline_response200.py -groundlight_openapi_client/model/inline_response2001.py -groundlight_openapi_client/model/inline_response2001_evaluation_results.py -groundlight_openapi_client/model/inline_response2002.py -groundlight_openapi_client/model/inline_response200_summary.py -groundlight_openapi_client/model/inline_response200_summary_class_counts.py -groundlight_openapi_client/model/label.py -groundlight_openapi_client/model/label_value.py -groundlight_openapi_client/model/label_value_request.py -groundlight_openapi_client/model/mode_enum.py -groundlight_openapi_client/model/multi_class_mode_configuration.py -groundlight_openapi_client/model/multi_classification_result.py -groundlight_openapi_client/model/note.py -groundlight_openapi_client/model/note_request.py -groundlight_openapi_client/model/paginated_detector_list.py -groundlight_openapi_client/model/paginated_image_query_list.py -groundlight_openapi_client/model/paginated_rule_list.py -groundlight_openapi_client/model/patched_detector_request.py -groundlight_openapi_client/model/payload_template.py -groundlight_openapi_client/model/payload_template_request.py -groundlight_openapi_client/model/result_type_enum.py -groundlight_openapi_client/model/roi.py -groundlight_openapi_client/model/roi_request.py -groundlight_openapi_client/model/rule.py -groundlight_openapi_client/model/rule_request.py -groundlight_openapi_client/model/snooze_time_unit_enum.py -groundlight_openapi_client/model/source.py -groundlight_openapi_client/model/source_enum.py -groundlight_openapi_client/model/status_enum.py -groundlight_openapi_client/model/text_mode_configuration.py -groundlight_openapi_client/model/text_recognition_result.py -groundlight_openapi_client/model/verb_enum.py -groundlight_openapi_client/model/webhook_action.py -groundlight_openapi_client/model/webhook_action_request.py -groundlight_openapi_client/model_utils.py groundlight_openapi_client/models/__init__.py +groundlight_openapi_client/models/action.py +groundlight_openapi_client/models/all_notes.py +groundlight_openapi_client/models/annotations_requested_enum.py +groundlight_openapi_client/models/b_box_geometry.py +groundlight_openapi_client/models/b_box_geometry_request.py +groundlight_openapi_client/models/binary_classification_result.py +groundlight_openapi_client/models/blank_enum.py +groundlight_openapi_client/models/bounding_box_mode_configuration.py +groundlight_openapi_client/models/bounding_box_result.py +groundlight_openapi_client/models/channel_enum.py +groundlight_openapi_client/models/condition.py +groundlight_openapi_client/models/condition_request.py +groundlight_openapi_client/models/count_mode_configuration.py +groundlight_openapi_client/models/counting_result.py +groundlight_openapi_client/models/detector.py +groundlight_openapi_client/models/detector_creation_input_request.py +groundlight_openapi_client/models/detector_creation_input_request_mode_configuration.py +groundlight_openapi_client/models/detector_group.py +groundlight_openapi_client/models/detector_group_request.py +groundlight_openapi_client/models/detector_status.py +groundlight_openapi_client/models/detector_type_enum.py +groundlight_openapi_client/models/edge_model_info.py +groundlight_openapi_client/models/escalation_type_enum.py +groundlight_openapi_client/models/get_detector_evaluation200_response.py +groundlight_openapi_client/models/get_detector_evaluation200_response_evaluation_results.py +groundlight_openapi_client/models/get_detector_metrics200_response.py +groundlight_openapi_client/models/get_detector_metrics200_response_summary.py +groundlight_openapi_client/models/get_detector_metrics200_response_summary_class_counts.py +groundlight_openapi_client/models/image_query.py +groundlight_openapi_client/models/image_query_result.py +groundlight_openapi_client/models/image_query_type_enum.py +groundlight_openapi_client/models/label.py +groundlight_openapi_client/models/label_value.py +groundlight_openapi_client/models/label_value_request.py +groundlight_openapi_client/models/mode_enum.py +groundlight_openapi_client/models/multi_class_mode_configuration.py +groundlight_openapi_client/models/multi_classification_result.py +groundlight_openapi_client/models/note.py +groundlight_openapi_client/models/note_request.py +groundlight_openapi_client/models/paginated_detector_list.py +groundlight_openapi_client/models/paginated_image_query_list.py +groundlight_openapi_client/models/paginated_rule_list.py +groundlight_openapi_client/models/patched_detector_request.py +groundlight_openapi_client/models/payload_template.py +groundlight_openapi_client/models/payload_template_request.py +groundlight_openapi_client/models/result_type_enum.py +groundlight_openapi_client/models/roi.py +groundlight_openapi_client/models/roi_request.py +groundlight_openapi_client/models/rule.py +groundlight_openapi_client/models/rule_action.py +groundlight_openapi_client/models/rule_request.py +groundlight_openapi_client/models/snooze_time_unit_enum.py +groundlight_openapi_client/models/source.py +groundlight_openapi_client/models/source_enum.py +groundlight_openapi_client/models/status_enum.py +groundlight_openapi_client/models/text_mode_configuration.py +groundlight_openapi_client/models/text_recognition_result.py +groundlight_openapi_client/models/verb_enum.py +groundlight_openapi_client/models/webhook_action.py +groundlight_openapi_client/models/webhook_action_request.py +groundlight_openapi_client/models/who_am_i200_response.py +groundlight_openapi_client/py.typed groundlight_openapi_client/rest.py +pyproject.toml requirements.txt setup.cfg setup.py test-requirements.txt test/__init__.py -test/test_label.py -test/test_source.py tox.ini diff --git a/generated/.openapi-generator/VERSION b/generated/.openapi-generator/VERSION index 1e20ec35..eb1dc6a5 100644 --- a/generated/.openapi-generator/VERSION +++ b/generated/.openapi-generator/VERSION @@ -1 +1 @@ -5.4.0 \ No newline at end of file +7.13.0 diff --git a/generated/README.md b/generated/README.md index f82c9211..313a0f58 100644 --- a/generated/README.md +++ b/generated/README.md @@ -5,11 +5,12 @@ This Python package is automatically generated by the [OpenAPI Generator](https: - API version: 0.18.2 - Package version: 1.0.0 -- Build package: org.openapitools.codegen.languages.PythonClientCodegen +- Generator version: 7.13.0 +- Build package: org.openapitools.codegen.languages.PythonPydanticV1ClientCodegen ## Requirements. -Python >=3.6 +Python 3.7+ ## Installation & Usage ### pip install @@ -40,6 +41,10 @@ Then import the package: import groundlight_openapi_client ``` +### Tests + +Execute `pytest` to run the tests. + ## Getting Started Please follow the [installation procedure](#installation--usage) and then run the following: @@ -48,11 +53,9 @@ Please follow the [installation procedure](#installation--usage) and then run th import time import groundlight_openapi_client +from groundlight_openapi_client.rest import ApiException from pprint import pprint -from groundlight_openapi_client.api import actions_api -from groundlight_openapi_client.model.paginated_rule_list import PaginatedRuleList -from groundlight_openapi_client.model.rule import Rule -from groundlight_openapi_client.model.rule_request import RuleRequest + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -65,7 +68,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -74,39 +77,17 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = actions_api.ActionsApi(api_client) - detector_id = "detector_id_example" # str | -rule_request = RuleRequest( - name="name_example", - enabled=True, - snooze_time_enabled=False, - snooze_time_value=0, - snooze_time_unit=None, - human_review_required=False, - condition=ConditionRequest( - verb="verb_example", - parameters={ - "key": None, - }, - ), - action=None, - webhook_action=[ - WebhookActionRequest( - url="url_example", - include_image=True, - payload_template=None, - last_message_failed=True, - last_failure_error="last_failure_error_example", - last_failed_at=dateutil_parser('1970-01-01T00:00:00.00Z'), - ), - ], - ) # RuleRequest | + api_instance = groundlight_openapi_client.ActionsApi(api_client) + detector_id = 'detector_id_example' # str | + rule_request = groundlight_openapi_client.RuleRequest() # RuleRequest | try: api_response = api_instance.create_rule(detector_id, rule_request) + print("The response of ActionsApi->create_rule:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except ApiException as e: print("Exception when calling ActionsApi->create_rule: %s\n" % e) + ``` ## Documentation for API Endpoints @@ -145,7 +126,6 @@ Class | Method | HTTP request | Description ## Documentation For Models - [Action](docs/Action.md) - - [ActionList](docs/ActionList.md) - [AllNotes](docs/AllNotes.md) - [AnnotationsRequestedEnum](docs/AnnotationsRequestedEnum.md) - [BBoxGeometry](docs/BBoxGeometry.md) @@ -161,19 +141,21 @@ Class | Method | HTTP request | Description - [CountingResult](docs/CountingResult.md) - [Detector](docs/Detector.md) - [DetectorCreationInputRequest](docs/DetectorCreationInputRequest.md) + - [DetectorCreationInputRequestModeConfiguration](docs/DetectorCreationInputRequestModeConfiguration.md) - [DetectorGroup](docs/DetectorGroup.md) - [DetectorGroupRequest](docs/DetectorGroupRequest.md) + - [DetectorStatus](docs/DetectorStatus.md) - [DetectorTypeEnum](docs/DetectorTypeEnum.md) - [EdgeModelInfo](docs/EdgeModelInfo.md) - [EscalationTypeEnum](docs/EscalationTypeEnum.md) + - [GetDetectorEvaluation200Response](docs/GetDetectorEvaluation200Response.md) + - [GetDetectorEvaluation200ResponseEvaluationResults](docs/GetDetectorEvaluation200ResponseEvaluationResults.md) + - [GetDetectorMetrics200Response](docs/GetDetectorMetrics200Response.md) + - [GetDetectorMetrics200ResponseSummary](docs/GetDetectorMetrics200ResponseSummary.md) + - [GetDetectorMetrics200ResponseSummaryClassCounts](docs/GetDetectorMetrics200ResponseSummaryClassCounts.md) - [ImageQuery](docs/ImageQuery.md) + - [ImageQueryResult](docs/ImageQueryResult.md) - [ImageQueryTypeEnum](docs/ImageQueryTypeEnum.md) - - [InlineResponse200](docs/InlineResponse200.md) - - [InlineResponse2001](docs/InlineResponse2001.md) - - [InlineResponse2001EvaluationResults](docs/InlineResponse2001EvaluationResults.md) - - [InlineResponse2002](docs/InlineResponse2002.md) - - [InlineResponse200Summary](docs/InlineResponse200Summary.md) - - [InlineResponse200SummaryClassCounts](docs/InlineResponse200SummaryClassCounts.md) - [Label](docs/Label.md) - [LabelValue](docs/LabelValue.md) - [LabelValueRequest](docs/LabelValueRequest.md) @@ -192,6 +174,7 @@ Class | Method | HTTP request | Description - [ROIRequest](docs/ROIRequest.md) - [ResultTypeEnum](docs/ResultTypeEnum.md) - [Rule](docs/Rule.md) + - [RuleAction](docs/RuleAction.md) - [RuleRequest](docs/RuleRequest.md) - [SnoozeTimeUnitEnum](docs/SnoozeTimeUnitEnum.md) - [Source](docs/Source.md) @@ -202,12 +185,16 @@ Class | Method | HTTP request | Description - [VerbEnum](docs/VerbEnum.md) - [WebhookAction](docs/WebhookAction.md) - [WebhookActionRequest](docs/WebhookActionRequest.md) + - [WhoAmI200Response](docs/WhoAmI200Response.md) + ## Documentation For Authorization -## ApiToken +Authentication schemes defined for the API: + +### ApiToken - **Type**: API key - **API key parameter name**: x-api-token @@ -219,22 +206,3 @@ Class | Method | HTTP request | Description support@groundlight.ai -## Notes for Large OpenAPI documents -If the OpenAPI document is large, imports in groundlight_openapi_client.apis and groundlight_openapi_client.models may fail with a -RecursionError indicating the maximum recursion limit has been exceeded. In that case, there are a couple of solutions: - -Solution 1: -Use specific imports for apis and models like: -- `from groundlight_openapi_client.api.default_api import DefaultApi` -- `from groundlight_openapi_client.model.pet import Pet` - -Solution 2: -Before importing the package, adjust the maximum recursion limit as shown below: -``` -import sys -sys.setrecursionlimit(1500) -import groundlight_openapi_client -from groundlight_openapi_client.apis import * -from groundlight_openapi_client.models import * -``` - diff --git a/generated/docs/Action.md b/generated/docs/Action.md index 0eee9350..e3e5982e 100644 --- a/generated/docs/Action.md +++ b/generated/docs/Action.md @@ -7,8 +7,24 @@ Name | Type | Description | Notes **channel** | [**ChannelEnum**](ChannelEnum.md) | | **recipient** | **str** | | **include_image** | **bool** | | -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +## Example + +```python +from groundlight_openapi_client.models.action import Action + +# TODO update the JSON string below +json = "{}" +# create an instance of Action from a JSON string +action_instance = Action.from_json(json) +# print the JSON string representation of the object +print Action.to_json() + +# convert the object into a dict +action_dict = action_instance.to_dict() +# create an instance of Action from a dict +action_from_dict = Action.from_dict(action_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/ActionList.md b/generated/docs/ActionList.md index 10624be3..c4c8528f 100644 --- a/generated/docs/ActionList.md +++ b/generated/docs/ActionList.md @@ -1,11 +1,8 @@ # ActionList - -## Properties -Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- -**value** | [**[Action]**](Action.md) | | +Type | Description | Notes +------------- | ------------- | ------------- +**[Action]** | | [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) - diff --git a/generated/docs/ActionsApi.md b/generated/docs/ActionsApi.md index 099d468f..84811dcf 100644 --- a/generated/docs/ActionsApi.md +++ b/generated/docs/ActionsApi.md @@ -21,14 +21,15 @@ Create a new rule for a detector ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import actions_api -from groundlight_openapi_client.model.rule import Rule -from groundlight_openapi_client.model.rule_request import RuleRequest +from groundlight_openapi_client.models.rule import Rule +from groundlight_openapi_client.models.rule_request import RuleRequest +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -41,7 +42,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -49,49 +50,26 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = actions_api.ActionsApi(api_client) - detector_id = "detector_id_example" # str | - rule_request = RuleRequest( - name="name_example", - enabled=True, - snooze_time_enabled=False, - snooze_time_value=0, - snooze_time_unit=None, - human_review_required=False, - condition=ConditionRequest( - verb="verb_example", - parameters={ - "key": None, - }, - ), - action=None, - webhook_action=[ - WebhookActionRequest( - url="url_example", - include_image=True, - payload_template=None, - last_message_failed=True, - last_failure_error="last_failure_error_example", - last_failed_at=dateutil_parser('1970-01-01T00:00:00.00Z'), - ), - ], - ) # RuleRequest | - - # example passing only required values which don't have defaults set + api_instance = groundlight_openapi_client.ActionsApi(api_client) + detector_id = 'detector_id_example' # str | + rule_request = groundlight_openapi_client.RuleRequest() # RuleRequest | + try: api_response = api_instance.create_rule(detector_id, rule_request) + print("The response of ActionsApi->create_rule:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling ActionsApi->create_rule: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **detector_id** | **str**| | - **rule_request** | [**RuleRequest**](RuleRequest.md)| | + **detector_id** | **str**| | + **rule_request** | [**RuleRequest**](RuleRequest.md)| | ### Return type @@ -106,9 +84,7 @@ Name | Type | Description | Notes - **Content-Type**: application/json, application/x-www-form-urlencoded, multipart/form-data - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **201** | | - | @@ -125,12 +101,13 @@ Delete a rule ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import actions_api +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -143,7 +120,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -151,22 +128,22 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = actions_api.ActionsApi(api_client) - id = 1 # int | + api_instance = groundlight_openapi_client.ActionsApi(api_client) + id = 56 # int | - # example passing only required values which don't have defaults set try: api_instance.delete_rule(id) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling ActionsApi->delete_rule: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **id** | **int**| | + **id** | **int**| | ### Return type @@ -181,9 +158,7 @@ void (empty response body) - **Content-Type**: Not defined - **Accept**: Not defined - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **204** | No response body | - | @@ -200,13 +175,14 @@ Retrieve a rule ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import actions_api -from groundlight_openapi_client.model.rule import Rule +from groundlight_openapi_client.models.rule import Rule +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -219,7 +195,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -227,23 +203,24 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = actions_api.ActionsApi(api_client) - id = 1 # int | + api_instance = groundlight_openapi_client.ActionsApi(api_client) + id = 56 # int | - # example passing only required values which don't have defaults set try: api_response = api_instance.get_rule(id) + print("The response of ActionsApi->get_rule:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling ActionsApi->get_rule: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **id** | **int**| | + **id** | **int**| | ### Return type @@ -258,9 +235,7 @@ Name | Type | Description | Notes - **Content-Type**: Not defined - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **200** | | - | @@ -268,7 +243,7 @@ Name | Type | Description | Notes [[Back to top]](#) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to Model list]](../README.md#documentation-for-models) [[Back to README]](../README.md) # **list_detector_rules** -> PaginatedRuleList list_detector_rules(detector_id) +> PaginatedRuleList list_detector_rules(detector_id, page=page, page_size=page_size) @@ -277,13 +252,14 @@ List all rules for a detector ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import actions_api -from groundlight_openapi_client.model.paginated_rule_list import PaginatedRuleList +from groundlight_openapi_client.models.paginated_rule_list import PaginatedRuleList +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -296,7 +272,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -304,35 +280,28 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = actions_api.ActionsApi(api_client) - detector_id = "detector_id_example" # str | - page = 1 # int | A page number within the paginated result set. (optional) - page_size = 1 # int | Number of results to return per page. (optional) - - # example passing only required values which don't have defaults set - try: - api_response = api_instance.list_detector_rules(detector_id) - pprint(api_response) - except groundlight_openapi_client.ApiException as e: - print("Exception when calling ActionsApi->list_detector_rules: %s\n" % e) + api_instance = groundlight_openapi_client.ActionsApi(api_client) + detector_id = 'detector_id_example' # str | + page = 56 # int | A page number within the paginated result set. (optional) + page_size = 56 # int | Number of results to return per page. (optional) - # example passing only required values which don't have defaults set - # and optional values try: api_response = api_instance.list_detector_rules(detector_id, page=page, page_size=page_size) + print("The response of ActionsApi->list_detector_rules:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling ActionsApi->list_detector_rules: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **detector_id** | **str**| | - **page** | **int**| A page number within the paginated result set. | [optional] - **page_size** | **int**| Number of results to return per page. | [optional] + **detector_id** | **str**| | + **page** | **int**| A page number within the paginated result set. | [optional] + **page_size** | **int**| Number of results to return per page. | [optional] ### Return type @@ -347,9 +316,7 @@ Name | Type | Description | Notes - **Content-Type**: Not defined - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **200** | | - | @@ -357,7 +324,7 @@ Name | Type | Description | Notes [[Back to top]](#) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to Model list]](../README.md#documentation-for-models) [[Back to README]](../README.md) # **list_rules** -> PaginatedRuleList list_rules() +> PaginatedRuleList list_rules(page=page, page_size=page_size) @@ -366,13 +333,14 @@ Lists all rules over all detectors owned by the requester. ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import actions_api -from groundlight_openapi_client.model.paginated_rule_list import PaginatedRuleList +from groundlight_openapi_client.models.paginated_rule_list import PaginatedRuleList +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -385,7 +353,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -393,26 +361,26 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = actions_api.ActionsApi(api_client) - page = 1 # int | A page number within the paginated result set. (optional) - page_size = 1 # int | Number of results to return per page. (optional) + api_instance = groundlight_openapi_client.ActionsApi(api_client) + page = 56 # int | A page number within the paginated result set. (optional) + page_size = 56 # int | Number of results to return per page. (optional) - # example passing only required values which don't have defaults set - # and optional values try: api_response = api_instance.list_rules(page=page, page_size=page_size) + print("The response of ActionsApi->list_rules:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling ActionsApi->list_rules: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **page** | **int**| A page number within the paginated result set. | [optional] - **page_size** | **int**| Number of results to return per page. | [optional] + **page** | **int**| A page number within the paginated result set. | [optional] + **page_size** | **int**| Number of results to return per page. | [optional] ### Return type @@ -427,9 +395,7 @@ Name | Type | Description | Notes - **Content-Type**: Not defined - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **200** | | - | diff --git a/generated/docs/AllNotes.md b/generated/docs/AllNotes.md index f14d4ea6..71957a6f 100644 --- a/generated/docs/AllNotes.md +++ b/generated/docs/AllNotes.md @@ -5,10 +5,26 @@ Serializes all notes for a given detector, grouped by type as listed in UserProf ## Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- -**customer** | [**[Note]**](Note.md) | | -**gl** | [**[Note]**](Note.md) | | -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**customer** | [**List[Note]**](Note.md) | | +**gl** | [**List[Note]**](Note.md) | | +## Example + +```python +from groundlight_openapi_client.models.all_notes import AllNotes + +# TODO update the JSON string below +json = "{}" +# create an instance of AllNotes from a JSON string +all_notes_instance = AllNotes.from_json(json) +# print the JSON string representation of the object +print AllNotes.to_json() + +# convert the object into a dict +all_notes_dict = all_notes_instance.to_dict() +# create an instance of AllNotes from a dict +all_notes_from_dict = AllNotes.from_dict(all_notes_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/AnnotationsRequestedEnum.md b/generated/docs/AnnotationsRequestedEnum.md index 31b444e4..668a7a47 100644 --- a/generated/docs/AnnotationsRequestedEnum.md +++ b/generated/docs/AnnotationsRequestedEnum.md @@ -1,10 +1,11 @@ # AnnotationsRequestedEnum -## Properties -Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- -**value** | **str** | | must be one of ["BINARY_CLASSIFICATION", "BOUNDING_BOXES", ] +## Enum + +* `BINARY_CLASSIFICATION` (value: `'BINARY_CLASSIFICATION'`) + +* `BOUNDING_BOXES` (value: `'BOUNDING_BOXES'`) [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/BBoxGeometry.md b/generated/docs/BBoxGeometry.md index 432bb48c..4b527dd5 100644 --- a/generated/docs/BBoxGeometry.md +++ b/generated/docs/BBoxGeometry.md @@ -11,8 +11,24 @@ Name | Type | Description | Notes **bottom** | **float** | | **x** | **float** | | [readonly] **y** | **float** | | [readonly] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +## Example + +```python +from groundlight_openapi_client.models.b_box_geometry import BBoxGeometry + +# TODO update the JSON string below +json = "{}" +# create an instance of BBoxGeometry from a JSON string +b_box_geometry_instance = BBoxGeometry.from_json(json) +# print the JSON string representation of the object +print BBoxGeometry.to_json() + +# convert the object into a dict +b_box_geometry_dict = b_box_geometry_instance.to_dict() +# create an instance of BBoxGeometry from a dict +b_box_geometry_from_dict = BBoxGeometry.from_dict(b_box_geometry_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/BBoxGeometryRequest.md b/generated/docs/BBoxGeometryRequest.md index 93432d84..01766e28 100644 --- a/generated/docs/BBoxGeometryRequest.md +++ b/generated/docs/BBoxGeometryRequest.md @@ -9,8 +9,24 @@ Name | Type | Description | Notes **top** | **float** | | **right** | **float** | | **bottom** | **float** | | -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +## Example + +```python +from groundlight_openapi_client.models.b_box_geometry_request import BBoxGeometryRequest + +# TODO update the JSON string below +json = "{}" +# create an instance of BBoxGeometryRequest from a JSON string +b_box_geometry_request_instance = BBoxGeometryRequest.from_json(json) +# print the JSON string representation of the object +print BBoxGeometryRequest.to_json() + +# convert the object into a dict +b_box_geometry_request_dict = b_box_geometry_request_instance.to_dict() +# create an instance of BBoxGeometryRequest from a dict +b_box_geometry_request_from_dict = BBoxGeometryRequest.from_dict(b_box_geometry_request_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/BinaryClassificationResult.md b/generated/docs/BinaryClassificationResult.md index a98a3b70..fa844263 100644 --- a/generated/docs/BinaryClassificationResult.md +++ b/generated/docs/BinaryClassificationResult.md @@ -4,13 +4,29 @@ ## Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- -**label** | **str** | | -**confidence** | **float, none_type** | | [optional] +**confidence** | **float** | | [optional] **source** | **str** | | [optional] -**result_type** | **str** | | [optional] if omitted the server will use the default value of "binary_classification" +**result_type** | **str** | | [optional] **from_edge** | **bool** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**label** | **str** | | + +## Example + +```python +from groundlight_openapi_client.models.binary_classification_result import BinaryClassificationResult + +# TODO update the JSON string below +json = "{}" +# create an instance of BinaryClassificationResult from a JSON string +binary_classification_result_instance = BinaryClassificationResult.from_json(json) +# print the JSON string representation of the object +print BinaryClassificationResult.to_json() +# convert the object into a dict +binary_classification_result_dict = binary_classification_result_instance.to_dict() +# create an instance of BinaryClassificationResult from a dict +binary_classification_result_from_dict = BinaryClassificationResult.from_dict(binary_classification_result_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/BlankEnum.md b/generated/docs/BlankEnum.md index 1531f56d..569c6616 100644 --- a/generated/docs/BlankEnum.md +++ b/generated/docs/BlankEnum.md @@ -1,10 +1,9 @@ # BlankEnum -## Properties -Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- -**value** | **str** | | defaults to "", must be one of ["", ] +## Enum + +* `EMPTY` (value: `''`) [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/BoundingBoxModeConfiguration.md b/generated/docs/BoundingBoxModeConfiguration.md index 5a27bed3..7c6b4c18 100644 --- a/generated/docs/BoundingBoxModeConfiguration.md +++ b/generated/docs/BoundingBoxModeConfiguration.md @@ -6,8 +6,24 @@ Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **class_name** | **str** | | **max_num_bboxes** | **int** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +## Example + +```python +from groundlight_openapi_client.models.bounding_box_mode_configuration import BoundingBoxModeConfiguration + +# TODO update the JSON string below +json = "{}" +# create an instance of BoundingBoxModeConfiguration from a JSON string +bounding_box_mode_configuration_instance = BoundingBoxModeConfiguration.from_json(json) +# print the JSON string representation of the object +print BoundingBoxModeConfiguration.to_json() + +# convert the object into a dict +bounding_box_mode_configuration_dict = bounding_box_mode_configuration_instance.to_dict() +# create an instance of BoundingBoxModeConfiguration from a dict +bounding_box_mode_configuration_from_dict = BoundingBoxModeConfiguration.from_dict(bounding_box_mode_configuration_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/BoundingBoxResult.md b/generated/docs/BoundingBoxResult.md index 7a089d95..d51b1f0e 100644 --- a/generated/docs/BoundingBoxResult.md +++ b/generated/docs/BoundingBoxResult.md @@ -4,13 +4,29 @@ ## Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- -**label** | **str** | | -**confidence** | **float, none_type** | | [optional] +**confidence** | **float** | | [optional] **source** | **str** | | [optional] -**result_type** | **str** | | [optional] if omitted the server will use the default value of "bounding_box" +**result_type** | **str** | | [optional] **from_edge** | **bool** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**label** | **str** | | + +## Example + +```python +from groundlight_openapi_client.models.bounding_box_result import BoundingBoxResult + +# TODO update the JSON string below +json = "{}" +# create an instance of BoundingBoxResult from a JSON string +bounding_box_result_instance = BoundingBoxResult.from_json(json) +# print the JSON string representation of the object +print BoundingBoxResult.to_json() +# convert the object into a dict +bounding_box_result_dict = bounding_box_result_instance.to_dict() +# create an instance of BoundingBoxResult from a dict +bounding_box_result_from_dict = BoundingBoxResult.from_dict(bounding_box_result_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/ChannelEnum.md b/generated/docs/ChannelEnum.md index f0dcfeaa..7b82d2a1 100644 --- a/generated/docs/ChannelEnum.md +++ b/generated/docs/ChannelEnum.md @@ -1,10 +1,11 @@ # ChannelEnum -## Properties -Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- -**value** | **str** | | must be one of ["TEXT", "EMAIL", ] +## Enum + +* `TEXT` (value: `'TEXT'`) + +* `EMAIL` (value: `'EMAIL'`) [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/Condition.md b/generated/docs/Condition.md index e54e1ad5..6274a91e 100644 --- a/generated/docs/Condition.md +++ b/generated/docs/Condition.md @@ -5,9 +5,25 @@ Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **verb** | **str** | | -**parameters** | **{str: (bool, date, datetime, dict, float, int, list, str, none_type)}** | | -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**parameters** | **Dict[str, object]** | | +## Example + +```python +from groundlight_openapi_client.models.condition import Condition + +# TODO update the JSON string below +json = "{}" +# create an instance of Condition from a JSON string +condition_instance = Condition.from_json(json) +# print the JSON string representation of the object +print Condition.to_json() + +# convert the object into a dict +condition_dict = condition_instance.to_dict() +# create an instance of Condition from a dict +condition_from_dict = Condition.from_dict(condition_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/ConditionRequest.md b/generated/docs/ConditionRequest.md index 60a6d034..8a95f21a 100644 --- a/generated/docs/ConditionRequest.md +++ b/generated/docs/ConditionRequest.md @@ -5,9 +5,25 @@ Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **verb** | **str** | | -**parameters** | **{str: (bool, date, datetime, dict, float, int, list, str, none_type)}** | | -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**parameters** | **Dict[str, object]** | | +## Example + +```python +from groundlight_openapi_client.models.condition_request import ConditionRequest + +# TODO update the JSON string below +json = "{}" +# create an instance of ConditionRequest from a JSON string +condition_request_instance = ConditionRequest.from_json(json) +# print the JSON string representation of the object +print ConditionRequest.to_json() + +# convert the object into a dict +condition_request_dict = condition_request_instance.to_dict() +# create an instance of ConditionRequest from a dict +condition_request_from_dict = ConditionRequest.from_dict(condition_request_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/CountModeConfiguration.md b/generated/docs/CountModeConfiguration.md index 0262ae0a..a9352295 100644 --- a/generated/docs/CountModeConfiguration.md +++ b/generated/docs/CountModeConfiguration.md @@ -4,10 +4,26 @@ ## Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- -**class_name** | **str** | | **max_count** | **int** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**class_name** | **str** | | + +## Example + +```python +from groundlight_openapi_client.models.count_mode_configuration import CountModeConfiguration + +# TODO update the JSON string below +json = "{}" +# create an instance of CountModeConfiguration from a JSON string +count_mode_configuration_instance = CountModeConfiguration.from_json(json) +# print the JSON string representation of the object +print CountModeConfiguration.to_json() +# convert the object into a dict +count_mode_configuration_dict = count_mode_configuration_instance.to_dict() +# create an instance of CountModeConfiguration from a dict +count_mode_configuration_from_dict = CountModeConfiguration.from_dict(count_mode_configuration_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/CountingResult.md b/generated/docs/CountingResult.md index f9db3d5d..9ffdfb50 100644 --- a/generated/docs/CountingResult.md +++ b/generated/docs/CountingResult.md @@ -4,14 +4,30 @@ ## Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- -**count** | **int, none_type** | | -**confidence** | **float, none_type** | | [optional] +**confidence** | **float** | | [optional] **source** | **str** | | [optional] -**result_type** | **str** | | [optional] if omitted the server will use the default value of "counting" +**result_type** | **str** | | [optional] **from_edge** | **bool** | | [optional] +**count** | **int** | | **greater_than_max** | **bool** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +## Example + +```python +from groundlight_openapi_client.models.counting_result import CountingResult + +# TODO update the JSON string below +json = "{}" +# create an instance of CountingResult from a JSON string +counting_result_instance = CountingResult.from_json(json) +# print the JSON string representation of the object +print CountingResult.to_json() + +# convert the object into a dict +counting_result_dict = counting_result_instance.to_dict() +# create an instance of CountingResult from a dict +counting_result_from_dict = CountingResult.from_dict(counting_result_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/Detector.md b/generated/docs/Detector.md index b2f9f86e..9536ea1e 100644 --- a/generated/docs/Detector.md +++ b/generated/docs/Detector.md @@ -6,20 +6,36 @@ Groundlight Detectors provide answers to natural language questions about images Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **id** | **str** | A unique ID for this object. | [readonly] -**type** | **bool, date, datetime, dict, float, int, list, str, none_type** | The type of this object. | [readonly] +**type** | [**DetectorTypeEnum**](DetectorTypeEnum.md) | The type of this object. | [readonly] **created_at** | **datetime** | When this detector was created. | [readonly] **name** | **str** | A short, descriptive name for the detector. | **query** | **str** | A question about the image. | [readonly] **group_name** | **str** | Which group should this detector be part of? | [readonly] -**metadata** | **{str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type** | Metadata about the detector. | [readonly] +**confidence_threshold** | **float** | If the detector's prediction is below this confidence threshold, send the image query for human review. | [optional] [default to 0.9] +**patience_time** | **float** | How long Groundlight will attempt to generate a confident prediction | [optional] [default to 30.0] +**metadata** | **Dict[str, object]** | Metadata about the detector. | [readonly] **mode** | **str** | | [readonly] -**mode_configuration** | **{str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type** | | [readonly] -**confidence_threshold** | **float** | If the detector's prediction is below this confidence threshold, send the image query for human review. | [optional] if omitted the server will use the default value of 0.9 -**patience_time** | **float** | How long Groundlight will attempt to generate a confident prediction | [optional] if omitted the server will use the default value of 30.0 -**status** | **bool, date, datetime, dict, float, int, list, str, none_type** | | [optional] +**mode_configuration** | **Dict[str, object]** | | [readonly] +**status** | [**DetectorStatus**](DetectorStatus.md) | | [optional] **escalation_type** | **str** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +## Example + +```python +from groundlight_openapi_client.models.detector import Detector + +# TODO update the JSON string below +json = "{}" +# create an instance of Detector from a JSON string +detector_instance = Detector.from_json(json) +# print the JSON string representation of the object +print Detector.to_json() + +# convert the object into a dict +detector_dict = detector_instance.to_dict() +# create an instance of Detector from a dict +detector_from_dict = Detector.from_dict(detector_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/DetectorCreationInputRequest.md b/generated/docs/DetectorCreationInputRequest.md index 16adba94..240c86e4 100644 --- a/generated/docs/DetectorCreationInputRequest.md +++ b/generated/docs/DetectorCreationInputRequest.md @@ -8,14 +8,30 @@ Name | Type | Description | Notes **name** | **str** | A short, descriptive name for the detector. | **query** | **str** | A question about the image. | **group_name** | **str** | Which group should this detector be part of? | [optional] -**confidence_threshold** | **float** | If the detector's prediction is below this confidence threshold, send the image query for human review. | [optional] if omitted the server will use the default value of 0.9 -**patience_time** | **float** | How long Groundlight will attempt to generate a confident prediction | [optional] if omitted the server will use the default value of 30.0 -**pipeline_config** | **str, none_type** | (Advanced usage) Configuration needed to instantiate a prediction pipeline. | [optional] +**confidence_threshold** | **float** | If the detector's prediction is below this confidence threshold, send the image query for human review. | [optional] [default to 0.9] +**patience_time** | **float** | How long Groundlight will attempt to generate a confident prediction | [optional] [default to 30.0] +**pipeline_config** | **str** | (Advanced usage) Configuration needed to instantiate a prediction pipeline. | [optional] **metadata** | **str** | Base64-encoded metadata for the detector. This should be a JSON object with string keys. The size after encoding should not exceed 1362 bytes, corresponding to 1KiB before encoding. | [optional] -**mode** | **bool, date, datetime, dict, float, int, list, str, none_type** | Mode in which this detector will work. * `BINARY` - BINARY * `COUNT` - COUNT * `MULTI_CLASS` - MULTI_CLASS * `TEXT` - TEXT * `BOUNDING_BOX` - BOUNDING_BOX | [optional] -**mode_configuration** | **bool, date, datetime, dict, float, int, list, str, none_type** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**mode** | [**ModeEnum**](ModeEnum.md) | Mode in which this detector will work. * `BINARY` - BINARY * `COUNT` - COUNT * `MULTI_CLASS` - MULTI_CLASS * `TEXT` - TEXT * `BOUNDING_BOX` - BOUNDING_BOX | [optional] +**mode_configuration** | [**DetectorCreationInputRequestModeConfiguration**](DetectorCreationInputRequestModeConfiguration.md) | | [optional] +## Example + +```python +from groundlight_openapi_client.models.detector_creation_input_request import DetectorCreationInputRequest + +# TODO update the JSON string below +json = "{}" +# create an instance of DetectorCreationInputRequest from a JSON string +detector_creation_input_request_instance = DetectorCreationInputRequest.from_json(json) +# print the JSON string representation of the object +print DetectorCreationInputRequest.to_json() + +# convert the object into a dict +detector_creation_input_request_dict = detector_creation_input_request_instance.to_dict() +# create an instance of DetectorCreationInputRequest from a dict +detector_creation_input_request_from_dict = DetectorCreationInputRequest.from_dict(detector_creation_input_request_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/DetectorCreationInputRequestModeConfiguration.md b/generated/docs/DetectorCreationInputRequestModeConfiguration.md new file mode 100644 index 00000000..f4f72b21 --- /dev/null +++ b/generated/docs/DetectorCreationInputRequestModeConfiguration.md @@ -0,0 +1,33 @@ +# DetectorCreationInputRequestModeConfiguration + + +## Properties +Name | Type | Description | Notes +------------ | ------------- | ------------- | ------------- +**max_count** | **int** | | [optional] +**class_name** | **str** | | +**class_names** | **List[str]** | | +**num_classes** | **int** | | [optional] +**value_max_length** | **int** | | [optional] +**max_num_bboxes** | **int** | | [optional] + +## Example + +```python +from groundlight_openapi_client.models.detector_creation_input_request_mode_configuration import DetectorCreationInputRequestModeConfiguration + +# TODO update the JSON string below +json = "{}" +# create an instance of DetectorCreationInputRequestModeConfiguration from a JSON string +detector_creation_input_request_mode_configuration_instance = DetectorCreationInputRequestModeConfiguration.from_json(json) +# print the JSON string representation of the object +print DetectorCreationInputRequestModeConfiguration.to_json() + +# convert the object into a dict +detector_creation_input_request_mode_configuration_dict = detector_creation_input_request_mode_configuration_instance.to_dict() +# create an instance of DetectorCreationInputRequestModeConfiguration from a dict +detector_creation_input_request_mode_configuration_from_dict = DetectorCreationInputRequestModeConfiguration.from_dict(detector_creation_input_request_mode_configuration_dict) +``` +[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) + + diff --git a/generated/docs/DetectorGroup.md b/generated/docs/DetectorGroup.md index c1f764be..a68e2447 100644 --- a/generated/docs/DetectorGroup.md +++ b/generated/docs/DetectorGroup.md @@ -6,8 +6,24 @@ Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **id** | **str** | | [readonly] **name** | **str** | | -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +## Example + +```python +from groundlight_openapi_client.models.detector_group import DetectorGroup + +# TODO update the JSON string below +json = "{}" +# create an instance of DetectorGroup from a JSON string +detector_group_instance = DetectorGroup.from_json(json) +# print the JSON string representation of the object +print DetectorGroup.to_json() + +# convert the object into a dict +detector_group_dict = detector_group_instance.to_dict() +# create an instance of DetectorGroup from a dict +detector_group_from_dict = DetectorGroup.from_dict(detector_group_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/DetectorGroupRequest.md b/generated/docs/DetectorGroupRequest.md index 8de4ad8a..808f5d4e 100644 --- a/generated/docs/DetectorGroupRequest.md +++ b/generated/docs/DetectorGroupRequest.md @@ -5,8 +5,24 @@ Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **name** | **str** | | -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +## Example + +```python +from groundlight_openapi_client.models.detector_group_request import DetectorGroupRequest + +# TODO update the JSON string below +json = "{}" +# create an instance of DetectorGroupRequest from a JSON string +detector_group_request_instance = DetectorGroupRequest.from_json(json) +# print the JSON string representation of the object +print DetectorGroupRequest.to_json() + +# convert the object into a dict +detector_group_request_dict = detector_group_request_instance.to_dict() +# create an instance of DetectorGroupRequest from a dict +detector_group_request_from_dict = DetectorGroupRequest.from_dict(detector_group_request_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/DetectorGroupsApi.md b/generated/docs/DetectorGroupsApi.md index 27212d12..ec1f734f 100644 --- a/generated/docs/DetectorGroupsApi.md +++ b/generated/docs/DetectorGroupsApi.md @@ -13,19 +13,24 @@ Method | HTTP request | Description -Create a new detector group POST data: Required: - name (str) - name of the predictor set +Create a new detector group + +POST data: + Required: + - name (str) - name of the predictor set ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import detector_groups_api -from groundlight_openapi_client.model.detector_group_request import DetectorGroupRequest -from groundlight_openapi_client.model.detector_group import DetectorGroup +from groundlight_openapi_client.models.detector_group import DetectorGroup +from groundlight_openapi_client.models.detector_group_request import DetectorGroupRequest +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -38,7 +43,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -46,25 +51,24 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = detector_groups_api.DetectorGroupsApi(api_client) - detector_group_request = DetectorGroupRequest( - name="name_example", - ) # DetectorGroupRequest | + api_instance = groundlight_openapi_client.DetectorGroupsApi(api_client) + detector_group_request = groundlight_openapi_client.DetectorGroupRequest() # DetectorGroupRequest | - # example passing only required values which don't have defaults set try: api_response = api_instance.create_detector_group(detector_group_request) + print("The response of DetectorGroupsApi->create_detector_group:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling DetectorGroupsApi->create_detector_group: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **detector_group_request** | [**DetectorGroupRequest**](DetectorGroupRequest.md)| | + **detector_group_request** | [**DetectorGroupRequest**](DetectorGroupRequest.md)| | ### Return type @@ -79,9 +83,7 @@ Name | Type | Description | Notes - **Content-Type**: application/json, application/x-www-form-urlencoded, multipart/form-data - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **201** | | - | @@ -89,7 +91,7 @@ Name | Type | Description | Notes [[Back to top]](#) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to Model list]](../README.md#documentation-for-models) [[Back to README]](../README.md) # **get_detector_groups** -> [DetectorGroup] get_detector_groups() +> List[DetectorGroup] get_detector_groups() @@ -98,13 +100,14 @@ List all detector groups ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import detector_groups_api -from groundlight_openapi_client.model.detector_group import DetectorGroup +from groundlight_openapi_client.models.detector_group import DetectorGroup +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -117,7 +120,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -125,23 +128,24 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = detector_groups_api.DetectorGroupsApi(api_client) + api_instance = groundlight_openapi_client.DetectorGroupsApi(api_client) - # example, this endpoint has no required or optional parameters try: api_response = api_instance.get_detector_groups() + print("The response of DetectorGroupsApi->get_detector_groups:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling DetectorGroupsApi->get_detector_groups: %s\n" % e) ``` + ### Parameters This endpoint does not need any parameter. ### Return type -[**[DetectorGroup]**](DetectorGroup.md) +[**List[DetectorGroup]**](DetectorGroup.md) ### Authorization @@ -152,9 +156,7 @@ This endpoint does not need any parameter. - **Content-Type**: Not defined - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **200** | | - | diff --git a/generated/docs/DetectorResetApi.md b/generated/docs/DetectorResetApi.md index 087d781c..e91992b3 100644 --- a/generated/docs/DetectorResetApi.md +++ b/generated/docs/DetectorResetApi.md @@ -17,12 +17,13 @@ Deletes all image queries on the detector ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import detector_reset_api +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -35,7 +36,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -43,22 +44,22 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = detector_reset_api.DetectorResetApi(api_client) - id = "id_example" # str | + api_instance = groundlight_openapi_client.DetectorResetApi(api_client) + id = 'id_example' # str | - # example passing only required values which don't have defaults set try: api_instance.reset_detector(id) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling DetectorResetApi->reset_detector: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **id** | **str**| | + **id** | **str**| | ### Return type @@ -73,9 +74,7 @@ void (empty response body) - **Content-Type**: Not defined - **Accept**: Not defined - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **204** | No response body | - | diff --git a/generated/docs/DetectorStatus.md b/generated/docs/DetectorStatus.md new file mode 100644 index 00000000..a99c9542 --- /dev/null +++ b/generated/docs/DetectorStatus.md @@ -0,0 +1,27 @@ +# DetectorStatus + + +## Properties +Name | Type | Description | Notes +------------ | ------------- | ------------- | ------------- + +## Example + +```python +from groundlight_openapi_client.models.detector_status import DetectorStatus + +# TODO update the JSON string below +json = "{}" +# create an instance of DetectorStatus from a JSON string +detector_status_instance = DetectorStatus.from_json(json) +# print the JSON string representation of the object +print DetectorStatus.to_json() + +# convert the object into a dict +detector_status_dict = detector_status_instance.to_dict() +# create an instance of DetectorStatus from a dict +detector_status_from_dict = DetectorStatus.from_dict(detector_status_dict) +``` +[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) + + diff --git a/generated/docs/DetectorTypeEnum.md b/generated/docs/DetectorTypeEnum.md index 36a12ea5..4a2480d3 100644 --- a/generated/docs/DetectorTypeEnum.md +++ b/generated/docs/DetectorTypeEnum.md @@ -1,10 +1,9 @@ # DetectorTypeEnum -## Properties -Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- -**value** | **str** | | defaults to "detector", must be one of ["detector", ] +## Enum + +* `DETECTOR` (value: `'detector'`) [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/DetectorsApi.md b/generated/docs/DetectorsApi.md index 9f5355e5..0acb7425 100644 --- a/generated/docs/DetectorsApi.md +++ b/generated/docs/DetectorsApi.md @@ -23,14 +23,15 @@ Create a new detector. ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import detectors_api -from groundlight_openapi_client.model.detector_creation_input_request import DetectorCreationInputRequest -from groundlight_openapi_client.model.detector import Detector +from groundlight_openapi_client.models.detector import Detector +from groundlight_openapi_client.models.detector_creation_input_request import DetectorCreationInputRequest +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -43,7 +44,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -51,33 +52,24 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = detectors_api.DetectorsApi(api_client) - detector_creation_input_request = DetectorCreationInputRequest( - name="name_example", - query="query_example", - group_name="group_name_example", - confidence_threshold=0.9, - patience_time=30.0, - pipeline_config="pipeline_config_example", - metadata="metadata_example", - mode=None, - mode_configuration=None, - ) # DetectorCreationInputRequest | - - # example passing only required values which don't have defaults set + api_instance = groundlight_openapi_client.DetectorsApi(api_client) + detector_creation_input_request = groundlight_openapi_client.DetectorCreationInputRequest() # DetectorCreationInputRequest | + try: api_response = api_instance.create_detector(detector_creation_input_request) + print("The response of DetectorsApi->create_detector:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling DetectorsApi->create_detector: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **detector_creation_input_request** | [**DetectorCreationInputRequest**](DetectorCreationInputRequest.md)| | + **detector_creation_input_request** | [**DetectorCreationInputRequest**](DetectorCreationInputRequest.md)| | ### Return type @@ -92,9 +84,7 @@ Name | Type | Description | Notes - **Content-Type**: application/json - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **201** | | - | @@ -111,12 +101,13 @@ Delete a detector by its ID. ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import detectors_api +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -129,7 +120,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -137,22 +128,22 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = detectors_api.DetectorsApi(api_client) - id = "id_example" # str | Choose a detector by its ID. + api_instance = groundlight_openapi_client.DetectorsApi(api_client) + id = 'id_example' # str | Choose a detector by its ID. - # example passing only required values which don't have defaults set try: api_instance.delete_detector(id) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling DetectorsApi->delete_detector: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **id** | **str**| Choose a detector by its ID. | + **id** | **str**| Choose a detector by its ID. | ### Return type @@ -167,9 +158,7 @@ void (empty response body) - **Content-Type**: Not defined - **Accept**: Not defined - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **204** | No response body | - | @@ -186,13 +175,14 @@ Retrieve a detector by its ID. ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import detectors_api -from groundlight_openapi_client.model.detector import Detector +from groundlight_openapi_client.models.detector import Detector +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -205,7 +195,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -213,23 +203,24 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = detectors_api.DetectorsApi(api_client) - id = "id_example" # str | Choose a detector by its ID. + api_instance = groundlight_openapi_client.DetectorsApi(api_client) + id = 'id_example' # str | Choose a detector by its ID. - # example passing only required values which don't have defaults set try: api_response = api_instance.get_detector(id) + print("The response of DetectorsApi->get_detector:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling DetectorsApi->get_detector: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **id** | **str**| Choose a detector by its ID. | + **id** | **str**| Choose a detector by its ID. | ### Return type @@ -244,9 +235,7 @@ Name | Type | Description | Notes - **Content-Type**: Not defined - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **200** | | - | @@ -254,7 +243,7 @@ Name | Type | Description | Notes [[Back to top]](#) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to Model list]](../README.md#documentation-for-models) [[Back to README]](../README.md) # **get_detector_evaluation** -> InlineResponse2001 get_detector_evaluation(id) +> GetDetectorEvaluation200Response get_detector_evaluation(id) @@ -263,13 +252,14 @@ Get Detector evaluation results. The result is null if there isn't enough ground ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import detectors_api -from groundlight_openapi_client.model.inline_response2001 import InlineResponse2001 +from groundlight_openapi_client.models.get_detector_evaluation200_response import GetDetectorEvaluation200Response +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -282,7 +272,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -290,27 +280,28 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = detectors_api.DetectorsApi(api_client) - id = "id_example" # str | + api_instance = groundlight_openapi_client.DetectorsApi(api_client) + id = 'id_example' # str | - # example passing only required values which don't have defaults set try: api_response = api_instance.get_detector_evaluation(id) + print("The response of DetectorsApi->get_detector_evaluation:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling DetectorsApi->get_detector_evaluation: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **id** | **str**| | + **id** | **str**| | ### Return type -[**InlineResponse2001**](InlineResponse2001.md) +[**GetDetectorEvaluation200Response**](GetDetectorEvaluation200Response.md) ### Authorization @@ -321,9 +312,7 @@ Name | Type | Description | Notes - **Content-Type**: Not defined - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **200** | | - | @@ -331,7 +320,7 @@ Name | Type | Description | Notes [[Back to top]](#) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to Model list]](../README.md#documentation-for-models) [[Back to README]](../README.md) # **get_detector_metrics** -> InlineResponse200 get_detector_metrics(detector_id) +> GetDetectorMetrics200Response get_detector_metrics(detector_id) @@ -340,13 +329,14 @@ Get Detector metrics, primarily the counts of different types of labels ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import detectors_api -from groundlight_openapi_client.model.inline_response200 import InlineResponse200 +from groundlight_openapi_client.models.get_detector_metrics200_response import GetDetectorMetrics200Response +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -359,7 +349,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -367,27 +357,28 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = detectors_api.DetectorsApi(api_client) - detector_id = "detector_id_example" # str | + api_instance = groundlight_openapi_client.DetectorsApi(api_client) + detector_id = 'detector_id_example' # str | - # example passing only required values which don't have defaults set try: api_response = api_instance.get_detector_metrics(detector_id) + print("The response of DetectorsApi->get_detector_metrics:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling DetectorsApi->get_detector_metrics: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **detector_id** | **str**| | + **detector_id** | **str**| | ### Return type -[**InlineResponse200**](InlineResponse200.md) +[**GetDetectorMetrics200Response**](GetDetectorMetrics200Response.md) ### Authorization @@ -398,9 +389,7 @@ Name | Type | Description | Notes - **Content-Type**: Not defined - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **200** | | - | @@ -408,7 +397,7 @@ Name | Type | Description | Notes [[Back to top]](#) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to Model list]](../README.md#documentation-for-models) [[Back to README]](../README.md) # **list_detectors** -> PaginatedDetectorList list_detectors() +> PaginatedDetectorList list_detectors(page=page, page_size=page_size) @@ -417,13 +406,14 @@ Retrieve a list of detectors. ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import detectors_api -from groundlight_openapi_client.model.paginated_detector_list import PaginatedDetectorList +from groundlight_openapi_client.models.paginated_detector_list import PaginatedDetectorList +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -436,7 +426,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -444,26 +434,26 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = detectors_api.DetectorsApi(api_client) - page = 1 # int | A page number within the paginated result set. (optional) - page_size = 1 # int | Number of items to return per page. (optional) + api_instance = groundlight_openapi_client.DetectorsApi(api_client) + page = 56 # int | A page number within the paginated result set. (optional) + page_size = 56 # int | Number of items to return per page. (optional) - # example passing only required values which don't have defaults set - # and optional values try: api_response = api_instance.list_detectors(page=page, page_size=page_size) + print("The response of DetectorsApi->list_detectors:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling DetectorsApi->list_detectors: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **page** | **int**| A page number within the paginated result set. | [optional] - **page_size** | **int**| Number of items to return per page. | [optional] + **page** | **int**| A page number within the paginated result set. | [optional] + **page_size** | **int**| Number of items to return per page. | [optional] ### Return type @@ -478,9 +468,7 @@ Name | Type | Description | Notes - **Content-Type**: Not defined - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **200** | | - | @@ -488,7 +476,7 @@ Name | Type | Description | Notes [[Back to top]](#) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to Model list]](../README.md#documentation-for-models) [[Back to README]](../README.md) # **update_detector** -> Detector update_detector(id) +> Detector update_detector(id, patched_detector_request=patched_detector_request) @@ -497,14 +485,15 @@ Update a detector ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import detectors_api -from groundlight_openapi_client.model.detector import Detector -from groundlight_openapi_client.model.patched_detector_request import PatchedDetectorRequest +from groundlight_openapi_client.models.detector import Detector +from groundlight_openapi_client.models.patched_detector_request import PatchedDetectorRequest +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -517,7 +506,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -525,39 +514,26 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = detectors_api.DetectorsApi(api_client) - id = "id_example" # str | - patched_detector_request = PatchedDetectorRequest( - name="name_example", - confidence_threshold=0.9, - patience_time=30.0, - status=None, - escalation_type="escalation_type_example", - ) # PatchedDetectorRequest | (optional) - - # example passing only required values which don't have defaults set - try: - api_response = api_instance.update_detector(id) - pprint(api_response) - except groundlight_openapi_client.ApiException as e: - print("Exception when calling DetectorsApi->update_detector: %s\n" % e) + api_instance = groundlight_openapi_client.DetectorsApi(api_client) + id = 'id_example' # str | + patched_detector_request = groundlight_openapi_client.PatchedDetectorRequest() # PatchedDetectorRequest | (optional) - # example passing only required values which don't have defaults set - # and optional values try: api_response = api_instance.update_detector(id, patched_detector_request=patched_detector_request) + print("The response of DetectorsApi->update_detector:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling DetectorsApi->update_detector: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **id** | **str**| | - **patched_detector_request** | [**PatchedDetectorRequest**](PatchedDetectorRequest.md)| | [optional] + **id** | **str**| | + **patched_detector_request** | [**PatchedDetectorRequest**](PatchedDetectorRequest.md)| | [optional] ### Return type @@ -572,9 +548,7 @@ Name | Type | Description | Notes - **Content-Type**: application/json, application/x-www-form-urlencoded, multipart/form-data - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **200** | | - | diff --git a/generated/docs/EdgeApi.md b/generated/docs/EdgeApi.md index 04976261..1355ec5f 100644 --- a/generated/docs/EdgeApi.md +++ b/generated/docs/EdgeApi.md @@ -13,17 +13,20 @@ Method | HTTP request | Description -Edge server periodically calls this to report metrics. POST body will have JSON data that we log. +Edge server periodically calls this to report metrics. + +POST body will have JSON data that we log. ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import edge_api +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -36,7 +39,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -44,16 +47,16 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = edge_api.EdgeApi(api_client) + api_instance = groundlight_openapi_client.EdgeApi(api_client) - # example, this endpoint has no required or optional parameters try: api_instance.edge_report_metrics_create() - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling EdgeApi->edge_report_metrics_create: %s\n" % e) ``` + ### Parameters This endpoint does not need any parameter. @@ -70,9 +73,7 @@ void (empty response body) - **Content-Type**: Not defined - **Accept**: Not defined - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **200** | No response body | - | @@ -89,13 +90,14 @@ Gets time limited pre-authenticated URLs to download a detector's edge model and ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import edge_api -from groundlight_openapi_client.model.edge_model_info import EdgeModelInfo +from groundlight_openapi_client.models.edge_model_info import EdgeModelInfo +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -108,7 +110,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -116,23 +118,24 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = edge_api.EdgeApi(api_client) - detector_id = "detector_id_example" # str | + api_instance = groundlight_openapi_client.EdgeApi(api_client) + detector_id = 'detector_id_example' # str | - # example passing only required values which don't have defaults set try: api_response = api_instance.get_model_urls(detector_id) + print("The response of EdgeApi->get_model_urls:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling EdgeApi->get_model_urls: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **detector_id** | **str**| | + **detector_id** | **str**| | ### Return type @@ -147,9 +150,7 @@ Name | Type | Description | Notes - **Content-Type**: Not defined - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **200** | | - | diff --git a/generated/docs/EdgeModelInfo.md b/generated/docs/EdgeModelInfo.md index 3141373f..175ebcc4 100644 --- a/generated/docs/EdgeModelInfo.md +++ b/generated/docs/EdgeModelInfo.md @@ -9,11 +9,27 @@ Name | Type | Description | Notes **model_binary_url** | **str** | | [optional] **oodd_model_binary_id** | **str** | | [optional] **oodd_model_binary_url** | **str** | | [optional] -**pipeline_config** | **bool, date, datetime, dict, float, int, list, str, none_type** | | [optional] -**oodd_pipeline_config** | **bool, date, datetime, dict, float, int, list, str, none_type** | | [optional] -**predictor_metadata** | **bool, date, datetime, dict, float, int, list, str, none_type** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**pipeline_config** | **object** | | [optional] +**oodd_pipeline_config** | **object** | | [optional] +**predictor_metadata** | **object** | | [optional] +## Example + +```python +from groundlight_openapi_client.models.edge_model_info import EdgeModelInfo + +# TODO update the JSON string below +json = "{}" +# create an instance of EdgeModelInfo from a JSON string +edge_model_info_instance = EdgeModelInfo.from_json(json) +# print the JSON string representation of the object +print EdgeModelInfo.to_json() + +# convert the object into a dict +edge_model_info_dict = edge_model_info_instance.to_dict() +# create an instance of EdgeModelInfo from a dict +edge_model_info_from_dict = EdgeModelInfo.from_dict(edge_model_info_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/EscalationTypeEnum.md b/generated/docs/EscalationTypeEnum.md index d7842681..3a3ce20a 100644 --- a/generated/docs/EscalationTypeEnum.md +++ b/generated/docs/EscalationTypeEnum.md @@ -1,10 +1,11 @@ # EscalationTypeEnum -## Properties -Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- -**value** | **str** | | must be one of ["STANDARD", "NO_HUMAN_LABELING", ] +## Enum + +* `STANDARD` (value: `'STANDARD'`) + +* `NO_HUMAN_LABELING` (value: `'NO_HUMAN_LABELING'`) [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/GetDetectorEvaluation200Response.md b/generated/docs/GetDetectorEvaluation200Response.md new file mode 100644 index 00000000..ab6116f9 --- /dev/null +++ b/generated/docs/GetDetectorEvaluation200Response.md @@ -0,0 +1,28 @@ +# GetDetectorEvaluation200Response + + +## Properties +Name | Type | Description | Notes +------------ | ------------- | ------------- | ------------- +**evaluation_results** | [**GetDetectorEvaluation200ResponseEvaluationResults**](GetDetectorEvaluation200ResponseEvaluationResults.md) | | [optional] + +## Example + +```python +from groundlight_openapi_client.models.get_detector_evaluation200_response import GetDetectorEvaluation200Response + +# TODO update the JSON string below +json = "{}" +# create an instance of GetDetectorEvaluation200Response from a JSON string +get_detector_evaluation200_response_instance = GetDetectorEvaluation200Response.from_json(json) +# print the JSON string representation of the object +print GetDetectorEvaluation200Response.to_json() + +# convert the object into a dict +get_detector_evaluation200_response_dict = get_detector_evaluation200_response_instance.to_dict() +# create an instance of GetDetectorEvaluation200Response from a dict +get_detector_evaluation200_response_from_dict = GetDetectorEvaluation200Response.from_dict(get_detector_evaluation200_response_dict) +``` +[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) + + diff --git a/generated/docs/GetDetectorEvaluation200ResponseEvaluationResults.md b/generated/docs/GetDetectorEvaluation200ResponseEvaluationResults.md new file mode 100644 index 00000000..6110abea --- /dev/null +++ b/generated/docs/GetDetectorEvaluation200ResponseEvaluationResults.md @@ -0,0 +1,46 @@ +# GetDetectorEvaluation200ResponseEvaluationResults + + +## Properties +Name | Type | Description | Notes +------------ | ------------- | ------------- | ------------- +**eval_timestamp** | **datetime** | | [optional] +**total_ground_truth_examples** | **int** | | [optional] +**total_labeled_examples** | **int** | | [optional] +**kfold_pooled__balanced_accuracy** | **float** | | [optional] +**kfold_pooled__positive_accuracy** | **float** | | [optional] +**kfold_pooled__negative_accuracy** | **float** | | [optional] +**precision__mean** | **float** | | [optional] +**recall__mean** | **float** | | [optional] +**roc_auc__mean** | **float** | | [optional] +**balanced_system_accuracies** | **Dict[str, object]** | | [optional] +**positive_system_accuracies** | **Dict[str, object]** | | [optional] +**negative_system_accuracies** | **Dict[str, object]** | | [optional] +**mean_absolute_error__mean** | **float** | | [optional] +**objdet_precision__mean** | **float** | | [optional] +**objdet_recall__mean** | **float** | | [optional] +**objdet_f1_score__mean** | **float** | | [optional] +**class_accuracies** | **Dict[str, object]** | | [optional] +**confusion_dict** | **Dict[str, object]** | | [optional] +**num_examples_per_class** | **Dict[str, object]** | | [optional] + +## Example + +```python +from groundlight_openapi_client.models.get_detector_evaluation200_response_evaluation_results import GetDetectorEvaluation200ResponseEvaluationResults + +# TODO update the JSON string below +json = "{}" +# create an instance of GetDetectorEvaluation200ResponseEvaluationResults from a JSON string +get_detector_evaluation200_response_evaluation_results_instance = GetDetectorEvaluation200ResponseEvaluationResults.from_json(json) +# print the JSON string representation of the object +print GetDetectorEvaluation200ResponseEvaluationResults.to_json() + +# convert the object into a dict +get_detector_evaluation200_response_evaluation_results_dict = get_detector_evaluation200_response_evaluation_results_instance.to_dict() +# create an instance of GetDetectorEvaluation200ResponseEvaluationResults from a dict +get_detector_evaluation200_response_evaluation_results_from_dict = GetDetectorEvaluation200ResponseEvaluationResults.from_dict(get_detector_evaluation200_response_evaluation_results_dict) +``` +[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) + + diff --git a/generated/docs/GetDetectorMetrics200Response.md b/generated/docs/GetDetectorMetrics200Response.md new file mode 100644 index 00000000..60ae020b --- /dev/null +++ b/generated/docs/GetDetectorMetrics200Response.md @@ -0,0 +1,28 @@ +# GetDetectorMetrics200Response + + +## Properties +Name | Type | Description | Notes +------------ | ------------- | ------------- | ------------- +**summary** | [**GetDetectorMetrics200ResponseSummary**](GetDetectorMetrics200ResponseSummary.md) | | [optional] + +## Example + +```python +from groundlight_openapi_client.models.get_detector_metrics200_response import GetDetectorMetrics200Response + +# TODO update the JSON string below +json = "{}" +# create an instance of GetDetectorMetrics200Response from a JSON string +get_detector_metrics200_response_instance = GetDetectorMetrics200Response.from_json(json) +# print the JSON string representation of the object +print GetDetectorMetrics200Response.to_json() + +# convert the object into a dict +get_detector_metrics200_response_dict = get_detector_metrics200_response_instance.to_dict() +# create an instance of GetDetectorMetrics200Response from a dict +get_detector_metrics200_response_from_dict = GetDetectorMetrics200Response.from_dict(get_detector_metrics200_response_dict) +``` +[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) + + diff --git a/generated/docs/GetDetectorMetrics200ResponseSummary.md b/generated/docs/GetDetectorMetrics200ResponseSummary.md new file mode 100644 index 00000000..21018836 --- /dev/null +++ b/generated/docs/GetDetectorMetrics200ResponseSummary.md @@ -0,0 +1,32 @@ +# GetDetectorMetrics200ResponseSummary + + +## Properties +Name | Type | Description | Notes +------------ | ------------- | ------------- | ------------- +**num_ground_truth** | **int** | | [optional] +**num_current_source_human** | **int** | | [optional] +**class_counts** | [**GetDetectorMetrics200ResponseSummaryClassCounts**](GetDetectorMetrics200ResponseSummaryClassCounts.md) | | [optional] +**unconfident_counts** | **object** | | [optional] +**total_iqs** | **int** | | [optional] + +## Example + +```python +from groundlight_openapi_client.models.get_detector_metrics200_response_summary import GetDetectorMetrics200ResponseSummary + +# TODO update the JSON string below +json = "{}" +# create an instance of GetDetectorMetrics200ResponseSummary from a JSON string +get_detector_metrics200_response_summary_instance = GetDetectorMetrics200ResponseSummary.from_json(json) +# print the JSON string representation of the object +print GetDetectorMetrics200ResponseSummary.to_json() + +# convert the object into a dict +get_detector_metrics200_response_summary_dict = get_detector_metrics200_response_summary_instance.to_dict() +# create an instance of GetDetectorMetrics200ResponseSummary from a dict +get_detector_metrics200_response_summary_from_dict = GetDetectorMetrics200ResponseSummary.from_dict(get_detector_metrics200_response_summary_dict) +``` +[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) + + diff --git a/generated/docs/GetDetectorMetrics200ResponseSummaryClassCounts.md b/generated/docs/GetDetectorMetrics200ResponseSummaryClassCounts.md new file mode 100644 index 00000000..5b5c9a30 --- /dev/null +++ b/generated/docs/GetDetectorMetrics200ResponseSummaryClassCounts.md @@ -0,0 +1,32 @@ +# GetDetectorMetrics200ResponseSummaryClassCounts + + +## Properties +Name | Type | Description | Notes +------------ | ------------- | ------------- | ------------- +**source_ml** | **object** | | [optional] +**source_human** | **object** | | [optional] +**cloud_labeler** | **object** | | [optional] +**cloud** | **object** | | [optional] +**total** | **object** | | [optional] + +## Example + +```python +from groundlight_openapi_client.models.get_detector_metrics200_response_summary_class_counts import GetDetectorMetrics200ResponseSummaryClassCounts + +# TODO update the JSON string below +json = "{}" +# create an instance of GetDetectorMetrics200ResponseSummaryClassCounts from a JSON string +get_detector_metrics200_response_summary_class_counts_instance = GetDetectorMetrics200ResponseSummaryClassCounts.from_json(json) +# print the JSON string representation of the object +print GetDetectorMetrics200ResponseSummaryClassCounts.to_json() + +# convert the object into a dict +get_detector_metrics200_response_summary_class_counts_dict = get_detector_metrics200_response_summary_class_counts_instance.to_dict() +# create an instance of GetDetectorMetrics200ResponseSummaryClassCounts from a dict +get_detector_metrics200_response_summary_class_counts_from_dict = GetDetectorMetrics200ResponseSummaryClassCounts.from_dict(get_detector_metrics200_response_summary_class_counts_dict) +``` +[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) + + diff --git a/generated/docs/ImageQueriesApi.md b/generated/docs/ImageQueriesApi.md index e3f91416..81f093e5 100644 --- a/generated/docs/ImageQueriesApi.md +++ b/generated/docs/ImageQueriesApi.md @@ -11,7 +11,7 @@ Method | HTTP request | Description # **get_image** -> file_type get_image(id) +> bytearray get_image(id) @@ -20,12 +20,13 @@ Retrieve an image by its ID. ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import image_queries_api +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -38,7 +39,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -46,27 +47,28 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = image_queries_api.ImageQueriesApi(api_client) - id = "id_example" # str | Retrieve the image associated with the image query ID. + api_instance = groundlight_openapi_client.ImageQueriesApi(api_client) + id = 'id_example' # str | Retrieve the image associated with the image query ID. - # example passing only required values which don't have defaults set try: api_response = api_instance.get_image(id) + print("The response of ImageQueriesApi->get_image:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling ImageQueriesApi->get_image: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **id** | **str**| Retrieve the image associated with the image query ID. | + **id** | **str**| Retrieve the image associated with the image query ID. | ### Return type -**file_type** +**bytearray** ### Authorization @@ -77,9 +79,7 @@ Name | Type | Description | Notes - **Content-Type**: Not defined - **Accept**: image/jpeg - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **200** | | - | @@ -96,13 +96,14 @@ Retrieve an image-query by its ID. ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import image_queries_api -from groundlight_openapi_client.model.image_query import ImageQuery +from groundlight_openapi_client.models.image_query import ImageQuery +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -115,7 +116,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -123,23 +124,24 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = image_queries_api.ImageQueriesApi(api_client) - id = "id_example" # str | Choose an image query by its ID. + api_instance = groundlight_openapi_client.ImageQueriesApi(api_client) + id = 'id_example' # str | Choose an image query by its ID. - # example passing only required values which don't have defaults set try: api_response = api_instance.get_image_query(id) + print("The response of ImageQueriesApi->get_image_query:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling ImageQueriesApi->get_image_query: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **id** | **str**| Choose an image query by its ID. | + **id** | **str**| Choose an image query by its ID. | ### Return type @@ -154,9 +156,7 @@ Name | Type | Description | Notes - **Content-Type**: Not defined - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **200** | | - | @@ -164,7 +164,7 @@ Name | Type | Description | Notes [[Back to top]](#) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to Model list]](../README.md#documentation-for-models) [[Back to README]](../README.md) # **list_image_queries** -> PaginatedImageQueryList list_image_queries() +> PaginatedImageQueryList list_image_queries(detector_id=detector_id, page=page, page_size=page_size) @@ -173,13 +173,14 @@ Retrieve a list of image-queries. ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import image_queries_api -from groundlight_openapi_client.model.paginated_image_query_list import PaginatedImageQueryList +from groundlight_openapi_client.models.paginated_image_query_list import PaginatedImageQueryList +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -192,7 +193,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -200,28 +201,28 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = image_queries_api.ImageQueriesApi(api_client) - detector_id = "detector_id_example" # str | Optionally filter image queries by detector ID. (optional) - page = 1 # int | A page number within the paginated result set. (optional) - page_size = 1 # int | Number of items to return per page. (optional) + api_instance = groundlight_openapi_client.ImageQueriesApi(api_client) + detector_id = 'detector_id_example' # str | Optionally filter image queries by detector ID. (optional) + page = 56 # int | A page number within the paginated result set. (optional) + page_size = 56 # int | Number of items to return per page. (optional) - # example passing only required values which don't have defaults set - # and optional values try: api_response = api_instance.list_image_queries(detector_id=detector_id, page=page, page_size=page_size) + print("The response of ImageQueriesApi->list_image_queries:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling ImageQueriesApi->list_image_queries: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **detector_id** | **str**| Optionally filter image queries by detector ID. | [optional] - **page** | **int**| A page number within the paginated result set. | [optional] - **page_size** | **int**| Number of items to return per page. | [optional] + **detector_id** | **str**| Optionally filter image queries by detector ID. | [optional] + **page** | **int**| A page number within the paginated result set. | [optional] + **page_size** | **int**| Number of items to return per page. | [optional] ### Return type @@ -236,9 +237,7 @@ Name | Type | Description | Notes - **Content-Type**: Not defined - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **200** | | - | @@ -246,22 +245,32 @@ Name | Type | Description | Notes [[Back to top]](#) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to Model list]](../README.md#documentation-for-models) [[Back to README]](../README.md) # **submit_image_query** -> ImageQuery submit_image_query(detector_id) +> ImageQuery submit_image_query(detector_id, confidence_threshold=confidence_threshold, human_review=human_review, image_query_id=image_query_id, inspection_id=inspection_id, metadata=metadata, patience_time=patience_time, want_async=want_async, body=body) + - Submit an image query against a detector. You must use `\"Content-Type: image/jpeg\"` or similar (image/png, image/webp, etc) for the image data. For example: ```Bash $ curl https://api.groundlight.ai/device-api/v1/image-queries?detector_id=det_abc123 \\ --header \"Content-Type: image/jpeg\" \\ --data-binary @path/to/filename.jpeg ``` +Submit an image query against a detector. + +You must use `"Content-Type: image/jpeg"` or similar (image/png, image/webp, etc) for the image data. For example: +```Bash +$ curl https://api.groundlight.ai/device-api/v1/image-queries?detector_id=det_abc123 \ + --header "Content-Type: image/jpeg" \ + --data-binary @path/to/filename.jpeg +``` + ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import image_queries_api -from groundlight_openapi_client.model.image_query import ImageQuery +from groundlight_openapi_client.models.image_query import ImageQuery +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -274,7 +283,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -282,47 +291,40 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = image_queries_api.ImageQueriesApi(api_client) - detector_id = "detector_id_example" # str | Choose a detector by its ID. - confidence_threshold = 0 # float | The confidence threshold for the image query. (optional) - human_review = "human_review_example" # str | If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review if the ML model is not confident). If set to `ALWAYS`, always send the image query for human review even if the ML model is confident. If set to `NEVER`, never send the image query for human review even if the ML model is not confident. (optional) - image_query_id = "image_query_id_example" # str | The ID to assign to the created image query. (optional) - inspection_id = "inspection_id_example" # str | Associate the image query with an inspection. (optional) - metadata = "metadata_example" # str | A dictionary of custom key/value metadata to associate with the image query (limited to 1KB). (optional) - patience_time = 3.14 # float | How long to wait for a confident response. (optional) - want_async = "want_async_example" # str | If \"true\" then submitting an image query returns immediately without a result. The result will be computed asynchronously and can be retrieved later. (optional) - body = open('/path/to/file', 'rb') # file_type | (optional) - - # example passing only required values which don't have defaults set - try: - api_response = api_instance.submit_image_query(detector_id) - pprint(api_response) - except groundlight_openapi_client.ApiException as e: - print("Exception when calling ImageQueriesApi->submit_image_query: %s\n" % e) + api_instance = groundlight_openapi_client.ImageQueriesApi(api_client) + detector_id = 'detector_id_example' # str | Choose a detector by its ID. + confidence_threshold = 3.4 # float | The confidence threshold for the image query. (optional) + human_review = 'human_review_example' # str | If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review if the ML model is not confident). If set to `ALWAYS`, always send the image query for human review even if the ML model is confident. If set to `NEVER`, never send the image query for human review even if the ML model is not confident. (optional) + image_query_id = 'image_query_id_example' # str | The ID to assign to the created image query. (optional) + inspection_id = 'inspection_id_example' # str | Associate the image query with an inspection. (optional) + metadata = 'metadata_example' # str | A dictionary of custom key/value metadata to associate with the image query (limited to 1KB). (optional) + patience_time = 3.4 # float | How long to wait for a confident response. (optional) + want_async = 'want_async_example' # str | If \"true\" then submitting an image query returns immediately without a result. The result will be computed asynchronously and can be retrieved later. (optional) + body = None # bytearray | (optional) - # example passing only required values which don't have defaults set - # and optional values try: api_response = api_instance.submit_image_query(detector_id, confidence_threshold=confidence_threshold, human_review=human_review, image_query_id=image_query_id, inspection_id=inspection_id, metadata=metadata, patience_time=patience_time, want_async=want_async, body=body) + print("The response of ImageQueriesApi->submit_image_query:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling ImageQueriesApi->submit_image_query: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **detector_id** | **str**| Choose a detector by its ID. | - **confidence_threshold** | **float**| The confidence threshold for the image query. | [optional] - **human_review** | **str**| If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review if the ML model is not confident). If set to `ALWAYS`, always send the image query for human review even if the ML model is confident. If set to `NEVER`, never send the image query for human review even if the ML model is not confident. | [optional] - **image_query_id** | **str**| The ID to assign to the created image query. | [optional] - **inspection_id** | **str**| Associate the image query with an inspection. | [optional] - **metadata** | **str**| A dictionary of custom key/value metadata to associate with the image query (limited to 1KB). | [optional] - **patience_time** | **float**| How long to wait for a confident response. | [optional] - **want_async** | **str**| If \"true\" then submitting an image query returns immediately without a result. The result will be computed asynchronously and can be retrieved later. | [optional] - **body** | **file_type**| | [optional] + **detector_id** | **str**| Choose a detector by its ID. | + **confidence_threshold** | **float**| The confidence threshold for the image query. | [optional] + **human_review** | **str**| If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review if the ML model is not confident). If set to `ALWAYS`, always send the image query for human review even if the ML model is confident. If set to `NEVER`, never send the image query for human review even if the ML model is not confident. | [optional] + **image_query_id** | **str**| The ID to assign to the created image query. | [optional] + **inspection_id** | **str**| Associate the image query with an inspection. | [optional] + **metadata** | **str**| A dictionary of custom key/value metadata to associate with the image query (limited to 1KB). | [optional] + **patience_time** | **float**| How long to wait for a confident response. | [optional] + **want_async** | **str**| If \"true\" then submitting an image query returns immediately without a result. The result will be computed asynchronously and can be retrieved later. | [optional] + **body** | **bytearray**| | [optional] ### Return type @@ -337,9 +339,7 @@ Name | Type | Description | Notes - **Content-Type**: image/jpeg, image/jpg, image/png, image/gif, image/webp, image/bmp, image/x-icon - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **201** | | - | diff --git a/generated/docs/ImageQuery.md b/generated/docs/ImageQuery.md index 6ed5ae92..d94bb8bd 100644 --- a/generated/docs/ImageQuery.md +++ b/generated/docs/ImageQuery.md @@ -5,21 +5,37 @@ ImageQuery objects are the answers to natural language questions about images cr ## Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- -**metadata** | **{str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type** | Metadata about the image query. | [readonly] +**metadata** | **Dict[str, object]** | Metadata about the image query. | [readonly] **id** | **str** | A unique ID for this object. | [readonly] -**type** | **bool, date, datetime, dict, float, int, list, str, none_type** | The type of this object. | [readonly] +**type** | [**ImageQueryTypeEnum**](ImageQueryTypeEnum.md) | The type of this object. | [readonly] **created_at** | **datetime** | When was this detector created? | [readonly] **query** | **str** | A question about the image. | [readonly] **detector_id** | **str** | Which detector was used on this image query? | [readonly] -**result_type** | **bool, date, datetime, dict, float, int, list, str, none_type** | What type of result are we returning? | [readonly] -**result** | **bool, date, datetime, dict, float, int, list, str, none_type** | | +**result_type** | [**ResultTypeEnum**](ResultTypeEnum.md) | What type of result are we returning? | [readonly] +**result** | [**ImageQueryResult**](ImageQueryResult.md) | | **patience_time** | **float** | How long to wait for a confident response. | [readonly] **confidence_threshold** | **float** | Min confidence needed to accept the response of the image query. | [readonly] -**rois** | [**[ROI], none_type**](ROI.md) | An array of regions of interest (bounding boxes) collected on image | [readonly] -**text** | **str, none_type** | A text field on image query. | [readonly] -**done_processing** | **bool** | EDGE ONLY - Whether the image query has completed escalating and will receive no new results. | [optional] if omitted the server will use the default value of False -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**rois** | [**List[ROI]**](ROI.md) | An array of regions of interest (bounding boxes) collected on image | [readonly] +**text** | **str** | A text field on image query. | [readonly] +**done_processing** | **bool** | EDGE ONLY - Whether the image query has completed escalating and will receive no new results. | [optional] [default to False] +## Example + +```python +from groundlight_openapi_client.models.image_query import ImageQuery + +# TODO update the JSON string below +json = "{}" +# create an instance of ImageQuery from a JSON string +image_query_instance = ImageQuery.from_json(json) +# print the JSON string representation of the object +print ImageQuery.to_json() + +# convert the object into a dict +image_query_dict = image_query_instance.to_dict() +# create an instance of ImageQuery from a dict +image_query_from_dict = ImageQuery.from_dict(image_query_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/ImageQueryResult.md b/generated/docs/ImageQueryResult.md new file mode 100644 index 00000000..fb86cc58 --- /dev/null +++ b/generated/docs/ImageQueryResult.md @@ -0,0 +1,36 @@ +# ImageQueryResult + + +## Properties +Name | Type | Description | Notes +------------ | ------------- | ------------- | ------------- +**confidence** | **float** | | [optional] +**source** | **str** | | [optional] +**result_type** | **str** | | [optional] +**from_edge** | **bool** | | [optional] +**label** | **str** | | +**count** | **int** | | +**greater_than_max** | **bool** | | [optional] +**text** | **str** | | +**truncated** | **bool** | | + +## Example + +```python +from groundlight_openapi_client.models.image_query_result import ImageQueryResult + +# TODO update the JSON string below +json = "{}" +# create an instance of ImageQueryResult from a JSON string +image_query_result_instance = ImageQueryResult.from_json(json) +# print the JSON string representation of the object +print ImageQueryResult.to_json() + +# convert the object into a dict +image_query_result_dict = image_query_result_instance.to_dict() +# create an instance of ImageQueryResult from a dict +image_query_result_from_dict = ImageQueryResult.from_dict(image_query_result_dict) +``` +[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) + + diff --git a/generated/docs/ImageQueryTypeEnum.md b/generated/docs/ImageQueryTypeEnum.md index 27a72eef..298ad42a 100644 --- a/generated/docs/ImageQueryTypeEnum.md +++ b/generated/docs/ImageQueryTypeEnum.md @@ -1,10 +1,9 @@ # ImageQueryTypeEnum -## Properties -Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- -**value** | **str** | | defaults to "image_query", must be one of ["image_query", ] +## Enum + +* `IMAGE_QUERY` (value: `'image_query'`) [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/InlineResponse200.md b/generated/docs/InlineResponse200.md index 23dc2df0..2ec59dfb 100644 --- a/generated/docs/InlineResponse200.md +++ b/generated/docs/InlineResponse200.md @@ -1,7 +1,6 @@ # InlineResponse200 - -## Properties +#### Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **summary** | [**InlineResponse200Summary**](InlineResponse200Summary.md) | | [optional] @@ -9,4 +8,3 @@ Name | Type | Description | Notes [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) - diff --git a/generated/docs/InlineResponse2001.md b/generated/docs/InlineResponse2001.md index aa7bbe38..31f0d33a 100644 --- a/generated/docs/InlineResponse2001.md +++ b/generated/docs/InlineResponse2001.md @@ -1,7 +1,6 @@ # InlineResponse2001 - -## Properties +#### Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **evaluation_results** | [**InlineResponse2001EvaluationResults**](InlineResponse2001EvaluationResults.md) | | [optional] @@ -9,4 +8,3 @@ Name | Type | Description | Notes [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) - diff --git a/generated/docs/InlineResponse2001EvaluationResults.md b/generated/docs/InlineResponse2001EvaluationResults.md index b73d0ff3..74fd9788 100644 --- a/generated/docs/InlineResponse2001EvaluationResults.md +++ b/generated/docs/InlineResponse2001EvaluationResults.md @@ -1,25 +1,24 @@ # InlineResponse2001EvaluationResults - -## Properties +#### Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **eval_timestamp** | **datetime** | | [optional] **total_ground_truth_examples** | **int, none_type** | | [optional] **total_labeled_examples** | **int, none_type** | | [optional] -**kfold_pooled__balanced_accuracy** | **float** | | [optional] -**kfold_pooled__positive_accuracy** | **float, none_type** | | [optional] -**kfold_pooled__negative_accuracy** | **float, none_type** | | [optional] -**precision__mean** | **float, none_type** | | [optional] -**recall__mean** | **float, none_type** | | [optional] -**roc_auc__mean** | **float, none_type** | | [optional] +**kfold_pooled__balanced_accuracy** | **int, float** | | [optional] +**kfold_pooled__positive_accuracy** | **int, float, none_type** | | [optional] +**kfold_pooled__negative_accuracy** | **int, float, none_type** | | [optional] +**precision__mean** | **int, float, none_type** | | [optional] +**recall__mean** | **int, float, none_type** | | [optional] +**roc_auc__mean** | **int, float, none_type** | | [optional] **balanced_system_accuracies** | **{str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type** | | [optional] **positive_system_accuracies** | **{str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type** | | [optional] **negative_system_accuracies** | **{str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type** | | [optional] -**mean_absolute_error__mean** | **float, none_type** | | [optional] -**objdet_precision__mean** | **float, none_type** | | [optional] -**objdet_recall__mean** | **float, none_type** | | [optional] -**objdet_f1_score__mean** | **float, none_type** | | [optional] +**mean_absolute_error__mean** | **int, float, none_type** | | [optional] +**objdet_precision__mean** | **int, float, none_type** | | [optional] +**objdet_recall__mean** | **int, float, none_type** | | [optional] +**objdet_f1_score__mean** | **int, float, none_type** | | [optional] **class_accuracies** | **{str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type** | | [optional] **confusion_dict** | **{str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type** | | [optional] **num_examples_per_class** | **{str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type** | | [optional] @@ -27,4 +26,3 @@ Name | Type | Description | Notes [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) - diff --git a/generated/docs/InlineResponse2002.md b/generated/docs/InlineResponse2002.md index 59b1a320..b30dd043 100644 --- a/generated/docs/InlineResponse2002.md +++ b/generated/docs/InlineResponse2002.md @@ -1,12 +1,10 @@ # InlineResponse2002 - -## Properties +#### Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- -**username** | **str** | The user's username | [optional] +**username** | **str** | The user's username | [optional] **any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) - diff --git a/generated/docs/InlineResponse200Summary.md b/generated/docs/InlineResponse200Summary.md index aebcac9c..7c2b559c 100644 --- a/generated/docs/InlineResponse200Summary.md +++ b/generated/docs/InlineResponse200Summary.md @@ -1,7 +1,6 @@ # InlineResponse200Summary - -## Properties +#### Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **num_ground_truth** | **int** | | [optional] @@ -13,4 +12,3 @@ Name | Type | Description | Notes [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) - diff --git a/generated/docs/InlineResponse200SummaryClassCounts.md b/generated/docs/InlineResponse200SummaryClassCounts.md index 463959ad..e40c6c91 100644 --- a/generated/docs/InlineResponse200SummaryClassCounts.md +++ b/generated/docs/InlineResponse200SummaryClassCounts.md @@ -1,7 +1,6 @@ # InlineResponse200SummaryClassCounts - -## Properties +#### Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **source_ml** | **{str: (bool, date, datetime, dict, float, int, list, str, none_type)}** | | [optional] @@ -13,4 +12,3 @@ Name | Type | Description | Notes [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) - diff --git a/generated/docs/Label.md b/generated/docs/Label.md index 8e75ee67..c541a004 100644 --- a/generated/docs/Label.md +++ b/generated/docs/Label.md @@ -1,10 +1,13 @@ # Label -## Properties -Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- -**value** | **str** | | must be one of ["YES", "NO", "UNCLEAR", ] +## Enum + +* `YES` (value: `'YES'`) + +* `NO` (value: `'NO'`) + +* `UNCLEAR` (value: `'UNCLEAR'`) [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/LabelValue.md b/generated/docs/LabelValue.md index da087687..6c50835f 100644 --- a/generated/docs/LabelValue.md +++ b/generated/docs/LabelValue.md @@ -4,16 +4,32 @@ ## Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- -**confidence** | **float, none_type** | | [readonly] -**class_name** | **str, none_type** | Return a human-readable class name for this label (e.g. YES/NO) | [readonly] -**annotations_requested** | **[str]** | | [readonly] +**confidence** | **float** | | [readonly] +**class_name** | **str** | Return a human-readable class name for this label (e.g. YES/NO) | [readonly] +**rois** | [**List[ROI]**](ROI.md) | | [optional] +**annotations_requested** | **List[str]** | | [readonly] **created_at** | **datetime** | | [readonly] -**detector_id** | **int, none_type** | | [readonly] +**detector_id** | **int** | | [readonly] **source** | **str** | | [readonly] -**text** | **str, none_type** | Text annotations | [readonly] -**rois** | [**[ROI], none_type**](ROI.md) | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**text** | **str** | Text annotations | [readonly] +## Example + +```python +from groundlight_openapi_client.models.label_value import LabelValue + +# TODO update the JSON string below +json = "{}" +# create an instance of LabelValue from a JSON string +label_value_instance = LabelValue.from_json(json) +# print the JSON string representation of the object +print LabelValue.to_json() + +# convert the object into a dict +label_value_dict = label_value_instance.to_dict() +# create an instance of LabelValue from a dict +label_value_from_dict = LabelValue.from_dict(label_value_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/LabelValueRequest.md b/generated/docs/LabelValueRequest.md index baa15c2f..8f8e672b 100644 --- a/generated/docs/LabelValueRequest.md +++ b/generated/docs/LabelValueRequest.md @@ -4,11 +4,27 @@ ## Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- -**label** | **str, none_type** | | +**label** | **str** | | **image_query_id** | **str** | | -**rois** | [**[ROIRequest], none_type**](ROIRequest.md) | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**rois** | [**List[ROIRequest]**](ROIRequest.md) | | [optional] +## Example + +```python +from groundlight_openapi_client.models.label_value_request import LabelValueRequest + +# TODO update the JSON string below +json = "{}" +# create an instance of LabelValueRequest from a JSON string +label_value_request_instance = LabelValueRequest.from_json(json) +# print the JSON string representation of the object +print LabelValueRequest.to_json() + +# convert the object into a dict +label_value_request_dict = label_value_request_instance.to_dict() +# create an instance of LabelValueRequest from a dict +label_value_request_from_dict = LabelValueRequest.from_dict(label_value_request_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/LabelsApi.md b/generated/docs/LabelsApi.md index 487e3fe4..20c729b8 100644 --- a/generated/docs/LabelsApi.md +++ b/generated/docs/LabelsApi.md @@ -12,19 +12,21 @@ Method | HTTP request | Description -Create a new LabelValue and attach it to an image query. This will trigger asynchronous fine-tuner model training. +Create a new LabelValue and attach it to an image query. This will trigger +asynchronous fine-tuner model training. ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import labels_api -from groundlight_openapi_client.model.label_value import LabelValue -from groundlight_openapi_client.model.label_value_request import LabelValueRequest +from groundlight_openapi_client.models.label_value import LabelValue +from groundlight_openapi_client.models.label_value_request import LabelValueRequest +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -37,7 +39,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -45,37 +47,24 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = labels_api.LabelsApi(api_client) - label_value_request = LabelValueRequest( - label="label_example", - image_query_id="image_query_id_example", - rois=[ - ROIRequest( - label="label_example", - geometry=BBoxGeometryRequest( - left=3.14, - top=3.14, - right=3.14, - bottom=3.14, - ), - ), - ], - ) # LabelValueRequest | - - # example passing only required values which don't have defaults set + api_instance = groundlight_openapi_client.LabelsApi(api_client) + label_value_request = groundlight_openapi_client.LabelValueRequest() # LabelValueRequest | + try: api_response = api_instance.create_label(label_value_request) + print("The response of LabelsApi->create_label:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling LabelsApi->create_label: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **label_value_request** | [**LabelValueRequest**](LabelValueRequest.md)| | + **label_value_request** | [**LabelValueRequest**](LabelValueRequest.md)| | ### Return type @@ -90,9 +79,7 @@ Name | Type | Description | Notes - **Content-Type**: application/json, application/x-www-form-urlencoded, multipart/form-data - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **201** | | - | diff --git a/generated/docs/ModeEnum.md b/generated/docs/ModeEnum.md index 720ccf3e..774c5a0c 100644 --- a/generated/docs/ModeEnum.md +++ b/generated/docs/ModeEnum.md @@ -1,10 +1,17 @@ # ModeEnum -## Properties -Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- -**value** | **str** | | must be one of ["BINARY", "COUNT", "MULTI_CLASS", "TEXT", "BOUNDING_BOX", ] +## Enum + +* `BINARY` (value: `'BINARY'`) + +* `COUNT` (value: `'COUNT'`) + +* `MULTI_CLASS` (value: `'MULTI_CLASS'`) + +* `TEXT` (value: `'TEXT'`) + +* `BOUNDING_BOX` (value: `'BOUNDING_BOX'`) [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/MultiClassModeConfiguration.md b/generated/docs/MultiClassModeConfiguration.md index 32570bcf..d9f612d3 100644 --- a/generated/docs/MultiClassModeConfiguration.md +++ b/generated/docs/MultiClassModeConfiguration.md @@ -4,10 +4,26 @@ ## Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- -**class_names** | **[str]** | | +**class_names** | **List[str]** | | **num_classes** | **int** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +## Example + +```python +from groundlight_openapi_client.models.multi_class_mode_configuration import MultiClassModeConfiguration + +# TODO update the JSON string below +json = "{}" +# create an instance of MultiClassModeConfiguration from a JSON string +multi_class_mode_configuration_instance = MultiClassModeConfiguration.from_json(json) +# print the JSON string representation of the object +print MultiClassModeConfiguration.to_json() + +# convert the object into a dict +multi_class_mode_configuration_dict = multi_class_mode_configuration_instance.to_dict() +# create an instance of MultiClassModeConfiguration from a dict +multi_class_mode_configuration_from_dict = MultiClassModeConfiguration.from_dict(multi_class_mode_configuration_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/MultiClassificationResult.md b/generated/docs/MultiClassificationResult.md index d048a5da..a8a74249 100644 --- a/generated/docs/MultiClassificationResult.md +++ b/generated/docs/MultiClassificationResult.md @@ -4,13 +4,29 @@ ## Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- -**label** | **str** | | -**confidence** | **float, none_type** | | [optional] +**confidence** | **float** | | [optional] **source** | **str** | | [optional] -**result_type** | **str** | | [optional] if omitted the server will use the default value of "multi_classification" +**result_type** | **str** | | [optional] **from_edge** | **bool** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**label** | **str** | | + +## Example + +```python +from groundlight_openapi_client.models.multi_classification_result import MultiClassificationResult + +# TODO update the JSON string below +json = "{}" +# create an instance of MultiClassificationResult from a JSON string +multi_classification_result_instance = MultiClassificationResult.from_json(json) +# print the JSON string representation of the object +print MultiClassificationResult.to_json() +# convert the object into a dict +multi_classification_result_dict = multi_classification_result_instance.to_dict() +# create an instance of MultiClassificationResult from a dict +multi_classification_result_from_dict = MultiClassificationResult.from_dict(multi_classification_result_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/Note.md b/generated/docs/Note.md index a5e80378..a86a1eb1 100644 --- a/generated/docs/Note.md +++ b/generated/docs/Note.md @@ -5,10 +5,26 @@ Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **detector_id** | **str** | | [readonly] -**content** | **str, none_type** | Text content of the note. | [optional] -**is_pinned** | **bool, none_type** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**content** | **str** | Text content of the note. | [optional] +**is_pinned** | **bool** | | [optional] +## Example + +```python +from groundlight_openapi_client.models.note import Note + +# TODO update the JSON string below +json = "{}" +# create an instance of Note from a JSON string +note_instance = Note.from_json(json) +# print the JSON string representation of the object +print Note.to_json() + +# convert the object into a dict +note_dict = note_instance.to_dict() +# create an instance of Note from a dict +note_from_dict = Note.from_dict(note_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/NoteRequest.md b/generated/docs/NoteRequest.md index 783cb431..3946ba5d 100644 --- a/generated/docs/NoteRequest.md +++ b/generated/docs/NoteRequest.md @@ -4,11 +4,27 @@ ## Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- -**content** | **str, none_type** | Text content of the note. | [optional] -**is_pinned** | **bool, none_type** | | [optional] -**image** | **file_type, none_type** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**content** | **str** | Text content of the note. | [optional] +**is_pinned** | **bool** | | [optional] +**image** | **bytearray** | | [optional] +## Example + +```python +from groundlight_openapi_client.models.note_request import NoteRequest + +# TODO update the JSON string below +json = "{}" +# create an instance of NoteRequest from a JSON string +note_request_instance = NoteRequest.from_json(json) +# print the JSON string representation of the object +print NoteRequest.to_json() + +# convert the object into a dict +note_request_dict = note_request_instance.to_dict() +# create an instance of NoteRequest from a dict +note_request_from_dict = NoteRequest.from_dict(note_request_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/NotesApi.md b/generated/docs/NotesApi.md index f4e20739..5285919d 100644 --- a/generated/docs/NotesApi.md +++ b/generated/docs/NotesApi.md @@ -9,7 +9,7 @@ Method | HTTP request | Description # **create_note** -> create_note(detector_id) +> create_note(detector_id, note_request=note_request) @@ -18,13 +18,14 @@ Creates a new note. ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import notes_api -from groundlight_openapi_client.model.note_request import NoteRequest +from groundlight_openapi_client.models.note_request import NoteRequest +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -37,7 +38,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -45,35 +46,24 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = notes_api.NotesApi(api_client) - detector_id = "detector_id_example" # str | the detector to associate the new note with - note_request = NoteRequest( - content="content_example", - is_pinned=True, - image=open('/path/to/file', 'rb'), - ) # NoteRequest | (optional) - - # example passing only required values which don't have defaults set - try: - api_instance.create_note(detector_id) - except groundlight_openapi_client.ApiException as e: - print("Exception when calling NotesApi->create_note: %s\n" % e) + api_instance = groundlight_openapi_client.NotesApi(api_client) + detector_id = 'detector_id_example' # str | the detector to associate the new note with + note_request = groundlight_openapi_client.NoteRequest() # NoteRequest | (optional) - # example passing only required values which don't have defaults set - # and optional values try: api_instance.create_note(detector_id, note_request=note_request) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling NotesApi->create_note: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **detector_id** | **str**| the detector to associate the new note with | - **note_request** | [**NoteRequest**](NoteRequest.md)| | [optional] + **detector_id** | **str**| the detector to associate the new note with | + **note_request** | [**NoteRequest**](NoteRequest.md)| | [optional] ### Return type @@ -88,9 +78,7 @@ void (empty response body) - **Content-Type**: application/json, application/x-www-form-urlencoded, multipart/form-data - **Accept**: Not defined - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **204** | No response body | - | @@ -107,13 +95,14 @@ Retrieves all notes from a given detector and returns them in lists, one for eac ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import notes_api -from groundlight_openapi_client.model.all_notes import AllNotes +from groundlight_openapi_client.models.all_notes import AllNotes +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -126,7 +115,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -134,23 +123,24 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = notes_api.NotesApi(api_client) - detector_id = "detector_id_example" # str | the detector whose notes to retrieve + api_instance = groundlight_openapi_client.NotesApi(api_client) + detector_id = 'detector_id_example' # str | the detector whose notes to retrieve - # example passing only required values which don't have defaults set try: api_response = api_instance.get_notes(detector_id) + print("The response of NotesApi->get_notes:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling NotesApi->get_notes: %s\n" % e) ``` + ### Parameters Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- - **detector_id** | **str**| the detector whose notes to retrieve | + **detector_id** | **str**| the detector whose notes to retrieve | ### Return type @@ -165,9 +155,7 @@ Name | Type | Description | Notes - **Content-Type**: Not defined - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **200** | | - | diff --git a/generated/docs/PaginatedDetectorList.md b/generated/docs/PaginatedDetectorList.md index 52c596a2..1f8b9918 100644 --- a/generated/docs/PaginatedDetectorList.md +++ b/generated/docs/PaginatedDetectorList.md @@ -5,11 +5,27 @@ Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **count** | **int** | | -**results** | [**[Detector]**](Detector.md) | | -**next** | **str, none_type** | | [optional] -**previous** | **str, none_type** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**next** | **str** | | [optional] +**previous** | **str** | | [optional] +**results** | [**List[Detector]**](Detector.md) | | +## Example + +```python +from groundlight_openapi_client.models.paginated_detector_list import PaginatedDetectorList + +# TODO update the JSON string below +json = "{}" +# create an instance of PaginatedDetectorList from a JSON string +paginated_detector_list_instance = PaginatedDetectorList.from_json(json) +# print the JSON string representation of the object +print PaginatedDetectorList.to_json() + +# convert the object into a dict +paginated_detector_list_dict = paginated_detector_list_instance.to_dict() +# create an instance of PaginatedDetectorList from a dict +paginated_detector_list_from_dict = PaginatedDetectorList.from_dict(paginated_detector_list_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/PaginatedImageQueryList.md b/generated/docs/PaginatedImageQueryList.md index f5711b9d..7ce7bd60 100644 --- a/generated/docs/PaginatedImageQueryList.md +++ b/generated/docs/PaginatedImageQueryList.md @@ -5,11 +5,27 @@ Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **count** | **int** | | -**results** | [**[ImageQuery]**](ImageQuery.md) | | -**next** | **str, none_type** | | [optional] -**previous** | **str, none_type** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**next** | **str** | | [optional] +**previous** | **str** | | [optional] +**results** | [**List[ImageQuery]**](ImageQuery.md) | | +## Example + +```python +from groundlight_openapi_client.models.paginated_image_query_list import PaginatedImageQueryList + +# TODO update the JSON string below +json = "{}" +# create an instance of PaginatedImageQueryList from a JSON string +paginated_image_query_list_instance = PaginatedImageQueryList.from_json(json) +# print the JSON string representation of the object +print PaginatedImageQueryList.to_json() + +# convert the object into a dict +paginated_image_query_list_dict = paginated_image_query_list_instance.to_dict() +# create an instance of PaginatedImageQueryList from a dict +paginated_image_query_list_from_dict = PaginatedImageQueryList.from_dict(paginated_image_query_list_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/PaginatedRuleList.md b/generated/docs/PaginatedRuleList.md index e0d1942b..cbe47f2f 100644 --- a/generated/docs/PaginatedRuleList.md +++ b/generated/docs/PaginatedRuleList.md @@ -5,11 +5,27 @@ Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **count** | **int** | | -**results** | [**[Rule]**](Rule.md) | | -**next** | **str, none_type** | | [optional] -**previous** | **str, none_type** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**next** | **str** | | [optional] +**previous** | **str** | | [optional] +**results** | [**List[Rule]**](Rule.md) | | +## Example + +```python +from groundlight_openapi_client.models.paginated_rule_list import PaginatedRuleList + +# TODO update the JSON string below +json = "{}" +# create an instance of PaginatedRuleList from a JSON string +paginated_rule_list_instance = PaginatedRuleList.from_json(json) +# print the JSON string representation of the object +print PaginatedRuleList.to_json() + +# convert the object into a dict +paginated_rule_list_dict = paginated_rule_list_instance.to_dict() +# create an instance of PaginatedRuleList from a dict +paginated_rule_list_from_dict = PaginatedRuleList.from_dict(paginated_rule_list_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/PatchedDetectorRequest.md b/generated/docs/PatchedDetectorRequest.md index eb594561..c240cb94 100644 --- a/generated/docs/PatchedDetectorRequest.md +++ b/generated/docs/PatchedDetectorRequest.md @@ -6,12 +6,28 @@ Groundlight Detectors provide answers to natural language questions about images Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **name** | **str** | A short, descriptive name for the detector. | [optional] -**confidence_threshold** | **float** | If the detector's prediction is below this confidence threshold, send the image query for human review. | [optional] if omitted the server will use the default value of 0.9 -**patience_time** | **float** | How long Groundlight will attempt to generate a confident prediction | [optional] if omitted the server will use the default value of 30.0 -**status** | **bool, date, datetime, dict, float, int, list, str, none_type** | | [optional] +**confidence_threshold** | **float** | If the detector's prediction is below this confidence threshold, send the image query for human review. | [optional] [default to 0.9] +**patience_time** | **float** | How long Groundlight will attempt to generate a confident prediction | [optional] [default to 30.0] +**status** | [**DetectorStatus**](DetectorStatus.md) | | [optional] **escalation_type** | **str** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +## Example + +```python +from groundlight_openapi_client.models.patched_detector_request import PatchedDetectorRequest + +# TODO update the JSON string below +json = "{}" +# create an instance of PatchedDetectorRequest from a JSON string +patched_detector_request_instance = PatchedDetectorRequest.from_json(json) +# print the JSON string representation of the object +print PatchedDetectorRequest.to_json() + +# convert the object into a dict +patched_detector_request_dict = patched_detector_request_instance.to_dict() +# create an instance of PatchedDetectorRequest from a dict +patched_detector_request_from_dict = PatchedDetectorRequest.from_dict(patched_detector_request_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/PayloadTemplate.md b/generated/docs/PayloadTemplate.md index b2f39495..4d70b726 100644 --- a/generated/docs/PayloadTemplate.md +++ b/generated/docs/PayloadTemplate.md @@ -5,9 +5,25 @@ Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **template** | **str** | | -**headers** | **{str: (str,)}, none_type** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**headers** | **Dict[str, str]** | | [optional] +## Example + +```python +from groundlight_openapi_client.models.payload_template import PayloadTemplate + +# TODO update the JSON string below +json = "{}" +# create an instance of PayloadTemplate from a JSON string +payload_template_instance = PayloadTemplate.from_json(json) +# print the JSON string representation of the object +print PayloadTemplate.to_json() + +# convert the object into a dict +payload_template_dict = payload_template_instance.to_dict() +# create an instance of PayloadTemplate from a dict +payload_template_from_dict = PayloadTemplate.from_dict(payload_template_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/PayloadTemplateRequest.md b/generated/docs/PayloadTemplateRequest.md index 83e8c3c3..6fb0ae39 100644 --- a/generated/docs/PayloadTemplateRequest.md +++ b/generated/docs/PayloadTemplateRequest.md @@ -5,9 +5,25 @@ Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **template** | **str** | | -**headers** | **{str: (str,)}, none_type** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**headers** | **Dict[str, str]** | | [optional] +## Example + +```python +from groundlight_openapi_client.models.payload_template_request import PayloadTemplateRequest + +# TODO update the JSON string below +json = "{}" +# create an instance of PayloadTemplateRequest from a JSON string +payload_template_request_instance = PayloadTemplateRequest.from_json(json) +# print the JSON string representation of the object +print PayloadTemplateRequest.to_json() + +# convert the object into a dict +payload_template_request_dict = payload_template_request_instance.to_dict() +# create an instance of PayloadTemplateRequest from a dict +payload_template_request_from_dict = PayloadTemplateRequest.from_dict(payload_template_request_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/ROI.md b/generated/docs/ROI.md index 60da87e6..0bb43e60 100644 --- a/generated/docs/ROI.md +++ b/generated/docs/ROI.md @@ -8,8 +8,24 @@ Name | Type | Description | Notes **label** | **str** | The label of the bounding box. | **score** | **float** | The confidence of the bounding box. | [readonly] **geometry** | [**BBoxGeometry**](BBoxGeometry.md) | | -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +## Example + +```python +from groundlight_openapi_client.models.roi import ROI + +# TODO update the JSON string below +json = "{}" +# create an instance of ROI from a JSON string +roi_instance = ROI.from_json(json) +# print the JSON string representation of the object +print ROI.to_json() + +# convert the object into a dict +roi_dict = roi_instance.to_dict() +# create an instance of ROI from a dict +roi_from_dict = ROI.from_dict(roi_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/ROIRequest.md b/generated/docs/ROIRequest.md index 8d675ec3..271abcdc 100644 --- a/generated/docs/ROIRequest.md +++ b/generated/docs/ROIRequest.md @@ -7,8 +7,24 @@ Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **label** | **str** | The label of the bounding box. | **geometry** | [**BBoxGeometryRequest**](BBoxGeometryRequest.md) | | -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +## Example + +```python +from groundlight_openapi_client.models.roi_request import ROIRequest + +# TODO update the JSON string below +json = "{}" +# create an instance of ROIRequest from a JSON string +roi_request_instance = ROIRequest.from_json(json) +# print the JSON string representation of the object +print ROIRequest.to_json() + +# convert the object into a dict +roi_request_dict = roi_request_instance.to_dict() +# create an instance of ROIRequest from a dict +roi_request_from_dict = ROIRequest.from_dict(roi_request_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/ResultTypeEnum.md b/generated/docs/ResultTypeEnum.md index 0146b794..c38a2c4b 100644 --- a/generated/docs/ResultTypeEnum.md +++ b/generated/docs/ResultTypeEnum.md @@ -1,10 +1,17 @@ # ResultTypeEnum -## Properties -Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- -**value** | **str** | | must be one of ["binary_classification", "counting", "multi_classification", "text_recognition", "bounding_box", ] +## Enum + +* `BINARY_CLASSIFICATION` (value: `'binary_classification'`) + +* `COUNTING` (value: `'counting'`) + +* `MULTI_CLASSIFICATION` (value: `'multi_classification'`) + +* `TEXT_RECOGNITION` (value: `'text_recognition'`) + +* `BOUNDING_BOX` (value: `'bounding_box'`) [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/Rule.md b/generated/docs/Rule.md index a3f0c9d5..d5799096 100644 --- a/generated/docs/Rule.md +++ b/generated/docs/Rule.md @@ -8,16 +8,32 @@ Name | Type | Description | Notes **detector_id** | **str** | | [readonly] **detector_name** | **str** | | [readonly] **name** | **str** | | +**enabled** | **bool** | | [optional] [default to True] +**snooze_time_enabled** | **bool** | | [optional] [default to False] +**snooze_time_value** | **int** | | [optional] [default to 0] +**snooze_time_unit** | [**SnoozeTimeUnitEnum**](SnoozeTimeUnitEnum.md) | | [optional] +**human_review_required** | **bool** | | [optional] [default to False] **condition** | [**Condition**](Condition.md) | | -**enabled** | **bool** | | [optional] if omitted the server will use the default value of True -**snooze_time_enabled** | **bool** | | [optional] if omitted the server will use the default value of False -**snooze_time_value** | **int** | | [optional] if omitted the server will use the default value of 0 -**snooze_time_unit** | **bool, date, datetime, dict, float, int, list, str, none_type** | | [optional] -**human_review_required** | **bool** | | [optional] if omitted the server will use the default value of False -**action** | **bool, date, datetime, dict, float, int, list, str, none_type** | | [optional] -**webhook_action** | [**[WebhookAction]**](WebhookAction.md) | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**action** | [**RuleAction**](RuleAction.md) | | [optional] +**webhook_action** | [**List[WebhookAction]**](WebhookAction.md) | | [optional] +## Example + +```python +from groundlight_openapi_client.models.rule import Rule + +# TODO update the JSON string below +json = "{}" +# create an instance of Rule from a JSON string +rule_instance = Rule.from_json(json) +# print the JSON string representation of the object +print Rule.to_json() + +# convert the object into a dict +rule_dict = rule_instance.to_dict() +# create an instance of Rule from a dict +rule_from_dict = Rule.from_dict(rule_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/RuleAction.md b/generated/docs/RuleAction.md new file mode 100644 index 00000000..474e9e13 --- /dev/null +++ b/generated/docs/RuleAction.md @@ -0,0 +1,30 @@ +# RuleAction + + +## Properties +Name | Type | Description | Notes +------------ | ------------- | ------------- | ------------- +**channel** | [**ChannelEnum**](ChannelEnum.md) | | +**recipient** | **str** | | +**include_image** | **bool** | | + +## Example + +```python +from groundlight_openapi_client.models.rule_action import RuleAction + +# TODO update the JSON string below +json = "{}" +# create an instance of RuleAction from a JSON string +rule_action_instance = RuleAction.from_json(json) +# print the JSON string representation of the object +print RuleAction.to_json() + +# convert the object into a dict +rule_action_dict = rule_action_instance.to_dict() +# create an instance of RuleAction from a dict +rule_action_from_dict = RuleAction.from_dict(rule_action_dict) +``` +[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) + + diff --git a/generated/docs/RuleRequest.md b/generated/docs/RuleRequest.md index 9e5b3294..9058f4c6 100644 --- a/generated/docs/RuleRequest.md +++ b/generated/docs/RuleRequest.md @@ -5,16 +5,32 @@ Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **name** | **str** | | +**enabled** | **bool** | | [optional] [default to True] +**snooze_time_enabled** | **bool** | | [optional] [default to False] +**snooze_time_value** | **int** | | [optional] [default to 0] +**snooze_time_unit** | [**SnoozeTimeUnitEnum**](SnoozeTimeUnitEnum.md) | | [optional] +**human_review_required** | **bool** | | [optional] [default to False] **condition** | [**ConditionRequest**](ConditionRequest.md) | | -**enabled** | **bool** | | [optional] if omitted the server will use the default value of True -**snooze_time_enabled** | **bool** | | [optional] if omitted the server will use the default value of False -**snooze_time_value** | **int** | | [optional] if omitted the server will use the default value of 0 -**snooze_time_unit** | **bool, date, datetime, dict, float, int, list, str, none_type** | | [optional] -**human_review_required** | **bool** | | [optional] if omitted the server will use the default value of False -**action** | **bool, date, datetime, dict, float, int, list, str, none_type** | | [optional] -**webhook_action** | [**[WebhookActionRequest]**](WebhookActionRequest.md) | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**action** | [**RuleAction**](RuleAction.md) | | [optional] +**webhook_action** | [**List[WebhookActionRequest]**](WebhookActionRequest.md) | | [optional] +## Example + +```python +from groundlight_openapi_client.models.rule_request import RuleRequest + +# TODO update the JSON string below +json = "{}" +# create an instance of RuleRequest from a JSON string +rule_request_instance = RuleRequest.from_json(json) +# print the JSON string representation of the object +print RuleRequest.to_json() + +# convert the object into a dict +rule_request_dict = rule_request_instance.to_dict() +# create an instance of RuleRequest from a dict +rule_request_from_dict = RuleRequest.from_dict(rule_request_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/SnoozeTimeUnitEnum.md b/generated/docs/SnoozeTimeUnitEnum.md index 778f7062..74e3d6ee 100644 --- a/generated/docs/SnoozeTimeUnitEnum.md +++ b/generated/docs/SnoozeTimeUnitEnum.md @@ -2,10 +2,15 @@ * `DAYS` - DAYS * `HOURS` - HOURS * `MINUTES` - MINUTES * `SECONDS` - SECONDS -## Properties -Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- -**value** | **str** | * `DAYS` - DAYS * `HOURS` - HOURS * `MINUTES` - MINUTES * `SECONDS` - SECONDS | must be one of ["DAYS", "HOURS", "MINUTES", "SECONDS", ] +## Enum + +* `DAYS` (value: `'DAYS'`) + +* `HOURS` (value: `'HOURS'`) + +* `MINUTES` (value: `'MINUTES'`) + +* `SECONDS` (value: `'SECONDS'`) [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/Source.md b/generated/docs/Source.md index fa6f1066..de54ce8d 100644 --- a/generated/docs/Source.md +++ b/generated/docs/Source.md @@ -1,10 +1,19 @@ # Source -## Properties -Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- -**value** | **str** | | must be one of ["STILL_PROCESSING", "CLOUD", "USER", "CLOUD_ENSEMBLE", "ALGORITHM", "EDGE", ] +## Enum + +* `STILL_PROCESSING` (value: `'STILL_PROCESSING'`) + +* `CLOUD` (value: `'CLOUD'`) + +* `USER` (value: `'USER'`) + +* `CLOUD_ENSEMBLE` (value: `'CLOUD_ENSEMBLE'`) + +* `ALGORITHM` (value: `'ALGORITHM'`) + +* `EDGE` (value: `'EDGE'`) [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/SourceEnum.md b/generated/docs/SourceEnum.md index a382414e..4acac60f 100644 --- a/generated/docs/SourceEnum.md +++ b/generated/docs/SourceEnum.md @@ -1,10 +1,23 @@ # SourceEnum -## Properties -Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- -**value** | **str** | | must be one of ["INITIAL_PLACEHOLDER", "CLOUD", "CUST", "HUMAN_CLOUD_ENSEMBLE", "ALG", "ALG_REC", "ALG_UNCLEAR", "EDGE", ] +## Enum + +* `INITIAL_PLACEHOLDER` (value: `'INITIAL_PLACEHOLDER'`) + +* `CLOUD` (value: `'CLOUD'`) + +* `CUST` (value: `'CUST'`) + +* `HUMAN_CLOUD_ENSEMBLE` (value: `'HUMAN_CLOUD_ENSEMBLE'`) + +* `ALG` (value: `'ALG'`) + +* `ALG_REC` (value: `'ALG_REC'`) + +* `ALG_UNCLEAR` (value: `'ALG_UNCLEAR'`) + +* `EDGE` (value: `'EDGE'`) [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/StatusEnum.md b/generated/docs/StatusEnum.md index 6d4c3312..e158c9f1 100644 --- a/generated/docs/StatusEnum.md +++ b/generated/docs/StatusEnum.md @@ -2,10 +2,11 @@ * `ON` - ON * `OFF` - OFF -## Properties -Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- -**value** | **str** | * `ON` - ON * `OFF` - OFF | must be one of ["ON", "OFF", ] +## Enum + +* `ON` (value: `'ON'`) + +* `OFF` (value: `'OFF'`) [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/TextModeConfiguration.md b/generated/docs/TextModeConfiguration.md index 01bcfdf1..a4e7291c 100644 --- a/generated/docs/TextModeConfiguration.md +++ b/generated/docs/TextModeConfiguration.md @@ -5,8 +5,24 @@ Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **value_max_length** | **int** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +## Example + +```python +from groundlight_openapi_client.models.text_mode_configuration import TextModeConfiguration + +# TODO update the JSON string below +json = "{}" +# create an instance of TextModeConfiguration from a JSON string +text_mode_configuration_instance = TextModeConfiguration.from_json(json) +# print the JSON string representation of the object +print TextModeConfiguration.to_json() + +# convert the object into a dict +text_mode_configuration_dict = text_mode_configuration_instance.to_dict() +# create an instance of TextModeConfiguration from a dict +text_mode_configuration_from_dict = TextModeConfiguration.from_dict(text_mode_configuration_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/TextRecognitionResult.md b/generated/docs/TextRecognitionResult.md index f518a26b..70580331 100644 --- a/generated/docs/TextRecognitionResult.md +++ b/generated/docs/TextRecognitionResult.md @@ -4,14 +4,30 @@ ## Properties Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- -**text** | **str, none_type** | | -**truncated** | **bool** | | -**confidence** | **float, none_type** | | [optional] +**confidence** | **float** | | [optional] **source** | **str** | | [optional] -**result_type** | **str** | | [optional] if omitted the server will use the default value of "text_recognition" +**result_type** | **str** | | [optional] **from_edge** | **bool** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**text** | **str** | | +**truncated** | **bool** | | + +## Example + +```python +from groundlight_openapi_client.models.text_recognition_result import TextRecognitionResult + +# TODO update the JSON string below +json = "{}" +# create an instance of TextRecognitionResult from a JSON string +text_recognition_result_instance = TextRecognitionResult.from_json(json) +# print the JSON string representation of the object +print TextRecognitionResult.to_json() +# convert the object into a dict +text_recognition_result_dict = text_recognition_result_instance.to_dict() +# create an instance of TextRecognitionResult from a dict +text_recognition_result_from_dict = TextRecognitionResult.from_dict(text_recognition_result_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/UserApi.md b/generated/docs/UserApi.md index b10f06bc..0bcb0350 100644 --- a/generated/docs/UserApi.md +++ b/generated/docs/UserApi.md @@ -8,7 +8,7 @@ Method | HTTP request | Description # **who_am_i** -> InlineResponse2002 who_am_i() +> WhoAmI200Response who_am_i() @@ -17,13 +17,14 @@ Retrieve the current user. ### Example * Api Key Authentication (ApiToken): - ```python import time +import os import groundlight_openapi_client -from groundlight_openapi_client.api import user_api -from groundlight_openapi_client.model.inline_response2002 import InlineResponse2002 +from groundlight_openapi_client.models.who_am_i200_response import WhoAmI200Response +from groundlight_openapi_client.rest import ApiException from pprint import pprint + # Defining the host is optional and defaults to https://api.groundlight.ai/device-api # See configuration.py for a list of all supported configuration parameters. configuration = groundlight_openapi_client.Configuration( @@ -36,7 +37,7 @@ configuration = groundlight_openapi_client.Configuration( # satisfies your auth use case. # Configure API key authorization: ApiToken -configuration.api_key['ApiToken'] = 'YOUR_API_KEY' +configuration.api_key['ApiToken'] = os.environ["API_KEY"] # Uncomment below to setup prefix (e.g. Bearer) for API key, if needed # configuration.api_key_prefix['ApiToken'] = 'Bearer' @@ -44,23 +45,24 @@ configuration.api_key['ApiToken'] = 'YOUR_API_KEY' # Enter a context with an instance of the API client with groundlight_openapi_client.ApiClient(configuration) as api_client: # Create an instance of the API class - api_instance = user_api.UserApi(api_client) + api_instance = groundlight_openapi_client.UserApi(api_client) - # example, this endpoint has no required or optional parameters try: api_response = api_instance.who_am_i() + print("The response of UserApi->who_am_i:\n") pprint(api_response) - except groundlight_openapi_client.ApiException as e: + except Exception as e: print("Exception when calling UserApi->who_am_i: %s\n" % e) ``` + ### Parameters This endpoint does not need any parameter. ### Return type -[**InlineResponse2002**](InlineResponse2002.md) +[**WhoAmI200Response**](WhoAmI200Response.md) ### Authorization @@ -71,9 +73,7 @@ This endpoint does not need any parameter. - **Content-Type**: Not defined - **Accept**: application/json - ### HTTP response details - | Status code | Description | Response headers | |-------------|-------------|------------------| **200** | | - | diff --git a/generated/docs/VerbEnum.md b/generated/docs/VerbEnum.md index 57a334c2..d010577d 100644 --- a/generated/docs/VerbEnum.md +++ b/generated/docs/VerbEnum.md @@ -1,10 +1,17 @@ # VerbEnum -## Properties -Name | Type | Description | Notes ------------- | ------------- | ------------- | ------------- -**value** | **str** | | must be one of ["ANSWERED_CONSECUTIVELY", "ANSWERED_WITHIN_TIME", "CHANGED_TO", "NO_CHANGE", "NO_QUERIES", ] +## Enum + +* `ANSWERED_CONSECUTIVELY` (value: `'ANSWERED_CONSECUTIVELY'`) + +* `ANSWERED_WITHIN_TIME` (value: `'ANSWERED_WITHIN_TIME'`) + +* `CHANGED_TO` (value: `'CHANGED_TO'`) + +* `NO_CHANGE` (value: `'NO_CHANGE'`) + +* `NO_QUERIES` (value: `'NO_QUERIES'`) [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/WebhookAction.md b/generated/docs/WebhookAction.md index 3635866d..ac391a34 100644 --- a/generated/docs/WebhookAction.md +++ b/generated/docs/WebhookAction.md @@ -6,12 +6,28 @@ Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **url** | **str** | | **include_image** | **bool** | | [optional] -**payload_template** | **bool, date, datetime, dict, float, int, list, str, none_type** | | [optional] +**payload_template** | [**PayloadTemplate**](PayloadTemplate.md) | | [optional] **last_message_failed** | **bool** | | [optional] -**last_failure_error** | **str, none_type** | | [optional] -**last_failed_at** | **datetime, none_type** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**last_failure_error** | **str** | | [optional] +**last_failed_at** | **datetime** | | [optional] +## Example + +```python +from groundlight_openapi_client.models.webhook_action import WebhookAction + +# TODO update the JSON string below +json = "{}" +# create an instance of WebhookAction from a JSON string +webhook_action_instance = WebhookAction.from_json(json) +# print the JSON string representation of the object +print WebhookAction.to_json() + +# convert the object into a dict +webhook_action_dict = webhook_action_instance.to_dict() +# create an instance of WebhookAction from a dict +webhook_action_from_dict = WebhookAction.from_dict(webhook_action_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/WebhookActionRequest.md b/generated/docs/WebhookActionRequest.md index 0910602a..919bc28d 100644 --- a/generated/docs/WebhookActionRequest.md +++ b/generated/docs/WebhookActionRequest.md @@ -6,12 +6,28 @@ Name | Type | Description | Notes ------------ | ------------- | ------------- | ------------- **url** | **str** | | **include_image** | **bool** | | [optional] -**payload_template** | **bool, date, datetime, dict, float, int, list, str, none_type** | | [optional] +**payload_template** | [**PayloadTemplateRequest**](PayloadTemplateRequest.md) | | [optional] **last_message_failed** | **bool** | | [optional] -**last_failure_error** | **str, none_type** | | [optional] -**last_failed_at** | **datetime, none_type** | | [optional] -**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional] +**last_failure_error** | **str** | | [optional] +**last_failed_at** | **datetime** | | [optional] +## Example + +```python +from groundlight_openapi_client.models.webhook_action_request import WebhookActionRequest + +# TODO update the JSON string below +json = "{}" +# create an instance of WebhookActionRequest from a JSON string +webhook_action_request_instance = WebhookActionRequest.from_json(json) +# print the JSON string representation of the object +print WebhookActionRequest.to_json() + +# convert the object into a dict +webhook_action_request_dict = webhook_action_request_instance.to_dict() +# create an instance of WebhookActionRequest from a dict +webhook_action_request_from_dict = WebhookActionRequest.from_dict(webhook_action_request_dict) +``` [[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) diff --git a/generated/docs/WhoAmI200Response.md b/generated/docs/WhoAmI200Response.md new file mode 100644 index 00000000..5c6adccf --- /dev/null +++ b/generated/docs/WhoAmI200Response.md @@ -0,0 +1,30 @@ +# WhoAmI200Response + + +## Properties +Name | Type | Description | Notes +------------ | ------------- | ------------- | ------------- +**username** | **str** | The user's username | [optional] +**email** | **str** | The user's email | [optional] +**is_superuser** | **bool** | Whether the user is a superuser | [optional] + +## Example + +```python +from groundlight_openapi_client.models.who_am_i200_response import WhoAmI200Response + +# TODO update the JSON string below +json = "{}" +# create an instance of WhoAmI200Response from a JSON string +who_am_i200_response_instance = WhoAmI200Response.from_json(json) +# print the JSON string representation of the object +print WhoAmI200Response.to_json() + +# convert the object into a dict +who_am_i200_response_dict = who_am_i200_response_instance.to_dict() +# create an instance of WhoAmI200Response from a dict +who_am_i200_response_from_dict = WhoAmI200Response.from_dict(who_am_i200_response_dict) +``` +[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) + + diff --git a/generated/groundlight_openapi_client/__init__.py b/generated/groundlight_openapi_client/__init__.py index ff1e9a1d..e9aac5c5 100644 --- a/generated/groundlight_openapi_client/__init__.py +++ b/generated/groundlight_openapi_client/__init__.py @@ -1,28 +1,111 @@ +# coding: utf-8 + # flake8: noqa """ Groundlight API - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. The version of the OpenAPI document: 0.18.2 Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 __version__ = "1.0.0" +# import apis into sdk package +from groundlight_openapi_client.api.actions_api import ActionsApi +from groundlight_openapi_client.api.detector_groups_api import DetectorGroupsApi +from groundlight_openapi_client.api.detector_reset_api import DetectorResetApi +from groundlight_openapi_client.api.detectors_api import DetectorsApi +from groundlight_openapi_client.api.edge_api import EdgeApi +from groundlight_openapi_client.api.image_queries_api import ImageQueriesApi +from groundlight_openapi_client.api.labels_api import LabelsApi +from groundlight_openapi_client.api.notes_api import NotesApi +from groundlight_openapi_client.api.user_api import UserApi + # import ApiClient +from groundlight_openapi_client.api_response import ApiResponse from groundlight_openapi_client.api_client import ApiClient - -# import Configuration from groundlight_openapi_client.configuration import Configuration - -# import exceptions from groundlight_openapi_client.exceptions import OpenApiException -from groundlight_openapi_client.exceptions import ApiAttributeError from groundlight_openapi_client.exceptions import ApiTypeError from groundlight_openapi_client.exceptions import ApiValueError from groundlight_openapi_client.exceptions import ApiKeyError +from groundlight_openapi_client.exceptions import ApiAttributeError from groundlight_openapi_client.exceptions import ApiException + +# import models into sdk package +from groundlight_openapi_client.models.action import Action +from groundlight_openapi_client.models.all_notes import AllNotes +from groundlight_openapi_client.models.annotations_requested_enum import AnnotationsRequestedEnum +from groundlight_openapi_client.models.b_box_geometry import BBoxGeometry +from groundlight_openapi_client.models.b_box_geometry_request import BBoxGeometryRequest +from groundlight_openapi_client.models.binary_classification_result import BinaryClassificationResult +from groundlight_openapi_client.models.blank_enum import BlankEnum +from groundlight_openapi_client.models.bounding_box_mode_configuration import BoundingBoxModeConfiguration +from groundlight_openapi_client.models.bounding_box_result import BoundingBoxResult +from groundlight_openapi_client.models.channel_enum import ChannelEnum +from groundlight_openapi_client.models.condition import Condition +from groundlight_openapi_client.models.condition_request import ConditionRequest +from groundlight_openapi_client.models.count_mode_configuration import CountModeConfiguration +from groundlight_openapi_client.models.counting_result import CountingResult +from groundlight_openapi_client.models.detector import Detector +from groundlight_openapi_client.models.detector_creation_input_request import DetectorCreationInputRequest +from groundlight_openapi_client.models.detector_creation_input_request_mode_configuration import ( + DetectorCreationInputRequestModeConfiguration, +) +from groundlight_openapi_client.models.detector_group import DetectorGroup +from groundlight_openapi_client.models.detector_group_request import DetectorGroupRequest +from groundlight_openapi_client.models.detector_status import DetectorStatus +from groundlight_openapi_client.models.detector_type_enum import DetectorTypeEnum +from groundlight_openapi_client.models.edge_model_info import EdgeModelInfo +from groundlight_openapi_client.models.escalation_type_enum import EscalationTypeEnum +from groundlight_openapi_client.models.get_detector_evaluation200_response import GetDetectorEvaluation200Response +from groundlight_openapi_client.models.get_detector_evaluation200_response_evaluation_results import ( + GetDetectorEvaluation200ResponseEvaluationResults, +) +from groundlight_openapi_client.models.get_detector_metrics200_response import GetDetectorMetrics200Response +from groundlight_openapi_client.models.get_detector_metrics200_response_summary import ( + GetDetectorMetrics200ResponseSummary, +) +from groundlight_openapi_client.models.get_detector_metrics200_response_summary_class_counts import ( + GetDetectorMetrics200ResponseSummaryClassCounts, +) +from groundlight_openapi_client.models.image_query import ImageQuery +from groundlight_openapi_client.models.image_query_result import ImageQueryResult +from groundlight_openapi_client.models.image_query_type_enum import ImageQueryTypeEnum +from groundlight_openapi_client.models.label import Label +from groundlight_openapi_client.models.label_value import LabelValue +from groundlight_openapi_client.models.label_value_request import LabelValueRequest +from groundlight_openapi_client.models.mode_enum import ModeEnum +from groundlight_openapi_client.models.multi_class_mode_configuration import MultiClassModeConfiguration +from groundlight_openapi_client.models.multi_classification_result import MultiClassificationResult +from groundlight_openapi_client.models.note import Note +from groundlight_openapi_client.models.note_request import NoteRequest +from groundlight_openapi_client.models.paginated_detector_list import PaginatedDetectorList +from groundlight_openapi_client.models.paginated_image_query_list import PaginatedImageQueryList +from groundlight_openapi_client.models.paginated_rule_list import PaginatedRuleList +from groundlight_openapi_client.models.patched_detector_request import PatchedDetectorRequest +from groundlight_openapi_client.models.payload_template import PayloadTemplate +from groundlight_openapi_client.models.payload_template_request import PayloadTemplateRequest +from groundlight_openapi_client.models.roi import ROI +from groundlight_openapi_client.models.roi_request import ROIRequest +from groundlight_openapi_client.models.result_type_enum import ResultTypeEnum +from groundlight_openapi_client.models.rule import Rule +from groundlight_openapi_client.models.rule_action import RuleAction +from groundlight_openapi_client.models.rule_request import RuleRequest +from groundlight_openapi_client.models.snooze_time_unit_enum import SnoozeTimeUnitEnum +from groundlight_openapi_client.models.source import Source +from groundlight_openapi_client.models.source_enum import SourceEnum +from groundlight_openapi_client.models.status_enum import StatusEnum +from groundlight_openapi_client.models.text_mode_configuration import TextModeConfiguration +from groundlight_openapi_client.models.text_recognition_result import TextRecognitionResult +from groundlight_openapi_client.models.verb_enum import VerbEnum +from groundlight_openapi_client.models.webhook_action import WebhookAction +from groundlight_openapi_client.models.webhook_action_request import WebhookActionRequest +from groundlight_openapi_client.models.who_am_i200_response import WhoAmI200Response diff --git a/generated/groundlight_openapi_client/api/__init__.py b/generated/groundlight_openapi_client/api/__init__.py index 895d519d..46ea63d6 100644 --- a/generated/groundlight_openapi_client/api/__init__.py +++ b/generated/groundlight_openapi_client/api/__init__.py @@ -1,3 +1,12 @@ -# do not import all apis into this module because that uses a lot of memory and stack frames -# if you need the ability to import all apis from one package, import them with -# from groundlight_openapi_client.apis import ActionsApi +# flake8: noqa + +# import apis into api package +from groundlight_openapi_client.api.actions_api import ActionsApi +from groundlight_openapi_client.api.detector_groups_api import DetectorGroupsApi +from groundlight_openapi_client.api.detector_reset_api import DetectorResetApi +from groundlight_openapi_client.api.detectors_api import DetectorsApi +from groundlight_openapi_client.api.edge_api import EdgeApi +from groundlight_openapi_client.api.image_queries_api import ImageQueriesApi +from groundlight_openapi_client.api.labels_api import LabelsApi +from groundlight_openapi_client.api.notes_api import NotesApi +from groundlight_openapi_client.api.user_api import UserApi diff --git a/generated/groundlight_openapi_client/api/actions_api.py b/generated/groundlight_openapi_client/api/actions_api.py index 4f9d42f5..9586e1c8 100644 --- a/generated/groundlight_openapi_client/api/actions_api.py +++ b/generated/groundlight_openapi_client/api/actions_api.py @@ -1,258 +1,52 @@ +# coding: utf-8 + """ Groundlight API - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. The version of the OpenAPI document: 0.18.2 Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.api_client import ApiClient, Endpoint as _Endpoint -from groundlight_openapi_client.model_utils import ( # noqa: F401 - check_allowed_values, - check_validations, - date, - datetime, - file_type, - none_type, - validate_and_convert_types, -) -from groundlight_openapi_client.model.paginated_rule_list import PaginatedRuleList -from groundlight_openapi_client.model.rule import Rule -from groundlight_openapi_client.model.rule_request import RuleRequest - - -class ActionsApi(object): +import io +import warnings + +from pydantic import validate_arguments, ValidationError + +from typing_extensions import Annotated +from pydantic import Field, StrictInt, StrictStr + +from typing import Optional + +from groundlight_openapi_client.models.paginated_rule_list import PaginatedRuleList +from groundlight_openapi_client.models.rule import Rule +from groundlight_openapi_client.models.rule_request import RuleRequest + +from groundlight_openapi_client.api_client import ApiClient +from groundlight_openapi_client.api_response import ApiResponse +from groundlight_openapi_client.exceptions import ApiTypeError, ApiValueError # noqa: F401 + + +class ActionsApi: """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ - def __init__(self, api_client=None): + def __init__(self, api_client=None) -> None: if api_client is None: - api_client = ApiClient() + api_client = ApiClient.get_default() self.api_client = api_client - self.create_rule_endpoint = _Endpoint( - settings={ - "response_type": (Rule,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/actions/detector/{detector_id}/rules", - "operation_id": "create_rule", - "http_method": "POST", - "servers": None, - }, - params_map={ - "all": [ - "detector_id", - "rule_request", - ], - "required": [ - "detector_id", - "rule_request", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "detector_id": (str,), - "rule_request": (RuleRequest,), - }, - "attribute_map": { - "detector_id": "detector_id", - }, - "location_map": { - "detector_id": "path", - "rule_request": "body", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": ["application/json", "application/x-www-form-urlencoded", "multipart/form-data"], - }, - api_client=api_client, - ) - self.delete_rule_endpoint = _Endpoint( - settings={ - "response_type": None, - "auth": ["ApiToken"], - "endpoint_path": "/v1/actions/rules/{id}", - "operation_id": "delete_rule", - "http_method": "DELETE", - "servers": None, - }, - params_map={ - "all": [ - "id", - ], - "required": [ - "id", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "id": (int,), - }, - "attribute_map": { - "id": "id", - }, - "location_map": { - "id": "path", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": [], - "content_type": [], - }, - api_client=api_client, - ) - self.get_rule_endpoint = _Endpoint( - settings={ - "response_type": (Rule,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/actions/rules/{id}", - "operation_id": "get_rule", - "http_method": "GET", - "servers": None, - }, - params_map={ - "all": [ - "id", - ], - "required": [ - "id", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "id": (int,), - }, - "attribute_map": { - "id": "id", - }, - "location_map": { - "id": "path", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": [], - }, - api_client=api_client, - ) - self.list_detector_rules_endpoint = _Endpoint( - settings={ - "response_type": (PaginatedRuleList,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/actions/detector/{detector_id}/rules", - "operation_id": "list_detector_rules", - "http_method": "GET", - "servers": None, - }, - params_map={ - "all": [ - "detector_id", - "page", - "page_size", - ], - "required": [ - "detector_id", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "detector_id": (str,), - "page": (int,), - "page_size": (int,), - }, - "attribute_map": { - "detector_id": "detector_id", - "page": "page", - "page_size": "page_size", - }, - "location_map": { - "detector_id": "path", - "page": "query", - "page_size": "query", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": [], - }, - api_client=api_client, - ) - self.list_rules_endpoint = _Endpoint( - settings={ - "response_type": (PaginatedRuleList,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/actions/rules", - "operation_id": "list_rules", - "http_method": "GET", - "servers": None, - }, - params_map={ - "all": [ - "page", - "page_size", - ], - "required": [], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "page": (int,), - "page_size": (int,), - }, - "attribute_map": { - "page": "page", - "page_size": "page_size", - }, - "location_map": { - "page": "query", - "page_size": "query", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": [], - }, - api_client=api_client, - ) - def create_rule(self, detector_id, rule_request, **kwargs): + @validate_arguments + def create_rule(self, detector_id: StrictStr, rule_request: RuleRequest, **kwargs) -> Rule: # noqa: E501 """create_rule # noqa: E501 Create a new rule for a detector # noqa: E501 @@ -262,57 +56,149 @@ def create_rule(self, detector_id, rule_request, **kwargs): >>> thread = api.create_rule(detector_id, rule_request, async_req=True) >>> result = thread.get() - Args: - detector_id (str): - rule_request (RuleRequest): - - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - Rule - If the method is called asynchronously, returns the request - thread. + :param detector_id: (required) + :type detector_id: str + :param rule_request: (required) + :type rule_request: RuleRequest + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: Rule + """ + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the create_rule_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.create_rule_with_http_info(detector_id, rule_request, **kwargs) # noqa: E501 + + @validate_arguments + def create_rule_with_http_info( + self, detector_id: StrictStr, rule_request: RuleRequest, **kwargs + ) -> ApiResponse: # noqa: E501 + """create_rule # noqa: E501 + + Create a new rule for a detector # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.create_rule_with_http_info(detector_id, rule_request, async_req=True) + >>> result = thread.get() + + :param detector_id: (required) + :type detector_id: str + :param rule_request: (required) + :type rule_request: RuleRequest + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(Rule, status_code(int), headers(HTTPHeaderDict)) """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["detector_id"] = detector_id - kwargs["rule_request"] = rule_request - return self.create_rule_endpoint.call_with_http_info(**kwargs) - - def delete_rule(self, id, **kwargs): + + _params = locals() + + _all_params = ["detector_id", "rule_request"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method create_rule" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + if _params["detector_id"] is not None: + _path_params["detector_id"] = _params["detector_id"] + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + if _params["rule_request"] is not None: + _body_params = _params["rule_request"] + + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # set the HTTP header `Content-Type` + _content_types_list = _params.get( + "_content_type", + self.api_client.select_header_content_type( + ["application/json", "application/x-www-form-urlencoded", "multipart/form-data"] + ), + ) + if _content_types_list: + _header_params["Content-Type"] = _content_types_list + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "201": "Rule", + } + + return self.api_client.call_api( + "/v1/actions/detector/{detector_id}/rules", + "POST", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) + + @validate_arguments + def delete_rule(self, id: StrictInt, **kwargs) -> None: # noqa: E501 """delete_rule # noqa: E501 Delete a rule # noqa: E501 @@ -322,55 +208,125 @@ def delete_rule(self, id, **kwargs): >>> thread = api.delete_rule(id, async_req=True) >>> result = thread.get() - Args: - id (int): - - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - None - If the method is called asynchronously, returns the request - thread. + :param id: (required) + :type id: int + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: None + """ + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the delete_rule_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.delete_rule_with_http_info(id, **kwargs) # noqa: E501 + + @validate_arguments + def delete_rule_with_http_info(self, id: StrictInt, **kwargs) -> ApiResponse: # noqa: E501 + """delete_rule # noqa: E501 + + Delete a rule # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.delete_rule_with_http_info(id, async_req=True) + >>> result = thread.get() + + :param id: (required) + :type id: int + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: None """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["id"] = id - return self.delete_rule_endpoint.call_with_http_info(**kwargs) - - def get_rule(self, id, **kwargs): + + _params = locals() + + _all_params = ["id"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method delete_rule" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + if _params["id"] is not None: + _path_params["id"] = _params["id"] + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = {} + + return self.api_client.call_api( + "/v1/actions/rules/{id}", + "DELETE", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) + + @validate_arguments + def get_rule(self, id: StrictInt, **kwargs) -> Rule: # noqa: E501 """get_rule # noqa: E501 Retrieve a rule # noqa: E501 @@ -380,167 +336,442 @@ def get_rule(self, id, **kwargs): >>> thread = api.get_rule(id, async_req=True) >>> result = thread.get() - Args: - id (int): - - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - Rule - If the method is called asynchronously, returns the request - thread. + :param id: (required) + :type id: int + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: Rule + """ + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the get_rule_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.get_rule_with_http_info(id, **kwargs) # noqa: E501 + + @validate_arguments + def get_rule_with_http_info(self, id: StrictInt, **kwargs) -> ApiResponse: # noqa: E501 + """get_rule # noqa: E501 + + Retrieve a rule # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.get_rule_with_http_info(id, async_req=True) + >>> result = thread.get() + + :param id: (required) + :type id: int + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(Rule, status_code(int), headers(HTTPHeaderDict)) + """ + + _params = locals() + + _all_params = ["id"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method get_rule" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + if _params["id"] is not None: + _path_params["id"] = _params["id"] + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "200": "Rule", + } + + return self.api_client.call_api( + "/v1/actions/rules/{id}", + "GET", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) + + @validate_arguments + def list_detector_rules( + self, + detector_id: StrictStr, + page: Annotated[ + Optional[StrictInt], Field(description="A page number within the paginated result set.") + ] = None, + page_size: Annotated[Optional[StrictInt], Field(description="Number of results to return per page.")] = None, + **kwargs, + ) -> PaginatedRuleList: # noqa: E501 + """list_detector_rules # noqa: E501 + + List all rules for a detector # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.list_detector_rules(detector_id, page, page_size, async_req=True) + >>> result = thread.get() + + :param detector_id: (required) + :type detector_id: str + :param page: A page number within the paginated result set. + :type page: int + :param page_size: Number of results to return per page. + :type page_size: int + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: PaginatedRuleList """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["id"] = id - return self.get_rule_endpoint.call_with_http_info(**kwargs) - - def list_detector_rules(self, detector_id, **kwargs): + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the list_detector_rules_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.list_detector_rules_with_http_info(detector_id, page, page_size, **kwargs) # noqa: E501 + + @validate_arguments + def list_detector_rules_with_http_info( + self, + detector_id: StrictStr, + page: Annotated[ + Optional[StrictInt], Field(description="A page number within the paginated result set.") + ] = None, + page_size: Annotated[Optional[StrictInt], Field(description="Number of results to return per page.")] = None, + **kwargs, + ) -> ApiResponse: # noqa: E501 """list_detector_rules # noqa: E501 List all rules for a detector # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True - >>> thread = api.list_detector_rules(detector_id, async_req=True) + >>> thread = api.list_detector_rules_with_http_info(detector_id, page, page_size, async_req=True) >>> result = thread.get() - Args: - detector_id (str): - - Keyword Args: - page (int): A page number within the paginated result set.. [optional] - page_size (int): Number of results to return per page.. [optional] - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - PaginatedRuleList - If the method is called asynchronously, returns the request - thread. + :param detector_id: (required) + :type detector_id: str + :param page: A page number within the paginated result set. + :type page: int + :param page_size: Number of results to return per page. + :type page_size: int + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(PaginatedRuleList, status_code(int), headers(HTTPHeaderDict)) """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["detector_id"] = detector_id - return self.list_detector_rules_endpoint.call_with_http_info(**kwargs) - - def list_rules(self, **kwargs): + + _params = locals() + + _all_params = ["detector_id", "page", "page_size"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method list_detector_rules" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + if _params["detector_id"] is not None: + _path_params["detector_id"] = _params["detector_id"] + + # process the query parameters + _query_params = [] + if _params.get("page") is not None: # noqa: E501 + _query_params.append(("page", _params["page"])) + + if _params.get("page_size") is not None: # noqa: E501 + _query_params.append(("page_size", _params["page_size"])) + + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "200": "PaginatedRuleList", + } + + return self.api_client.call_api( + "/v1/actions/detector/{detector_id}/rules", + "GET", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) + + @validate_arguments + def list_rules( + self, + page: Annotated[ + Optional[StrictInt], Field(description="A page number within the paginated result set.") + ] = None, + page_size: Annotated[Optional[StrictInt], Field(description="Number of results to return per page.")] = None, + **kwargs, + ) -> PaginatedRuleList: # noqa: E501 """list_rules # noqa: E501 Lists all rules over all detectors owned by the requester. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True - >>> thread = api.list_rules(async_req=True) + >>> thread = api.list_rules(page, page_size, async_req=True) >>> result = thread.get() + :param page: A page number within the paginated result set. + :type page: int + :param page_size: Number of results to return per page. + :type page_size: int + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: PaginatedRuleList + """ + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the list_rules_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.list_rules_with_http_info(page, page_size, **kwargs) # noqa: E501 + + @validate_arguments + def list_rules_with_http_info( + self, + page: Annotated[ + Optional[StrictInt], Field(description="A page number within the paginated result set.") + ] = None, + page_size: Annotated[Optional[StrictInt], Field(description="Number of results to return per page.")] = None, + **kwargs, + ) -> ApiResponse: # noqa: E501 + """list_rules # noqa: E501 + + Lists all rules over all detectors owned by the requester. # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True - Keyword Args: - page (int): A page number within the paginated result set.. [optional] - page_size (int): Number of results to return per page.. [optional] - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - PaginatedRuleList - If the method is called asynchronously, returns the request - thread. + >>> thread = api.list_rules_with_http_info(page, page_size, async_req=True) + >>> result = thread.get() + + :param page: A page number within the paginated result set. + :type page: int + :param page_size: Number of results to return per page. + :type page_size: int + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(PaginatedRuleList, status_code(int), headers(HTTPHeaderDict)) """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - return self.list_rules_endpoint.call_with_http_info(**kwargs) + + _params = locals() + + _all_params = ["page", "page_size"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method list_rules" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + + # process the query parameters + _query_params = [] + if _params.get("page") is not None: # noqa: E501 + _query_params.append(("page", _params["page"])) + + if _params.get("page_size") is not None: # noqa: E501 + _query_params.append(("page_size", _params["page_size"])) + + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "200": "PaginatedRuleList", + } + + return self.api_client.call_api( + "/v1/actions/rules", + "GET", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) diff --git a/generated/groundlight_openapi_client/api/detector_groups_api.py b/generated/groundlight_openapi_client/api/detector_groups_api.py index 83a2cb4f..967b5888 100644 --- a/generated/groundlight_openapi_client/api/detector_groups_api.py +++ b/generated/groundlight_openapi_client/api/detector_groups_api.py @@ -1,105 +1,50 @@ +# coding: utf-8 + """ Groundlight API - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. The version of the OpenAPI document: 0.18.2 Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.api_client import ApiClient, Endpoint as _Endpoint -from groundlight_openapi_client.model_utils import ( # noqa: F401 - check_allowed_values, - check_validations, - date, - datetime, - file_type, - none_type, - validate_and_convert_types, -) -from groundlight_openapi_client.model.detector_group import DetectorGroup -from groundlight_openapi_client.model.detector_group_request import DetectorGroupRequest - - -class DetectorGroupsApi(object): +import io +import warnings + +from pydantic import validate_arguments, ValidationError + +from typing import List + +from groundlight_openapi_client.models.detector_group import DetectorGroup +from groundlight_openapi_client.models.detector_group_request import DetectorGroupRequest + +from groundlight_openapi_client.api_client import ApiClient +from groundlight_openapi_client.api_response import ApiResponse +from groundlight_openapi_client.exceptions import ApiTypeError, ApiValueError # noqa: F401 + + +class DetectorGroupsApi: """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ - def __init__(self, api_client=None): + def __init__(self, api_client=None) -> None: if api_client is None: - api_client = ApiClient() + api_client = ApiClient.get_default() self.api_client = api_client - self.create_detector_group_endpoint = _Endpoint( - settings={ - "response_type": (DetectorGroup,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/detector-groups", - "operation_id": "create_detector_group", - "http_method": "POST", - "servers": None, - }, - params_map={ - "all": [ - "detector_group_request", - ], - "required": [ - "detector_group_request", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "detector_group_request": (DetectorGroupRequest,), - }, - "attribute_map": {}, - "location_map": { - "detector_group_request": "body", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": ["application/json", "application/x-www-form-urlencoded", "multipart/form-data"], - }, - api_client=api_client, - ) - self.get_detector_groups_endpoint = _Endpoint( - settings={ - "response_type": ([DetectorGroup],), - "auth": ["ApiToken"], - "endpoint_path": "/v1/detector-groups", - "operation_id": "get_detector_groups", - "http_method": "GET", - "servers": None, - }, - params_map={"all": [], "required": [], "nullable": [], "enum": [], "validation": []}, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": {}, - "attribute_map": {}, - "location_map": {}, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": [], - }, - api_client=api_client, - ) - def create_detector_group(self, detector_group_request, **kwargs): + @validate_arguments + def create_detector_group( + self, detector_group_request: DetectorGroupRequest, **kwargs + ) -> DetectorGroup: # noqa: E501 """create_detector_group # noqa: E501 Create a new detector group POST data: Required: - name (str) - name of the predictor set # noqa: E501 @@ -109,55 +54,143 @@ def create_detector_group(self, detector_group_request, **kwargs): >>> thread = api.create_detector_group(detector_group_request, async_req=True) >>> result = thread.get() - Args: - detector_group_request (DetectorGroupRequest): - - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - DetectorGroup - If the method is called asynchronously, returns the request - thread. + :param detector_group_request: (required) + :type detector_group_request: DetectorGroupRequest + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: DetectorGroup + """ + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the create_detector_group_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.create_detector_group_with_http_info(detector_group_request, **kwargs) # noqa: E501 + + @validate_arguments + def create_detector_group_with_http_info( + self, detector_group_request: DetectorGroupRequest, **kwargs + ) -> ApiResponse: # noqa: E501 + """create_detector_group # noqa: E501 + + Create a new detector group POST data: Required: - name (str) - name of the predictor set # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.create_detector_group_with_http_info(detector_group_request, async_req=True) + >>> result = thread.get() + + :param detector_group_request: (required) + :type detector_group_request: DetectorGroupRequest + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(DetectorGroup, status_code(int), headers(HTTPHeaderDict)) """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["detector_group_request"] = detector_group_request - return self.create_detector_group_endpoint.call_with_http_info(**kwargs) - - def get_detector_groups(self, **kwargs): + + _params = locals() + + _all_params = ["detector_group_request"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method create_detector_group" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + if _params["detector_group_request"] is not None: + _body_params = _params["detector_group_request"] + + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # set the HTTP header `Content-Type` + _content_types_list = _params.get( + "_content_type", + self.api_client.select_header_content_type( + ["application/json", "application/x-www-form-urlencoded", "multipart/form-data"] + ), + ) + if _content_types_list: + _header_params["Content-Type"] = _content_types_list + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "201": "DetectorGroup", + } + + return self.api_client.call_api( + "/v1/detector-groups", + "POST", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) + + @validate_arguments + def get_detector_groups(self, **kwargs) -> List[DetectorGroup]: # noqa: E501 """get_detector_groups # noqa: E501 List all detector groups # noqa: E501 @@ -167,47 +200,118 @@ def get_detector_groups(self, **kwargs): >>> thread = api.get_detector_groups(async_req=True) >>> result = thread.get() + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: List[DetectorGroup] + """ + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the get_detector_groups_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.get_detector_groups_with_http_info(**kwargs) # noqa: E501 + + @validate_arguments + def get_detector_groups_with_http_info(self, **kwargs) -> ApiResponse: # noqa: E501 + """get_detector_groups # noqa: E501 + + List all detector groups # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.get_detector_groups_with_http_info(async_req=True) + >>> result = thread.get() - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - [DetectorGroup] - If the method is called asynchronously, returns the request - thread. + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(List[DetectorGroup], status_code(int), headers(HTTPHeaderDict)) """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - return self.get_detector_groups_endpoint.call_with_http_info(**kwargs) + + _params = locals() + + _all_params = [] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method get_detector_groups" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "200": "List[DetectorGroup]", + } + + return self.api_client.call_api( + "/v1/detector-groups", + "GET", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) diff --git a/generated/groundlight_openapi_client/api/detector_reset_api.py b/generated/groundlight_openapi_client/api/detector_reset_api.py index c50532f8..5c5ab2ee 100644 --- a/generated/groundlight_openapi_client/api/detector_reset_api.py +++ b/generated/groundlight_openapi_client/api/detector_reset_api.py @@ -1,81 +1,46 @@ +# coding: utf-8 + """ Groundlight API - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. The version of the OpenAPI document: 0.18.2 Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + import re # noqa: F401 -import sys # noqa: F401 +import io +import warnings + +from pydantic import validate_arguments, ValidationError + +from pydantic import StrictStr -from groundlight_openapi_client.api_client import ApiClient, Endpoint as _Endpoint -from groundlight_openapi_client.model_utils import ( # noqa: F401 - check_allowed_values, - check_validations, - date, - datetime, - file_type, - none_type, - validate_and_convert_types, -) +from groundlight_openapi_client.api_client import ApiClient +from groundlight_openapi_client.api_response import ApiResponse +from groundlight_openapi_client.exceptions import ApiTypeError, ApiValueError # noqa: F401 -class DetectorResetApi(object): + +class DetectorResetApi: """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ - def __init__(self, api_client=None): + def __init__(self, api_client=None) -> None: if api_client is None: - api_client = ApiClient() + api_client = ApiClient.get_default() self.api_client = api_client - self.reset_detector_endpoint = _Endpoint( - settings={ - "response_type": None, - "auth": ["ApiToken"], - "endpoint_path": "/v1/detector-reset/{id}", - "operation_id": "reset_detector", - "http_method": "DELETE", - "servers": None, - }, - params_map={ - "all": [ - "id", - ], - "required": [ - "id", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "id": (str,), - }, - "attribute_map": { - "id": "id", - }, - "location_map": { - "id": "path", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": [], - "content_type": [], - }, - api_client=api_client, - ) - def reset_detector(self, id, **kwargs): + @validate_arguments + def reset_detector(self, id: StrictStr, **kwargs) -> None: # noqa: E501 """reset_detector # noqa: E501 Deletes all image queries on the detector # noqa: E501 @@ -85,50 +50,119 @@ def reset_detector(self, id, **kwargs): >>> thread = api.reset_detector(id, async_req=True) >>> result = thread.get() - Args: - id (str): - - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - None - If the method is called asynchronously, returns the request - thread. + :param id: (required) + :type id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: None + """ + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the reset_detector_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.reset_detector_with_http_info(id, **kwargs) # noqa: E501 + + @validate_arguments + def reset_detector_with_http_info(self, id: StrictStr, **kwargs) -> ApiResponse: # noqa: E501 + """reset_detector # noqa: E501 + + Deletes all image queries on the detector # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.reset_detector_with_http_info(id, async_req=True) + >>> result = thread.get() + + :param id: (required) + :type id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: None """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["id"] = id - return self.reset_detector_endpoint.call_with_http_info(**kwargs) + + _params = locals() + + _all_params = ["id"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method reset_detector" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + if _params["id"] is not None: + _path_params["id"] = _params["id"] + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = {} + + return self.api_client.call_api( + "/v1/detector-reset/{id}", + "DELETE", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) diff --git a/generated/groundlight_openapi_client/api/detectors_api.py b/generated/groundlight_openapi_client/api/detectors_api.py index eca18f45..09a5236b 100644 --- a/generated/groundlight_openapi_client/api/detectors_api.py +++ b/generated/groundlight_openapi_client/api/detectors_api.py @@ -1,327 +1,57 @@ +# coding: utf-8 + """ Groundlight API - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. The version of the OpenAPI document: 0.18.2 Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.api_client import ApiClient, Endpoint as _Endpoint -from groundlight_openapi_client.model_utils import ( # noqa: F401 - check_allowed_values, - check_validations, - date, - datetime, - file_type, - none_type, - validate_and_convert_types, -) -from groundlight_openapi_client.model.detector import Detector -from groundlight_openapi_client.model.detector_creation_input_request import DetectorCreationInputRequest -from groundlight_openapi_client.model.inline_response200 import InlineResponse200 -from groundlight_openapi_client.model.inline_response2001 import InlineResponse2001 -from groundlight_openapi_client.model.paginated_detector_list import PaginatedDetectorList -from groundlight_openapi_client.model.patched_detector_request import PatchedDetectorRequest - - -class DetectorsApi(object): +import io +import warnings + +from pydantic import validate_arguments, ValidationError + +from typing_extensions import Annotated +from pydantic import Field, StrictInt, StrictStr + +from typing import Optional + +from groundlight_openapi_client.models.detector import Detector +from groundlight_openapi_client.models.detector_creation_input_request import DetectorCreationInputRequest +from groundlight_openapi_client.models.get_detector_evaluation200_response import GetDetectorEvaluation200Response +from groundlight_openapi_client.models.get_detector_metrics200_response import GetDetectorMetrics200Response +from groundlight_openapi_client.models.paginated_detector_list import PaginatedDetectorList +from groundlight_openapi_client.models.patched_detector_request import PatchedDetectorRequest + +from groundlight_openapi_client.api_client import ApiClient +from groundlight_openapi_client.api_response import ApiResponse +from groundlight_openapi_client.exceptions import ApiTypeError, ApiValueError # noqa: F401 + + +class DetectorsApi: """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ - def __init__(self, api_client=None): + def __init__(self, api_client=None) -> None: if api_client is None: - api_client = ApiClient() + api_client = ApiClient.get_default() self.api_client = api_client - self.create_detector_endpoint = _Endpoint( - settings={ - "response_type": (Detector,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/detectors", - "operation_id": "create_detector", - "http_method": "POST", - "servers": None, - }, - params_map={ - "all": [ - "detector_creation_input_request", - ], - "required": [ - "detector_creation_input_request", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "detector_creation_input_request": (DetectorCreationInputRequest,), - }, - "attribute_map": {}, - "location_map": { - "detector_creation_input_request": "body", - }, - "collection_format_map": {}, - }, - headers_map={"accept": ["application/json"], "content_type": ["application/json"]}, - api_client=api_client, - ) - self.delete_detector_endpoint = _Endpoint( - settings={ - "response_type": None, - "auth": ["ApiToken"], - "endpoint_path": "/v1/detectors/{id}", - "operation_id": "delete_detector", - "http_method": "DELETE", - "servers": None, - }, - params_map={ - "all": [ - "id", - ], - "required": [ - "id", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "id": (str,), - }, - "attribute_map": { - "id": "id", - }, - "location_map": { - "id": "path", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": [], - "content_type": [], - }, - api_client=api_client, - ) - self.get_detector_endpoint = _Endpoint( - settings={ - "response_type": (Detector,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/detectors/{id}", - "operation_id": "get_detector", - "http_method": "GET", - "servers": None, - }, - params_map={ - "all": [ - "id", - ], - "required": [ - "id", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "id": (str,), - }, - "attribute_map": { - "id": "id", - }, - "location_map": { - "id": "path", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": [], - }, - api_client=api_client, - ) - self.get_detector_evaluation_endpoint = _Endpoint( - settings={ - "response_type": (InlineResponse2001,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/detectors/{id}/evaluation", - "operation_id": "get_detector_evaluation", - "http_method": "GET", - "servers": None, - }, - params_map={ - "all": [ - "id", - ], - "required": [ - "id", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "id": (str,), - }, - "attribute_map": { - "id": "id", - }, - "location_map": { - "id": "path", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": [], - }, - api_client=api_client, - ) - self.get_detector_metrics_endpoint = _Endpoint( - settings={ - "response_type": (InlineResponse200,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/detectors/{detector_id}/metrics", - "operation_id": "get_detector_metrics", - "http_method": "GET", - "servers": None, - }, - params_map={ - "all": [ - "detector_id", - ], - "required": [ - "detector_id", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "detector_id": (str,), - }, - "attribute_map": { - "detector_id": "detector_id", - }, - "location_map": { - "detector_id": "path", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": [], - }, - api_client=api_client, - ) - self.list_detectors_endpoint = _Endpoint( - settings={ - "response_type": (PaginatedDetectorList,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/detectors", - "operation_id": "list_detectors", - "http_method": "GET", - "servers": None, - }, - params_map={ - "all": [ - "page", - "page_size", - ], - "required": [], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "page": (int,), - "page_size": (int,), - }, - "attribute_map": { - "page": "page", - "page_size": "page_size", - }, - "location_map": { - "page": "query", - "page_size": "query", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": [], - }, - api_client=api_client, - ) - self.update_detector_endpoint = _Endpoint( - settings={ - "response_type": (Detector,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/detectors/{id}", - "operation_id": "update_detector", - "http_method": "PATCH", - "servers": None, - }, - params_map={ - "all": [ - "id", - "patched_detector_request", - ], - "required": [ - "id", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "id": (str,), - "patched_detector_request": (PatchedDetectorRequest,), - }, - "attribute_map": { - "id": "id", - }, - "location_map": { - "id": "path", - "patched_detector_request": "body", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": ["application/json", "application/x-www-form-urlencoded", "multipart/form-data"], - }, - api_client=api_client, - ) - def create_detector(self, detector_creation_input_request, **kwargs): + @validate_arguments + def create_detector( + self, detector_creation_input_request: DetectorCreationInputRequest, **kwargs + ) -> Detector: # noqa: E501 """create_detector # noqa: E501 Create a new detector. # noqa: E501 @@ -331,55 +61,141 @@ def create_detector(self, detector_creation_input_request, **kwargs): >>> thread = api.create_detector(detector_creation_input_request, async_req=True) >>> result = thread.get() - Args: - detector_creation_input_request (DetectorCreationInputRequest): - - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - Detector - If the method is called asynchronously, returns the request - thread. + :param detector_creation_input_request: (required) + :type detector_creation_input_request: DetectorCreationInputRequest + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: Detector """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["detector_creation_input_request"] = detector_creation_input_request - return self.create_detector_endpoint.call_with_http_info(**kwargs) - - def delete_detector(self, id, **kwargs): + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the create_detector_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.create_detector_with_http_info(detector_creation_input_request, **kwargs) # noqa: E501 + + @validate_arguments + def create_detector_with_http_info( + self, detector_creation_input_request: DetectorCreationInputRequest, **kwargs + ) -> ApiResponse: # noqa: E501 + """create_detector # noqa: E501 + + Create a new detector. # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.create_detector_with_http_info(detector_creation_input_request, async_req=True) + >>> result = thread.get() + + :param detector_creation_input_request: (required) + :type detector_creation_input_request: DetectorCreationInputRequest + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(Detector, status_code(int), headers(HTTPHeaderDict)) + """ + + _params = locals() + + _all_params = ["detector_creation_input_request"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method create_detector" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + if _params["detector_creation_input_request"] is not None: + _body_params = _params["detector_creation_input_request"] + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # set the HTTP header `Content-Type` + _content_types_list = _params.get( + "_content_type", self.api_client.select_header_content_type(["application/json"]) + ) + if _content_types_list: + _header_params["Content-Type"] = _content_types_list + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "201": "Detector", + } + + return self.api_client.call_api( + "/v1/detectors", + "POST", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) + + @validate_arguments + def delete_detector( + self, id: Annotated[StrictStr, Field(..., description="Choose a detector by its ID.")], **kwargs + ) -> None: # noqa: E501 """delete_detector # noqa: E501 Delete a detector by its ID. # noqa: E501 @@ -389,55 +205,129 @@ def delete_detector(self, id, **kwargs): >>> thread = api.delete_detector(id, async_req=True) >>> result = thread.get() - Args: - id (str): Choose a detector by its ID. - - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - None - If the method is called asynchronously, returns the request - thread. + :param id: Choose a detector by its ID. (required) + :type id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: None """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["id"] = id - return self.delete_detector_endpoint.call_with_http_info(**kwargs) - - def get_detector(self, id, **kwargs): + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the delete_detector_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.delete_detector_with_http_info(id, **kwargs) # noqa: E501 + + @validate_arguments + def delete_detector_with_http_info( + self, id: Annotated[StrictStr, Field(..., description="Choose a detector by its ID.")], **kwargs + ) -> ApiResponse: # noqa: E501 + """delete_detector # noqa: E501 + + Delete a detector by its ID. # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.delete_detector_with_http_info(id, async_req=True) + >>> result = thread.get() + + :param id: Choose a detector by its ID. (required) + :type id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: None + """ + + _params = locals() + + _all_params = ["id"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method delete_detector" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + if _params["id"] is not None: + _path_params["id"] = _params["id"] + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = {} + + return self.api_client.call_api( + "/v1/detectors/{id}", + "DELETE", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) + + @validate_arguments + def get_detector( + self, id: Annotated[StrictStr, Field(..., description="Choose a detector by its ID.")], **kwargs + ) -> Detector: # noqa: E501 """get_detector # noqa: E501 Retrieve a detector by its ID. # noqa: E501 @@ -447,55 +337,132 @@ def get_detector(self, id, **kwargs): >>> thread = api.get_detector(id, async_req=True) >>> result = thread.get() - Args: - id (str): Choose a detector by its ID. - - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - Detector - If the method is called asynchronously, returns the request - thread. + :param id: Choose a detector by its ID. (required) + :type id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: Detector + """ + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the get_detector_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.get_detector_with_http_info(id, **kwargs) # noqa: E501 + + @validate_arguments + def get_detector_with_http_info( + self, id: Annotated[StrictStr, Field(..., description="Choose a detector by its ID.")], **kwargs + ) -> ApiResponse: # noqa: E501 + """get_detector # noqa: E501 + + Retrieve a detector by its ID. # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.get_detector_with_http_info(id, async_req=True) + >>> result = thread.get() + + :param id: Choose a detector by its ID. (required) + :type id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(Detector, status_code(int), headers(HTTPHeaderDict)) """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["id"] = id - return self.get_detector_endpoint.call_with_http_info(**kwargs) - - def get_detector_evaluation(self, id, **kwargs): + + _params = locals() + + _all_params = ["id"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method get_detector" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + if _params["id"] is not None: + _path_params["id"] = _params["id"] + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "200": "Detector", + } + + return self.api_client.call_api( + "/v1/detectors/{id}", + "GET", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) + + @validate_arguments + def get_detector_evaluation(self, id: StrictStr, **kwargs) -> GetDetectorEvaluation200Response: # noqa: E501 """get_detector_evaluation # noqa: E501 Get Detector evaluation results. The result is null if there isn't enough ground truth data to evaluate the detector. Returns the time of the evaulation, total ground truth labels, the ml based kfold accuracies, and the system accuracies at different confidence thresholds # noqa: E501 @@ -505,55 +472,130 @@ def get_detector_evaluation(self, id, **kwargs): >>> thread = api.get_detector_evaluation(id, async_req=True) >>> result = thread.get() - Args: - id (str): - - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - InlineResponse2001 - If the method is called asynchronously, returns the request - thread. + :param id: (required) + :type id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: GetDetectorEvaluation200Response + """ + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the get_detector_evaluation_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.get_detector_evaluation_with_http_info(id, **kwargs) # noqa: E501 + + @validate_arguments + def get_detector_evaluation_with_http_info(self, id: StrictStr, **kwargs) -> ApiResponse: # noqa: E501 + """get_detector_evaluation # noqa: E501 + + Get Detector evaluation results. The result is null if there isn't enough ground truth data to evaluate the detector. Returns the time of the evaulation, total ground truth labels, the ml based kfold accuracies, and the system accuracies at different confidence thresholds # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.get_detector_evaluation_with_http_info(id, async_req=True) + >>> result = thread.get() + + :param id: (required) + :type id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(GetDetectorEvaluation200Response, status_code(int), headers(HTTPHeaderDict)) """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["id"] = id - return self.get_detector_evaluation_endpoint.call_with_http_info(**kwargs) - - def get_detector_metrics(self, detector_id, **kwargs): + + _params = locals() + + _all_params = ["id"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method get_detector_evaluation" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + if _params["id"] is not None: + _path_params["id"] = _params["id"] + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "200": "GetDetectorEvaluation200Response", + } + + return self.api_client.call_api( + "/v1/detectors/{id}/evaluation", + "GET", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) + + @validate_arguments + def get_detector_metrics(self, detector_id: StrictStr, **kwargs) -> GetDetectorMetrics200Response: # noqa: E501 """get_detector_metrics # noqa: E501 Get Detector metrics, primarily the counts of different types of labels # noqa: E501 @@ -563,166 +605,433 @@ def get_detector_metrics(self, detector_id, **kwargs): >>> thread = api.get_detector_metrics(detector_id, async_req=True) >>> result = thread.get() - Args: - detector_id (str): - - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - InlineResponse200 - If the method is called asynchronously, returns the request - thread. + :param detector_id: (required) + :type detector_id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: GetDetectorMetrics200Response """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["detector_id"] = detector_id - return self.get_detector_metrics_endpoint.call_with_http_info(**kwargs) - - def list_detectors(self, **kwargs): + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the get_detector_metrics_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.get_detector_metrics_with_http_info(detector_id, **kwargs) # noqa: E501 + + @validate_arguments + def get_detector_metrics_with_http_info(self, detector_id: StrictStr, **kwargs) -> ApiResponse: # noqa: E501 + """get_detector_metrics # noqa: E501 + + Get Detector metrics, primarily the counts of different types of labels # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.get_detector_metrics_with_http_info(detector_id, async_req=True) + >>> result = thread.get() + + :param detector_id: (required) + :type detector_id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(GetDetectorMetrics200Response, status_code(int), headers(HTTPHeaderDict)) + """ + + _params = locals() + + _all_params = ["detector_id"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method get_detector_metrics" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + if _params["detector_id"] is not None: + _path_params["detector_id"] = _params["detector_id"] + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "200": "GetDetectorMetrics200Response", + } + + return self.api_client.call_api( + "/v1/detectors/{detector_id}/metrics", + "GET", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) + + @validate_arguments + def list_detectors( + self, + page: Annotated[ + Optional[StrictInt], Field(description="A page number within the paginated result set.") + ] = None, + page_size: Annotated[Optional[StrictInt], Field(description="Number of items to return per page.")] = None, + **kwargs, + ) -> PaginatedDetectorList: # noqa: E501 """list_detectors # noqa: E501 Retrieve a list of detectors. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True - >>> thread = api.list_detectors(async_req=True) + >>> thread = api.list_detectors(page, page_size, async_req=True) >>> result = thread.get() + :param page: A page number within the paginated result set. + :type page: int + :param page_size: Number of items to return per page. + :type page_size: int + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: PaginatedDetectorList + """ + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the list_detectors_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.list_detectors_with_http_info(page, page_size, **kwargs) # noqa: E501 + + @validate_arguments + def list_detectors_with_http_info( + self, + page: Annotated[ + Optional[StrictInt], Field(description="A page number within the paginated result set.") + ] = None, + page_size: Annotated[Optional[StrictInt], Field(description="Number of items to return per page.")] = None, + **kwargs, + ) -> ApiResponse: # noqa: E501 + """list_detectors # noqa: E501 - Keyword Args: - page (int): A page number within the paginated result set.. [optional] - page_size (int): Number of items to return per page.. [optional] - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - PaginatedDetectorList - If the method is called asynchronously, returns the request - thread. + Retrieve a list of detectors. # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.list_detectors_with_http_info(page, page_size, async_req=True) + >>> result = thread.get() + + :param page: A page number within the paginated result set. + :type page: int + :param page_size: Number of items to return per page. + :type page_size: int + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(PaginatedDetectorList, status_code(int), headers(HTTPHeaderDict)) + """ + + _params = locals() + + _all_params = ["page", "page_size"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method list_detectors" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + + # process the query parameters + _query_params = [] + if _params.get("page") is not None: # noqa: E501 + _query_params.append(("page", _params["page"])) + + if _params.get("page_size") is not None: # noqa: E501 + _query_params.append(("page_size", _params["page_size"])) + + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "200": "PaginatedDetectorList", + } + + return self.api_client.call_api( + "/v1/detectors", + "GET", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) + + @validate_arguments + def update_detector( + self, id: StrictStr, patched_detector_request: Optional[PatchedDetectorRequest] = None, **kwargs + ) -> Detector: # noqa: E501 + """update_detector # noqa: E501 + + Update a detector # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.update_detector(id, patched_detector_request, async_req=True) + >>> result = thread.get() + + :param id: (required) + :type id: str + :param patched_detector_request: + :type patched_detector_request: PatchedDetectorRequest + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: Detector """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - return self.list_detectors_endpoint.call_with_http_info(**kwargs) - - def update_detector(self, id, **kwargs): + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the update_detector_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.update_detector_with_http_info(id, patched_detector_request, **kwargs) # noqa: E501 + + @validate_arguments + def update_detector_with_http_info( + self, id: StrictStr, patched_detector_request: Optional[PatchedDetectorRequest] = None, **kwargs + ) -> ApiResponse: # noqa: E501 """update_detector # noqa: E501 Update a detector # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True - >>> thread = api.update_detector(id, async_req=True) + >>> thread = api.update_detector_with_http_info(id, patched_detector_request, async_req=True) >>> result = thread.get() - Args: - id (str): - - Keyword Args: - patched_detector_request (PatchedDetectorRequest): [optional] - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - Detector - If the method is called asynchronously, returns the request - thread. + :param id: (required) + :type id: str + :param patched_detector_request: + :type patched_detector_request: PatchedDetectorRequest + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(Detector, status_code(int), headers(HTTPHeaderDict)) """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["id"] = id - return self.update_detector_endpoint.call_with_http_info(**kwargs) + + _params = locals() + + _all_params = ["id", "patched_detector_request"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method update_detector" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + if _params["id"] is not None: + _path_params["id"] = _params["id"] + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + if _params["patched_detector_request"] is not None: + _body_params = _params["patched_detector_request"] + + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # set the HTTP header `Content-Type` + _content_types_list = _params.get( + "_content_type", + self.api_client.select_header_content_type( + ["application/json", "application/x-www-form-urlencoded", "multipart/form-data"] + ), + ) + if _content_types_list: + _header_params["Content-Type"] = _content_types_list + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "200": "Detector", + } + + return self.api_client.call_api( + "/v1/detectors/{id}", + "PATCH", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) diff --git a/generated/groundlight_openapi_client/api/edge_api.py b/generated/groundlight_openapi_client/api/edge_api.py index a0b3187d..ef448034 100644 --- a/generated/groundlight_openapi_client/api/edge_api.py +++ b/generated/groundlight_openapi_client/api/edge_api.py @@ -1,106 +1,47 @@ +# coding: utf-8 + """ Groundlight API - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. The version of the OpenAPI document: 0.18.2 Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.api_client import ApiClient, Endpoint as _Endpoint -from groundlight_openapi_client.model_utils import ( # noqa: F401 - check_allowed_values, - check_validations, - date, - datetime, - file_type, - none_type, - validate_and_convert_types, -) -from groundlight_openapi_client.model.edge_model_info import EdgeModelInfo - - -class EdgeApi(object): +import io +import warnings + +from pydantic import validate_arguments, ValidationError + +from pydantic import StrictStr + +from groundlight_openapi_client.models.edge_model_info import EdgeModelInfo + +from groundlight_openapi_client.api_client import ApiClient +from groundlight_openapi_client.api_response import ApiResponse +from groundlight_openapi_client.exceptions import ApiTypeError, ApiValueError # noqa: F401 + + +class EdgeApi: """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ - def __init__(self, api_client=None): + def __init__(self, api_client=None) -> None: if api_client is None: - api_client = ApiClient() + api_client = ApiClient.get_default() self.api_client = api_client - self.edge_report_metrics_create_endpoint = _Endpoint( - settings={ - "response_type": None, - "auth": ["ApiToken"], - "endpoint_path": "/v1/edge/report-metrics", - "operation_id": "edge_report_metrics_create", - "http_method": "POST", - "servers": None, - }, - params_map={"all": [], "required": [], "nullable": [], "enum": [], "validation": []}, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": {}, - "attribute_map": {}, - "location_map": {}, - "collection_format_map": {}, - }, - headers_map={ - "accept": [], - "content_type": [], - }, - api_client=api_client, - ) - self.get_model_urls_endpoint = _Endpoint( - settings={ - "response_type": (EdgeModelInfo,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/edge/fetch-model-urls/{detector_id}/", - "operation_id": "get_model_urls", - "http_method": "GET", - "servers": None, - }, - params_map={ - "all": [ - "detector_id", - ], - "required": [ - "detector_id", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "detector_id": (str,), - }, - "attribute_map": { - "detector_id": "detector_id", - }, - "location_map": { - "detector_id": "path", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": [], - }, - api_client=api_client, - ) - def edge_report_metrics_create(self, **kwargs): + @validate_arguments + def edge_report_metrics_create(self, **kwargs) -> None: # noqa: E501 """edge_report_metrics_create # noqa: E501 Edge server periodically calls this to report metrics. POST body will have JSON data that we log. # noqa: E501 @@ -110,52 +51,121 @@ def edge_report_metrics_create(self, **kwargs): >>> thread = api.edge_report_metrics_create(async_req=True) >>> result = thread.get() + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: None + """ + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the edge_report_metrics_create_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.edge_report_metrics_create_with_http_info(**kwargs) # noqa: E501 + + @validate_arguments + def edge_report_metrics_create_with_http_info(self, **kwargs) -> ApiResponse: # noqa: E501 + """edge_report_metrics_create # noqa: E501 + + Edge server periodically calls this to report metrics. POST body will have JSON data that we log. # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - None - If the method is called asynchronously, returns the request - thread. + >>> thread = api.edge_report_metrics_create_with_http_info(async_req=True) + >>> result = thread.get() + + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: None """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - return self.edge_report_metrics_create_endpoint.call_with_http_info(**kwargs) - - def get_model_urls(self, detector_id, **kwargs): + + _params = locals() + + _all_params = [] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError( + "Got an unexpected keyword argument '%s' to method edge_report_metrics_create" % _key + ) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = {} + + return self.api_client.call_api( + "/v1/edge/report-metrics", + "POST", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) + + @validate_arguments + def get_model_urls(self, detector_id: StrictStr, **kwargs) -> EdgeModelInfo: # noqa: E501 """get_model_urls # noqa: E501 Gets time limited pre-authenticated URLs to download a detector's edge model and oodd model. # noqa: E501 @@ -165,50 +175,124 @@ def get_model_urls(self, detector_id, **kwargs): >>> thread = api.get_model_urls(detector_id, async_req=True) >>> result = thread.get() - Args: - detector_id (str): - - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - EdgeModelInfo - If the method is called asynchronously, returns the request - thread. + :param detector_id: (required) + :type detector_id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: EdgeModelInfo + """ + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the get_model_urls_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.get_model_urls_with_http_info(detector_id, **kwargs) # noqa: E501 + + @validate_arguments + def get_model_urls_with_http_info(self, detector_id: StrictStr, **kwargs) -> ApiResponse: # noqa: E501 + """get_model_urls # noqa: E501 + + Gets time limited pre-authenticated URLs to download a detector's edge model and oodd model. # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.get_model_urls_with_http_info(detector_id, async_req=True) + >>> result = thread.get() + + :param detector_id: (required) + :type detector_id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(EdgeModelInfo, status_code(int), headers(HTTPHeaderDict)) """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["detector_id"] = detector_id - return self.get_model_urls_endpoint.call_with_http_info(**kwargs) + + _params = locals() + + _all_params = ["detector_id"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method get_model_urls" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + if _params["detector_id"] is not None: + _path_params["detector_id"] = _params["detector_id"] + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "200": "EdgeModelInfo", + } + + return self.api_client.call_api( + "/v1/edge/fetch-model-urls/{detector_id}/", + "GET", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) diff --git a/generated/groundlight_openapi_client/api/image_queries_api.py b/generated/groundlight_openapi_client/api/image_queries_api.py index 03ad2600..4e05ae1b 100644 --- a/generated/groundlight_openapi_client/api/image_queries_api.py +++ b/generated/groundlight_openapi_client/api/image_queries_api.py @@ -1,255 +1,55 @@ +# coding: utf-8 + """ Groundlight API - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. The version of the OpenAPI document: 0.18.2 Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.api_client import ApiClient, Endpoint as _Endpoint -from groundlight_openapi_client.model_utils import ( # noqa: F401 - check_allowed_values, - check_validations, - date, - datetime, - file_type, - none_type, - validate_and_convert_types, -) -from groundlight_openapi_client.model.image_query import ImageQuery -from groundlight_openapi_client.model.paginated_image_query_list import PaginatedImageQueryList - - -class ImageQueriesApi(object): +import io +import warnings + +from pydantic import validate_arguments, ValidationError + +from typing_extensions import Annotated +from pydantic import Field, StrictBytes, StrictFloat, StrictInt, StrictStr, confloat, conint + +from typing import Optional, Union + +from groundlight_openapi_client.models.image_query import ImageQuery +from groundlight_openapi_client.models.paginated_image_query_list import PaginatedImageQueryList + +from groundlight_openapi_client.api_client import ApiClient +from groundlight_openapi_client.api_response import ApiResponse +from groundlight_openapi_client.exceptions import ApiTypeError, ApiValueError # noqa: F401 + + +class ImageQueriesApi: """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ - def __init__(self, api_client=None): + def __init__(self, api_client=None) -> None: if api_client is None: - api_client = ApiClient() + api_client = ApiClient.get_default() self.api_client = api_client - self.get_image_endpoint = _Endpoint( - settings={ - "response_type": (file_type,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/image-queries/{id}/image", - "operation_id": "get_image", - "http_method": "GET", - "servers": None, - }, - params_map={ - "all": [ - "id", - ], - "required": [ - "id", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "id": (str,), - }, - "attribute_map": { - "id": "id", - }, - "location_map": { - "id": "path", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["image/jpeg"], - "content_type": [], - }, - api_client=api_client, - ) - self.get_image_query_endpoint = _Endpoint( - settings={ - "response_type": (ImageQuery,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/image-queries/{id}", - "operation_id": "get_image_query", - "http_method": "GET", - "servers": None, - }, - params_map={ - "all": [ - "id", - ], - "required": [ - "id", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "id": (str,), - }, - "attribute_map": { - "id": "id", - }, - "location_map": { - "id": "path", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": [], - }, - api_client=api_client, - ) - self.list_image_queries_endpoint = _Endpoint( - settings={ - "response_type": (PaginatedImageQueryList,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/image-queries", - "operation_id": "list_image_queries", - "http_method": "GET", - "servers": None, - }, - params_map={ - "all": [ - "detector_id", - "page", - "page_size", - ], - "required": [], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "detector_id": (str,), - "page": (int,), - "page_size": (int,), - }, - "attribute_map": { - "detector_id": "detector_id", - "page": "page", - "page_size": "page_size", - }, - "location_map": { - "detector_id": "query", - "page": "query", - "page_size": "query", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": [], - }, - api_client=api_client, - ) - self.submit_image_query_endpoint = _Endpoint( - settings={ - "response_type": (ImageQuery,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/image-queries", - "operation_id": "submit_image_query", - "http_method": "POST", - "servers": None, - }, - params_map={ - "all": [ - "detector_id", - "confidence_threshold", - "human_review", - "image_query_id", - "inspection_id", - "metadata", - "patience_time", - "want_async", - "body", - ], - "required": [ - "detector_id", - ], - "nullable": [], - "enum": [], - "validation": [ - "confidence_threshold", - ], - }, - root_map={ - "validations": { - ("confidence_threshold",): { - "inclusive_maximum": 1, - "inclusive_minimum": 0, - }, - }, - "allowed_values": {}, - "openapi_types": { - "detector_id": (str,), - "confidence_threshold": (float,), - "human_review": (str,), - "image_query_id": (str,), - "inspection_id": (str,), - "metadata": (str,), - "patience_time": (float,), - "want_async": (str,), - "body": (file_type,), - }, - "attribute_map": { - "detector_id": "detector_id", - "confidence_threshold": "confidence_threshold", - "human_review": "human_review", - "image_query_id": "image_query_id", - "inspection_id": "inspection_id", - "metadata": "metadata", - "patience_time": "patience_time", - "want_async": "want_async", - }, - "location_map": { - "detector_id": "query", - "confidence_threshold": "query", - "human_review": "query", - "image_query_id": "query", - "inspection_id": "query", - "metadata": "query", - "patience_time": "query", - "want_async": "query", - "body": "body", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": [ - "image/jpeg", - "image/jpg", - "image/png", - "image/gif", - "image/webp", - "image/bmp", - "image/x-icon", - ], - }, - api_client=api_client, - ) - def get_image(self, id, **kwargs): + @validate_arguments + def get_image( + self, + id: Annotated[StrictStr, Field(..., description="Retrieve the image associated with the image query ID.")], + **kwargs, + ) -> bytearray: # noqa: E501 """get_image # noqa: E501 Retrieve an image by its ID. # noqa: E501 @@ -259,55 +59,136 @@ def get_image(self, id, **kwargs): >>> thread = api.get_image(id, async_req=True) >>> result = thread.get() - Args: - id (str): Retrieve the image associated with the image query ID. - - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - file_type - If the method is called asynchronously, returns the request - thread. + :param id: Retrieve the image associated with the image query ID. (required) + :type id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: bytearray """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["id"] = id - return self.get_image_endpoint.call_with_http_info(**kwargs) - - def get_image_query(self, id, **kwargs): + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the get_image_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.get_image_with_http_info(id, **kwargs) # noqa: E501 + + @validate_arguments + def get_image_with_http_info( + self, + id: Annotated[StrictStr, Field(..., description="Retrieve the image associated with the image query ID.")], + **kwargs, + ) -> ApiResponse: # noqa: E501 + """get_image # noqa: E501 + + Retrieve an image by its ID. # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.get_image_with_http_info(id, async_req=True) + >>> result = thread.get() + + :param id: Retrieve the image associated with the image query ID. (required) + :type id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(bytearray, status_code(int), headers(HTTPHeaderDict)) + """ + + _params = locals() + + _all_params = ["id"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method get_image" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + if _params["id"] is not None: + _path_params["id"] = _params["id"] + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["image/jpeg"]) # noqa: E501 + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "200": "bytearray", + } + + return self.api_client.call_api( + "/v1/image-queries/{id}/image", + "GET", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) + + @validate_arguments + def get_image_query( + self, id: Annotated[StrictStr, Field(..., description="Choose an image query by its ID.")], **kwargs + ) -> ImageQuery: # noqa: E501 """get_image_query # noqa: E501 Retrieve an image-query by its ID. # noqa: E501 @@ -317,174 +198,612 @@ def get_image_query(self, id, **kwargs): >>> thread = api.get_image_query(id, async_req=True) >>> result = thread.get() - Args: - id (str): Choose an image query by its ID. - - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - ImageQuery - If the method is called asynchronously, returns the request - thread. + :param id: Choose an image query by its ID. (required) + :type id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: ImageQuery """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["id"] = id - return self.get_image_query_endpoint.call_with_http_info(**kwargs) - - def list_image_queries(self, **kwargs): + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the get_image_query_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.get_image_query_with_http_info(id, **kwargs) # noqa: E501 + + @validate_arguments + def get_image_query_with_http_info( + self, id: Annotated[StrictStr, Field(..., description="Choose an image query by its ID.")], **kwargs + ) -> ApiResponse: # noqa: E501 + """get_image_query # noqa: E501 + + Retrieve an image-query by its ID. # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.get_image_query_with_http_info(id, async_req=True) + >>> result = thread.get() + + :param id: Choose an image query by its ID. (required) + :type id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(ImageQuery, status_code(int), headers(HTTPHeaderDict)) + """ + + _params = locals() + + _all_params = ["id"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method get_image_query" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + if _params["id"] is not None: + _path_params["id"] = _params["id"] + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "200": "ImageQuery", + } + + return self.api_client.call_api( + "/v1/image-queries/{id}", + "GET", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) + + @validate_arguments + def list_image_queries( + self, + detector_id: Annotated[ + Optional[StrictStr], Field(description="Optionally filter image queries by detector ID.") + ] = None, + page: Annotated[ + Optional[StrictInt], Field(description="A page number within the paginated result set.") + ] = None, + page_size: Annotated[Optional[StrictInt], Field(description="Number of items to return per page.")] = None, + **kwargs, + ) -> PaginatedImageQueryList: # noqa: E501 """list_image_queries # noqa: E501 Retrieve a list of image-queries. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True - >>> thread = api.list_image_queries(async_req=True) + >>> thread = api.list_image_queries(detector_id, page, page_size, async_req=True) >>> result = thread.get() + :param detector_id: Optionally filter image queries by detector ID. + :type detector_id: str + :param page: A page number within the paginated result set. + :type page: int + :param page_size: Number of items to return per page. + :type page_size: int + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: PaginatedImageQueryList + """ + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the list_image_queries_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.list_image_queries_with_http_info(detector_id, page, page_size, **kwargs) # noqa: E501 + + @validate_arguments + def list_image_queries_with_http_info( + self, + detector_id: Annotated[ + Optional[StrictStr], Field(description="Optionally filter image queries by detector ID.") + ] = None, + page: Annotated[ + Optional[StrictInt], Field(description="A page number within the paginated result set.") + ] = None, + page_size: Annotated[Optional[StrictInt], Field(description="Number of items to return per page.")] = None, + **kwargs, + ) -> ApiResponse: # noqa: E501 + """list_image_queries # noqa: E501 + + Retrieve a list of image-queries. # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.list_image_queries_with_http_info(detector_id, page, page_size, async_req=True) + >>> result = thread.get() - Keyword Args: - detector_id (str): Optionally filter image queries by detector ID.. [optional] - page (int): A page number within the paginated result set.. [optional] - page_size (int): Number of items to return per page.. [optional] - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - PaginatedImageQueryList - If the method is called asynchronously, returns the request - thread. + :param detector_id: Optionally filter image queries by detector ID. + :type detector_id: str + :param page: A page number within the paginated result set. + :type page: int + :param page_size: Number of items to return per page. + :type page_size: int + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(PaginatedImageQueryList, status_code(int), headers(HTTPHeaderDict)) """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - return self.list_image_queries_endpoint.call_with_http_info(**kwargs) - - def submit_image_query(self, detector_id, **kwargs): + + _params = locals() + + _all_params = ["detector_id", "page", "page_size"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method list_image_queries" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + + # process the query parameters + _query_params = [] + if _params.get("detector_id") is not None: # noqa: E501 + _query_params.append(("detector_id", _params["detector_id"])) + + if _params.get("page") is not None: # noqa: E501 + _query_params.append(("page", _params["page"])) + + if _params.get("page_size") is not None: # noqa: E501 + _query_params.append(("page_size", _params["page_size"])) + + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "200": "PaginatedImageQueryList", + } + + return self.api_client.call_api( + "/v1/image-queries", + "GET", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) + + @validate_arguments + def submit_image_query( + self, + detector_id: Annotated[StrictStr, Field(..., description="Choose a detector by its ID.")], + confidence_threshold: Annotated[ + Optional[Union[confloat(le=1, ge=0, strict=True), conint(le=1.0, ge=0.0, strict=True)]], + Field(description="The confidence threshold for the image query."), + ] = None, + human_review: Annotated[ + Optional[StrictStr], + Field( + description=( + "If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review" + " if the ML model is not confident). If set to `ALWAYS`, always send the image query for human" + " review even if the ML model is confident. If set to `NEVER`, never send the image query for human" + " review even if the ML model is not confident." + ) + ), + ] = None, + image_query_id: Annotated[ + Optional[StrictStr], Field(description="The ID to assign to the created image query.") + ] = None, + inspection_id: Annotated[ + Optional[StrictStr], Field(description="Associate the image query with an inspection.") + ] = None, + metadata: Annotated[ + Optional[StrictStr], + Field( + description=( + "A dictionary of custom key/value metadata to associate with the image query (limited to 1KB)." + ) + ), + ] = None, + patience_time: Annotated[ + Optional[Union[StrictFloat, StrictInt]], Field(description="How long to wait for a confident response.") + ] = None, + want_async: Annotated[ + Optional[StrictStr], + Field( + description=( + 'If "true" then submitting an image query returns immediately without a result. The result will be' + " computed asynchronously and can be retrieved later." + ) + ), + ] = None, + body: Optional[Union[StrictBytes, StrictStr]] = None, + **kwargs, + ) -> ImageQuery: # noqa: E501 """submit_image_query # noqa: E501 Submit an image query against a detector. You must use `\"Content-Type: image/jpeg\"` or similar (image/png, image/webp, etc) for the image data. For example: ```Bash $ curl https://api.groundlight.ai/device-api/v1/image-queries?detector_id=det_abc123 \\ --header \"Content-Type: image/jpeg\" \\ --data-binary @path/to/filename.jpeg ``` # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True - >>> thread = api.submit_image_query(detector_id, async_req=True) + >>> thread = api.submit_image_query(detector_id, confidence_threshold, human_review, image_query_id, inspection_id, metadata, patience_time, want_async, body, async_req=True) >>> result = thread.get() - Args: - detector_id (str): Choose a detector by its ID. - - Keyword Args: - confidence_threshold (float): The confidence threshold for the image query.. [optional] - human_review (str): If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review if the ML model is not confident). If set to `ALWAYS`, always send the image query for human review even if the ML model is confident. If set to `NEVER`, never send the image query for human review even if the ML model is not confident.. [optional] - image_query_id (str): The ID to assign to the created image query.. [optional] - inspection_id (str): Associate the image query with an inspection.. [optional] - metadata (str): A dictionary of custom key/value metadata to associate with the image query (limited to 1KB).. [optional] - patience_time (float): How long to wait for a confident response.. [optional] - want_async (str): If \"true\" then submitting an image query returns immediately without a result. The result will be computed asynchronously and can be retrieved later.. [optional] - body (file_type): [optional] - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - ImageQuery - If the method is called asynchronously, returns the request - thread. + :param detector_id: Choose a detector by its ID. (required) + :type detector_id: str + :param confidence_threshold: The confidence threshold for the image query. + :type confidence_threshold: float + :param human_review: If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review if the ML model is not confident). If set to `ALWAYS`, always send the image query for human review even if the ML model is confident. If set to `NEVER`, never send the image query for human review even if the ML model is not confident. + :type human_review: str + :param image_query_id: The ID to assign to the created image query. + :type image_query_id: str + :param inspection_id: Associate the image query with an inspection. + :type inspection_id: str + :param metadata: A dictionary of custom key/value metadata to associate with the image query (limited to 1KB). + :type metadata: str + :param patience_time: How long to wait for a confident response. + :type patience_time: float + :param want_async: If \"true\" then submitting an image query returns immediately without a result. The result will be computed asynchronously and can be retrieved later. + :type want_async: str + :param body: + :type body: bytearray + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: ImageQuery """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["detector_id"] = detector_id - return self.submit_image_query_endpoint.call_with_http_info(**kwargs) + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the submit_image_query_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.submit_image_query_with_http_info( + detector_id, + confidence_threshold, + human_review, + image_query_id, + inspection_id, + metadata, + patience_time, + want_async, + body, + **kwargs, + ) # noqa: E501 + + @validate_arguments + def submit_image_query_with_http_info( + self, + detector_id: Annotated[StrictStr, Field(..., description="Choose a detector by its ID.")], + confidence_threshold: Annotated[ + Optional[Union[confloat(le=1, ge=0, strict=True), conint(le=1.0, ge=0.0, strict=True)]], + Field(description="The confidence threshold for the image query."), + ] = None, + human_review: Annotated[ + Optional[StrictStr], + Field( + description=( + "If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review" + " if the ML model is not confident). If set to `ALWAYS`, always send the image query for human" + " review even if the ML model is confident. If set to `NEVER`, never send the image query for human" + " review even if the ML model is not confident." + ) + ), + ] = None, + image_query_id: Annotated[ + Optional[StrictStr], Field(description="The ID to assign to the created image query.") + ] = None, + inspection_id: Annotated[ + Optional[StrictStr], Field(description="Associate the image query with an inspection.") + ] = None, + metadata: Annotated[ + Optional[StrictStr], + Field( + description=( + "A dictionary of custom key/value metadata to associate with the image query (limited to 1KB)." + ) + ), + ] = None, + patience_time: Annotated[ + Optional[Union[StrictFloat, StrictInt]], Field(description="How long to wait for a confident response.") + ] = None, + want_async: Annotated[ + Optional[StrictStr], + Field( + description=( + 'If "true" then submitting an image query returns immediately without a result. The result will be' + " computed asynchronously and can be retrieved later." + ) + ), + ] = None, + body: Optional[Union[StrictBytes, StrictStr]] = None, + **kwargs, + ) -> ApiResponse: # noqa: E501 + """submit_image_query # noqa: E501 + + Submit an image query against a detector. You must use `\"Content-Type: image/jpeg\"` or similar (image/png, image/webp, etc) for the image data. For example: ```Bash $ curl https://api.groundlight.ai/device-api/v1/image-queries?detector_id=det_abc123 \\ --header \"Content-Type: image/jpeg\" \\ --data-binary @path/to/filename.jpeg ``` # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.submit_image_query_with_http_info(detector_id, confidence_threshold, human_review, image_query_id, inspection_id, metadata, patience_time, want_async, body, async_req=True) + >>> result = thread.get() + + :param detector_id: Choose a detector by its ID. (required) + :type detector_id: str + :param confidence_threshold: The confidence threshold for the image query. + :type confidence_threshold: float + :param human_review: If set to `DEFAULT`, use the regular escalation logic (i.e., send the image query for human review if the ML model is not confident). If set to `ALWAYS`, always send the image query for human review even if the ML model is confident. If set to `NEVER`, never send the image query for human review even if the ML model is not confident. + :type human_review: str + :param image_query_id: The ID to assign to the created image query. + :type image_query_id: str + :param inspection_id: Associate the image query with an inspection. + :type inspection_id: str + :param metadata: A dictionary of custom key/value metadata to associate with the image query (limited to 1KB). + :type metadata: str + :param patience_time: How long to wait for a confident response. + :type patience_time: float + :param want_async: If \"true\" then submitting an image query returns immediately without a result. The result will be computed asynchronously and can be retrieved later. + :type want_async: str + :param body: + :type body: bytearray + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(ImageQuery, status_code(int), headers(HTTPHeaderDict)) + """ + + _params = locals() + + _all_params = [ + "detector_id", + "confidence_threshold", + "human_review", + "image_query_id", + "inspection_id", + "metadata", + "patience_time", + "want_async", + "body", + ] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method submit_image_query" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + + # process the query parameters + _query_params = [] + if _params.get("confidence_threshold") is not None: # noqa: E501 + _query_params.append(("confidence_threshold", _params["confidence_threshold"])) + + if _params.get("detector_id") is not None: # noqa: E501 + _query_params.append(("detector_id", _params["detector_id"])) + + if _params.get("human_review") is not None: # noqa: E501 + _query_params.append(("human_review", _params["human_review"])) + + if _params.get("image_query_id") is not None: # noqa: E501 + _query_params.append(("image_query_id", _params["image_query_id"])) + + if _params.get("inspection_id") is not None: # noqa: E501 + _query_params.append(("inspection_id", _params["inspection_id"])) + + if _params.get("metadata") is not None: # noqa: E501 + _query_params.append(("metadata", _params["metadata"])) + + if _params.get("patience_time") is not None: # noqa: E501 + _query_params.append(("patience_time", _params["patience_time"])) + + if _params.get("want_async") is not None: # noqa: E501 + _query_params.append(("want_async", _params["want_async"])) + + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + if _params["body"] is not None: + _body_params = _params["body"] + # convert to byte array if the input is a file name (str) + if isinstance(_body_params, str): + with io.open(_body_params, "rb") as _fp: + _body_params_from_file = _fp.read() + _body_params = _body_params_from_file + + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # set the HTTP header `Content-Type` + _content_types_list = _params.get( + "_content_type", + self.api_client.select_header_content_type( + ["image/jpeg", "image/jpg", "image/png", "image/gif", "image/webp", "image/bmp", "image/x-icon"] + ), + ) + if _content_types_list: + _header_params["Content-Type"] = _content_types_list + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "201": "ImageQuery", + } + + return self.api_client.call_api( + "/v1/image-queries", + "POST", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) diff --git a/generated/groundlight_openapi_client/api/labels_api.py b/generated/groundlight_openapi_client/api/labels_api.py index fd948a9f..d70a9b54 100644 --- a/generated/groundlight_openapi_client/api/labels_api.py +++ b/generated/groundlight_openapi_client/api/labels_api.py @@ -1,81 +1,46 @@ +# coding: utf-8 + """ Groundlight API - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. The version of the OpenAPI document: 0.18.2 Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.api_client import ApiClient, Endpoint as _Endpoint -from groundlight_openapi_client.model_utils import ( # noqa: F401 - check_allowed_values, - check_validations, - date, - datetime, - file_type, - none_type, - validate_and_convert_types, -) -from groundlight_openapi_client.model.label_value import LabelValue -from groundlight_openapi_client.model.label_value_request import LabelValueRequest - - -class LabelsApi(object): +import io +import warnings + +from pydantic import validate_arguments, ValidationError + +from groundlight_openapi_client.models.label_value import LabelValue +from groundlight_openapi_client.models.label_value_request import LabelValueRequest + +from groundlight_openapi_client.api_client import ApiClient +from groundlight_openapi_client.api_response import ApiResponse +from groundlight_openapi_client.exceptions import ApiTypeError, ApiValueError # noqa: F401 + + +class LabelsApi: """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ - def __init__(self, api_client=None): + def __init__(self, api_client=None) -> None: if api_client is None: - api_client = ApiClient() + api_client = ApiClient.get_default() self.api_client = api_client - self.create_label_endpoint = _Endpoint( - settings={ - "response_type": (LabelValue,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/labels", - "operation_id": "create_label", - "http_method": "POST", - "servers": None, - }, - params_map={ - "all": [ - "label_value_request", - ], - "required": [ - "label_value_request", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "label_value_request": (LabelValueRequest,), - }, - "attribute_map": {}, - "location_map": { - "label_value_request": "body", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": ["application/json", "application/x-www-form-urlencoded", "multipart/form-data"], - }, - api_client=api_client, - ) - def create_label(self, label_value_request, **kwargs): + @validate_arguments + def create_label(self, label_value_request: LabelValueRequest, **kwargs) -> LabelValue: # noqa: E501 """create_label # noqa: E501 Create a new LabelValue and attach it to an image query. This will trigger asynchronous fine-tuner model training. # noqa: E501 @@ -85,50 +50,137 @@ def create_label(self, label_value_request, **kwargs): >>> thread = api.create_label(label_value_request, async_req=True) >>> result = thread.get() - Args: - label_value_request (LabelValueRequest): - - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - LabelValue - If the method is called asynchronously, returns the request - thread. + :param label_value_request: (required) + :type label_value_request: LabelValueRequest + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: LabelValue + """ + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the create_label_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.create_label_with_http_info(label_value_request, **kwargs) # noqa: E501 + + @validate_arguments + def create_label_with_http_info( + self, label_value_request: LabelValueRequest, **kwargs + ) -> ApiResponse: # noqa: E501 + """create_label # noqa: E501 + + Create a new LabelValue and attach it to an image query. This will trigger asynchronous fine-tuner model training. # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.create_label_with_http_info(label_value_request, async_req=True) + >>> result = thread.get() + + :param label_value_request: (required) + :type label_value_request: LabelValueRequest + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(LabelValue, status_code(int), headers(HTTPHeaderDict)) """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["label_value_request"] = label_value_request - return self.create_label_endpoint.call_with_http_info(**kwargs) + + _params = locals() + + _all_params = ["label_value_request"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method create_label" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + if _params["label_value_request"] is not None: + _body_params = _params["label_value_request"] + + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # set the HTTP header `Content-Type` + _content_types_list = _params.get( + "_content_type", + self.api_client.select_header_content_type( + ["application/json", "application/x-www-form-urlencoded", "multipart/form-data"] + ), + ) + if _content_types_list: + _header_params["Content-Type"] = _content_types_list + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "201": "LabelValue", + } + + return self.api_client.call_api( + "/v1/labels", + "POST", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) diff --git a/generated/groundlight_openapi_client/api/notes_api.py b/generated/groundlight_openapi_client/api/notes_api.py index f1af2cb5..bfdd8bcd 100644 --- a/generated/groundlight_openapi_client/api/notes_api.py +++ b/generated/groundlight_openapi_client/api/notes_api.py @@ -1,185 +1,211 @@ +# coding: utf-8 + """ Groundlight API - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. The version of the OpenAPI document: 0.18.2 Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.api_client import ApiClient, Endpoint as _Endpoint -from groundlight_openapi_client.model_utils import ( # noqa: F401 - check_allowed_values, - check_validations, - date, - datetime, - file_type, - none_type, - validate_and_convert_types, -) -from groundlight_openapi_client.model.all_notes import AllNotes -from groundlight_openapi_client.model.note_request import NoteRequest - - -class NotesApi(object): +import io +import warnings + +from pydantic import validate_arguments, ValidationError + +from typing_extensions import Annotated +from pydantic import Field, StrictStr + +from typing import Optional + +from groundlight_openapi_client.models.all_notes import AllNotes +from groundlight_openapi_client.models.note_request import NoteRequest + +from groundlight_openapi_client.api_client import ApiClient +from groundlight_openapi_client.api_response import ApiResponse +from groundlight_openapi_client.exceptions import ApiTypeError, ApiValueError # noqa: F401 + + +class NotesApi: """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ - def __init__(self, api_client=None): + def __init__(self, api_client=None) -> None: if api_client is None: - api_client = ApiClient() + api_client = ApiClient.get_default() self.api_client = api_client - self.create_note_endpoint = _Endpoint( - settings={ - "response_type": None, - "auth": ["ApiToken"], - "endpoint_path": "/v1/notes", - "operation_id": "create_note", - "http_method": "POST", - "servers": None, - }, - params_map={ - "all": [ - "detector_id", - "note_request", - ], - "required": [ - "detector_id", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "detector_id": (str,), - "note_request": (NoteRequest,), - }, - "attribute_map": { - "detector_id": "detector_id", - }, - "location_map": { - "detector_id": "query", - "note_request": "body", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": [], - "content_type": ["application/json", "application/x-www-form-urlencoded", "multipart/form-data"], - }, - api_client=api_client, - ) - self.get_notes_endpoint = _Endpoint( - settings={ - "response_type": (AllNotes,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/notes", - "operation_id": "get_notes", - "http_method": "GET", - "servers": None, - }, - params_map={ - "all": [ - "detector_id", - ], - "required": [ - "detector_id", - ], - "nullable": [], - "enum": [], - "validation": [], - }, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": { - "detector_id": (str,), - }, - "attribute_map": { - "detector_id": "detector_id", - }, - "location_map": { - "detector_id": "query", - }, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": [], - }, - api_client=api_client, - ) - def create_note(self, detector_id, **kwargs): + @validate_arguments + def create_note( + self, + detector_id: Annotated[StrictStr, Field(..., description="the detector to associate the new note with")], + note_request: Optional[NoteRequest] = None, + **kwargs, + ) -> None: # noqa: E501 + """create_note # noqa: E501 + + Creates a new note. # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.create_note(detector_id, note_request, async_req=True) + >>> result = thread.get() + + :param detector_id: the detector to associate the new note with (required) + :type detector_id: str + :param note_request: + :type note_request: NoteRequest + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: None + """ + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the create_note_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.create_note_with_http_info(detector_id, note_request, **kwargs) # noqa: E501 + + @validate_arguments + def create_note_with_http_info( + self, + detector_id: Annotated[StrictStr, Field(..., description="the detector to associate the new note with")], + note_request: Optional[NoteRequest] = None, + **kwargs, + ) -> ApiResponse: # noqa: E501 """create_note # noqa: E501 Creates a new note. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True - >>> thread = api.create_note(detector_id, async_req=True) + >>> thread = api.create_note_with_http_info(detector_id, note_request, async_req=True) >>> result = thread.get() - Args: - detector_id (str): the detector to associate the new note with - - Keyword Args: - note_request (NoteRequest): [optional] - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - None - If the method is called asynchronously, returns the request - thread. + :param detector_id: the detector to associate the new note with (required) + :type detector_id: str + :param note_request: + :type note_request: NoteRequest + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: None """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["detector_id"] = detector_id - return self.create_note_endpoint.call_with_http_info(**kwargs) - - def get_notes(self, detector_id, **kwargs): + + _params = locals() + + _all_params = ["detector_id", "note_request"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method create_note" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + + # process the query parameters + _query_params = [] + if _params.get("detector_id") is not None: # noqa: E501 + _query_params.append(("detector_id", _params["detector_id"])) + + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + if _params["note_request"] is not None: + _body_params = _params["note_request"] + + # set the HTTP header `Content-Type` + _content_types_list = _params.get( + "_content_type", + self.api_client.select_header_content_type( + ["application/json", "application/x-www-form-urlencoded", "multipart/form-data"] + ), + ) + if _content_types_list: + _header_params["Content-Type"] = _content_types_list + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = {} + + return self.api_client.call_api( + "/v1/notes", + "POST", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) + + @validate_arguments + def get_notes( + self, + detector_id: Annotated[StrictStr, Field(..., description="the detector whose notes to retrieve")], + **kwargs, + ) -> AllNotes: # noqa: E501 """get_notes # noqa: E501 Retrieves all notes from a given detector and returns them in lists, one for each note_category. # noqa: E501 @@ -189,50 +215,129 @@ def get_notes(self, detector_id, **kwargs): >>> thread = api.get_notes(detector_id, async_req=True) >>> result = thread.get() - Args: - detector_id (str): the detector whose notes to retrieve - - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - AllNotes - If the method is called asynchronously, returns the request - thread. + :param detector_id: the detector whose notes to retrieve (required) + :type detector_id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: AllNotes """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - kwargs["detector_id"] = detector_id - return self.get_notes_endpoint.call_with_http_info(**kwargs) + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the get_notes_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.get_notes_with_http_info(detector_id, **kwargs) # noqa: E501 + + @validate_arguments + def get_notes_with_http_info( + self, + detector_id: Annotated[StrictStr, Field(..., description="the detector whose notes to retrieve")], + **kwargs, + ) -> ApiResponse: # noqa: E501 + """get_notes # noqa: E501 + + Retrieves all notes from a given detector and returns them in lists, one for each note_category. # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.get_notes_with_http_info(detector_id, async_req=True) + >>> result = thread.get() + + :param detector_id: the detector whose notes to retrieve (required) + :type detector_id: str + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(AllNotes, status_code(int), headers(HTTPHeaderDict)) + """ + + _params = locals() + + _all_params = ["detector_id"] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method get_notes" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + + # process the query parameters + _query_params = [] + if _params.get("detector_id") is not None: # noqa: E501 + _query_params.append(("detector_id", _params["detector_id"])) + + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "200": "AllNotes", + } + + return self.api_client.call_api( + "/v1/notes", + "GET", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) diff --git a/generated/groundlight_openapi_client/api/user_api.py b/generated/groundlight_openapi_client/api/user_api.py index a14200f2..95a589cd 100644 --- a/generated/groundlight_openapi_client/api/user_api.py +++ b/generated/groundlight_openapi_client/api/user_api.py @@ -1,66 +1,45 @@ +# coding: utf-8 + """ Groundlight API - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. The version of the OpenAPI document: 0.18.2 Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.api_client import ApiClient, Endpoint as _Endpoint -from groundlight_openapi_client.model_utils import ( # noqa: F401 - check_allowed_values, - check_validations, - date, - datetime, - file_type, - none_type, - validate_and_convert_types, -) -from groundlight_openapi_client.model.inline_response2002 import InlineResponse2002 - - -class UserApi(object): +import io +import warnings + +from pydantic import validate_arguments, ValidationError + +from groundlight_openapi_client.models.who_am_i200_response import WhoAmI200Response + +from groundlight_openapi_client.api_client import ApiClient +from groundlight_openapi_client.api_response import ApiResponse +from groundlight_openapi_client.exceptions import ApiTypeError, ApiValueError # noqa: F401 + + +class UserApi: """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ - def __init__(self, api_client=None): + def __init__(self, api_client=None) -> None: if api_client is None: - api_client = ApiClient() + api_client = ApiClient.get_default() self.api_client = api_client - self.who_am_i_endpoint = _Endpoint( - settings={ - "response_type": (InlineResponse2002,), - "auth": ["ApiToken"], - "endpoint_path": "/v1/me", - "operation_id": "who_am_i", - "http_method": "GET", - "servers": None, - }, - params_map={"all": [], "required": [], "nullable": [], "enum": [], "validation": []}, - root_map={ - "validations": {}, - "allowed_values": {}, - "openapi_types": {}, - "attribute_map": {}, - "location_map": {}, - "collection_format_map": {}, - }, - headers_map={ - "accept": ["application/json"], - "content_type": [], - }, - api_client=api_client, - ) - def who_am_i(self, **kwargs): + @validate_arguments + def who_am_i(self, **kwargs) -> WhoAmI200Response: # noqa: E501 """who_am_i # noqa: E501 Retrieve the current user. # noqa: E501 @@ -70,47 +49,118 @@ def who_am_i(self, **kwargs): >>> thread = api.who_am_i(async_req=True) >>> result = thread.get() + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _request_timeout: timeout setting for this request. + If one number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: WhoAmI200Response + """ + kwargs["_return_http_data_only"] = True + if "_preload_content" in kwargs: + message = "Error! Please call the who_am_i_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data" # noqa: E501 + raise ValueError(message) + return self.who_am_i_with_http_info(**kwargs) # noqa: E501 + + @validate_arguments + def who_am_i_with_http_info(self, **kwargs) -> ApiResponse: # noqa: E501 + """who_am_i # noqa: E501 + + Retrieve the current user. # noqa: E501 + This method makes a synchronous HTTP request by default. To make an + asynchronous HTTP request, please pass async_req=True + + >>> thread = api.who_am_i_with_http_info(async_req=True) + >>> result = thread.get() - Keyword Args: - _return_http_data_only (bool): response data without head status - code and headers. Default is True. - _preload_content (bool): if False, the urllib3.HTTPResponse object - will be returned without reading/decoding response data. - Default is True. - _request_timeout (int/float/tuple): timeout setting for this request. If - one number provided, it will be total request timeout. It can also - be a pair (tuple) of (connection, read) timeouts. - Default is None. - _check_input_type (bool): specifies if type checking - should be done one the data sent to the server. - Default is True. - _check_return_type (bool): specifies if type checking - should be done one the data received from the server. - Default is True. - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _content_type (str/None): force body content-type. - Default is None and content-type will be predicted by allowed - content-types and body. - _host_index (int/None): specifies the index of the server - that we want to use. - Default is read from the configuration. - async_req (bool): execute request asynchronously - - Returns: - InlineResponse2002 - If the method is called asynchronously, returns the request - thread. + :param async_req: Whether to execute the request asynchronously. + :type async_req: bool, optional + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. + :type _preload_content: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :type _return_http_data_only: bool, optional + :param _request_timeout: timeout setting for this request. If one + number provided, it will be total request + timeout. It can also be a pair (tuple) of + (connection, read) timeouts. + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_auth: dict, optional + :type _content_type: string, optional: force content-type for the request + :return: Returns the result object. + If the method is called asynchronously, + returns the request thread. + :rtype: tuple(WhoAmI200Response, status_code(int), headers(HTTPHeaderDict)) """ - kwargs["async_req"] = kwargs.get("async_req", False) - kwargs["_return_http_data_only"] = kwargs.get("_return_http_data_only", True) - kwargs["_preload_content"] = kwargs.get("_preload_content", True) - kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) - kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) - kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) - kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) - kwargs["_content_type"] = kwargs.get("_content_type") - kwargs["_host_index"] = kwargs.get("_host_index") - return self.who_am_i_endpoint.call_with_http_info(**kwargs) + + _params = locals() + + _all_params = [] + _all_params.extend([ + "async_req", + "_return_http_data_only", + "_preload_content", + "_request_timeout", + "_request_auth", + "_content_type", + "_headers", + ]) + + # validate the arguments + for _key, _val in _params["kwargs"].items(): + if _key not in _all_params: + raise ApiTypeError("Got an unexpected keyword argument '%s' to method who_am_i" % _key) + _params[_key] = _val + del _params["kwargs"] + + _collection_formats = {} + + # process the path parameters + _path_params = {} + + # process the query parameters + _query_params = [] + # process the header parameters + _header_params = dict(_params.get("_headers", {})) + # process the form parameters + _form_params = [] + _files = {} + # process the body parameter + _body_params = None + # set the HTTP header `Accept` + _header_params["Accept"] = self.api_client.select_header_accept(["application/json"]) # noqa: E501 + + # authentication setting + _auth_settings = ["ApiToken"] # noqa: E501 + + _response_types_map = { + "200": "WhoAmI200Response", + } + + return self.api_client.call_api( + "/v1/me", + "GET", + _path_params, + _query_params, + _header_params, + body=_body_params, + post_params=_form_params, + files=_files, + response_types_map=_response_types_map, + auth_settings=_auth_settings, + async_req=_params.get("async_req"), + _return_http_data_only=_params.get("_return_http_data_only"), # noqa: E501 + _preload_content=_params.get("_preload_content", True), + _request_timeout=_params.get("_request_timeout"), + collection_formats=_collection_formats, + _request_auth=_params.get("_request_auth"), + ) diff --git a/generated/groundlight_openapi_client/api_client.py b/generated/groundlight_openapi_client/api_client.py index da2e348a..db3bf61f 100644 --- a/generated/groundlight_openapi_client/api_client.py +++ b/generated/groundlight_openapi_client/api_client.py @@ -1,45 +1,39 @@ +# coding: utf-8 + """ Groundlight API - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. The version of the OpenAPI document: 0.18.2 Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + -import json import atexit +import datetime +from dateutil.parser import parse +import json import mimetypes from multiprocessing.pool import ThreadPool -import io import os import re -import typing -from urllib.parse import quote -from urllib3.fields import RequestField +import tempfile +from urllib.parse import quote +from pydantic import SecretStr -from groundlight_openapi_client import rest from groundlight_openapi_client.configuration import Configuration -from groundlight_openapi_client.exceptions import ApiTypeError, ApiValueError, ApiException -from groundlight_openapi_client.model_utils import ( - ModelNormal, - ModelSimple, - ModelComposed, - check_allowed_values, - check_validations, - date, - datetime, - deserialize_file, - file_type, - model_to_dict, - none_type, - validate_and_convert_types, -) - - -class ApiClient(object): +from groundlight_openapi_client.api_response import ApiResponse +import groundlight_openapi_client.models +from groundlight_openapi_client import rest +from groundlight_openapi_client.exceptions import ApiValueError, ApiException + + +class ApiClient: """Generic API client for OpenAPI client library builds. OpenAPI generic API client. This client handles the client- @@ -47,10 +41,6 @@ class ApiClient(object): the methods and models for each application are generated from the OpenAPI templates. - NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - Do not edit the class manually. - :param configuration: .Configuration object for this client :param header_name: a header to pass when making calls to the API. :param header_value: a header value to pass when making calls to @@ -61,11 +51,23 @@ class ApiClient(object): to the API. More threads means more concurrent API requests. """ + PRIMITIVE_TYPES = (float, bool, bytes, str, int) + NATIVE_TYPES_MAPPING = { + "int": int, + "long": int, # TODO remove as only py3 is supported? + "float": float, + "str": str, + "bool": bool, + "date": datetime.date, + "datetime": datetime.datetime, + "object": object, + } _pool = None - def __init__(self, configuration=None, header_name=None, header_value=None, cookie=None, pool_threads=1): + def __init__(self, configuration=None, header_name=None, header_value=None, cookie=None, pool_threads=1) -> None: + # use default configuration if none is provided if configuration is None: - configuration = Configuration.get_default_copy() + configuration = Configuration.get_default() self.configuration = configuration self.pool_threads = pool_threads @@ -76,6 +78,7 @@ def __init__(self, configuration=None, header_name=None, header_value=None, cook self.cookie = cookie # Set default User-Agent. self.user_agent = "OpenAPI-Generator/1.0.0/python" + self.client_side_validation = configuration.client_side_validation def __enter__(self): return self @@ -113,25 +116,50 @@ def user_agent(self, value): def set_default_header(self, header_name, header_value): self.default_headers[header_name] = header_value + _default = None + + @classmethod + def get_default(cls): + """Return new instance of ApiClient. + + This method returns newly created, based on default constructor, + object of ApiClient class or returns a copy of default + ApiClient. + + :return: The ApiClient object. + """ + if cls._default is None: + cls._default = ApiClient() + return cls._default + + @classmethod + def set_default(cls, default): + """Set default instance of ApiClient. + + It stores default ApiClient. + + :param default: object of ApiClient. + """ + cls._default = default + def __call_api( self, - resource_path: str, - method: str, - path_params: typing.Optional[typing.Dict[str, typing.Any]] = None, - query_params: typing.Optional[typing.List[typing.Tuple[str, typing.Any]]] = None, - header_params: typing.Optional[typing.Dict[str, typing.Any]] = None, - body: typing.Optional[typing.Any] = None, - post_params: typing.Optional[typing.List[typing.Tuple[str, typing.Any]]] = None, - files: typing.Optional[typing.Dict[str, typing.List[io.IOBase]]] = None, - response_type: typing.Optional[typing.Tuple[typing.Any]] = None, - auth_settings: typing.Optional[typing.List[str]] = None, - _return_http_data_only: typing.Optional[bool] = None, - collection_formats: typing.Optional[typing.Dict[str, str]] = None, - _preload_content: bool = True, - _request_timeout: typing.Optional[typing.Union[int, float, typing.Tuple]] = None, - _host: typing.Optional[str] = None, - _check_type: typing.Optional[bool] = None, - _content_type: typing.Optional[str] = None, + resource_path, + method, + path_params=None, + query_params=None, + header_params=None, + body=None, + post_params=None, + files=None, + response_types_map=None, + auth_settings=None, + _return_http_data_only=None, + collection_formats=None, + _preload_content=True, + _request_timeout=None, + _host=None, + _request_auth=None, ): config = self.configuration @@ -153,27 +181,22 @@ def __call_api( # specified safe chars, encode everything resource_path = resource_path.replace("{%s}" % k, quote(str(v), safe=config.safe_chars_for_path_param)) - # query parameters - if query_params: - query_params = self.sanitize_for_serialization(query_params) - query_params = self.parameters_to_tuples(query_params, collection_formats) - # post parameters if post_params or files: post_params = post_params if post_params else [] post_params = self.sanitize_for_serialization(post_params) post_params = self.parameters_to_tuples(post_params, collection_formats) post_params.extend(self.files_parameters(files)) - if header_params["Content-Type"].startswith("multipart"): - post_params = self.parameters_to_multipart(post_params, (dict)) + + # auth setting + self.update_params_for_auth( + header_params, query_params, auth_settings, resource_path, method, body, request_auth=_request_auth + ) # body if body: body = self.sanitize_for_serialization(body) - # auth setting - self.update_params_for_auth(header_params, query_params, auth_settings, resource_path, method, body) - # request url if _host is None: url = self.configuration.host + resource_path @@ -181,6 +204,12 @@ def __call_api( # use server/host defined in path or operation instead url = _host + resource_path + # query parameters + if query_params: + query_params = self.sanitize_for_serialization(query_params) + url_query = self.parameters_to_url_query(query_params, collection_formats) + url += "?" + url_query + try: # perform request and return response response_data = self.request( @@ -194,145 +223,169 @@ def __call_api( _request_timeout=_request_timeout, ) except ApiException as e: - e.body = e.body.decode("utf-8") + if e.body: + e.body = e.body.decode("utf-8") raise e self.last_response = response_data - return_data = response_data + return_data = None # assuming deserialization is not needed + # data needs deserialization or returns HTTP data (deserialized) only + if _preload_content or _return_http_data_only: + response_type = response_types_map.get(str(response_data.status), None) + if not response_type and isinstance(response_data.status, int) and 100 <= response_data.status <= 599: + # if not found, look for '1XX', '2XX', etc. + response_type = response_types_map.get(str(response_data.status)[0] + "XX", None) - if not _preload_content: - return return_data - return return_data - - # deserialize response data - if response_type: - if response_type != (file_type,): - encoding = "utf-8" + if response_type == "bytearray": + response_data.data = response_data.data + else: + match = None content_type = response_data.getheader("content-type") if content_type is not None: - match = re.search(r"charset=([a-zA-Z\-\d]+)[\s\;]?", content_type) - if match: - encoding = match.group(1) + match = re.search(r"charset=([a-zA-Z\-\d]+)[\s;]?", content_type) + encoding = match.group(1) if match else "utf-8" response_data.data = response_data.data.decode(encoding) - return_data = self.deserialize(response_data, response_type, _check_type) - else: - return_data = None + # deserialize response data + if response_type == "bytearray": + return_data = response_data.data + elif response_type: + return_data = self.deserialize(response_data, response_type) + else: + return_data = None if _return_http_data_only: return return_data else: - return (return_data, response_data.status, response_data.getheaders()) - - def parameters_to_multipart(self, params, collection_types): - """Get parameters as list of tuples, formatting as json if value is collection_types + return ApiResponse( + status_code=response_data.status, + data=return_data, + headers=response_data.getheaders(), + raw_data=response_data.data, + ) - :param params: Parameters as list of two-tuples - :param dict collection_types: Parameter collection types - :return: Parameters as list of tuple or urllib3.fields.RequestField - """ - new_params = [] - if collection_types is None: - collection_types = dict - for k, v in params.items() if isinstance(params, dict) else params: # noqa: E501 - if isinstance(v, collection_types): # v is instance of collection_type, formatting as application/json - v = json.dumps(v, ensure_ascii=False).encode("utf-8") - field = RequestField(k, v) - field.make_multipart(content_type="application/json; charset=utf-8") - new_params.append(field) - else: - new_params.append((k, v)) - return new_params + def sanitize_for_serialization(self, obj): + """Builds a JSON POST object. - @classmethod - def sanitize_for_serialization(cls, obj): - """Prepares data for transmission before it is sent with the rest client If obj is None, return None. + If obj is SecretStr, return obj.get_secret_value() If obj is str, int, long, float, bool, return directly. If obj is datetime.datetime, datetime.date convert to string in iso8601 format. If obj is list, sanitize each element in the list. If obj is dict, return the dict. If obj is OpenAPI model, return the properties dict. - If obj is io.IOBase, return the bytes + :param obj: The data to serialize. :return: The serialized form of data. """ - if isinstance(obj, (ModelNormal, ModelComposed)): - return {key: cls.sanitize_for_serialization(val) for key, val in model_to_dict(obj, serialize=True).items()} - elif isinstance(obj, io.IOBase): - return cls.get_file_data_and_close_file(obj) - elif isinstance(obj, (str, int, float, none_type, bool)): + if obj is None: + return None + elif isinstance(obj, SecretStr): + return obj.get_secret_value() + elif isinstance(obj, self.PRIMITIVE_TYPES): return obj - elif isinstance(obj, (datetime, date)): + elif isinstance(obj, list): + return [self.sanitize_for_serialization(sub_obj) for sub_obj in obj] + elif isinstance(obj, tuple): + return tuple(self.sanitize_for_serialization(sub_obj) for sub_obj in obj) + elif isinstance(obj, (datetime.datetime, datetime.date)): return obj.isoformat() - elif isinstance(obj, ModelSimple): - return cls.sanitize_for_serialization(obj.value) - elif isinstance(obj, (list, tuple)): - return [cls.sanitize_for_serialization(item) for item in obj] + if isinstance(obj, dict): - return {key: cls.sanitize_for_serialization(val) for key, val in obj.items()} - raise ApiValueError("Unable to prepare type {} for serialization".format(obj.__class__.__name__)) + obj_dict = obj + else: + # Convert model obj to dict except + # attributes `openapi_types`, `attribute_map` + # and attributes which value is not None. + # Convert attribute name to json key in + # model definition for request. + if hasattr(obj, "to_dict") and callable(getattr(obj, "to_dict")): + obj_dict = obj.to_dict() + else: + obj_dict = obj.__dict__ - def deserialize(self, response, response_type, _check_type): + return {key: self.sanitize_for_serialization(val) for key, val in obj_dict.items()} + + def deserialize(self, response, response_type): """Deserializes response into an object. :param response: RESTResponse object to be deserialized. - :param response_type: For the response, a tuple containing: - valid classes - a list containing valid classes (for list schemas) - a dict containing a tuple of valid classes as the value - Example values: - (str,) - (Pet,) - (float, none_type) - ([int, none_type],) - ({str: (bool, str, int, float, date, datetime, str, none_type)},) - :param _check_type: boolean, whether to check the types of the data - received from the server - :type _check_type: bool + :param response_type: class literal for + deserialized object, or string of class name. :return: deserialized object. """ # handle file downloading # save response body into a tmp file and return the instance - if response_type == (file_type,): - content_disposition = response.getheader("Content-Disposition") - return deserialize_file(response.data, self.configuration, content_disposition=content_disposition) + if response_type == "file": + return self.__deserialize_file(response) # fetch data from response object try: - received_data = json.loads(response.data) + data = json.loads(response.data) except ValueError: - received_data = response.data + data = response.data - # store our data under the key of 'received_data' so users have some - # context if they are deserializing a string and the data type is wrong - deserialized_data = validate_and_convert_types( - received_data, response_type, ["received_data"], True, _check_type, configuration=self.configuration - ) - return deserialized_data + return self.__deserialize(data, response_type) + + def __deserialize(self, data, klass): + """Deserializes dict, list, str into an object. + + :param data: dict, list or str. + :param klass: class literal, or string of class name. + + :return: object. + """ + if data is None: + return None + + if isinstance(klass, str): + if klass.startswith("List["): + sub_kls = re.match(r"List\[(.*)]", klass).group(1) + return [self.__deserialize(sub_data, sub_kls) for sub_data in data] + + if klass.startswith("Dict["): + sub_kls = re.match(r"Dict\[([^,]*), (.*)]", klass).group(2) + return {k: self.__deserialize(v, sub_kls) for k, v in data.items()} + + # convert str to class + if klass in self.NATIVE_TYPES_MAPPING: + klass = self.NATIVE_TYPES_MAPPING[klass] + else: + klass = getattr(groundlight_openapi_client.models, klass) + + if klass in self.PRIMITIVE_TYPES: + return self.__deserialize_primitive(data, klass) + elif klass == object: + return self.__deserialize_object(data) + elif klass == datetime.date: + return self.__deserialize_date(data) + elif klass == datetime.datetime: + return self.__deserialize_datetime(data) + else: + return self.__deserialize_model(data, klass) def call_api( self, - resource_path: str, - method: str, - path_params: typing.Optional[typing.Dict[str, typing.Any]] = None, - query_params: typing.Optional[typing.List[typing.Tuple[str, typing.Any]]] = None, - header_params: typing.Optional[typing.Dict[str, typing.Any]] = None, - body: typing.Optional[typing.Any] = None, - post_params: typing.Optional[typing.List[typing.Tuple[str, typing.Any]]] = None, - files: typing.Optional[typing.Dict[str, typing.List[io.IOBase]]] = None, - response_type: typing.Optional[typing.Tuple[typing.Any]] = None, - auth_settings: typing.Optional[typing.List[str]] = None, - async_req: typing.Optional[bool] = None, - _return_http_data_only: typing.Optional[bool] = None, - collection_formats: typing.Optional[typing.Dict[str, str]] = None, - _preload_content: bool = True, - _request_timeout: typing.Optional[typing.Union[int, float, typing.Tuple]] = None, - _host: typing.Optional[str] = None, - _check_type: typing.Optional[bool] = None, + resource_path, + method, + path_params=None, + query_params=None, + header_params=None, + body=None, + post_params=None, + files=None, + response_types_map=None, + auth_settings=None, + async_req=None, + _return_http_data_only=None, + collection_formats=None, + _preload_content=True, + _request_timeout=None, + _host=None, + _request_auth=None, ): """Makes the HTTP request (synchronous) and returns deserialized data. @@ -348,38 +401,26 @@ def call_api( :param post_params dict: Request post form parameters, for `application/x-www-form-urlencoded`, `multipart/form-data`. :param auth_settings list: Auth Settings names for the request. - :param response_type: For the response, a tuple containing: - valid classes - a list containing valid classes (for list schemas) - a dict containing a tuple of valid classes as the value - Example values: - (str,) - (Pet,) - (float, none_type) - ([int, none_type],) - ({str: (bool, str, int, float, date, datetime, str, none_type)},) - :param files: key -> field name, value -> a list of open file - objects for `multipart/form-data`. - :type files: dict + :param response: Response data type. + :param files dict: key -> filename, value -> filepath, + for `multipart/form-data`. :param async_req bool: execute request asynchronously - :type async_req: bool, optional - :param _return_http_data_only: response data without head status code - and headers - :type _return_http_data_only: bool, optional + :param _return_http_data_only: response data instead of ApiResponse + object with status code, headers, etc + :param _preload_content: if False, the ApiResponse.data will + be set to none and raw_data will store the + HTTP response body without reading/decoding. + Default is True. :param collection_formats: dict of collection formats for path, query, header, and post parameters. - :type collection_formats: dict, optional - :param _preload_content: if False, the urllib3.HTTPResponse object will - be returned without reading/decoding response - data. Default is True. - :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. - :param _check_type: boolean describing if the data back from the server - should have its type checked. - :type _check_type: bool, optional + :param _request_auth: set to override the auth_settings for an a single + request; this effectively ignores the authentication + in the spec for a single request. + :type _request_token: dict, optional :return: If async_req parameter is True, the request will be called asynchronously. @@ -387,47 +428,28 @@ def call_api( If parameter async_req is False or missing, then the method will return the response directly. """ + args = ( + resource_path, + method, + path_params, + query_params, + header_params, + body, + post_params, + files, + response_types_map, + auth_settings, + _return_http_data_only, + collection_formats, + _preload_content, + _request_timeout, + _host, + _request_auth, + ) if not async_req: - return self.__call_api( - resource_path, - method, - path_params, - query_params, - header_params, - body, - post_params, - files, - response_type, - auth_settings, - _return_http_data_only, - collection_formats, - _preload_content, - _request_timeout, - _host, - _check_type, - ) + return self.__call_api(*args) - return self.pool.apply_async( - self.__call_api, - ( - resource_path, - method, - path_params, - query_params, - header_params, - body, - post_params, - files, - response_type, - auth_settings, - _return_http_data_only, - collection_formats, - _preload_content, - _request_timeout, - _host, - _check_type, - ), - ) + return self.pool.apply_async(self.__call_api, args) def request( self, @@ -442,7 +464,7 @@ def request( ): """Makes the HTTP request using RESTClient.""" if method == "GET": - return self.rest_client.GET( + return self.rest_client.get_request( url, query_params=query_params, _preload_content=_preload_content, @@ -450,7 +472,7 @@ def request( headers=headers, ) elif method == "HEAD": - return self.rest_client.HEAD( + return self.rest_client.head_request( url, query_params=query_params, _preload_content=_preload_content, @@ -458,17 +480,15 @@ def request( headers=headers, ) elif method == "OPTIONS": - return self.rest_client.OPTIONS( + return self.rest_client.options_request( url, query_params=query_params, headers=headers, - post_params=post_params, _preload_content=_preload_content, _request_timeout=_request_timeout, - body=body, ) elif method == "POST": - return self.rest_client.POST( + return self.rest_client.post_request( url, query_params=query_params, headers=headers, @@ -478,7 +498,7 @@ def request( body=body, ) elif method == "PUT": - return self.rest_client.PUT( + return self.rest_client.put_request( url, query_params=query_params, headers=headers, @@ -488,7 +508,7 @@ def request( body=body, ) elif method == "PATCH": - return self.rest_client.PATCH( + return self.rest_client.patch_request( url, query_params=query_params, headers=headers, @@ -498,7 +518,7 @@ def request( body=body, ) elif method == "DELETE": - return self.rest_client.DELETE( + return self.rest_client.delete_request( url, query_params=query_params, headers=headers, @@ -538,39 +558,62 @@ def parameters_to_tuples(self, params, collection_formats): new_params.append((k, v)) return new_params - @staticmethod - def get_file_data_and_close_file(file_instance: io.IOBase) -> bytes: - file_data = file_instance.read() - file_instance.close() - return file_data + def parameters_to_url_query(self, params, collection_formats): + """Get parameters as list of tuples, formatting collections. + + :param params: Parameters as dict or list of two-tuples + :param dict collection_formats: Parameter collection formats + :return: URL query string (e.g. a=Hello%20World&b=123) + """ + new_params = [] + if collection_formats is None: + collection_formats = {} + for k, v in params.items() if isinstance(params, dict) else params: # noqa: E501 + if isinstance(v, bool): + v = str(v).lower() + if isinstance(v, (int, float)): + v = str(v) + if isinstance(v, dict): + v = json.dumps(v) - def files_parameters(self, files: typing.Optional[typing.Dict[str, typing.List[io.IOBase]]] = None): + if k in collection_formats: + collection_format = collection_formats[k] + if collection_format == "multi": + new_params.extend((k, quote(str(value))) for value in v) + else: + if collection_format == "ssv": + delimiter = " " + elif collection_format == "tsv": + delimiter = "\t" + elif collection_format == "pipes": + delimiter = "|" + else: # csv is the default + delimiter = "," + new_params.append((k, delimiter.join(quote(str(value)) for value in v))) + else: + new_params.append((k, quote(str(v)))) + + return "&".join(["=".join(map(str, item)) for item in new_params]) + + def files_parameters(self, files=None): """Builds form parameters. - :param files: None or a dict with key=param_name and - value is a list of open file objects - :return: List of tuples of form parameters with file data + :param files: File parameters. + :return: Form parameters with files. """ - if files is None: - return [] - params = [] - for param_name, file_instances in files.items(): - if file_instances is None: - # if the file field is nullable, skip None values - continue - for file_instance in file_instances: - if file_instance is None: - # if the file field is nullable, skip None values + + if files: + for k, v in files.items(): + if not v: continue - if file_instance.closed is True: - raise ApiValueError( - "Cannot read a closed file. The passed in file_type for %s must be open." % param_name - ) - filename = os.path.basename(file_instance.name) - filedata = self.get_file_data_and_close_file(file_instance) - mimetype = mimetypes.guess_type(filename)[0] or "application/octet-stream" - params.append(tuple([param_name, tuple([filename, filedata, mimetype])])) + file_names = v if type(v) is list else [v] + for n in file_names: + with open(n, "rb") as f: + filename = os.path.basename(f.name) + filedata = f.read() + mimetype = mimetypes.guess_type(filename)[0] or "application/octet-stream" + params.append(tuple([k, tuple([filename, filedata, mimetype])])) return params @@ -583,275 +626,152 @@ def select_header_accept(self, accepts): if not accepts: return - accepts = [x.lower() for x in accepts] + for accept in accepts: + if re.search("json", accept, re.IGNORECASE): + return accept - if "application/json" in accepts: - return "application/json" - else: - return ", ".join(accepts) + return accepts[0] - def select_header_content_type(self, content_types, method=None, body=None): + def select_header_content_type(self, content_types): """Returns `Content-Type` based on an array of content_types provided. :param content_types: List of content-types. - :param method: http method (e.g. POST, PATCH). - :param body: http body to send. :return: Content-Type (e.g. application/json). """ if not content_types: - return "application/json" + return None - content_types = [x.lower() for x in content_types] + for content_type in content_types: + if re.search("json", content_type, re.IGNORECASE): + return content_type - if method == "PATCH" and "application/json-patch+json" in content_types and isinstance(body, list): - return "application/json-patch+json" - - if "application/json" in content_types or "*/*" in content_types: - return "application/json" - else: - return content_types[0] + return content_types[0] - def update_params_for_auth(self, headers, queries, auth_settings, resource_path, method, body): + def update_params_for_auth(self, headers, queries, auth_settings, resource_path, method, body, request_auth=None): """Updates header and query params based on authentication setting. :param headers: Header parameters dict to be updated. :param queries: Query parameters tuple list to be updated. :param auth_settings: Authentication setting identifiers list. - :param resource_path: A string representation of the HTTP request resource path. - :param method: A string representation of the HTTP request method. - :param body: A object representing the body of the HTTP request. - The object type is the return value of _encoder.default(). + :resource_path: A string representation of the HTTP request resource path. + :method: A string representation of the HTTP request method. + :body: A object representing the body of the HTTP request. + The object type is the return value of sanitize_for_serialization(). + :param request_auth: if set, the provided settings will + override the token in the configuration. """ if not auth_settings: return + if request_auth: + self._apply_auth_params(headers, queries, resource_path, method, body, request_auth) + return + for auth in auth_settings: auth_setting = self.configuration.auth_settings().get(auth) if auth_setting: - if auth_setting["in"] == "cookie": - headers["Cookie"] = auth_setting["value"] - elif auth_setting["in"] == "header": - if auth_setting["type"] != "http-signature": - headers[auth_setting["key"]] = auth_setting["value"] - elif auth_setting["in"] == "query": - queries.append((auth_setting["key"], auth_setting["value"])) - else: - raise ApiValueError("Authentication token must be in `query` or `header`") - - -class Endpoint(object): - def __init__(self, settings=None, params_map=None, root_map=None, headers_map=None, api_client=None, callable=None): - """Creates an endpoint - - Args: - settings (dict): see below key value pairs - 'response_type' (tuple/None): response type - 'auth' (list): a list of auth type keys - 'endpoint_path' (str): the endpoint path - 'operation_id' (str): endpoint string identifier - 'http_method' (str): POST/PUT/PATCH/GET etc - 'servers' (list): list of str servers that this endpoint is at - params_map (dict): see below key value pairs - 'all' (list): list of str endpoint parameter names - 'required' (list): list of required parameter names - 'nullable' (list): list of nullable parameter names - 'enum' (list): list of parameters with enum values - 'validation' (list): list of parameters with validations - root_map - 'validations' (dict): the dict mapping endpoint parameter tuple - paths to their validation dictionaries - 'allowed_values' (dict): the dict mapping endpoint parameter - tuple paths to their allowed_values (enum) dictionaries - 'openapi_types' (dict): param_name to openapi type - 'attribute_map' (dict): param_name to camelCase name - 'location_map' (dict): param_name to 'body', 'file', 'form', - 'header', 'path', 'query' - collection_format_map (dict): param_name to `csv` etc. - headers_map (dict): see below key value pairs - 'accept' (list): list of Accept header strings - 'content_type' (list): list of Content-Type header strings - api_client (ApiClient) api client instance - callable (function): the function which is invoked when the - Endpoint is called + self._apply_auth_params(headers, queries, resource_path, method, body, auth_setting) + + def _apply_auth_params(self, headers, queries, resource_path, method, body, auth_setting): + """Updates the request parameters based on a single auth_setting + + :param headers: Header parameters dict to be updated. + :param queries: Query parameters tuple list to be updated. + :resource_path: A string representation of the HTTP request resource path. + :method: A string representation of the HTTP request method. + :body: A object representing the body of the HTTP request. + The object type is the return value of sanitize_for_serialization(). + :param auth_setting: auth settings for the endpoint """ - self.settings = settings - self.params_map = params_map - self.params_map["all"].extend([ - "async_req", - "_host_index", - "_preload_content", - "_request_timeout", - "_return_http_data_only", - "_check_input_type", - "_check_return_type", - "_content_type", - "_spec_property_naming", - ]) - self.params_map["nullable"].extend(["_request_timeout"]) - self.validations = root_map["validations"] - self.allowed_values = root_map["allowed_values"] - self.openapi_types = root_map["openapi_types"] - extra_types = { - "async_req": (bool,), - "_host_index": (none_type, int), - "_preload_content": (bool,), - "_request_timeout": (none_type, float, (float,), [float], int, (int,), [int]), - "_return_http_data_only": (bool,), - "_check_input_type": (bool,), - "_check_return_type": (bool,), - "_spec_property_naming": (bool,), - "_content_type": (none_type, str), - } - self.openapi_types.update(extra_types) - self.attribute_map = root_map["attribute_map"] - self.location_map = root_map["location_map"] - self.collection_format_map = root_map["collection_format_map"] - self.headers_map = headers_map - self.api_client = api_client - self.callable = callable - - def __validate_inputs(self, kwargs): - for param in self.params_map["enum"]: - if param in kwargs: - check_allowed_values(self.allowed_values, (param,), kwargs[param]) - - for param in self.params_map["validation"]: - if param in kwargs: - check_validations( - self.validations, (param,), kwargs[param], configuration=self.api_client.configuration - ) - - if kwargs["_check_input_type"] is False: - return + if auth_setting["in"] == "cookie": + headers["Cookie"] = auth_setting["value"] + elif auth_setting["in"] == "header": + if auth_setting["type"] != "http-signature": + headers[auth_setting["key"]] = auth_setting["value"] + elif auth_setting["in"] == "query": + queries.append((auth_setting["key"], auth_setting["value"])) + else: + raise ApiValueError("Authentication token must be in `query` or `header`") - for key, value in kwargs.items(): - fixed_val = validate_and_convert_types( - value, - self.openapi_types[key], - [key], - kwargs["_spec_property_naming"], - kwargs["_check_input_type"], - configuration=self.api_client.configuration, - ) - kwargs[key] = fixed_val - - def __gather_params(self, kwargs): - params = {"body": None, "collection_format": {}, "file": {}, "form": [], "header": {}, "path": {}, "query": []} - - for param_name, param_value in kwargs.items(): - param_location = self.location_map.get(param_name) - if param_location is None: - continue - if param_location: - if param_location == "body": - params["body"] = param_value - continue - base_name = self.attribute_map[param_name] - if param_location == "form" and self.openapi_types[param_name] == (file_type,): - params["file"][base_name] = [param_value] - elif param_location == "form" and self.openapi_types[param_name] == ([file_type],): - # param_value is already a list - params["file"][base_name] = param_value - elif param_location in {"form", "query"}: - param_value_full = (base_name, param_value) - params[param_location].append(param_value_full) - if param_location not in {"form", "query"}: - params[param_location][base_name] = param_value - collection_format = self.collection_format_map.get(param_name) - if collection_format: - params["collection_format"][base_name] = collection_format + def __deserialize_file(self, response): + """Deserializes body to file - return params + Saves response body into a file in a temporary folder, + using the filename from the `Content-Disposition` header if provided. + + :param response: RESTResponse. + :return: file path. + """ + fd, path = tempfile.mkstemp(dir=self.configuration.temp_folder_path) + os.close(fd) + os.remove(path) + + content_disposition = response.getheader("Content-Disposition") + if content_disposition: + filename = re.search(r'filename=[\'"]?([^\'"\s]+)[\'"]?', content_disposition).group(1) + path = os.path.join(os.path.dirname(path), filename) + + with open(path, "wb") as f: + f.write(response.data) + + return path + + def __deserialize_primitive(self, data, klass): + """Deserializes string to primitive type. + + :param data: str. + :param klass: class literal. - def __call__(self, *args, **kwargs): - """This method is invoked when endpoints are called - Example: + :return: int, long, float, str, bool. + """ + try: + return klass(data) + except UnicodeEncodeError: + return str(data) + except TypeError: + return data - api_instance = ActionsApi() - api_instance.create_rule # this is an instance of the class Endpoint - api_instance.create_rule() # this invokes api_instance.create_rule.__call__() - which then invokes the callable functions stored in that endpoint at - api_instance.create_rule.callable or self.callable in this class + def __deserialize_object(self, value): + """Return an original value. + :return: object. """ - return self.callable(self, *args, **kwargs) + return value - def call_with_http_info(self, **kwargs): + def __deserialize_date(self, string): + """Deserializes string to date. + :param string: str. + :return: date. + """ try: - index = ( - self.api_client.configuration.server_operation_index.get( - self.settings["operation_id"], self.api_client.configuration.server_index - ) - if kwargs["_host_index"] is None - else kwargs["_host_index"] - ) - server_variables = self.api_client.configuration.server_operation_variables.get( - self.settings["operation_id"], self.api_client.configuration.server_variables - ) - _host = self.api_client.configuration.get_host_from_settings( - index, variables=server_variables, servers=self.settings["servers"] - ) - except IndexError: - if self.settings["servers"]: - raise ApiValueError("Invalid host index. Must be 0 <= index < %s" % len(self.settings["servers"])) - _host = None - - for key, value in kwargs.items(): - if key not in self.params_map["all"]: - raise ApiTypeError( - "Got an unexpected parameter '%s' to method `%s`" % (key, self.settings["operation_id"]) - ) - # only throw this nullable ApiValueError if _check_input_type - # is False, if _check_input_type==True we catch this case - # in self.__validate_inputs - if key not in self.params_map["nullable"] and value is None and kwargs["_check_input_type"] is False: - raise ApiValueError( - "Value may not be None for non-nullable parameter `%s` when calling `%s`" - % (key, self.settings["operation_id"]) - ) - - for key in self.params_map["required"]: - if key not in kwargs.keys(): - raise ApiValueError( - "Missing the required parameter `%s` when calling `%s`" % (key, self.settings["operation_id"]) - ) - - self.__validate_inputs(kwargs) - - params = self.__gather_params(kwargs) - - accept_headers_list = self.headers_map["accept"] - if accept_headers_list: - params["header"]["Accept"] = self.api_client.select_header_accept(accept_headers_list) - - if kwargs.get("_content_type"): - params["header"]["Content-Type"] = kwargs["_content_type"] - else: - content_type_headers_list = self.headers_map["content_type"] - if content_type_headers_list: - if params["body"] != "": - header_list = self.api_client.select_header_content_type( - content_type_headers_list, self.settings["http_method"], params["body"] - ) - params["header"]["Content-Type"] = header_list - - return self.api_client.call_api( - self.settings["endpoint_path"], - self.settings["http_method"], - params["path"], - params["query"], - params["header"], - body=params["body"], - post_params=params["form"], - files=params["file"], - response_type=self.settings["response_type"], - auth_settings=self.settings["auth"], - async_req=kwargs["async_req"], - _check_type=kwargs["_check_return_type"], - _return_http_data_only=kwargs["_return_http_data_only"], - _preload_content=kwargs["_preload_content"], - _request_timeout=kwargs["_request_timeout"], - _host=_host, - collection_formats=params["collection_format"], - ) + return parse(string).date() + except ImportError: + return string + except ValueError: + raise rest.ApiException(status=0, reason="Failed to parse `{0}` as date object".format(string)) + + def __deserialize_datetime(self, string): + """Deserializes string to datetime. + + The string should be in iso8601 datetime format. + + :param string: str. + :return: datetime. + """ + try: + return parse(string) + except ImportError: + return string + except ValueError: + raise rest.ApiException(status=0, reason="Failed to parse `{0}` as datetime object".format(string)) + + def __deserialize_model(self, data, klass): + """Deserializes list or dict to model. + + :param data: dict, list. + :param klass: class literal. + :return: model object. + """ + + return klass.from_dict(data) diff --git a/generated/groundlight_openapi_client/api_response.py b/generated/groundlight_openapi_client/api_response.py new file mode 100644 index 00000000..7bc03cb6 --- /dev/null +++ b/generated/groundlight_openapi_client/api_response.py @@ -0,0 +1,22 @@ +"""API response object.""" + +from __future__ import annotations +from typing import Any, Dict, Optional +from pydantic import Field, StrictInt, StrictStr + + +class ApiResponse: + """ + API response object + """ + + status_code: Optional[StrictInt] = Field(None, description="HTTP status code") + headers: Optional[Dict[StrictStr, StrictStr]] = Field(None, description="HTTP headers") + data: Optional[Any] = Field(None, description="Deserialized data given the data type") + raw_data: Optional[Any] = Field(None, description="Raw data (HTTP response body)") + + def __init__(self, status_code=None, headers=None, data=None, raw_data=None) -> None: + self.status_code = status_code + self.headers = headers + self.data = data + self.raw_data = raw_data diff --git a/generated/groundlight_openapi_client/apis/__init__.py b/generated/groundlight_openapi_client/apis/__init__.py deleted file mode 100644 index 28edf7d0..00000000 --- a/generated/groundlight_openapi_client/apis/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -# flake8: noqa - -# Import all APIs into this package. -# If you have many APIs here with many many models used in each API this may -# raise a `RecursionError`. -# In order to avoid this, import only the API that you directly need like: -# -# from .api.actions_api import ActionsApi -# -# or import this package, but before doing it, use: -# -# import sys -# sys.setrecursionlimit(n) - -# Import APIs into API package: -from groundlight_openapi_client.api.actions_api import ActionsApi -from groundlight_openapi_client.api.detector_groups_api import DetectorGroupsApi -from groundlight_openapi_client.api.detector_reset_api import DetectorResetApi -from groundlight_openapi_client.api.detectors_api import DetectorsApi -from groundlight_openapi_client.api.edge_api import EdgeApi -from groundlight_openapi_client.api.image_queries_api import ImageQueriesApi -from groundlight_openapi_client.api.labels_api import LabelsApi -from groundlight_openapi_client.api.notes_api import NotesApi -from groundlight_openapi_client.api.user_api import UserApi diff --git a/generated/groundlight_openapi_client/configuration.py b/generated/groundlight_openapi_client/configuration.py index 654b832f..261f7b3b 100644 --- a/generated/groundlight_openapi_client/configuration.py +++ b/generated/groundlight_openapi_client/configuration.py @@ -1,12 +1,17 @@ +# coding: utf-8 + """ Groundlight API - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. The version of the OpenAPI document: 0.18.2 Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + import copy import logging @@ -14,9 +19,7 @@ import sys import urllib3 -from http import client as http_client -from groundlight_openapi_client.exceptions import ApiValueError - +import http.client as httplib JSON_SCHEMA_VALIDATION_KEYWORDS = { "multipleOf", @@ -32,46 +35,20 @@ } -class Configuration(object): - """NOTE: This class is auto generated by OpenAPI Generator - - Ref: https://openapi-generator.tech - Do not edit the class manually. +class Configuration: + """This class contains various settings of the API client. - :param host: Base url + :param host: Base url. :param api_key: Dict to store API key(s). Each entry in the dict specifies an API key. The dict key is the name of the security scheme in the OAS specification. The dict value is the API key secret. - :param api_key_prefix: Dict to store API prefix (e.g. Bearer) + :param api_key_prefix: Dict to store API prefix (e.g. Bearer). The dict key is the name of the security scheme in the OAS specification. The dict value is an API key prefix when generating the auth data. - :param username: Username for HTTP basic authentication - :param password: Password for HTTP basic authentication - :param discard_unknown_keys: Boolean value indicating whether to discard - unknown properties. A server may send a response that includes additional - properties that are not known by the client in the following scenarios: - 1. The OpenAPI document is incomplete, i.e. it does not match the server - implementation. - 2. The client was generated using an older version of the OpenAPI document - and the server has been upgraded since then. - If a schema in the OpenAPI document defines the additionalProperties attribute, - then all undeclared properties received by the server are injected into the - additional properties map. In that case, there are undeclared properties, and - nothing to discard. - :param disabled_client_side_validations (string): Comma-separated list of - JSON schema validation keywords to disable JSON schema structural validation - rules. The following keywords may be specified: multipleOf, maximum, - exclusiveMaximum, minimum, exclusiveMinimum, maxLength, minLength, pattern, - maxItems, minItems. - By default, the validation is performed for data generated locally by the client - and data received from the server, independent of any validation performed by - the server side. If the input data does not satisfy the JSON schema validation - rules specified in the OpenAPI document, an exception is raised. - If disabled_client_side_validations is set, structural validation is - disabled. This can be useful to troubleshoot data validation problem, such as - when the OpenAPI document validation rules do not match the actual API data - received by the server. + :param username: Username for HTTP basic authentication. + :param password: Password for HTTP basic authentication. + :param access_token: Access token. :param server_index: Index to servers configuration. :param server_variables: Mapping with string values to replace variables in templated server configuration. The validation of enums is performed for @@ -80,9 +57,10 @@ class Configuration(object): configuration. :param server_operation_variables: Mapping from operation ID to a mapping with string values to replace variables in templated server configuration. - The validation of enums is performed for variables with defined enum values before. + The validation of enums is performed for variables with defined enum + values before. :param ssl_ca_cert: str - the path to a file of concatenated CA certificates - in PEM format + in PEM format. :Example: @@ -113,17 +91,15 @@ def __init__( host=None, api_key=None, api_key_prefix=None, - access_token=None, username=None, password=None, - discard_unknown_keys=False, - disabled_client_side_validations="", + access_token=None, server_index=None, server_variables=None, server_operation_index=None, server_operation_variables=None, ssl_ca_cert=None, - ): + ) -> None: """Constructor""" self._base_path = "https://api.groundlight.ai/device-api" if host is None else host """Default Base url @@ -140,7 +116,6 @@ def __init__( """Temp file folder for downloading files """ # Authentication Settings - self.access_token = access_token self.api_key = {} if api_key: self.api_key = api_key @@ -160,8 +135,9 @@ def __init__( self.password = password """Password for HTTP basic authentication """ - self.discard_unknown_keys = discard_unknown_keys - self.disabled_client_side_validations = disabled_client_side_validations + self.access_token = access_token + """Access token + """ self.logger = {} """Logging Settings """ @@ -200,6 +176,10 @@ def __init__( self.assert_hostname = None """Set this to True/False to enable/disable SSL hostname verification. """ + self.tls_server_name = None + """SSL/TLS Server Name Indication (SNI) + Set this to the SNI value expected by the server. + """ self.connection_pool_maxsize = multiprocessing.cpu_count() * 5 """urllib3 connection pool's maximum number of connections saved @@ -212,9 +192,6 @@ def __init__( self.proxy = None """Proxy URL """ - self.no_proxy = None - """bypass proxy for host in the no_proxy list. - """ self.proxy_headers = None """Proxy headers """ @@ -227,8 +204,17 @@ def __init__( # Enable client side validation self.client_side_validation = True - # Options to pass down to the underlying urllib3 socket self.socket_options = None + """Options to pass down to the underlying urllib3 socket + """ + + self.datetime_format = "%Y-%m-%dT%H:%M:%S.%f%z" + """datetime format + """ + + self.date_format = "%Y-%m-%d" + """date format + """ def __deepcopy__(self, memo): cls = self.__class__ @@ -246,12 +232,6 @@ def __deepcopy__(self, memo): def __setattr__(self, name, value): object.__setattr__(self, name, value) - if name == "disabled_client_side_validations": - s = set(filter(None, value.split(","))) - for v in s: - if v not in JSON_SCHEMA_VALIDATION_KEYWORDS: - raise ApiValueError("Invalid keyword: '{0}''".format(v)) - self._disabled_client_side_validations = s @classmethod def set_default(cls, default): @@ -262,21 +242,31 @@ def set_default(cls, default): :param default: object of Configuration """ - cls._default = copy.deepcopy(default) + cls._default = default @classmethod def get_default_copy(cls): - """Return new instance of configuration. + """Deprecated. Please use `get_default` instead. + + Deprecated. Please use `get_default` instead. + + :return: The configuration object. + """ + return cls.get_default() + + @classmethod + def get_default(cls): + """Return the default configuration. This method returns newly created, based on default constructor, object of Configuration class or returns a copy of default - configuration passed by the set_default method. + configuration. :return: The configuration object. """ - if cls._default is not None: - return copy.deepcopy(cls._default) - return Configuration() + if cls._default is None: + cls._default = Configuration() + return cls._default @property def logger_file(self): @@ -330,15 +320,15 @@ def debug(self, value): # if debug status is True, turn on debug logging for _, logger in self.logger.items(): logger.setLevel(logging.DEBUG) - # turn on http_client debug - http_client.HTTPConnection.debuglevel = 1 + # turn on httplib debug + httplib.HTTPConnection.debuglevel = 1 else: # if debug status is False, turn off debug logging, # setting log level to default `logging.WARNING` for _, logger in self.logger.items(): logger.setLevel(logging.WARNING) - # turn off http_client debug - http_client.HTTPConnection.debuglevel = 0 + # turn off httplib debug + httplib.HTTPConnection.debuglevel = 0 @property def logger_format(self): diff --git a/generated/groundlight_openapi_client/exceptions.py b/generated/groundlight_openapi_client/exceptions.py index 393dbba8..8686286a 100644 --- a/generated/groundlight_openapi_client/exceptions.py +++ b/generated/groundlight_openapi_client/exceptions.py @@ -1,12 +1,16 @@ +# coding: utf-8 + """ Groundlight API - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. The version of the OpenAPI document: 0.18.2 Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 class OpenApiException(Exception): @@ -14,7 +18,7 @@ class OpenApiException(Exception): class ApiTypeError(OpenApiException, TypeError): - def __init__(self, msg, path_to_item=None, valid_classes=None, key_type=None): + def __init__(self, msg, path_to_item=None, valid_classes=None, key_type=None) -> None: """Raises an exception for TypeErrors Args: @@ -42,7 +46,7 @@ def __init__(self, msg, path_to_item=None, valid_classes=None, key_type=None): class ApiValueError(OpenApiException, ValueError): - def __init__(self, msg, path_to_item=None): + def __init__(self, msg, path_to_item=None) -> None: """ Args: msg (str): the exception message @@ -60,7 +64,7 @@ def __init__(self, msg, path_to_item=None): class ApiAttributeError(OpenApiException, AttributeError): - def __init__(self, msg, path_to_item=None): + def __init__(self, msg, path_to_item=None) -> None: """ Raised when an attribute reference or assignment fails. @@ -79,7 +83,7 @@ def __init__(self, msg, path_to_item=None): class ApiKeyError(OpenApiException, KeyError): - def __init__(self, msg, path_to_item=None): + def __init__(self, msg, path_to_item=None) -> None: """ Args: msg (str): the exception message @@ -96,7 +100,7 @@ def __init__(self, msg, path_to_item=None): class ApiException(OpenApiException): - def __init__(self, status=None, reason=None, http_resp=None): + def __init__(self, status=None, reason=None, http_resp=None) -> None: if http_resp: self.status = http_resp.status self.reason = http_resp.reason @@ -120,23 +124,28 @@ def __str__(self): return error_message +class BadRequestException(ApiException): + def __init__(self, status=None, reason=None, http_resp=None) -> None: + super(BadRequestException, self).__init__(status, reason, http_resp) + + class NotFoundException(ApiException): - def __init__(self, status=None, reason=None, http_resp=None): + def __init__(self, status=None, reason=None, http_resp=None) -> None: super(NotFoundException, self).__init__(status, reason, http_resp) class UnauthorizedException(ApiException): - def __init__(self, status=None, reason=None, http_resp=None): + def __init__(self, status=None, reason=None, http_resp=None) -> None: super(UnauthorizedException, self).__init__(status, reason, http_resp) class ForbiddenException(ApiException): - def __init__(self, status=None, reason=None, http_resp=None): + def __init__(self, status=None, reason=None, http_resp=None) -> None: super(ForbiddenException, self).__init__(status, reason, http_resp) class ServiceException(ApiException): - def __init__(self, status=None, reason=None, http_resp=None): + def __init__(self, status=None, reason=None, http_resp=None) -> None: super(ServiceException, self).__init__(status, reason, http_resp) diff --git a/generated/groundlight_openapi_client/model/__init__.py b/generated/groundlight_openapi_client/model/__init__.py deleted file mode 100644 index cfe32b78..00000000 --- a/generated/groundlight_openapi_client/model/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -# we can not import model classes here because that would create a circular -# reference which would not work in python2 -# do not import all models into this module because that uses a lot of memory and stack frames -# if you need the ability to import all models from one package, import them with -# from {{packageName}.models import ModelA, ModelB diff --git a/generated/groundlight_openapi_client/model/action.py b/generated/groundlight_openapi_client/model/action.py deleted file mode 100644 index 8308d44b..00000000 --- a/generated/groundlight_openapi_client/model/action.py +++ /dev/null @@ -1,294 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.channel_enum import ChannelEnum - - globals()["ChannelEnum"] = ChannelEnum - - -class Action(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "channel": (ChannelEnum,), # noqa: E501 - "recipient": (str,), # noqa: E501 - "include_image": (bool,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "channel": "channel", # noqa: E501 - "recipient": "recipient", # noqa: E501 - "include_image": "include_image", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, channel, recipient, include_image, *args, **kwargs): # noqa: E501 - """Action - a model defined in OpenAPI - - Args: - channel (ChannelEnum): - recipient (str): - include_image (bool): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.channel = channel - self.recipient = recipient - self.include_image = include_image - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, channel, recipient, include_image, *args, **kwargs): # noqa: E501 - """Action - a model defined in OpenAPI - - Args: - channel (ChannelEnum): - recipient (str): - include_image (bool): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.channel = channel - self.recipient = recipient - self.include_image = include_image - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/action_list.py b/generated/groundlight_openapi_client/model/action_list.py deleted file mode 100644 index a38fae04..00000000 --- a/generated/groundlight_openapi_client/model/action_list.py +++ /dev/null @@ -1,285 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.action import Action - - globals()["Action"] = Action - - -class ActionList(ModelSimple): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - additional_properties_type = None - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "value": ([Action],), - } - - @cached_property - def discriminator(): - return None - - attribute_map = {} - - read_only_vars = set() - - _composed_schemas = None - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): - """ActionList - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] ([Action]): # noqa: E501 - - Keyword Args: - value ([Action]): # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): - """ActionList - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] ([Action]): # noqa: E501 - - Keyword Args: - value ([Action]): # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - return self diff --git a/generated/groundlight_openapi_client/model/action_request.py b/generated/groundlight_openapi_client/model/action_request.py deleted file mode 100644 index 921399b4..00000000 --- a/generated/groundlight_openapi_client/model/action_request.py +++ /dev/null @@ -1,298 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.15.3 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.channel_enum import ChannelEnum - - globals()["ChannelEnum"] = ChannelEnum - - -class ActionRequest(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("recipient",): { - "min_length": 1, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "channel": (ChannelEnum,), # noqa: E501 - "recipient": (str,), # noqa: E501 - "include_image": (bool,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "channel": "channel", # noqa: E501 - "recipient": "recipient", # noqa: E501 - "include_image": "include_image", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, channel, recipient, include_image, *args, **kwargs): # noqa: E501 - """ActionRequest - a model defined in OpenAPI - - Args: - channel (ChannelEnum): - recipient (str): - include_image (bool): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.channel = channel - self.recipient = recipient - self.include_image = include_image - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, channel, recipient, include_image, *args, **kwargs): # noqa: E501 - """ActionRequest - a model defined in OpenAPI - - Args: - channel (ChannelEnum): - recipient (str): - include_image (bool): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.channel = channel - self.recipient = recipient - self.include_image = include_image - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/all_notes.py b/generated/groundlight_openapi_client/model/all_notes.py deleted file mode 100644 index 89da5af1..00000000 --- a/generated/groundlight_openapi_client/model/all_notes.py +++ /dev/null @@ -1,288 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.note import Note - - globals()["Note"] = Note - - -class AllNotes(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "customer": ([Note],), # noqa: E501 - "gl": ([Note],), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "customer": "CUSTOMER", # noqa: E501 - "gl": "GL", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, customer, gl, *args, **kwargs): # noqa: E501 - """AllNotes - a model defined in OpenAPI - - Args: - customer ([Note]): - gl ([Note]): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.customer = customer - self.gl = gl - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, customer, gl, *args, **kwargs): # noqa: E501 - """AllNotes - a model defined in OpenAPI - - Args: - customer ([Note]): - gl ([Note]): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.customer = customer - self.gl = gl - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/annotations_requested_enum.py b/generated/groundlight_openapi_client/model/annotations_requested_enum.py deleted file mode 100644 index 1e1f6b8f..00000000 --- a/generated/groundlight_openapi_client/model/annotations_requested_enum.py +++ /dev/null @@ -1,283 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class AnnotationsRequestedEnum(ModelSimple): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("value",): { - "BINARY_CLASSIFICATION": "BINARY_CLASSIFICATION", - "BOUNDING_BOXES": "BOUNDING_BOXES", - }, - } - - validations = {} - - additional_properties_type = None - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "value": (str,), - } - - @cached_property - def discriminator(): - return None - - attribute_map = {} - - read_only_vars = set() - - _composed_schemas = None - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): - """AnnotationsRequestedEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["BINARY_CLASSIFICATION", "BOUNDING_BOXES", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["BINARY_CLASSIFICATION", "BOUNDING_BOXES", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): - """AnnotationsRequestedEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["BINARY_CLASSIFICATION", "BOUNDING_BOXES", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["BINARY_CLASSIFICATION", "BOUNDING_BOXES", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - return self diff --git a/generated/groundlight_openapi_client/model/b_box_geometry.py b/generated/groundlight_openapi_client/model/b_box_geometry.py deleted file mode 100644 index 2282dfd7..00000000 --- a/generated/groundlight_openapi_client/model/b_box_geometry.py +++ /dev/null @@ -1,302 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class BBoxGeometry(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "left": (float,), # noqa: E501 - "top": (float,), # noqa: E501 - "right": (float,), # noqa: E501 - "bottom": (float,), # noqa: E501 - "x": (float,), # noqa: E501 - "y": (float,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "left": "left", # noqa: E501 - "top": "top", # noqa: E501 - "right": "right", # noqa: E501 - "bottom": "bottom", # noqa: E501 - "x": "x", # noqa: E501 - "y": "y", # noqa: E501 - } - - read_only_vars = { - "x", # noqa: E501 - "y", # noqa: E501 - } - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, left, top, right, bottom, x, y, *args, **kwargs): # noqa: E501 - """BBoxGeometry - a model defined in OpenAPI - - Args: - left (float): - top (float): - right (float): - bottom (float): - x (float): - y (float): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.left = left - self.top = top - self.right = right - self.bottom = bottom - self.x = x - self.y = y - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, left, top, right, bottom, *args, **kwargs): # noqa: E501 - """BBoxGeometry - a model defined in OpenAPI - - Args: - left (float): - top (float): - right (float): - bottom (float): - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.left = left - self.top = top - self.right = right - self.bottom = bottom - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/b_box_geometry_request.py b/generated/groundlight_openapi_client/model/b_box_geometry_request.py deleted file mode 100644 index fcac579f..00000000 --- a/generated/groundlight_openapi_client/model/b_box_geometry_request.py +++ /dev/null @@ -1,292 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class BBoxGeometryRequest(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "left": (float,), # noqa: E501 - "top": (float,), # noqa: E501 - "right": (float,), # noqa: E501 - "bottom": (float,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "left": "left", # noqa: E501 - "top": "top", # noqa: E501 - "right": "right", # noqa: E501 - "bottom": "bottom", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, left, top, right, bottom, *args, **kwargs): # noqa: E501 - """BBoxGeometryRequest - a model defined in OpenAPI - - Args: - left (float): - top (float): - right (float): - bottom (float): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.left = left - self.top = top - self.right = right - self.bottom = bottom - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, left, top, right, bottom, *args, **kwargs): # noqa: E501 - """BBoxGeometryRequest - a model defined in OpenAPI - - Args: - left (float): - top (float): - right (float): - bottom (float): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.left = left - self.top = top - self.right = right - self.bottom = bottom - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/binary_classification_result.py b/generated/groundlight_openapi_client/model/binary_classification_result.py deleted file mode 100644 index 018a6f3e..00000000 --- a/generated/groundlight_openapi_client/model/binary_classification_result.py +++ /dev/null @@ -1,302 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class BinaryClassificationResult(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("result_type",): { - "BINARY_CLASSIFICATION": "binary_classification", - }, - } - - validations = { - ("confidence",): { - "inclusive_maximum": 1.0, - "inclusive_minimum": 0.0, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "label": (str,), # noqa: E501 - "confidence": ( - float, - none_type, - ), # noqa: E501 - "source": (str,), # noqa: E501 - "result_type": (str,), # noqa: E501 - "from_edge": (bool,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "label": "label", # noqa: E501 - "confidence": "confidence", # noqa: E501 - "source": "source", # noqa: E501 - "result_type": "result_type", # noqa: E501 - "from_edge": "from_edge", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, label, *args, **kwargs): # noqa: E501 - """BinaryClassificationResult - a model defined in OpenAPI - - Args: - label (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - confidence (float, none_type): [optional] # noqa: E501 - source (str): [optional] # noqa: E501 - result_type (str): [optional] if omitted the server will use the default value of "binary_classification" # noqa: E501 - from_edge (bool): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.label = label - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, label, *args, **kwargs): # noqa: E501 - """BinaryClassificationResult - a model defined in OpenAPI - - Args: - label (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - confidence (float, none_type): [optional] # noqa: E501 - source (str): [optional] # noqa: E501 - result_type (str): [optional] if omitted the server will use the default value of "binary_classification" # noqa: E501 - from_edge (bool): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.label = label - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/blank_enum.py b/generated/groundlight_openapi_client/model/blank_enum.py deleted file mode 100644 index aa466bb8..00000000 --- a/generated/groundlight_openapi_client/model/blank_enum.py +++ /dev/null @@ -1,274 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class BlankEnum(ModelSimple): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("value",): { - "EMPTY": "", - }, - } - - validations = {} - - additional_properties_type = None - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "value": (str,), - } - - @cached_property - def discriminator(): - return None - - attribute_map = {} - - read_only_vars = set() - - _composed_schemas = None - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): - """BlankEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str): if omitted defaults to "", must be one of ["", ] # noqa: E501 - - Keyword Args: - value (str): if omitted defaults to "", must be one of ["", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - value = "" - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): - """BlankEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str): if omitted defaults to "", must be one of ["", ] # noqa: E501 - - Keyword Args: - value (str): if omitted defaults to "", must be one of ["", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - value = "" - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - return self diff --git a/generated/groundlight_openapi_client/model/bounding_box_mode_configuration.py b/generated/groundlight_openapi_client/model/bounding_box_mode_configuration.py deleted file mode 100644 index bcf98f27..00000000 --- a/generated/groundlight_openapi_client/model/bounding_box_mode_configuration.py +++ /dev/null @@ -1,283 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class BoundingBoxModeConfiguration(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("max_num_bboxes",): { - "inclusive_maximum": 50, - "inclusive_minimum": 1, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "class_name": (str,), # noqa: E501 - "max_num_bboxes": (int,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "class_name": "class_name", # noqa: E501 - "max_num_bboxes": "max_num_bboxes", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, class_name, *args, **kwargs): # noqa: E501 - """BoundingBoxModeConfiguration - a model defined in OpenAPI - - Args: - class_name (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - max_num_bboxes (int): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.class_name = class_name - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, class_name, *args, **kwargs): # noqa: E501 - """BoundingBoxModeConfiguration - a model defined in OpenAPI - - Args: - class_name (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - max_num_bboxes (int): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.class_name = class_name - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/bounding_box_result.py b/generated/groundlight_openapi_client/model/bounding_box_result.py deleted file mode 100644 index 8fc4c417..00000000 --- a/generated/groundlight_openapi_client/model/bounding_box_result.py +++ /dev/null @@ -1,302 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class BoundingBoxResult(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("result_type",): { - "BOUNDING_BOX": "bounding_box", - }, - } - - validations = { - ("confidence",): { - "inclusive_maximum": 1.0, - "inclusive_minimum": 0.0, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "label": (str,), # noqa: E501 - "confidence": ( - float, - none_type, - ), # noqa: E501 - "source": (str,), # noqa: E501 - "result_type": (str,), # noqa: E501 - "from_edge": (bool,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "label": "label", # noqa: E501 - "confidence": "confidence", # noqa: E501 - "source": "source", # noqa: E501 - "result_type": "result_type", # noqa: E501 - "from_edge": "from_edge", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, label, *args, **kwargs): # noqa: E501 - """BoundingBoxResult - a model defined in OpenAPI - - Args: - label (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - confidence (float, none_type): [optional] # noqa: E501 - source (str): [optional] # noqa: E501 - result_type (str): [optional] if omitted the server will use the default value of "bounding_box" # noqa: E501 - from_edge (bool): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.label = label - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, label, *args, **kwargs): # noqa: E501 - """BoundingBoxResult - a model defined in OpenAPI - - Args: - label (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - confidence (float, none_type): [optional] # noqa: E501 - source (str): [optional] # noqa: E501 - result_type (str): [optional] if omitted the server will use the default value of "bounding_box" # noqa: E501 - from_edge (bool): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.label = label - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/channel_enum.py b/generated/groundlight_openapi_client/model/channel_enum.py deleted file mode 100644 index 720dac0d..00000000 --- a/generated/groundlight_openapi_client/model/channel_enum.py +++ /dev/null @@ -1,283 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class ChannelEnum(ModelSimple): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("value",): { - "TEXT": "TEXT", - "EMAIL": "EMAIL", - }, - } - - validations = {} - - additional_properties_type = None - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "value": (str,), - } - - @cached_property - def discriminator(): - return None - - attribute_map = {} - - read_only_vars = set() - - _composed_schemas = None - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): - """ChannelEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["TEXT", "EMAIL", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["TEXT", "EMAIL", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): - """ChannelEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["TEXT", "EMAIL", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["TEXT", "EMAIL", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - return self diff --git a/generated/groundlight_openapi_client/model/condition.py b/generated/groundlight_openapi_client/model/condition.py deleted file mode 100644 index 65a2861f..00000000 --- a/generated/groundlight_openapi_client/model/condition.py +++ /dev/null @@ -1,280 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class Condition(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "verb": (str,), # noqa: E501 - "parameters": ({str: (bool, date, datetime, dict, float, int, list, str, none_type)},), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "verb": "verb", # noqa: E501 - "parameters": "parameters", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, verb, parameters, *args, **kwargs): # noqa: E501 - """Condition - a model defined in OpenAPI - - Args: - verb (str): - parameters ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.verb = verb - self.parameters = parameters - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, verb, parameters, *args, **kwargs): # noqa: E501 - """Condition - a model defined in OpenAPI - - Args: - verb (str): - parameters ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.verb = verb - self.parameters = parameters - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/condition_request.py b/generated/groundlight_openapi_client/model/condition_request.py deleted file mode 100644 index 674578a6..00000000 --- a/generated/groundlight_openapi_client/model/condition_request.py +++ /dev/null @@ -1,280 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class ConditionRequest(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "verb": (str,), # noqa: E501 - "parameters": ({str: (bool, date, datetime, dict, float, int, list, str, none_type)},), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "verb": "verb", # noqa: E501 - "parameters": "parameters", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, verb, parameters, *args, **kwargs): # noqa: E501 - """ConditionRequest - a model defined in OpenAPI - - Args: - verb (str): - parameters ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.verb = verb - self.parameters = parameters - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, verb, parameters, *args, **kwargs): # noqa: E501 - """ConditionRequest - a model defined in OpenAPI - - Args: - verb (str): - parameters ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.verb = verb - self.parameters = parameters - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/count_mode_configuration.py b/generated/groundlight_openapi_client/model/count_mode_configuration.py deleted file mode 100644 index 9c2d3be9..00000000 --- a/generated/groundlight_openapi_client/model/count_mode_configuration.py +++ /dev/null @@ -1,283 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class CountModeConfiguration(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("max_count",): { - "inclusive_maximum": 50, - "inclusive_minimum": 1, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "class_name": (str,), # noqa: E501 - "max_count": (int,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "class_name": "class_name", # noqa: E501 - "max_count": "max_count", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, class_name, *args, **kwargs): # noqa: E501 - """CountModeConfiguration - a model defined in OpenAPI - - Args: - class_name (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - max_count (int): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.class_name = class_name - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, class_name, *args, **kwargs): # noqa: E501 - """CountModeConfiguration - a model defined in OpenAPI - - Args: - class_name (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - max_count (int): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.class_name = class_name - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/count_mode_configuration_serializer.py b/generated/groundlight_openapi_client/model/count_mode_configuration_serializer.py deleted file mode 100644 index 5b2d0dda..00000000 --- a/generated/groundlight_openapi_client/model/count_mode_configuration_serializer.py +++ /dev/null @@ -1,280 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.15.3 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class CountModeConfigurationSerializer(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "id": (str,), # noqa: E501 - "max_count": (int,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "id": "id", # noqa: E501 - "max_count": "max_count", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, id, max_count, *args, **kwargs): # noqa: E501 - """CountModeConfigurationSerializer - a model defined in OpenAPI - - Args: - id (str): - max_count (int): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.id = id - self.max_count = max_count - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, id, max_count, *args, **kwargs): # noqa: E501 - """CountModeConfigurationSerializer - a model defined in OpenAPI - - Args: - id (str): - max_count (int): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.id = id - self.max_count = max_count - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/counting_result.py b/generated/groundlight_openapi_client/model/counting_result.py deleted file mode 100644 index 85e692e5..00000000 --- a/generated/groundlight_openapi_client/model/counting_result.py +++ /dev/null @@ -1,312 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class CountingResult(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("result_type",): { - "COUNTING": "counting", - }, - } - - validations = { - ("count",): { - "inclusive_minimum": 0, - }, - ("confidence",): { - "inclusive_maximum": 1.0, - "inclusive_minimum": 0.0, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "count": ( - int, - none_type, - ), # noqa: E501 - "confidence": ( - float, - none_type, - ), # noqa: E501 - "source": (str,), # noqa: E501 - "result_type": (str,), # noqa: E501 - "from_edge": (bool,), # noqa: E501 - "greater_than_max": (bool,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "count": "count", # noqa: E501 - "confidence": "confidence", # noqa: E501 - "source": "source", # noqa: E501 - "result_type": "result_type", # noqa: E501 - "from_edge": "from_edge", # noqa: E501 - "greater_than_max": "greater_than_max", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, count, *args, **kwargs): # noqa: E501 - """CountingResult - a model defined in OpenAPI - - Args: - count (int, none_type): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - confidence (float, none_type): [optional] # noqa: E501 - source (str): [optional] # noqa: E501 - result_type (str): [optional] if omitted the server will use the default value of "counting" # noqa: E501 - from_edge (bool): [optional] # noqa: E501 - greater_than_max (bool): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.count = count - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, count, *args, **kwargs): # noqa: E501 - """CountingResult - a model defined in OpenAPI - - Args: - count (int, none_type): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - confidence (float, none_type): [optional] # noqa: E501 - source (str): [optional] # noqa: E501 - result_type (str): [optional] if omitted the server will use the default value of "counting" # noqa: E501 - from_edge (bool): [optional] # noqa: E501 - greater_than_max (bool): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.count = count - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/detector.py b/generated/groundlight_openapi_client/model/detector.py deleted file mode 100644 index 2cde7991..00000000 --- a/generated/groundlight_openapi_client/model/detector.py +++ /dev/null @@ -1,381 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.blank_enum import BlankEnum - from groundlight_openapi_client.model.detector_type_enum import DetectorTypeEnum - from groundlight_openapi_client.model.status_enum import StatusEnum - - globals()["BlankEnum"] = BlankEnum - globals()["DetectorTypeEnum"] = DetectorTypeEnum - globals()["StatusEnum"] = StatusEnum - - -class Detector(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("name",): { - "max_length": 200, - }, - ("confidence_threshold",): { - "inclusive_maximum": 1.0, - "inclusive_minimum": 0.0, - }, - ("patience_time",): { - "inclusive_maximum": 3600, - "inclusive_minimum": 0, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "id": (str,), # noqa: E501 - "type": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - "created_at": (datetime,), # noqa: E501 - "name": (str,), # noqa: E501 - "query": (str,), # noqa: E501 - "group_name": (str,), # noqa: E501 - "metadata": ( - {str: (bool, date, datetime, dict, float, int, list, str, none_type)}, - none_type, - ), # noqa: E501 - "mode": (str,), # noqa: E501 - "mode_configuration": ( - {str: (bool, date, datetime, dict, float, int, list, str, none_type)}, - none_type, - ), # noqa: E501 - "confidence_threshold": (float,), # noqa: E501 - "patience_time": (float,), # noqa: E501 - "status": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - "escalation_type": (str,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "id": "id", # noqa: E501 - "type": "type", # noqa: E501 - "created_at": "created_at", # noqa: E501 - "name": "name", # noqa: E501 - "query": "query", # noqa: E501 - "group_name": "group_name", # noqa: E501 - "metadata": "metadata", # noqa: E501 - "mode": "mode", # noqa: E501 - "mode_configuration": "mode_configuration", # noqa: E501 - "confidence_threshold": "confidence_threshold", # noqa: E501 - "patience_time": "patience_time", # noqa: E501 - "status": "status", # noqa: E501 - "escalation_type": "escalation_type", # noqa: E501 - } - - read_only_vars = { - "id", # noqa: E501 - "type", # noqa: E501 - "created_at", # noqa: E501 - "query", # noqa: E501 - "group_name", # noqa: E501 - "metadata", # noqa: E501 - "mode", # noqa: E501 - "mode_configuration", # noqa: E501 - } - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data( - cls, id, type, created_at, name, query, group_name, metadata, mode, mode_configuration, *args, **kwargs - ): # noqa: E501 - """Detector - a model defined in OpenAPI - - Args: - id (str): A unique ID for this object. - type (bool, date, datetime, dict, float, int, list, str, none_type): The type of this object. - created_at (datetime): When this detector was created. - name (str): A short, descriptive name for the detector. - query (str): A question about the image. - group_name (str): Which group should this detector be part of? - metadata ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): Metadata about the detector. - mode (str): - mode_configuration ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - confidence_threshold (float): If the detector's prediction is below this confidence threshold, send the image query for human review.. [optional] if omitted the server will use the default value of 0.9 # noqa: E501 - patience_time (float): How long Groundlight will attempt to generate a confident prediction. [optional] if omitted the server will use the default value of 30.0 # noqa: E501 - status (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - escalation_type (str): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.id = id - self.type = type - self.created_at = created_at - self.name = name - self.query = query - self.group_name = group_name - self.metadata = metadata - self.mode = mode - self.mode_configuration = mode_configuration - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, name, *args, **kwargs): # noqa: E501 - """Detector - a model defined in OpenAPI - - name (str): A short, descriptive name for the detector. - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - confidence_threshold (float): If the detector's prediction is below this confidence threshold, send the image query for human review.. [optional] if omitted the server will use the default value of 0.9 # noqa: E501 - patience_time (float): How long Groundlight will attempt to generate a confident prediction. [optional] if omitted the server will use the default value of 30.0 # noqa: E501 - status (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - escalation_type (str): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.name = name - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/detector_creation_input_request.py b/generated/groundlight_openapi_client/model/detector_creation_input_request.py deleted file mode 100644 index ca1b1563..00000000 --- a/generated/groundlight_openapi_client/model/detector_creation_input_request.py +++ /dev/null @@ -1,375 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.bounding_box_mode_configuration import BoundingBoxModeConfiguration - from groundlight_openapi_client.model.count_mode_configuration import CountModeConfiguration - from groundlight_openapi_client.model.mode_enum import ModeEnum - from groundlight_openapi_client.model.multi_class_mode_configuration import MultiClassModeConfiguration - from groundlight_openapi_client.model.text_mode_configuration import TextModeConfiguration - - globals()["BoundingBoxModeConfiguration"] = BoundingBoxModeConfiguration - globals()["CountModeConfiguration"] = CountModeConfiguration - globals()["ModeEnum"] = ModeEnum - globals()["MultiClassModeConfiguration"] = MultiClassModeConfiguration - globals()["TextModeConfiguration"] = TextModeConfiguration - - -class DetectorCreationInputRequest(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("name",): { - "max_length": 200, - "min_length": 1, - }, - ("query",): { - "max_length": 300, - "min_length": 1, - }, - ("group_name",): { - "max_length": 100, - "min_length": 1, - }, - ("confidence_threshold",): { - "inclusive_maximum": 1.0, - "inclusive_minimum": 0.0, - }, - ("patience_time",): { - "inclusive_maximum": 3600, - "inclusive_minimum": 0, - }, - ("pipeline_config",): { - "max_length": 100, - }, - ("metadata",): { - "max_length": 1362, - "min_length": 1, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "name": (str,), # noqa: E501 - "query": (str,), # noqa: E501 - "group_name": (str,), # noqa: E501 - "confidence_threshold": (float,), # noqa: E501 - "patience_time": (float,), # noqa: E501 - "pipeline_config": ( - str, - none_type, - ), # noqa: E501 - "metadata": (str,), # noqa: E501 - "mode": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - "mode_configuration": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "name": "name", # noqa: E501 - "query": "query", # noqa: E501 - "group_name": "group_name", # noqa: E501 - "confidence_threshold": "confidence_threshold", # noqa: E501 - "patience_time": "patience_time", # noqa: E501 - "pipeline_config": "pipeline_config", # noqa: E501 - "metadata": "metadata", # noqa: E501 - "mode": "mode", # noqa: E501 - "mode_configuration": "mode_configuration", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, name, query, *args, **kwargs): # noqa: E501 - """DetectorCreationInputRequest - a model defined in OpenAPI - - Args: - name (str): A short, descriptive name for the detector. - query (str): A question about the image. - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - group_name (str): Which group should this detector be part of?. [optional] # noqa: E501 - confidence_threshold (float): If the detector's prediction is below this confidence threshold, send the image query for human review.. [optional] if omitted the server will use the default value of 0.9 # noqa: E501 - patience_time (float): How long Groundlight will attempt to generate a confident prediction. [optional] if omitted the server will use the default value of 30.0 # noqa: E501 - pipeline_config (str, none_type): (Advanced usage) Configuration needed to instantiate a prediction pipeline.. [optional] # noqa: E501 - metadata (str): Base64-encoded metadata for the detector. This should be a JSON object with string keys. The size after encoding should not exceed 1362 bytes, corresponding to 1KiB before encoding.. [optional] # noqa: E501 - mode (bool, date, datetime, dict, float, int, list, str, none_type): Mode in which this detector will work. * `BINARY` - BINARY * `COUNT` - COUNT * `MULTI_CLASS` - MULTI_CLASS * `TEXT` - TEXT * `BOUNDING_BOX` - BOUNDING_BOX. [optional] # noqa: E501 - mode_configuration (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.name = name - self.query = query - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, name, query, *args, **kwargs): # noqa: E501 - """DetectorCreationInputRequest - a model defined in OpenAPI - - Args: - name (str): A short, descriptive name for the detector. - query (str): A question about the image. - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - group_name (str): Which group should this detector be part of?. [optional] # noqa: E501 - confidence_threshold (float): If the detector's prediction is below this confidence threshold, send the image query for human review.. [optional] if omitted the server will use the default value of 0.9 # noqa: E501 - patience_time (float): How long Groundlight will attempt to generate a confident prediction. [optional] if omitted the server will use the default value of 30.0 # noqa: E501 - pipeline_config (str, none_type): (Advanced usage) Configuration needed to instantiate a prediction pipeline.. [optional] # noqa: E501 - metadata (str): Base64-encoded metadata for the detector. This should be a JSON object with string keys. The size after encoding should not exceed 1362 bytes, corresponding to 1KiB before encoding.. [optional] # noqa: E501 - mode (bool, date, datetime, dict, float, int, list, str, none_type): Mode in which this detector will work. * `BINARY` - BINARY * `COUNT` - COUNT * `MULTI_CLASS` - MULTI_CLASS * `TEXT` - TEXT * `BOUNDING_BOX` - BOUNDING_BOX. [optional] # noqa: E501 - mode_configuration (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.name = name - self.query = query - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/detector_group.py b/generated/groundlight_openapi_client/model/detector_group.py deleted file mode 100644 index 5620b0c9..00000000 --- a/generated/groundlight_openapi_client/model/detector_group.py +++ /dev/null @@ -1,283 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class DetectorGroup(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("name",): { - "max_length": 100, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "id": (str,), # noqa: E501 - "name": (str,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "id": "id", # noqa: E501 - "name": "name", # noqa: E501 - } - - read_only_vars = { - "id", # noqa: E501 - } - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, id, name, *args, **kwargs): # noqa: E501 - """DetectorGroup - a model defined in OpenAPI - - Args: - id (str): - name (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.id = id - self.name = name - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, name, *args, **kwargs): # noqa: E501 - """DetectorGroup - a model defined in OpenAPI - - name (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.name = name - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/detector_group_request.py b/generated/groundlight_openapi_client/model/detector_group_request.py deleted file mode 100644 index 3302860f..00000000 --- a/generated/groundlight_openapi_client/model/detector_group_request.py +++ /dev/null @@ -1,279 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class DetectorGroupRequest(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("name",): { - "max_length": 100, - "min_length": 1, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "name": (str,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "name": "name", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, name, *args, **kwargs): # noqa: E501 - """DetectorGroupRequest - a model defined in OpenAPI - - Args: - name (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.name = name - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, name, *args, **kwargs): # noqa: E501 - """DetectorGroupRequest - a model defined in OpenAPI - - Args: - name (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.name = name - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/detector_request.py b/generated/groundlight_openapi_client/model/detector_request.py deleted file mode 100644 index c7f708f1..00000000 --- a/generated/groundlight_openapi_client/model/detector_request.py +++ /dev/null @@ -1,335 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.15.3 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.blank_enum import BlankEnum - from groundlight_openapi_client.model.escalation_type_enum import EscalationTypeEnum - from groundlight_openapi_client.model.status_enum import StatusEnum - - globals()["BlankEnum"] = BlankEnum - globals()["EscalationTypeEnum"] = EscalationTypeEnum - globals()["StatusEnum"] = StatusEnum - - -class DetectorRequest(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("name",): { - "max_length": 200, - "min_length": 1, - }, - ("confidence_threshold",): { - "inclusive_maximum": 1.0, - "inclusive_minimum": 0.0, - }, - ("patience_time",): { - "inclusive_maximum": 3600, - "inclusive_minimum": 0, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "name": (str,), # noqa: E501 - "confidence_threshold": (float,), # noqa: E501 - "patience_time": (float,), # noqa: E501 - "status": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - "escalation_type": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "name": "name", # noqa: E501 - "confidence_threshold": "confidence_threshold", # noqa: E501 - "patience_time": "patience_time", # noqa: E501 - "status": "status", # noqa: E501 - "escalation_type": "escalation_type", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, name, *args, **kwargs): # noqa: E501 - """DetectorRequest - a model defined in OpenAPI - - Args: - name (str): A short, descriptive name for the detector. - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - confidence_threshold (float): If the detector's prediction is below this confidence threshold, send the image query for human review.. [optional] if omitted the server will use the default value of 0.9 # noqa: E501 - patience_time (float): How long Groundlight will attempt to generate a confident prediction. [optional] if omitted the server will use the default value of 30.0 # noqa: E501 - status (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - escalation_type (bool, date, datetime, dict, float, int, list, str, none_type): Category that define internal proccess for labeling image queries * `STANDARD` - STANDARD * `NO_HUMAN_LABELING` - NO_HUMAN_LABELING. [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.name = name - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, name, *args, **kwargs): # noqa: E501 - """DetectorRequest - a model defined in OpenAPI - - Args: - name (str): A short, descriptive name for the detector. - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - confidence_threshold (float): If the detector's prediction is below this confidence threshold, send the image query for human review.. [optional] if omitted the server will use the default value of 0.9 # noqa: E501 - patience_time (float): How long Groundlight will attempt to generate a confident prediction. [optional] if omitted the server will use the default value of 30.0 # noqa: E501 - status (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - escalation_type (bool, date, datetime, dict, float, int, list, str, none_type): Category that define internal proccess for labeling image queries * `STANDARD` - STANDARD * `NO_HUMAN_LABELING` - NO_HUMAN_LABELING. [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.name = name - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/detector_type_enum.py b/generated/groundlight_openapi_client/model/detector_type_enum.py deleted file mode 100644 index 94d446da..00000000 --- a/generated/groundlight_openapi_client/model/detector_type_enum.py +++ /dev/null @@ -1,274 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class DetectorTypeEnum(ModelSimple): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("value",): { - "DETECTOR": "detector", - }, - } - - validations = {} - - additional_properties_type = None - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "value": (str,), - } - - @cached_property - def discriminator(): - return None - - attribute_map = {} - - read_only_vars = set() - - _composed_schemas = None - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): - """DetectorTypeEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str): if omitted defaults to "detector", must be one of ["detector", ] # noqa: E501 - - Keyword Args: - value (str): if omitted defaults to "detector", must be one of ["detector", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - value = "detector" - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): - """DetectorTypeEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str): if omitted defaults to "detector", must be one of ["detector", ] # noqa: E501 - - Keyword Args: - value (str): if omitted defaults to "detector", must be one of ["detector", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - value = "detector" - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - return self diff --git a/generated/groundlight_openapi_client/model/edge_model_info.py b/generated/groundlight_openapi_client/model/edge_model_info.py deleted file mode 100644 index 66826368..00000000 --- a/generated/groundlight_openapi_client/model/edge_model_info.py +++ /dev/null @@ -1,322 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class EdgeModelInfo(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "model_binary_id": (str,), # noqa: E501 - "model_binary_url": (str,), # noqa: E501 - "oodd_model_binary_id": (str,), # noqa: E501 - "oodd_model_binary_url": (str,), # noqa: E501 - "pipeline_config": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - "oodd_pipeline_config": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - "predictor_metadata": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "model_binary_id": "model_binary_id", # noqa: E501 - "model_binary_url": "model_binary_url", # noqa: E501 - "oodd_model_binary_id": "oodd_model_binary_id", # noqa: E501 - "oodd_model_binary_url": "oodd_model_binary_url", # noqa: E501 - "pipeline_config": "pipeline_config", # noqa: E501 - "oodd_pipeline_config": "oodd_pipeline_config", # noqa: E501 - "predictor_metadata": "predictor_metadata", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 - """EdgeModelInfo - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - model_binary_id (str): [optional] # noqa: E501 - model_binary_url (str): [optional] # noqa: E501 - oodd_model_binary_id (str): [optional] # noqa: E501 - oodd_model_binary_url (str): [optional] # noqa: E501 - pipeline_config (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - oodd_pipeline_config (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - predictor_metadata (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): # noqa: E501 - """EdgeModelInfo - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - model_binary_id (str): [optional] # noqa: E501 - model_binary_url (str): [optional] # noqa: E501 - oodd_model_binary_id (str): [optional] # noqa: E501 - oodd_model_binary_url (str): [optional] # noqa: E501 - pipeline_config (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - oodd_pipeline_config (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - predictor_metadata (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/escalation_type_enum.py b/generated/groundlight_openapi_client/model/escalation_type_enum.py deleted file mode 100644 index 2b80360e..00000000 --- a/generated/groundlight_openapi_client/model/escalation_type_enum.py +++ /dev/null @@ -1,283 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class EscalationTypeEnum(ModelSimple): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("value",): { - "STANDARD": "STANDARD", - "NO_HUMAN_LABELING": "NO_HUMAN_LABELING", - }, - } - - validations = {} - - additional_properties_type = None - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "value": (str,), - } - - @cached_property - def discriminator(): - return None - - attribute_map = {} - - read_only_vars = set() - - _composed_schemas = None - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): - """EscalationTypeEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["STANDARD", "NO_HUMAN_LABELING", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["STANDARD", "NO_HUMAN_LABELING", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): - """EscalationTypeEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["STANDARD", "NO_HUMAN_LABELING", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["STANDARD", "NO_HUMAN_LABELING", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - return self diff --git a/generated/groundlight_openapi_client/model/image_query.py b/generated/groundlight_openapi_client/model/image_query.py deleted file mode 100644 index 79cf0179..00000000 --- a/generated/groundlight_openapi_client/model/image_query.py +++ /dev/null @@ -1,409 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.binary_classification_result import BinaryClassificationResult - from groundlight_openapi_client.model.bounding_box_result import BoundingBoxResult - from groundlight_openapi_client.model.counting_result import CountingResult - from groundlight_openapi_client.model.image_query_type_enum import ImageQueryTypeEnum - from groundlight_openapi_client.model.multi_classification_result import MultiClassificationResult - from groundlight_openapi_client.model.result_type_enum import ResultTypeEnum - from groundlight_openapi_client.model.roi import ROI - from groundlight_openapi_client.model.text_recognition_result import TextRecognitionResult - - globals()["BinaryClassificationResult"] = BinaryClassificationResult - globals()["BoundingBoxResult"] = BoundingBoxResult - globals()["CountingResult"] = CountingResult - globals()["ImageQueryTypeEnum"] = ImageQueryTypeEnum - globals()["MultiClassificationResult"] = MultiClassificationResult - globals()["ROI"] = ROI - globals()["ResultTypeEnum"] = ResultTypeEnum - globals()["TextRecognitionResult"] = TextRecognitionResult - - -class ImageQuery(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "metadata": ( - {str: (bool, date, datetime, dict, float, int, list, str, none_type)}, - none_type, - ), # noqa: E501 - "id": (str,), # noqa: E501 - "type": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - "created_at": (datetime,), # noqa: E501 - "query": (str,), # noqa: E501 - "detector_id": (str,), # noqa: E501 - "result_type": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - "result": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - "patience_time": (float,), # noqa: E501 - "confidence_threshold": (float,), # noqa: E501 - "rois": ( - [ROI], - none_type, - ), # noqa: E501 - "text": ( - str, - none_type, - ), # noqa: E501 - "done_processing": (bool,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "metadata": "metadata", # noqa: E501 - "id": "id", # noqa: E501 - "type": "type", # noqa: E501 - "created_at": "created_at", # noqa: E501 - "query": "query", # noqa: E501 - "detector_id": "detector_id", # noqa: E501 - "result_type": "result_type", # noqa: E501 - "result": "result", # noqa: E501 - "patience_time": "patience_time", # noqa: E501 - "confidence_threshold": "confidence_threshold", # noqa: E501 - "rois": "rois", # noqa: E501 - "text": "text", # noqa: E501 - "done_processing": "done_processing", # noqa: E501 - } - - read_only_vars = { - "metadata", # noqa: E501 - "id", # noqa: E501 - "type", # noqa: E501 - "created_at", # noqa: E501 - "query", # noqa: E501 - "detector_id", # noqa: E501 - "result_type", # noqa: E501 - "patience_time", # noqa: E501 - "confidence_threshold", # noqa: E501 - "rois", # noqa: E501 - "text", # noqa: E501 - } - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data( - cls, - metadata, - id, - type, - created_at, - query, - detector_id, - result_type, - result, - patience_time, - confidence_threshold, - rois, - text, - *args, - **kwargs, - ): # noqa: E501 - """ImageQuery - a model defined in OpenAPI - - Args: - metadata ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): Metadata about the image query. - id (str): A unique ID for this object. - type (bool, date, datetime, dict, float, int, list, str, none_type): The type of this object. - created_at (datetime): When was this detector created? - query (str): A question about the image. - detector_id (str): Which detector was used on this image query? - result_type (bool, date, datetime, dict, float, int, list, str, none_type): What type of result are we returning? - result (bool, date, datetime, dict, float, int, list, str, none_type): - patience_time (float): How long to wait for a confident response. - confidence_threshold (float): Min confidence needed to accept the response of the image query. - rois ([ROI], none_type): An array of regions of interest (bounding boxes) collected on image - text (str, none_type): A text field on image query. - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - done_processing (bool): EDGE ONLY - Whether the image query has completed escalating and will receive no new results.. [optional] if omitted the server will use the default value of False # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.metadata = metadata - self.id = id - self.type = type - self.created_at = created_at - self.query = query - self.detector_id = detector_id - self.result_type = result_type - self.result = result - self.patience_time = patience_time - self.confidence_threshold = confidence_threshold - self.rois = rois - self.text = text - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, result, *args, **kwargs): # noqa: E501 - """ImageQuery - a model defined in OpenAPI - - result (bool, date, datetime, dict, float, int, list, str, none_type): - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - done_processing (bool): EDGE ONLY - Whether the image query has completed escalating and will receive no new results.. [optional] if omitted the server will use the default value of False # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.result = result - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/image_query_type_enum.py b/generated/groundlight_openapi_client/model/image_query_type_enum.py deleted file mode 100644 index 42470730..00000000 --- a/generated/groundlight_openapi_client/model/image_query_type_enum.py +++ /dev/null @@ -1,274 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class ImageQueryTypeEnum(ModelSimple): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("value",): { - "IMAGE_QUERY": "image_query", - }, - } - - validations = {} - - additional_properties_type = None - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "value": (str,), - } - - @cached_property - def discriminator(): - return None - - attribute_map = {} - - read_only_vars = set() - - _composed_schemas = None - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): - """ImageQueryTypeEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str): if omitted defaults to "image_query", must be one of ["image_query", ] # noqa: E501 - - Keyword Args: - value (str): if omitted defaults to "image_query", must be one of ["image_query", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - value = "image_query" - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): - """ImageQueryTypeEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str): if omitted defaults to "image_query", must be one of ["image_query", ] # noqa: E501 - - Keyword Args: - value (str): if omitted defaults to "image_query", must be one of ["image_query", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - value = "image_query" - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - return self diff --git a/generated/groundlight_openapi_client/model/inline_response200.py b/generated/groundlight_openapi_client/model/inline_response200.py deleted file mode 100644 index 62e2494f..00000000 --- a/generated/groundlight_openapi_client/model/inline_response200.py +++ /dev/null @@ -1,276 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.inline_response200_summary import InlineResponse200Summary - - globals()["InlineResponse200Summary"] = InlineResponse200Summary - - -class InlineResponse200(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "summary": (InlineResponse200Summary,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "summary": "summary", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 - """InlineResponse200 - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - summary (InlineResponse200Summary): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): # noqa: E501 - """InlineResponse200 - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - summary (InlineResponse200Summary): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/inline_response2001.py b/generated/groundlight_openapi_client/model/inline_response2001.py deleted file mode 100644 index cdb30032..00000000 --- a/generated/groundlight_openapi_client/model/inline_response2001.py +++ /dev/null @@ -1,278 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.inline_response2001_evaluation_results import ( - InlineResponse2001EvaluationResults, - ) - - globals()["InlineResponse2001EvaluationResults"] = InlineResponse2001EvaluationResults - - -class InlineResponse2001(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "evaluation_results": (InlineResponse2001EvaluationResults,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "evaluation_results": "evaluation_results", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 - """InlineResponse2001 - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - evaluation_results (InlineResponse2001EvaluationResults): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): # noqa: E501 - """InlineResponse2001 - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - evaluation_results (InlineResponse2001EvaluationResults): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/inline_response2001_evaluation_results.py b/generated/groundlight_openapi_client/model/inline_response2001_evaluation_results.py deleted file mode 100644 index 9ee33a84..00000000 --- a/generated/groundlight_openapi_client/model/inline_response2001_evaluation_results.py +++ /dev/null @@ -1,391 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class InlineResponse2001EvaluationResults(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = True - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "eval_timestamp": (datetime,), # noqa: E501 - "total_ground_truth_examples": ( - int, - none_type, - ), # noqa: E501 - "total_labeled_examples": ( - int, - none_type, - ), # noqa: E501 - "kfold_pooled__balanced_accuracy": (float,), # noqa: E501 - "kfold_pooled__positive_accuracy": ( - float, - none_type, - ), # noqa: E501 - "kfold_pooled__negative_accuracy": ( - float, - none_type, - ), # noqa: E501 - "precision__mean": ( - float, - none_type, - ), # noqa: E501 - "recall__mean": ( - float, - none_type, - ), # noqa: E501 - "roc_auc__mean": ( - float, - none_type, - ), # noqa: E501 - "balanced_system_accuracies": ( - {str: (bool, date, datetime, dict, float, int, list, str, none_type)}, - none_type, - ), # noqa: E501 - "positive_system_accuracies": ( - {str: (bool, date, datetime, dict, float, int, list, str, none_type)}, - none_type, - ), # noqa: E501 - "negative_system_accuracies": ( - {str: (bool, date, datetime, dict, float, int, list, str, none_type)}, - none_type, - ), # noqa: E501 - "mean_absolute_error__mean": ( - float, - none_type, - ), # noqa: E501 - "objdet_precision__mean": ( - float, - none_type, - ), # noqa: E501 - "objdet_recall__mean": ( - float, - none_type, - ), # noqa: E501 - "objdet_f1_score__mean": ( - float, - none_type, - ), # noqa: E501 - "class_accuracies": ( - {str: (bool, date, datetime, dict, float, int, list, str, none_type)}, - none_type, - ), # noqa: E501 - "confusion_dict": ( - {str: (bool, date, datetime, dict, float, int, list, str, none_type)}, - none_type, - ), # noqa: E501 - "num_examples_per_class": ( - {str: (bool, date, datetime, dict, float, int, list, str, none_type)}, - none_type, - ), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "eval_timestamp": "eval_timestamp", # noqa: E501 - "total_ground_truth_examples": "total_ground_truth_examples", # noqa: E501 - "total_labeled_examples": "total_labeled_examples", # noqa: E501 - "kfold_pooled__balanced_accuracy": "kfold_pooled__balanced_accuracy", # noqa: E501 - "kfold_pooled__positive_accuracy": "kfold_pooled__positive_accuracy", # noqa: E501 - "kfold_pooled__negative_accuracy": "kfold_pooled__negative_accuracy", # noqa: E501 - "precision__mean": "precision__mean", # noqa: E501 - "recall__mean": "recall__mean", # noqa: E501 - "roc_auc__mean": "roc_auc__mean", # noqa: E501 - "balanced_system_accuracies": "balanced_system_accuracies", # noqa: E501 - "positive_system_accuracies": "positive_system_accuracies", # noqa: E501 - "negative_system_accuracies": "negative_system_accuracies", # noqa: E501 - "mean_absolute_error__mean": "mean_absolute_error__mean", # noqa: E501 - "objdet_precision__mean": "objdet_precision__mean", # noqa: E501 - "objdet_recall__mean": "objdet_recall__mean", # noqa: E501 - "objdet_f1_score__mean": "objdet_f1_score__mean", # noqa: E501 - "class_accuracies": "class_accuracies", # noqa: E501 - "confusion_dict": "confusion_dict", # noqa: E501 - "num_examples_per_class": "num_examples_per_class", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 - """InlineResponse2001EvaluationResults - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - eval_timestamp (datetime): [optional] # noqa: E501 - total_ground_truth_examples (int, none_type): [optional] # noqa: E501 - total_labeled_examples (int, none_type): [optional] # noqa: E501 - kfold_pooled__balanced_accuracy (float): [optional] # noqa: E501 - kfold_pooled__positive_accuracy (float, none_type): [optional] # noqa: E501 - kfold_pooled__negative_accuracy (float, none_type): [optional] # noqa: E501 - precision__mean (float, none_type): [optional] # noqa: E501 - recall__mean (float, none_type): [optional] # noqa: E501 - roc_auc__mean (float, none_type): [optional] # noqa: E501 - balanced_system_accuracies ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): [optional] # noqa: E501 - positive_system_accuracies ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): [optional] # noqa: E501 - negative_system_accuracies ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): [optional] # noqa: E501 - mean_absolute_error__mean (float, none_type): [optional] # noqa: E501 - objdet_precision__mean (float, none_type): [optional] # noqa: E501 - objdet_recall__mean (float, none_type): [optional] # noqa: E501 - objdet_f1_score__mean (float, none_type): [optional] # noqa: E501 - class_accuracies ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): [optional] # noqa: E501 - confusion_dict ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): [optional] # noqa: E501 - num_examples_per_class ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): # noqa: E501 - """InlineResponse2001EvaluationResults - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - eval_timestamp (datetime): [optional] # noqa: E501 - total_ground_truth_examples (int, none_type): [optional] # noqa: E501 - total_labeled_examples (int, none_type): [optional] # noqa: E501 - kfold_pooled__balanced_accuracy (float): [optional] # noqa: E501 - kfold_pooled__positive_accuracy (float, none_type): [optional] # noqa: E501 - kfold_pooled__negative_accuracy (float, none_type): [optional] # noqa: E501 - precision__mean (float, none_type): [optional] # noqa: E501 - recall__mean (float, none_type): [optional] # noqa: E501 - roc_auc__mean (float, none_type): [optional] # noqa: E501 - balanced_system_accuracies ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): [optional] # noqa: E501 - positive_system_accuracies ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): [optional] # noqa: E501 - negative_system_accuracies ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): [optional] # noqa: E501 - mean_absolute_error__mean (float, none_type): [optional] # noqa: E501 - objdet_precision__mean (float, none_type): [optional] # noqa: E501 - objdet_recall__mean (float, none_type): [optional] # noqa: E501 - objdet_f1_score__mean (float, none_type): [optional] # noqa: E501 - class_accuracies ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): [optional] # noqa: E501 - confusion_dict ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): [optional] # noqa: E501 - num_examples_per_class ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/inline_response2002.py b/generated/groundlight_openapi_client/model/inline_response2002.py deleted file mode 100644 index 1fbfd51d..00000000 --- a/generated/groundlight_openapi_client/model/inline_response2002.py +++ /dev/null @@ -1,268 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class InlineResponse2002(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "username": (str,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "username": "username", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 - """InlineResponse2002 - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - username (str): The user's username. [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): # noqa: E501 - """InlineResponse2002 - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - username (str): The user's username. [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/inline_response200_summary.py b/generated/groundlight_openapi_client/model/inline_response200_summary.py deleted file mode 100644 index d5c30a12..00000000 --- a/generated/groundlight_openapi_client/model/inline_response200_summary.py +++ /dev/null @@ -1,296 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.inline_response200_summary_class_counts import ( - InlineResponse200SummaryClassCounts, - ) - - globals()["InlineResponse200SummaryClassCounts"] = InlineResponse200SummaryClassCounts - - -class InlineResponse200Summary(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = True - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "num_ground_truth": (int,), # noqa: E501 - "num_current_source_human": (int,), # noqa: E501 - "class_counts": (InlineResponse200SummaryClassCounts,), # noqa: E501 - "unconfident_counts": ( - {str: (bool, date, datetime, dict, float, int, list, str, none_type)}, - ), # noqa: E501 - "total_iqs": (int,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "num_ground_truth": "num_ground_truth", # noqa: E501 - "num_current_source_human": "num_current_source_human", # noqa: E501 - "class_counts": "class_counts", # noqa: E501 - "unconfident_counts": "unconfident_counts", # noqa: E501 - "total_iqs": "total_iqs", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 - """InlineResponse200Summary - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - num_ground_truth (int): [optional] # noqa: E501 - num_current_source_human (int): [optional] # noqa: E501 - class_counts (InlineResponse200SummaryClassCounts): [optional] # noqa: E501 - unconfident_counts ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): [optional] # noqa: E501 - total_iqs (int): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): # noqa: E501 - """InlineResponse200Summary - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - num_ground_truth (int): [optional] # noqa: E501 - num_current_source_human (int): [optional] # noqa: E501 - class_counts (InlineResponse200SummaryClassCounts): [optional] # noqa: E501 - unconfident_counts ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): [optional] # noqa: E501 - total_iqs (int): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/inline_response200_summary_class_counts.py b/generated/groundlight_openapi_client/model/inline_response200_summary_class_counts.py deleted file mode 100644 index b2eb180b..00000000 --- a/generated/groundlight_openapi_client/model/inline_response200_summary_class_counts.py +++ /dev/null @@ -1,284 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class InlineResponse200SummaryClassCounts(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "source_ml": ({str: (bool, date, datetime, dict, float, int, list, str, none_type)},), # noqa: E501 - "source_human": ({str: (bool, date, datetime, dict, float, int, list, str, none_type)},), # noqa: E501 - "cloud_labeler": ({str: (bool, date, datetime, dict, float, int, list, str, none_type)},), # noqa: E501 - "cloud": ({str: (bool, date, datetime, dict, float, int, list, str, none_type)},), # noqa: E501 - "total": ({str: (bool, date, datetime, dict, float, int, list, str, none_type)},), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "source_ml": "source_ml", # noqa: E501 - "source_human": "source_human", # noqa: E501 - "cloud_labeler": "cloud_labeler", # noqa: E501 - "cloud": "cloud", # noqa: E501 - "total": "total", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 - """InlineResponse200SummaryClassCounts - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - source_ml ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): [optional] # noqa: E501 - source_human ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): [optional] # noqa: E501 - cloud_labeler ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): [optional] # noqa: E501 - cloud ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): [optional] # noqa: E501 - total ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): # noqa: E501 - """InlineResponse200SummaryClassCounts - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - source_ml ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): [optional] # noqa: E501 - source_human ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): [optional] # noqa: E501 - cloud_labeler ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): [optional] # noqa: E501 - cloud ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): [optional] # noqa: E501 - total ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/label.py b/generated/groundlight_openapi_client/model/label.py deleted file mode 100644 index 810b9e2e..00000000 --- a/generated/groundlight_openapi_client/model/label.py +++ /dev/null @@ -1,284 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class Label(ModelSimple): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("value",): { - "YES": "YES", - "NO": "NO", - "UNCLEAR": "UNCLEAR", - }, - } - - validations = {} - - additional_properties_type = None - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "value": (str,), - } - - @cached_property - def discriminator(): - return None - - attribute_map = {} - - read_only_vars = set() - - _composed_schemas = None - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): - """Label - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["YES", "NO", "UNCLEAR", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["YES", "NO", "UNCLEAR", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): - """Label - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["YES", "NO", "UNCLEAR", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["YES", "NO", "UNCLEAR", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - return self diff --git a/generated/groundlight_openapi_client/model/label_value.py b/generated/groundlight_openapi_client/model/label_value.py deleted file mode 100644 index ee9b73e9..00000000 --- a/generated/groundlight_openapi_client/model/label_value.py +++ /dev/null @@ -1,331 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.roi import ROI - - globals()["ROI"] = ROI - - -class LabelValue(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "confidence": ( - float, - none_type, - ), # noqa: E501 - "class_name": ( - str, - none_type, - ), # noqa: E501 - "annotations_requested": ([str],), # noqa: E501 - "created_at": (datetime,), # noqa: E501 - "detector_id": ( - int, - none_type, - ), # noqa: E501 - "source": (str,), # noqa: E501 - "text": ( - str, - none_type, - ), # noqa: E501 - "rois": ( - [ROI], - none_type, - ), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "confidence": "confidence", # noqa: E501 - "class_name": "class_name", # noqa: E501 - "annotations_requested": "annotations_requested", # noqa: E501 - "created_at": "created_at", # noqa: E501 - "detector_id": "detector_id", # noqa: E501 - "source": "source", # noqa: E501 - "text": "text", # noqa: E501 - "rois": "rois", # noqa: E501 - } - - read_only_vars = { - "confidence", # noqa: E501 - "class_name", # noqa: E501 - "annotations_requested", # noqa: E501 - "created_at", # noqa: E501 - "detector_id", # noqa: E501 - "source", # noqa: E501 - "text", # noqa: E501 - } - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data( - cls, confidence, class_name, annotations_requested, created_at, detector_id, source, text, *args, **kwargs - ): # noqa: E501 - """LabelValue - a model defined in OpenAPI - - Args: - confidence (float, none_type): - class_name (str, none_type): Return a human-readable class name for this label (e.g. YES/NO) - annotations_requested ([str]): - created_at (datetime): - detector_id (int, none_type): - source (str): - text (str, none_type): Text annotations - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - rois ([ROI], none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.confidence = confidence - self.class_name = class_name - self.annotations_requested = annotations_requested - self.created_at = created_at - self.detector_id = detector_id - self.source = source - self.text = text - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): # noqa: E501 - """LabelValue - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - rois ([ROI], none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/label_value_request.py b/generated/groundlight_openapi_client/model/label_value_request.py deleted file mode 100644 index 434f8ad8..00000000 --- a/generated/groundlight_openapi_client/model/label_value_request.py +++ /dev/null @@ -1,302 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.roi_request import ROIRequest - - globals()["ROIRequest"] = ROIRequest - - -class LabelValueRequest(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("image_query_id",): { - "min_length": 1, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "label": ( - str, - none_type, - ), # noqa: E501 - "image_query_id": (str,), # noqa: E501 - "rois": ( - [ROIRequest], - none_type, - ), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "label": "label", # noqa: E501 - "image_query_id": "image_query_id", # noqa: E501 - "rois": "rois", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, label, image_query_id, *args, **kwargs): # noqa: E501 - """LabelValueRequest - a model defined in OpenAPI - - Args: - label (str, none_type): - image_query_id (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - rois ([ROIRequest], none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.label = label - self.image_query_id = image_query_id - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, label, image_query_id, *args, **kwargs): # noqa: E501 - """LabelValueRequest - a model defined in OpenAPI - - Args: - label (str, none_type): - image_query_id (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - rois ([ROIRequest], none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.label = label - self.image_query_id = image_query_id - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/mode_enum.py b/generated/groundlight_openapi_client/model/mode_enum.py deleted file mode 100644 index b35a3833..00000000 --- a/generated/groundlight_openapi_client/model/mode_enum.py +++ /dev/null @@ -1,286 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class ModeEnum(ModelSimple): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("value",): { - "BINARY": "BINARY", - "COUNT": "COUNT", - "MULTI_CLASS": "MULTI_CLASS", - "TEXT": "TEXT", - "BOUNDING_BOX": "BOUNDING_BOX", - }, - } - - validations = {} - - additional_properties_type = None - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "value": (str,), - } - - @cached_property - def discriminator(): - return None - - attribute_map = {} - - read_only_vars = set() - - _composed_schemas = None - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): - """ModeEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["BINARY", "COUNT", "MULTI_CLASS", "TEXT", "BOUNDING_BOX", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["BINARY", "COUNT", "MULTI_CLASS", "TEXT", "BOUNDING_BOX", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): - """ModeEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["BINARY", "COUNT", "MULTI_CLASS", "TEXT", "BOUNDING_BOX", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["BINARY", "COUNT", "MULTI_CLASS", "TEXT", "BOUNDING_BOX", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - return self diff --git a/generated/groundlight_openapi_client/model/multi_class_mode_configuration.py b/generated/groundlight_openapi_client/model/multi_class_mode_configuration.py deleted file mode 100644 index f08ee27c..00000000 --- a/generated/groundlight_openapi_client/model/multi_class_mode_configuration.py +++ /dev/null @@ -1,278 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class MultiClassModeConfiguration(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "class_names": ([str],), # noqa: E501 - "num_classes": (int,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "class_names": "class_names", # noqa: E501 - "num_classes": "num_classes", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, class_names, *args, **kwargs): # noqa: E501 - """MultiClassModeConfiguration - a model defined in OpenAPI - - Args: - class_names ([str]): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - num_classes (int): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.class_names = class_names - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, class_names, *args, **kwargs): # noqa: E501 - """MultiClassModeConfiguration - a model defined in OpenAPI - - Args: - class_names ([str]): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - num_classes (int): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.class_names = class_names - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/multi_class_mode_configuration_serializer.py b/generated/groundlight_openapi_client/model/multi_class_mode_configuration_serializer.py deleted file mode 100644 index 22241510..00000000 --- a/generated/groundlight_openapi_client/model/multi_class_mode_configuration_serializer.py +++ /dev/null @@ -1,280 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.15.3 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class MultiClassModeConfigurationSerializer(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "id": (str,), # noqa: E501 - "num_classes": (int,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "id": "id", # noqa: E501 - "num_classes": "num_classes", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, id, num_classes, *args, **kwargs): # noqa: E501 - """MultiClassModeConfigurationSerializer - a model defined in OpenAPI - - Args: - id (str): - num_classes (int): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.id = id - self.num_classes = num_classes - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, id, num_classes, *args, **kwargs): # noqa: E501 - """MultiClassModeConfigurationSerializer - a model defined in OpenAPI - - Args: - id (str): - num_classes (int): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.id = id - self.num_classes = num_classes - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/multi_classification_result.py b/generated/groundlight_openapi_client/model/multi_classification_result.py deleted file mode 100644 index bfc778c5..00000000 --- a/generated/groundlight_openapi_client/model/multi_classification_result.py +++ /dev/null @@ -1,302 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class MultiClassificationResult(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("result_type",): { - "MULTI_CLASSIFICATION": "multi_classification", - }, - } - - validations = { - ("confidence",): { - "inclusive_maximum": 1.0, - "inclusive_minimum": 0.0, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "label": (str,), # noqa: E501 - "confidence": ( - float, - none_type, - ), # noqa: E501 - "source": (str,), # noqa: E501 - "result_type": (str,), # noqa: E501 - "from_edge": (bool,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "label": "label", # noqa: E501 - "confidence": "confidence", # noqa: E501 - "source": "source", # noqa: E501 - "result_type": "result_type", # noqa: E501 - "from_edge": "from_edge", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, label, *args, **kwargs): # noqa: E501 - """MultiClassificationResult - a model defined in OpenAPI - - Args: - label (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - confidence (float, none_type): [optional] # noqa: E501 - source (str): [optional] # noqa: E501 - result_type (str): [optional] if omitted the server will use the default value of "multi_classification" # noqa: E501 - from_edge (bool): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.label = label - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, label, *args, **kwargs): # noqa: E501 - """MultiClassificationResult - a model defined in OpenAPI - - Args: - label (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - confidence (float, none_type): [optional] # noqa: E501 - source (str): [optional] # noqa: E501 - result_type (str): [optional] if omitted the server will use the default value of "multi_classification" # noqa: E501 - from_edge (bool): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.label = label - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/note.py b/generated/groundlight_openapi_client/model/note.py deleted file mode 100644 index 64f139a7..00000000 --- a/generated/groundlight_openapi_client/model/note.py +++ /dev/null @@ -1,286 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class Note(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "detector_id": (str,), # noqa: E501 - "content": ( - str, - none_type, - ), # noqa: E501 - "is_pinned": ( - bool, - none_type, - ), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "detector_id": "detector_id", # noqa: E501 - "content": "content", # noqa: E501 - "is_pinned": "is_pinned", # noqa: E501 - } - - read_only_vars = { - "detector_id", # noqa: E501 - } - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, detector_id, *args, **kwargs): # noqa: E501 - """Note - a model defined in OpenAPI - - Args: - detector_id (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - content (str, none_type): Text content of the note.. [optional] # noqa: E501 - is_pinned (bool, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.detector_id = detector_id - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): # noqa: E501 - """Note - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - content (str, none_type): Text content of the note.. [optional] # noqa: E501 - is_pinned (bool, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/note_request.py b/generated/groundlight_openapi_client/model/note_request.py deleted file mode 100644 index 95889010..00000000 --- a/generated/groundlight_openapi_client/model/note_request.py +++ /dev/null @@ -1,285 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class NoteRequest(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "content": ( - str, - none_type, - ), # noqa: E501 - "is_pinned": ( - bool, - none_type, - ), # noqa: E501 - "image": ( - file_type, - none_type, - ), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "content": "content", # noqa: E501 - "is_pinned": "is_pinned", # noqa: E501 - "image": "image", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 - """NoteRequest - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - content (str, none_type): Text content of the note.. [optional] # noqa: E501 - is_pinned (bool, none_type): [optional] # noqa: E501 - image (file_type, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): # noqa: E501 - """NoteRequest - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - content (str, none_type): Text content of the note.. [optional] # noqa: E501 - is_pinned (bool, none_type): [optional] # noqa: E501 - image (file_type, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/paginated_detector_list.py b/generated/groundlight_openapi_client/model/paginated_detector_list.py deleted file mode 100644 index 5972205e..00000000 --- a/generated/groundlight_openapi_client/model/paginated_detector_list.py +++ /dev/null @@ -1,302 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.detector import Detector - - globals()["Detector"] = Detector - - -class PaginatedDetectorList(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "count": (int,), # noqa: E501 - "results": ([Detector],), # noqa: E501 - "next": ( - str, - none_type, - ), # noqa: E501 - "previous": ( - str, - none_type, - ), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "count": "count", # noqa: E501 - "results": "results", # noqa: E501 - "next": "next", # noqa: E501 - "previous": "previous", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, count, results, *args, **kwargs): # noqa: E501 - """PaginatedDetectorList - a model defined in OpenAPI - - Args: - count (int): - results ([Detector]): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - next (str, none_type): [optional] # noqa: E501 - previous (str, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.count = count - self.results = results - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, count, results, *args, **kwargs): # noqa: E501 - """PaginatedDetectorList - a model defined in OpenAPI - - Args: - count (int): - results ([Detector]): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - next (str, none_type): [optional] # noqa: E501 - previous (str, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.count = count - self.results = results - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/paginated_image_query_list.py b/generated/groundlight_openapi_client/model/paginated_image_query_list.py deleted file mode 100644 index 13dccab7..00000000 --- a/generated/groundlight_openapi_client/model/paginated_image_query_list.py +++ /dev/null @@ -1,302 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.image_query import ImageQuery - - globals()["ImageQuery"] = ImageQuery - - -class PaginatedImageQueryList(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "count": (int,), # noqa: E501 - "results": ([ImageQuery],), # noqa: E501 - "next": ( - str, - none_type, - ), # noqa: E501 - "previous": ( - str, - none_type, - ), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "count": "count", # noqa: E501 - "results": "results", # noqa: E501 - "next": "next", # noqa: E501 - "previous": "previous", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, count, results, *args, **kwargs): # noqa: E501 - """PaginatedImageQueryList - a model defined in OpenAPI - - Args: - count (int): - results ([ImageQuery]): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - next (str, none_type): [optional] # noqa: E501 - previous (str, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.count = count - self.results = results - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, count, results, *args, **kwargs): # noqa: E501 - """PaginatedImageQueryList - a model defined in OpenAPI - - Args: - count (int): - results ([ImageQuery]): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - next (str, none_type): [optional] # noqa: E501 - previous (str, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.count = count - self.results = results - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/paginated_rule_list.py b/generated/groundlight_openapi_client/model/paginated_rule_list.py deleted file mode 100644 index 6d007e1d..00000000 --- a/generated/groundlight_openapi_client/model/paginated_rule_list.py +++ /dev/null @@ -1,302 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.rule import Rule - - globals()["Rule"] = Rule - - -class PaginatedRuleList(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "count": (int,), # noqa: E501 - "results": ([Rule],), # noqa: E501 - "next": ( - str, - none_type, - ), # noqa: E501 - "previous": ( - str, - none_type, - ), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "count": "count", # noqa: E501 - "results": "results", # noqa: E501 - "next": "next", # noqa: E501 - "previous": "previous", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, count, results, *args, **kwargs): # noqa: E501 - """PaginatedRuleList - a model defined in OpenAPI - - Args: - count (int): - results ([Rule]): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - next (str, none_type): [optional] # noqa: E501 - previous (str, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.count = count - self.results = results - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, count, results, *args, **kwargs): # noqa: E501 - """PaginatedRuleList - a model defined in OpenAPI - - Args: - count (int): - results ([Rule]): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - next (str, none_type): [optional] # noqa: E501 - previous (str, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.count = count - self.results = results - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/patched_detector_request.py b/generated/groundlight_openapi_client/model/patched_detector_request.py deleted file mode 100644 index 7e7a5813..00000000 --- a/generated/groundlight_openapi_client/model/patched_detector_request.py +++ /dev/null @@ -1,320 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.blank_enum import BlankEnum - from groundlight_openapi_client.model.status_enum import StatusEnum - - globals()["BlankEnum"] = BlankEnum - globals()["StatusEnum"] = StatusEnum - - -class PatchedDetectorRequest(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("name",): { - "max_length": 200, - "min_length": 1, - }, - ("confidence_threshold",): { - "inclusive_maximum": 1.0, - "inclusive_minimum": 0.0, - }, - ("patience_time",): { - "inclusive_maximum": 3600, - "inclusive_minimum": 0, - }, - ("escalation_type",): { - "min_length": 1, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "name": (str,), # noqa: E501 - "confidence_threshold": (float,), # noqa: E501 - "patience_time": (float,), # noqa: E501 - "status": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - "escalation_type": (str,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "name": "name", # noqa: E501 - "confidence_threshold": "confidence_threshold", # noqa: E501 - "patience_time": "patience_time", # noqa: E501 - "status": "status", # noqa: E501 - "escalation_type": "escalation_type", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 - """PatchedDetectorRequest - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - name (str): A short, descriptive name for the detector.. [optional] # noqa: E501 - confidence_threshold (float): If the detector's prediction is below this confidence threshold, send the image query for human review.. [optional] if omitted the server will use the default value of 0.9 # noqa: E501 - patience_time (float): How long Groundlight will attempt to generate a confident prediction. [optional] if omitted the server will use the default value of 30.0 # noqa: E501 - status (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - escalation_type (str): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): # noqa: E501 - """PatchedDetectorRequest - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - name (str): A short, descriptive name for the detector.. [optional] # noqa: E501 - confidence_threshold (float): If the detector's prediction is below this confidence threshold, send the image query for human review.. [optional] if omitted the server will use the default value of 0.9 # noqa: E501 - patience_time (float): How long Groundlight will attempt to generate a confident prediction. [optional] if omitted the server will use the default value of 30.0 # noqa: E501 - status (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - escalation_type (str): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/payload_template.py b/generated/groundlight_openapi_client/model/payload_template.py deleted file mode 100644 index a721bb4c..00000000 --- a/generated/groundlight_openapi_client/model/payload_template.py +++ /dev/null @@ -1,281 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class PayloadTemplate(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "template": (str,), # noqa: E501 - "headers": ( - {str: (str,)}, - none_type, - ), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "template": "template", # noqa: E501 - "headers": "headers", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, template, *args, **kwargs): # noqa: E501 - """PayloadTemplate - a model defined in OpenAPI - - Args: - template (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - headers ({str: (str,)}, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.template = template - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, template, *args, **kwargs): # noqa: E501 - """PayloadTemplate - a model defined in OpenAPI - - Args: - template (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - headers ({str: (str,)}, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.template = template - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/payload_template_request.py b/generated/groundlight_openapi_client/model/payload_template_request.py deleted file mode 100644 index 3a0f12a2..00000000 --- a/generated/groundlight_openapi_client/model/payload_template_request.py +++ /dev/null @@ -1,285 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class PayloadTemplateRequest(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("template",): { - "min_length": 1, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "template": (str,), # noqa: E501 - "headers": ( - {str: (str,)}, - none_type, - ), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "template": "template", # noqa: E501 - "headers": "headers", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, template, *args, **kwargs): # noqa: E501 - """PayloadTemplateRequest - a model defined in OpenAPI - - Args: - template (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - headers ({str: (str,)}, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.template = template - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, template, *args, **kwargs): # noqa: E501 - """PayloadTemplateRequest - a model defined in OpenAPI - - Args: - template (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - headers ({str: (str,)}, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.template = template - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/result_type_enum.py b/generated/groundlight_openapi_client/model/result_type_enum.py deleted file mode 100644 index c4b954fd..00000000 --- a/generated/groundlight_openapi_client/model/result_type_enum.py +++ /dev/null @@ -1,286 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class ResultTypeEnum(ModelSimple): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("value",): { - "BINARY_CLASSIFICATION": "binary_classification", - "COUNTING": "counting", - "MULTI_CLASSIFICATION": "multi_classification", - "TEXT_RECOGNITION": "text_recognition", - "BOUNDING_BOX": "bounding_box", - }, - } - - validations = {} - - additional_properties_type = None - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "value": (str,), - } - - @cached_property - def discriminator(): - return None - - attribute_map = {} - - read_only_vars = set() - - _composed_schemas = None - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): - """ResultTypeEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["binary_classification", "counting", "multi_classification", "text_recognition", "bounding_box", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["binary_classification", "counting", "multi_classification", "text_recognition", "bounding_box", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): - """ResultTypeEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["binary_classification", "counting", "multi_classification", "text_recognition", "bounding_box", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["binary_classification", "counting", "multi_classification", "text_recognition", "bounding_box", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - return self diff --git a/generated/groundlight_openapi_client/model/roi.py b/generated/groundlight_openapi_client/model/roi.py deleted file mode 100644 index 74c4fc66..00000000 --- a/generated/groundlight_openapi_client/model/roi.py +++ /dev/null @@ -1,294 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.b_box_geometry import BBoxGeometry - - globals()["BBoxGeometry"] = BBoxGeometry - - -class ROI(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = {} - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "label": (str,), # noqa: E501 - "score": (float,), # noqa: E501 - "geometry": (BBoxGeometry,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "label": "label", # noqa: E501 - "score": "score", # noqa: E501 - "geometry": "geometry", # noqa: E501 - } - - read_only_vars = { - "score", # noqa: E501 - } - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, label, score, geometry, *args, **kwargs): # noqa: E501 - """ROI - a model defined in OpenAPI - - Args: - label (str): The label of the bounding box. - score (float): The confidence of the bounding box. - geometry (BBoxGeometry): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.label = label - self.score = score - self.geometry = geometry - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, label, geometry, *args, **kwargs): # noqa: E501 - """ROI - a model defined in OpenAPI - - Args: - label (str): The label of the bounding box. - geometry (BBoxGeometry): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.label = label - self.geometry = geometry - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/roi_request.py b/generated/groundlight_openapi_client/model/roi_request.py deleted file mode 100644 index 07fb54e2..00000000 --- a/generated/groundlight_openapi_client/model/roi_request.py +++ /dev/null @@ -1,292 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.b_box_geometry_request import BBoxGeometryRequest - - globals()["BBoxGeometryRequest"] = BBoxGeometryRequest - - -class ROIRequest(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("label",): { - "min_length": 1, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "label": (str,), # noqa: E501 - "geometry": (BBoxGeometryRequest,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "label": "label", # noqa: E501 - "geometry": "geometry", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, label, geometry, *args, **kwargs): # noqa: E501 - """ROIRequest - a model defined in OpenAPI - - Args: - label (str): The label of the bounding box. - geometry (BBoxGeometryRequest): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.label = label - self.geometry = geometry - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, label, geometry, *args, **kwargs): # noqa: E501 - """ROIRequest - a model defined in OpenAPI - - Args: - label (str): The label of the bounding box. - geometry (BBoxGeometryRequest): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.label = label - self.geometry = geometry - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/rule.py b/generated/groundlight_openapi_client/model/rule.py deleted file mode 100644 index 7f1be14a..00000000 --- a/generated/groundlight_openapi_client/model/rule.py +++ /dev/null @@ -1,366 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.action import Action - from groundlight_openapi_client.model.action_list import ActionList - from groundlight_openapi_client.model.condition import Condition - from groundlight_openapi_client.model.snooze_time_unit_enum import SnoozeTimeUnitEnum - from groundlight_openapi_client.model.webhook_action import WebhookAction - - globals()["Action"] = Action - globals()["ActionList"] = ActionList - globals()["Condition"] = Condition - globals()["SnoozeTimeUnitEnum"] = SnoozeTimeUnitEnum - globals()["WebhookAction"] = WebhookAction - - -class Rule(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("name",): { - "max_length": 44, - }, - ("snooze_time_value",): { - "inclusive_minimum": 0, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "id": (int,), # noqa: E501 - "detector_id": (str,), # noqa: E501 - "detector_name": (str,), # noqa: E501 - "name": (str,), # noqa: E501 - "condition": (Condition,), # noqa: E501 - "enabled": (bool,), # noqa: E501 - "snooze_time_enabled": (bool,), # noqa: E501 - "snooze_time_value": (int,), # noqa: E501 - "snooze_time_unit": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - "human_review_required": (bool,), # noqa: E501 - "action": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - "webhook_action": ([WebhookAction],), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "id": "id", # noqa: E501 - "detector_id": "detector_id", # noqa: E501 - "detector_name": "detector_name", # noqa: E501 - "name": "name", # noqa: E501 - "condition": "condition", # noqa: E501 - "enabled": "enabled", # noqa: E501 - "snooze_time_enabled": "snooze_time_enabled", # noqa: E501 - "snooze_time_value": "snooze_time_value", # noqa: E501 - "snooze_time_unit": "snooze_time_unit", # noqa: E501 - "human_review_required": "human_review_required", # noqa: E501 - "action": "action", # noqa: E501 - "webhook_action": "webhook_action", # noqa: E501 - } - - read_only_vars = { - "id", # noqa: E501 - "detector_id", # noqa: E501 - "detector_name", # noqa: E501 - } - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, id, detector_id, detector_name, name, condition, *args, **kwargs): # noqa: E501 - """Rule - a model defined in OpenAPI - - Args: - id (int): - detector_id (str): - detector_name (str): - name (str): - condition (Condition): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - enabled (bool): [optional] if omitted the server will use the default value of True # noqa: E501 - snooze_time_enabled (bool): [optional] if omitted the server will use the default value of False # noqa: E501 - snooze_time_value (int): [optional] if omitted the server will use the default value of 0 # noqa: E501 - snooze_time_unit (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - human_review_required (bool): [optional] if omitted the server will use the default value of False # noqa: E501 - action (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - webhook_action ([WebhookAction]): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.id = id - self.detector_id = detector_id - self.detector_name = detector_name - self.name = name - self.condition = condition - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, name, condition, *args, **kwargs): # noqa: E501 - """Rule - a model defined in OpenAPI - - name (str): - condition (Condition): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - enabled (bool): [optional] if omitted the server will use the default value of True # noqa: E501 - snooze_time_enabled (bool): [optional] if omitted the server will use the default value of False # noqa: E501 - snooze_time_value (int): [optional] if omitted the server will use the default value of 0 # noqa: E501 - snooze_time_unit (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - human_review_required (bool): [optional] if omitted the server will use the default value of False # noqa: E501 - action (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - webhook_action ([WebhookAction]): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.name = name - self.condition = condition - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/rule_request.py b/generated/groundlight_openapi_client/model/rule_request.py deleted file mode 100644 index 8fdf8fe1..00000000 --- a/generated/groundlight_openapi_client/model/rule_request.py +++ /dev/null @@ -1,352 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.action import Action - from groundlight_openapi_client.model.action_list import ActionList - from groundlight_openapi_client.model.condition_request import ConditionRequest - from groundlight_openapi_client.model.snooze_time_unit_enum import SnoozeTimeUnitEnum - from groundlight_openapi_client.model.webhook_action_request import WebhookActionRequest - - globals()["Action"] = Action - globals()["ActionList"] = ActionList - globals()["ConditionRequest"] = ConditionRequest - globals()["SnoozeTimeUnitEnum"] = SnoozeTimeUnitEnum - globals()["WebhookActionRequest"] = WebhookActionRequest - - -class RuleRequest(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("name",): { - "max_length": 44, - "min_length": 1, - }, - ("snooze_time_value",): { - "inclusive_minimum": 0, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "name": (str,), # noqa: E501 - "condition": (ConditionRequest,), # noqa: E501 - "enabled": (bool,), # noqa: E501 - "snooze_time_enabled": (bool,), # noqa: E501 - "snooze_time_value": (int,), # noqa: E501 - "snooze_time_unit": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - "human_review_required": (bool,), # noqa: E501 - "action": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - "webhook_action": ([WebhookActionRequest],), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "name": "name", # noqa: E501 - "condition": "condition", # noqa: E501 - "enabled": "enabled", # noqa: E501 - "snooze_time_enabled": "snooze_time_enabled", # noqa: E501 - "snooze_time_value": "snooze_time_value", # noqa: E501 - "snooze_time_unit": "snooze_time_unit", # noqa: E501 - "human_review_required": "human_review_required", # noqa: E501 - "action": "action", # noqa: E501 - "webhook_action": "webhook_action", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, name, condition, *args, **kwargs): # noqa: E501 - """RuleRequest - a model defined in OpenAPI - - Args: - name (str): - condition (ConditionRequest): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - enabled (bool): [optional] if omitted the server will use the default value of True # noqa: E501 - snooze_time_enabled (bool): [optional] if omitted the server will use the default value of False # noqa: E501 - snooze_time_value (int): [optional] if omitted the server will use the default value of 0 # noqa: E501 - snooze_time_unit (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - human_review_required (bool): [optional] if omitted the server will use the default value of False # noqa: E501 - action (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - webhook_action ([WebhookActionRequest]): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.name = name - self.condition = condition - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, name, condition, *args, **kwargs): # noqa: E501 - """RuleRequest - a model defined in OpenAPI - - Args: - name (str): - condition (ConditionRequest): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - enabled (bool): [optional] if omitted the server will use the default value of True # noqa: E501 - snooze_time_enabled (bool): [optional] if omitted the server will use the default value of False # noqa: E501 - snooze_time_value (int): [optional] if omitted the server will use the default value of 0 # noqa: E501 - snooze_time_unit (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - human_review_required (bool): [optional] if omitted the server will use the default value of False # noqa: E501 - action (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - webhook_action ([WebhookActionRequest]): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.name = name - self.condition = condition - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/snooze_time_unit_enum.py b/generated/groundlight_openapi_client/model/snooze_time_unit_enum.py deleted file mode 100644 index f5586bb6..00000000 --- a/generated/groundlight_openapi_client/model/snooze_time_unit_enum.py +++ /dev/null @@ -1,285 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class SnoozeTimeUnitEnum(ModelSimple): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("value",): { - "DAYS": "DAYS", - "HOURS": "HOURS", - "MINUTES": "MINUTES", - "SECONDS": "SECONDS", - }, - } - - validations = {} - - additional_properties_type = None - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "value": (str,), - } - - @cached_property - def discriminator(): - return None - - attribute_map = {} - - read_only_vars = set() - - _composed_schemas = None - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): - """SnoozeTimeUnitEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str): * `DAYS` - DAYS * `HOURS` - HOURS * `MINUTES` - MINUTES * `SECONDS` - SECONDS., must be one of ["DAYS", "HOURS", "MINUTES", "SECONDS", ] # noqa: E501 - - Keyword Args: - value (str): * `DAYS` - DAYS * `HOURS` - HOURS * `MINUTES` - MINUTES * `SECONDS` - SECONDS., must be one of ["DAYS", "HOURS", "MINUTES", "SECONDS", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): - """SnoozeTimeUnitEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str): * `DAYS` - DAYS * `HOURS` - HOURS * `MINUTES` - MINUTES * `SECONDS` - SECONDS., must be one of ["DAYS", "HOURS", "MINUTES", "SECONDS", ] # noqa: E501 - - Keyword Args: - value (str): * `DAYS` - DAYS * `HOURS` - HOURS * `MINUTES` - MINUTES * `SECONDS` - SECONDS., must be one of ["DAYS", "HOURS", "MINUTES", "SECONDS", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - return self diff --git a/generated/groundlight_openapi_client/model/source.py b/generated/groundlight_openapi_client/model/source.py deleted file mode 100644 index 7e3e832a..00000000 --- a/generated/groundlight_openapi_client/model/source.py +++ /dev/null @@ -1,287 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class Source(ModelSimple): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("value",): { - "STILL_PROCESSING": "STILL_PROCESSING", - "CLOUD": "CLOUD", - "USER": "USER", - "CLOUD_ENSEMBLE": "CLOUD_ENSEMBLE", - "ALGORITHM": "ALGORITHM", - "EDGE": "EDGE", - }, - } - - validations = {} - - additional_properties_type = None - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "value": (str,), - } - - @cached_property - def discriminator(): - return None - - attribute_map = {} - - read_only_vars = set() - - _composed_schemas = None - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): - """Source - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["STILL_PROCESSING", "CLOUD", "USER", "CLOUD_ENSEMBLE", "ALGORITHM", "EDGE", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["STILL_PROCESSING", "CLOUD", "USER", "CLOUD_ENSEMBLE", "ALGORITHM", "EDGE", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): - """Source - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["STILL_PROCESSING", "CLOUD", "USER", "CLOUD_ENSEMBLE", "ALGORITHM", "EDGE", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["STILL_PROCESSING", "CLOUD", "USER", "CLOUD_ENSEMBLE", "ALGORITHM", "EDGE", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - return self diff --git a/generated/groundlight_openapi_client/model/source_enum.py b/generated/groundlight_openapi_client/model/source_enum.py deleted file mode 100644 index 8a2746ab..00000000 --- a/generated/groundlight_openapi_client/model/source_enum.py +++ /dev/null @@ -1,289 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class SourceEnum(ModelSimple): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("value",): { - "INITIAL_PLACEHOLDER": "INITIAL_PLACEHOLDER", - "CLOUD": "CLOUD", - "CUST": "CUST", - "HUMAN_CLOUD_ENSEMBLE": "HUMAN_CLOUD_ENSEMBLE", - "ALG": "ALG", - "ALG_REC": "ALG_REC", - "ALG_UNCLEAR": "ALG_UNCLEAR", - "EDGE": "EDGE", - }, - } - - validations = {} - - additional_properties_type = None - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "value": (str,), - } - - @cached_property - def discriminator(): - return None - - attribute_map = {} - - read_only_vars = set() - - _composed_schemas = None - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): - """SourceEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["INITIAL_PLACEHOLDER", "CLOUD", "CUST", "HUMAN_CLOUD_ENSEMBLE", "ALG", "ALG_REC", "ALG_UNCLEAR", "EDGE", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["INITIAL_PLACEHOLDER", "CLOUD", "CUST", "HUMAN_CLOUD_ENSEMBLE", "ALG", "ALG_REC", "ALG_UNCLEAR", "EDGE", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): - """SourceEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["INITIAL_PLACEHOLDER", "CLOUD", "CUST", "HUMAN_CLOUD_ENSEMBLE", "ALG", "ALG_REC", "ALG_UNCLEAR", "EDGE", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["INITIAL_PLACEHOLDER", "CLOUD", "CUST", "HUMAN_CLOUD_ENSEMBLE", "ALG", "ALG_REC", "ALG_UNCLEAR", "EDGE", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - return self diff --git a/generated/groundlight_openapi_client/model/status_enum.py b/generated/groundlight_openapi_client/model/status_enum.py deleted file mode 100644 index b41c2871..00000000 --- a/generated/groundlight_openapi_client/model/status_enum.py +++ /dev/null @@ -1,283 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class StatusEnum(ModelSimple): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("value",): { - "ON": "ON", - "OFF": "OFF", - }, - } - - validations = {} - - additional_properties_type = None - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "value": (str,), - } - - @cached_property - def discriminator(): - return None - - attribute_map = {} - - read_only_vars = set() - - _composed_schemas = None - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): - """StatusEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str): * `ON` - ON * `OFF` - OFF., must be one of ["ON", "OFF", ] # noqa: E501 - - Keyword Args: - value (str): * `ON` - ON * `OFF` - OFF., must be one of ["ON", "OFF", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): - """StatusEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str): * `ON` - ON * `OFF` - OFF., must be one of ["ON", "OFF", ] # noqa: E501 - - Keyword Args: - value (str): * `ON` - ON * `OFF` - OFF., must be one of ["ON", "OFF", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - return self diff --git a/generated/groundlight_openapi_client/model/text_mode_configuration.py b/generated/groundlight_openapi_client/model/text_mode_configuration.py deleted file mode 100644 index ee4c4e73..00000000 --- a/generated/groundlight_openapi_client/model/text_mode_configuration.py +++ /dev/null @@ -1,273 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class TextModeConfiguration(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("value_max_length",): { - "inclusive_maximum": 250, - "inclusive_minimum": 1, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "value_max_length": (int,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "value_max_length": "value_max_length", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 - """TextModeConfiguration - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - value_max_length (int): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): # noqa: E501 - """TextModeConfiguration - a model defined in OpenAPI - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - value_max_length (int): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/text_recognition_result.py b/generated/groundlight_openapi_client/model/text_recognition_result.py deleted file mode 100644 index 0e769567..00000000 --- a/generated/groundlight_openapi_client/model/text_recognition_result.py +++ /dev/null @@ -1,311 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class TextRecognitionResult(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("result_type",): { - "TEXT_RECOGNITION": "text_recognition", - }, - } - - validations = { - ("confidence",): { - "inclusive_maximum": 1.0, - "inclusive_minimum": 0.0, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "text": ( - str, - none_type, - ), # noqa: E501 - "truncated": (bool,), # noqa: E501 - "confidence": ( - float, - none_type, - ), # noqa: E501 - "source": (str,), # noqa: E501 - "result_type": (str,), # noqa: E501 - "from_edge": (bool,), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "text": "text", # noqa: E501 - "truncated": "truncated", # noqa: E501 - "confidence": "confidence", # noqa: E501 - "source": "source", # noqa: E501 - "result_type": "result_type", # noqa: E501 - "from_edge": "from_edge", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, text, truncated, *args, **kwargs): # noqa: E501 - """TextRecognitionResult - a model defined in OpenAPI - - Args: - text (str, none_type): - truncated (bool): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - confidence (float, none_type): [optional] # noqa: E501 - source (str): [optional] # noqa: E501 - result_type (str): [optional] if omitted the server will use the default value of "text_recognition" # noqa: E501 - from_edge (bool): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.text = text - self.truncated = truncated - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, text, truncated, *args, **kwargs): # noqa: E501 - """TextRecognitionResult - a model defined in OpenAPI - - Args: - text (str, none_type): - truncated (bool): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - confidence (float, none_type): [optional] # noqa: E501 - source (str): [optional] # noqa: E501 - result_type (str): [optional] if omitted the server will use the default value of "text_recognition" # noqa: E501 - from_edge (bool): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.text = text - self.truncated = truncated - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/verb_enum.py b/generated/groundlight_openapi_client/model/verb_enum.py deleted file mode 100644 index 789dd77c..00000000 --- a/generated/groundlight_openapi_client/model/verb_enum.py +++ /dev/null @@ -1,286 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -class VerbEnum(ModelSimple): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = { - ("value",): { - "ANSWERED_CONSECUTIVELY": "ANSWERED_CONSECUTIVELY", - "ANSWERED_WITHIN_TIME": "ANSWERED_WITHIN_TIME", - "CHANGED_TO": "CHANGED_TO", - "NO_CHANGE": "NO_CHANGE", - "NO_QUERIES": "NO_QUERIES", - }, - } - - validations = {} - - additional_properties_type = None - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - return { - "value": (str,), - } - - @cached_property - def discriminator(): - return None - - attribute_map = {} - - read_only_vars = set() - - _composed_schemas = None - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, *args, **kwargs): - """VerbEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["ANSWERED_CONSECUTIVELY", "ANSWERED_WITHIN_TIME", "CHANGED_TO", "NO_CHANGE", "NO_QUERIES", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["ANSWERED_CONSECUTIVELY", "ANSWERED_WITHIN_TIME", "CHANGED_TO", "NO_CHANGE", "NO_QUERIES", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, *args, **kwargs): - """VerbEnum - a model defined in OpenAPI - - Note that value can be passed either in args or in kwargs, but not in both. - - Args: - args[0] (str):, must be one of ["ANSWERED_CONSECUTIVELY", "ANSWERED_WITHIN_TIME", "CHANGED_TO", "NO_CHANGE", "NO_QUERIES", ] # noqa: E501 - - Keyword Args: - value (str):, must be one of ["ANSWERED_CONSECUTIVELY", "ANSWERED_WITHIN_TIME", "CHANGED_TO", "NO_CHANGE", "NO_QUERIES", ] # noqa: E501 - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - """ - # required up here when default value is not given - _path_to_item = kwargs.pop("_path_to_item", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if "value" in kwargs: - value = kwargs.pop("value") - elif args: - args = list(args) - value = args.pop(0) - else: - raise ApiTypeError( - "value is required, but not passed in args or kwargs and doesn't have default", - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - self.value = value - if kwargs: - raise ApiTypeError( - "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." - % ( - kwargs, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - return self diff --git a/generated/groundlight_openapi_client/model/webhook_action.py b/generated/groundlight_openapi_client/model/webhook_action.py deleted file mode 100644 index 33f70f3c..00000000 --- a/generated/groundlight_openapi_client/model/webhook_action.py +++ /dev/null @@ -1,322 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.payload_template import PayloadTemplate - - globals()["PayloadTemplate"] = PayloadTemplate - - -class WebhookAction(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("url",): { - "max_length": 200, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "url": (str,), # noqa: E501 - "include_image": (bool,), # noqa: E501 - "payload_template": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - "last_message_failed": (bool,), # noqa: E501 - "last_failure_error": ( - str, - none_type, - ), # noqa: E501 - "last_failed_at": ( - datetime, - none_type, - ), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "url": "url", # noqa: E501 - "include_image": "include_image", # noqa: E501 - "payload_template": "payload_template", # noqa: E501 - "last_message_failed": "last_message_failed", # noqa: E501 - "last_failure_error": "last_failure_error", # noqa: E501 - "last_failed_at": "last_failed_at", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, url, *args, **kwargs): # noqa: E501 - """WebhookAction - a model defined in OpenAPI - - Args: - url (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - include_image (bool): [optional] # noqa: E501 - payload_template (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - last_message_failed (bool): [optional] # noqa: E501 - last_failure_error (str, none_type): [optional] # noqa: E501 - last_failed_at (datetime, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.url = url - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, url, *args, **kwargs): # noqa: E501 - """WebhookAction - a model defined in OpenAPI - - Args: - url (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - include_image (bool): [optional] # noqa: E501 - payload_template (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - last_message_failed (bool): [optional] # noqa: E501 - last_failure_error (str, none_type): [optional] # noqa: E501 - last_failed_at (datetime, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.url = url - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model/webhook_action_request.py b/generated/groundlight_openapi_client/model/webhook_action_request.py deleted file mode 100644 index cd98a99b..00000000 --- a/generated/groundlight_openapi_client/model/webhook_action_request.py +++ /dev/null @@ -1,323 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -import re # noqa: F401 -import sys # noqa: F401 - -from groundlight_openapi_client.model_utils import ( # noqa: F401 - ApiTypeError, - ModelComposed, - ModelNormal, - ModelSimple, - cached_property, - change_keys_js_to_python, - convert_js_args_to_python_args, - date, - datetime, - file_type, - none_type, - validate_get_composed_info, - OpenApiModel, -) -from groundlight_openapi_client.exceptions import ApiAttributeError - - -def lazy_import(): - from groundlight_openapi_client.model.payload_template_request import PayloadTemplateRequest - - globals()["PayloadTemplateRequest"] = PayloadTemplateRequest - - -class WebhookActionRequest(ModelNormal): - """NOTE: This class is auto generated by OpenAPI Generator. - Ref: https://openapi-generator.tech - - Do not edit the class manually. - - Attributes: - allowed_values (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - with a capitalized key describing the allowed value and an allowed - value. These dicts store the allowed enum values. - attribute_map (dict): The key is attribute name - and the value is json key in definition. - discriminator_value_class_map (dict): A dict to go from the discriminator - variable value to the discriminator class name. - validations (dict): The key is the tuple path to the attribute - and the for var_name this is (var_name,). The value is a dict - that stores validations for max_length, min_length, max_items, - min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, - inclusive_minimum, and regex. - additional_properties_type (tuple): A tuple of classes accepted - as additional properties values. - """ - - allowed_values = {} - - validations = { - ("url",): { - "max_length": 200, - "min_length": 1, - }, - } - - @cached_property - def additional_properties_type(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - """ - lazy_import() - return ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ) # noqa: E501 - - _nullable = False - - @cached_property - def openapi_types(): - """ - This must be a method because a model may have properties that are - of type self, this must run after the class is loaded - - Returns - openapi_types (dict): The key is attribute name - and the value is attribute type. - """ - lazy_import() - return { - "url": (str,), # noqa: E501 - "include_image": (bool,), # noqa: E501 - "payload_template": ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ), # noqa: E501 - "last_message_failed": (bool,), # noqa: E501 - "last_failure_error": ( - str, - none_type, - ), # noqa: E501 - "last_failed_at": ( - datetime, - none_type, - ), # noqa: E501 - } - - @cached_property - def discriminator(): - return None - - attribute_map = { - "url": "url", # noqa: E501 - "include_image": "include_image", # noqa: E501 - "payload_template": "payload_template", # noqa: E501 - "last_message_failed": "last_message_failed", # noqa: E501 - "last_failure_error": "last_failure_error", # noqa: E501 - "last_failed_at": "last_failed_at", # noqa: E501 - } - - read_only_vars = {} - - _composed_schemas = {} - - @classmethod - @convert_js_args_to_python_args - def _from_openapi_data(cls, url, *args, **kwargs): # noqa: E501 - """WebhookActionRequest - a model defined in OpenAPI - - Args: - url (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - include_image (bool): [optional] # noqa: E501 - payload_template (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - last_message_failed (bool): [optional] # noqa: E501 - last_failure_error (str, none_type): [optional] # noqa: E501 - last_failed_at (datetime, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - self = super(OpenApiModel, cls).__new__(cls) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.url = url - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - return self - - required_properties = set([ - "_data_store", - "_check_type", - "_spec_property_naming", - "_path_to_item", - "_configuration", - "_visited_composed_classes", - ]) - - @convert_js_args_to_python_args - def __init__(self, url, *args, **kwargs): # noqa: E501 - """WebhookActionRequest - a model defined in OpenAPI - - Args: - url (str): - - Keyword Args: - _check_type (bool): if True, values for parameters in openapi_types - will be type checked and a TypeError will be - raised if the wrong type is input. - Defaults to True - _path_to_item (tuple/list): This is a list of keys or values to - drill down to the model in received_data - when deserializing a response - _spec_property_naming (bool): True if the variable names in the input data - are serialized names, as specified in the OpenAPI document. - False if the variable names in the input data - are pythonic names, e.g. snake case (default) - _configuration (Configuration): the instance to use when - deserializing a file_type parameter. - If passed, type conversion is attempted - If omitted no type conversion is done. - _visited_composed_classes (tuple): This stores a tuple of - classes that we have traveled through so that - if we see that class again we will not use its - discriminator again. - When traveling through a discriminator, the - composed schema that is - is traveled through is added to this set. - For example if Animal has a discriminator - petType and we pass in "Dog", and the class Dog - allOf includes Animal, we move through Animal - once using the discriminator, and pick Dog. - Then in Dog, we will make an instance of the - Animal class but this time we won't travel - through its discriminator because we passed in - _visited_composed_classes = (Animal,) - include_image (bool): [optional] # noqa: E501 - payload_template (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501 - last_message_failed (bool): [optional] # noqa: E501 - last_failure_error (str, none_type): [optional] # noqa: E501 - last_failed_at (datetime, none_type): [optional] # noqa: E501 - """ - - _check_type = kwargs.pop("_check_type", True) - _spec_property_naming = kwargs.pop("_spec_property_naming", False) - _path_to_item = kwargs.pop("_path_to_item", ()) - _configuration = kwargs.pop("_configuration", None) - _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) - - if args: - raise ApiTypeError( - "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." - % ( - args, - self.__class__.__name__, - ), - path_to_item=_path_to_item, - valid_classes=(self.__class__,), - ) - - self._data_store = {} - self._check_type = _check_type - self._spec_property_naming = _spec_property_naming - self._path_to_item = _path_to_item - self._configuration = _configuration - self._visited_composed_classes = _visited_composed_classes + (self.__class__,) - - self.url = url - for var_name, var_value in kwargs.items(): - if ( - var_name not in self.attribute_map - and self._configuration is not None - and self._configuration.discard_unknown_keys - and self.additional_properties_type is None - ): - # discard variable. - continue - setattr(self, var_name, var_value) - if var_name in self.read_only_vars: - raise ApiAttributeError( - f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " - "class with read only attributes." - ) diff --git a/generated/groundlight_openapi_client/model_utils.py b/generated/groundlight_openapi_client/model_utils.py deleted file mode 100644 index cf7bd6d4..00000000 --- a/generated/groundlight_openapi_client/model_utils.py +++ /dev/null @@ -1,1932 +0,0 @@ -""" - Groundlight API - - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - - The version of the OpenAPI document: 0.18.2 - Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" - -from datetime import date, datetime # noqa: F401 -from copy import deepcopy -import inspect -import io -import os -import pprint -import re -import tempfile - -from dateutil.parser import parse - -from groundlight_openapi_client.exceptions import ( - ApiKeyError, - ApiAttributeError, - ApiTypeError, - ApiValueError, -) - -none_type = type(None) -file_type = io.IOBase - - -def convert_js_args_to_python_args(fn): - from functools import wraps - - @wraps(fn) - def wrapped_init(_self, *args, **kwargs): - """ - An attribute named `self` received from the api will conflicts with the reserved `self` - parameter of a class method. During generation, `self` attributes are mapped - to `_self` in models. Here, we name `_self` instead of `self` to avoid conflicts. - """ - spec_property_naming = kwargs.get("_spec_property_naming", False) - if spec_property_naming: - kwargs = change_keys_js_to_python(kwargs, _self if isinstance(_self, type) else _self.__class__) - return fn(_self, *args, **kwargs) - - return wrapped_init - - -class cached_property(object): - # this caches the result of the function call for fn with no inputs - # use this as a decorator on function methods that you want converted - # into cached properties - result_key = "_results" - - def __init__(self, fn): - self._fn = fn - - def __get__(self, instance, cls=None): - if self.result_key in vars(self): - return vars(self)[self.result_key] - else: - result = self._fn() - setattr(self, self.result_key, result) - return result - - -PRIMITIVE_TYPES = (list, float, int, bool, datetime, date, str, file_type) - - -def allows_single_value_input(cls): - """ - This function returns True if the input composed schema model or any - descendant model allows a value only input - This is true for cases where oneOf contains items like: - oneOf: - - float - - NumberWithValidation - - StringEnum - - ArrayModel - - null - TODO: lru_cache this - """ - if issubclass(cls, ModelSimple) or cls in PRIMITIVE_TYPES: - return True - elif issubclass(cls, ModelComposed): - if not cls._composed_schemas["oneOf"]: - return False - return any(allows_single_value_input(c) for c in cls._composed_schemas["oneOf"]) - return False - - -def composed_model_input_classes(cls): - """ - This function returns a list of the possible models that can be accepted as - inputs. - TODO: lru_cache this - """ - if issubclass(cls, ModelSimple) or cls in PRIMITIVE_TYPES: - return [cls] - elif issubclass(cls, ModelNormal): - if cls.discriminator is None: - return [cls] - else: - return get_discriminated_classes(cls) - elif issubclass(cls, ModelComposed): - if not cls._composed_schemas["oneOf"]: - return [] - if cls.discriminator is None: - input_classes = [] - for c in cls._composed_schemas["oneOf"]: - input_classes.extend(composed_model_input_classes(c)) - return input_classes - else: - return get_discriminated_classes(cls) - return [] - - -class OpenApiModel(object): - """The base class for all OpenAPIModels""" - - def set_attribute(self, name, value): - # this is only used to set properties on self - - path_to_item = [] - if self._path_to_item: - path_to_item.extend(self._path_to_item) - path_to_item.append(name) - - if name in self.openapi_types: - required_types_mixed = self.openapi_types[name] - elif self.additional_properties_type is None: - raise ApiAttributeError("{0} has no attribute '{1}'".format(type(self).__name__, name), path_to_item) - elif self.additional_properties_type is not None: - required_types_mixed = self.additional_properties_type - - if get_simple_class(name) != str: - error_msg = type_error_message(var_name=name, var_value=name, valid_classes=(str,), key_type=True) - raise ApiTypeError(error_msg, path_to_item=path_to_item, valid_classes=(str,), key_type=True) - - if self._check_type: - value = validate_and_convert_types( - value, - required_types_mixed, - path_to_item, - self._spec_property_naming, - self._check_type, - configuration=self._configuration, - ) - if (name,) in self.allowed_values: - check_allowed_values(self.allowed_values, (name,), value) - if (name,) in self.validations: - check_validations(self.validations, (name,), value, self._configuration) - self.__dict__["_data_store"][name] = value - - def __repr__(self): - """For `print` and `pprint`""" - return self.to_str() - - def __ne__(self, other): - """Returns true if both objects are not equal""" - return not self == other - - def __setattr__(self, attr, value): - """set the value of an attribute using dot notation: `instance.attr = val`""" - self[attr] = value - - def __getattr__(self, attr): - """get the value of an attribute using dot notation: `instance.attr`""" - return self.__getitem__(attr) - - def __copy__(self): - cls = self.__class__ - if self.get("_spec_property_naming", False): - return cls._new_from_openapi_data(**self.__dict__) - else: - return new_cls.__new__(cls, **self.__dict__) - - def __deepcopy__(self, memo): - cls = self.__class__ - - if self.get("_spec_property_naming", False): - new_inst = cls._new_from_openapi_data() - else: - new_inst = cls.__new__(cls) - - for k, v in self.__dict__.items(): - setattr(new_inst, k, deepcopy(v, memo)) - return new_inst - - def __new__(cls, *args, **kwargs): - # this function uses the discriminator to - # pick a new schema/class to instantiate because a discriminator - # propertyName value was passed in - - if len(args) == 1: - arg = args[0] - if arg is None and is_type_nullable(cls): - # The input data is the 'null' value and the type is nullable. - return None - - if issubclass(cls, ModelComposed) and allows_single_value_input(cls): - model_kwargs = {} - oneof_instance = get_oneof_instance(cls, model_kwargs, kwargs, model_arg=arg) - return oneof_instance - - visited_composed_classes = kwargs.get("_visited_composed_classes", ()) - if cls.discriminator is None or cls in visited_composed_classes: - # Use case 1: this openapi schema (cls) does not have a discriminator - # Use case 2: we have already visited this class before and are sure that we - # want to instantiate it this time. We have visited this class deserializing - # a payload with a discriminator. During that process we traveled through - # this class but did not make an instance of it. Now we are making an - # instance of a composed class which contains cls in it, so this time make an instance of cls. - # - # Here's an example of use case 2: If Animal has a discriminator - # petType and we pass in "Dog", and the class Dog - # allOf includes Animal, we move through Animal - # once using the discriminator, and pick Dog. - # Then in the composed schema dog Dog, we will make an instance of the - # Animal class (because Dal has allOf: Animal) but this time we won't travel - # through Animal's discriminator because we passed in - # _visited_composed_classes = (Animal,) - - return super(OpenApiModel, cls).__new__(cls) - - # Get the name and value of the discriminator property. - # The discriminator name is obtained from the discriminator meta-data - # and the discriminator value is obtained from the input data. - discr_propertyname_py = list(cls.discriminator.keys())[0] - discr_propertyname_js = cls.attribute_map[discr_propertyname_py] - if discr_propertyname_js in kwargs: - discr_value = kwargs[discr_propertyname_js] - elif discr_propertyname_py in kwargs: - discr_value = kwargs[discr_propertyname_py] - else: - # The input data does not contain the discriminator property. - path_to_item = kwargs.get("_path_to_item", ()) - raise ApiValueError( - "Cannot deserialize input data due to missing discriminator. " - "The discriminator property '%s' is missing at path: %s" % (discr_propertyname_js, path_to_item) - ) - - # Implementation note: the last argument to get_discriminator_class - # is a list of visited classes. get_discriminator_class may recursively - # call itself and update the list of visited classes, and the initial - # value must be an empty list. Hence not using 'visited_composed_classes' - new_cls = get_discriminator_class(cls, discr_propertyname_py, discr_value, []) - if new_cls is None: - path_to_item = kwargs.get("_path_to_item", ()) - disc_prop_value = kwargs.get(discr_propertyname_js, kwargs.get(discr_propertyname_py)) - raise ApiValueError( - "Cannot deserialize input data due to invalid discriminator " - "value. The OpenAPI document has no mapping for discriminator " - "property '%s'='%s' at path: %s" % (discr_propertyname_js, disc_prop_value, path_to_item) - ) - - if new_cls in visited_composed_classes: - # if we are making an instance of a composed schema Descendent - # which allOf includes Ancestor, then Ancestor contains - # a discriminator that includes Descendent. - # So if we make an instance of Descendent, we have to make an - # instance of Ancestor to hold the allOf properties. - # This code detects that use case and makes the instance of Ancestor - # For example: - # When making an instance of Dog, _visited_composed_classes = (Dog,) - # then we make an instance of Animal to include in dog._composed_instances - # so when we are here, cls is Animal - # cls.discriminator != None - # cls not in _visited_composed_classes - # new_cls = Dog - # but we know we know that we already have Dog - # because it is in visited_composed_classes - # so make Animal here - return super(OpenApiModel, cls).__new__(cls) - - # Build a list containing all oneOf and anyOf descendants. - oneof_anyof_classes = None - if cls._composed_schemas is not None: - oneof_anyof_classes = cls._composed_schemas.get("oneOf", ()) + cls._composed_schemas.get("anyOf", ()) - oneof_anyof_child = new_cls in oneof_anyof_classes - kwargs["_visited_composed_classes"] = visited_composed_classes + (cls,) - - if cls._composed_schemas.get("allOf") and oneof_anyof_child: - # Validate that we can make self because when we make the - # new_cls it will not include the allOf validations in self - self_inst = super(OpenApiModel, cls).__new__(cls) - self_inst.__init__(*args, **kwargs) - - if kwargs.get("_spec_property_naming", False): - # when true, implies new is from deserialization - new_inst = new_cls._new_from_openapi_data(*args, **kwargs) - else: - new_inst = new_cls.__new__(new_cls, *args, **kwargs) - new_inst.__init__(*args, **kwargs) - - return new_inst - - @classmethod - @convert_js_args_to_python_args - def _new_from_openapi_data(cls, *args, **kwargs): - # this function uses the discriminator to - # pick a new schema/class to instantiate because a discriminator - # propertyName value was passed in - - if len(args) == 1: - arg = args[0] - if arg is None and is_type_nullable(cls): - # The input data is the 'null' value and the type is nullable. - return None - - if issubclass(cls, ModelComposed) and allows_single_value_input(cls): - model_kwargs = {} - oneof_instance = get_oneof_instance(cls, model_kwargs, kwargs, model_arg=arg) - return oneof_instance - - visited_composed_classes = kwargs.get("_visited_composed_classes", ()) - if cls.discriminator is None or cls in visited_composed_classes: - # Use case 1: this openapi schema (cls) does not have a discriminator - # Use case 2: we have already visited this class before and are sure that we - # want to instantiate it this time. We have visited this class deserializing - # a payload with a discriminator. During that process we traveled through - # this class but did not make an instance of it. Now we are making an - # instance of a composed class which contains cls in it, so this time make an instance of cls. - # - # Here's an example of use case 2: If Animal has a discriminator - # petType and we pass in "Dog", and the class Dog - # allOf includes Animal, we move through Animal - # once using the discriminator, and pick Dog. - # Then in the composed schema dog Dog, we will make an instance of the - # Animal class (because Dal has allOf: Animal) but this time we won't travel - # through Animal's discriminator because we passed in - # _visited_composed_classes = (Animal,) - - return cls._from_openapi_data(*args, **kwargs) - - # Get the name and value of the discriminator property. - # The discriminator name is obtained from the discriminator meta-data - # and the discriminator value is obtained from the input data. - discr_propertyname_py = list(cls.discriminator.keys())[0] - discr_propertyname_js = cls.attribute_map[discr_propertyname_py] - if discr_propertyname_js in kwargs: - discr_value = kwargs[discr_propertyname_js] - elif discr_propertyname_py in kwargs: - discr_value = kwargs[discr_propertyname_py] - else: - # The input data does not contain the discriminator property. - path_to_item = kwargs.get("_path_to_item", ()) - raise ApiValueError( - "Cannot deserialize input data due to missing discriminator. " - "The discriminator property '%s' is missing at path: %s" % (discr_propertyname_js, path_to_item) - ) - - # Implementation note: the last argument to get_discriminator_class - # is a list of visited classes. get_discriminator_class may recursively - # call itself and update the list of visited classes, and the initial - # value must be an empty list. Hence not using 'visited_composed_classes' - new_cls = get_discriminator_class(cls, discr_propertyname_py, discr_value, []) - if new_cls is None: - path_to_item = kwargs.get("_path_to_item", ()) - disc_prop_value = kwargs.get(discr_propertyname_js, kwargs.get(discr_propertyname_py)) - raise ApiValueError( - "Cannot deserialize input data due to invalid discriminator " - "value. The OpenAPI document has no mapping for discriminator " - "property '%s'='%s' at path: %s" % (discr_propertyname_js, disc_prop_value, path_to_item) - ) - - if new_cls in visited_composed_classes: - # if we are making an instance of a composed schema Descendent - # which allOf includes Ancestor, then Ancestor contains - # a discriminator that includes Descendent. - # So if we make an instance of Descendent, we have to make an - # instance of Ancestor to hold the allOf properties. - # This code detects that use case and makes the instance of Ancestor - # For example: - # When making an instance of Dog, _visited_composed_classes = (Dog,) - # then we make an instance of Animal to include in dog._composed_instances - # so when we are here, cls is Animal - # cls.discriminator != None - # cls not in _visited_composed_classes - # new_cls = Dog - # but we know we know that we already have Dog - # because it is in visited_composed_classes - # so make Animal here - return cls._from_openapi_data(*args, **kwargs) - - # Build a list containing all oneOf and anyOf descendants. - oneof_anyof_classes = None - if cls._composed_schemas is not None: - oneof_anyof_classes = cls._composed_schemas.get("oneOf", ()) + cls._composed_schemas.get("anyOf", ()) - oneof_anyof_child = new_cls in oneof_anyof_classes - kwargs["_visited_composed_classes"] = visited_composed_classes + (cls,) - - if cls._composed_schemas.get("allOf") and oneof_anyof_child: - # Validate that we can make self because when we make the - # new_cls it will not include the allOf validations in self - self_inst = cls._from_openapi_data(*args, **kwargs) - - new_inst = new_cls._new_from_openapi_data(*args, **kwargs) - return new_inst - - -class ModelSimple(OpenApiModel): - """the parent class of models whose type != object in their - swagger/openapi""" - - def __setitem__(self, name, value): - """set the value of an attribute using square-bracket notation: `instance[attr] = val`""" - if name in self.required_properties: - self.__dict__[name] = value - return - - self.set_attribute(name, value) - - def get(self, name, default=None): - """returns the value of an attribute or some default value if the attribute was not set""" - if name in self.required_properties: - return self.__dict__[name] - - return self.__dict__["_data_store"].get(name, default) - - def __getitem__(self, name): - """get the value of an attribute using square-bracket notation: `instance[attr]`""" - if name in self: - return self.get(name) - - raise ApiAttributeError( - "{0} has no attribute '{1}'".format(type(self).__name__, name), [e for e in [self._path_to_item, name] if e] - ) - - def __contains__(self, name): - """used by `in` operator to check if an attribute value was set in an instance: `'attr' in instance`""" - if name in self.required_properties: - return name in self.__dict__ - - return name in self.__dict__["_data_store"] - - def to_str(self): - """Returns the string representation of the model""" - return str(self.value) - - def __eq__(self, other): - """Returns true if both objects are equal""" - if not isinstance(other, self.__class__): - return False - - this_val = self._data_store["value"] - that_val = other._data_store["value"] - types = set() - types.add(this_val.__class__) - types.add(that_val.__class__) - vals_equal = this_val == that_val - return vals_equal - - -class ModelNormal(OpenApiModel): - """the parent class of models whose type == object in their - swagger/openapi""" - - def __setitem__(self, name, value): - """set the value of an attribute using square-bracket notation: `instance[attr] = val`""" - if name in self.required_properties: - self.__dict__[name] = value - return - - self.set_attribute(name, value) - - def get(self, name, default=None): - """returns the value of an attribute or some default value if the attribute was not set""" - if name in self.required_properties: - return self.__dict__[name] - - return self.__dict__["_data_store"].get(name, default) - - def __getitem__(self, name): - """get the value of an attribute using square-bracket notation: `instance[attr]`""" - if name in self: - return self.get(name) - - raise ApiAttributeError( - "{0} has no attribute '{1}'".format(type(self).__name__, name), [e for e in [self._path_to_item, name] if e] - ) - - def __contains__(self, name): - """used by `in` operator to check if an attribute value was set in an instance: `'attr' in instance`""" - if name in self.required_properties: - return name in self.__dict__ - - return name in self.__dict__["_data_store"] - - def to_dict(self): - """Returns the model properties as a dict""" - return model_to_dict(self, serialize=False) - - def to_str(self): - """Returns the string representation of the model""" - return pprint.pformat(self.to_dict()) - - def __eq__(self, other): - """Returns true if both objects are equal""" - if not isinstance(other, self.__class__): - return False - - if not set(self._data_store.keys()) == set(other._data_store.keys()): - return False - for _var_name, this_val in self._data_store.items(): - that_val = other._data_store[_var_name] - types = set() - types.add(this_val.__class__) - types.add(that_val.__class__) - vals_equal = this_val == that_val - if not vals_equal: - return False - return True - - -class ModelComposed(OpenApiModel): - """the parent class of models whose type == object in their - swagger/openapi and have oneOf/allOf/anyOf - - When one sets a property we use var_name_to_model_instances to store the value in - the correct class instances + run any type checking + validation code. - When one gets a property we use var_name_to_model_instances to get the value - from the correct class instances. - This allows multiple composed schemas to contain the same property with additive - constraints on the value. - - _composed_schemas (dict) stores the anyOf/allOf/oneOf classes - key (str): allOf/oneOf/anyOf - value (list): the classes in the XOf definition. - Note: none_type can be included when the openapi document version >= 3.1.0 - _composed_instances (list): stores a list of instances of the composed schemas - defined in _composed_schemas. When properties are accessed in the self instance, - they are returned from the self._data_store or the data stores in the instances - in self._composed_schemas - _var_name_to_model_instances (dict): maps between a variable name on self and - the composed instances (self included) which contain that data - key (str): property name - value (list): list of class instances, self or instances in _composed_instances - which contain the value that the key is referring to. - """ - - def __setitem__(self, name, value): - """set the value of an attribute using square-bracket notation: `instance[attr] = val`""" - if name in self.required_properties: - self.__dict__[name] = value - return - - """ - Use cases: - 1. additional_properties_type is None (additionalProperties == False in spec) - Check for property presence in self.openapi_types - if not present then throw an error - if present set in self, set attribute - always set on composed schemas - 2. additional_properties_type exists - set attribute on self - always set on composed schemas - """ - if self.additional_properties_type is None: - """ - For an attribute to exist on a composed schema it must: - - fulfill schema_requirements in the self composed schema not considering oneOf/anyOf/allOf schemas AND - - fulfill schema_requirements in each oneOf/anyOf/allOf schemas - - schema_requirements: - For an attribute to exist on a schema it must: - - be present in properties at the schema OR - - have additionalProperties unset (defaults additionalProperties = any type) OR - - have additionalProperties set - """ - if name not in self.openapi_types: - raise ApiAttributeError( - "{0} has no attribute '{1}'".format(type(self).__name__, name), - [e for e in [self._path_to_item, name] if e], - ) - # attribute must be set on self and composed instances - self.set_attribute(name, value) - for model_instance in self._composed_instances: - setattr(model_instance, name, value) - if name not in self._var_name_to_model_instances: - # we assigned an additional property - self.__dict__["_var_name_to_model_instances"][name] = self._composed_instances + [self] - return None - - __unset_attribute_value__ = object() - - def get(self, name, default=None): - """returns the value of an attribute or some default value if the attribute was not set""" - if name in self.required_properties: - return self.__dict__[name] - - # get the attribute from the correct instance - model_instances = self._var_name_to_model_instances.get(name) - values = [] - # A composed model stores self and child (oneof/anyOf/allOf) models under - # self._var_name_to_model_instances. - # Any property must exist in self and all model instances - # The value stored in all model instances must be the same - if model_instances: - for model_instance in model_instances: - if name in model_instance._data_store: - v = model_instance._data_store[name] - if v not in values: - values.append(v) - len_values = len(values) - if len_values == 0: - return default - elif len_values == 1: - return values[0] - elif len_values > 1: - raise ApiValueError( - "Values stored for property {0} in {1} differ when looking " - "at self and self's composed instances. All values must be " - "the same".format(name, type(self).__name__), - [e for e in [self._path_to_item, name] if e], - ) - - def __getitem__(self, name): - """get the value of an attribute using square-bracket notation: `instance[attr]`""" - value = self.get(name, self.__unset_attribute_value__) - if value is self.__unset_attribute_value__: - raise ApiAttributeError( - "{0} has no attribute '{1}'".format(type(self).__name__, name), - [e for e in [self._path_to_item, name] if e], - ) - return value - - def __contains__(self, name): - """used by `in` operator to check if an attribute value was set in an instance: `'attr' in instance`""" - - if name in self.required_properties: - return name in self.__dict__ - - model_instances = self._var_name_to_model_instances.get(name, self._additional_properties_model_instances) - - if model_instances: - for model_instance in model_instances: - if name in model_instance._data_store: - return True - - return False - - def to_dict(self): - """Returns the model properties as a dict""" - return model_to_dict(self, serialize=False) - - def to_str(self): - """Returns the string representation of the model""" - return pprint.pformat(self.to_dict()) - - def __eq__(self, other): - """Returns true if both objects are equal""" - if not isinstance(other, self.__class__): - return False - - if not set(self._data_store.keys()) == set(other._data_store.keys()): - return False - for _var_name, this_val in self._data_store.items(): - that_val = other._data_store[_var_name] - types = set() - types.add(this_val.__class__) - types.add(that_val.__class__) - vals_equal = this_val == that_val - if not vals_equal: - return False - return True - - -COERCION_INDEX_BY_TYPE = { - ModelComposed: 0, - ModelNormal: 1, - ModelSimple: 2, - none_type: 3, # The type of 'None'. - list: 4, - dict: 5, - float: 6, - int: 7, - bool: 8, - datetime: 9, - date: 10, - str: 11, - file_type: 12, # 'file_type' is an alias for the built-in 'file' or 'io.IOBase' type. -} - -# these are used to limit what type conversions we try to do -# when we have a valid type already and we want to try converting -# to another type -UPCONVERSION_TYPE_PAIRS = ( - (str, datetime), - (str, date), - (int, float), # A float may be serialized as an integer, e.g. '3' is a valid serialized float. - (list, ModelComposed), - (dict, ModelComposed), - (str, ModelComposed), - (int, ModelComposed), - (float, ModelComposed), - (list, ModelComposed), - (list, ModelNormal), - (dict, ModelNormal), - (str, ModelSimple), - (int, ModelSimple), - (float, ModelSimple), - (list, ModelSimple), -) - -COERCIBLE_TYPE_PAIRS = { - False: ( # client instantiation of a model with client data - # (dict, ModelComposed), - # (list, ModelComposed), - # (dict, ModelNormal), - # (list, ModelNormal), - # (str, ModelSimple), - # (int, ModelSimple), - # (float, ModelSimple), - # (list, ModelSimple), - # (str, int), - # (str, float), - # (str, datetime), - # (str, date), - # (int, str), - # (float, str), - ), - True: ( # server -> client data - (dict, ModelComposed), - (list, ModelComposed), - (dict, ModelNormal), - (list, ModelNormal), - (str, ModelSimple), - (int, ModelSimple), - (float, ModelSimple), - (list, ModelSimple), - # (str, int), - # (str, float), - (str, datetime), - (str, date), - # (int, str), - # (float, str), - (str, file_type), - ), -} - - -def get_simple_class(input_value): - """Returns an input_value's simple class that we will use for type checking - Python2: - float and int will return int, where int is the python3 int backport - str and unicode will return str, where str is the python3 str backport - Note: float and int ARE both instances of int backport - Note: str_py2 and unicode_py2 are NOT both instances of str backport - - Args: - input_value (class/class_instance): the item for which we will return - the simple class - """ - if isinstance(input_value, type): - # input_value is a class - return input_value - elif isinstance(input_value, tuple): - return tuple - elif isinstance(input_value, list): - return list - elif isinstance(input_value, dict): - return dict - elif isinstance(input_value, none_type): - return none_type - elif isinstance(input_value, file_type): - return file_type - elif isinstance(input_value, bool): - # this must be higher than the int check because - # isinstance(True, int) == True - return bool - elif isinstance(input_value, int): - return int - elif isinstance(input_value, datetime): - # this must be higher than the date check because - # isinstance(datetime_instance, date) == True - return datetime - elif isinstance(input_value, date): - return date - elif isinstance(input_value, str): - return str - return type(input_value) - - -def check_allowed_values(allowed_values, input_variable_path, input_values): - """Raises an exception if the input_values are not allowed - - Args: - allowed_values (dict): the allowed_values dict - input_variable_path (tuple): the path to the input variable - input_values (list/str/int/float/date/datetime): the values that we - are checking to see if they are in allowed_values - """ - these_allowed_values = list(allowed_values[input_variable_path].values()) - if isinstance(input_values, list) and not set(input_values).issubset(set(these_allowed_values)): - invalid_values = (", ".join(map(str, set(input_values) - set(these_allowed_values))),) - raise ApiValueError( - "Invalid values for `%s` [%s], must be a subset of [%s]" - % (input_variable_path[0], invalid_values, ", ".join(map(str, these_allowed_values))) - ) - elif isinstance(input_values, dict) and not set(input_values.keys()).issubset(set(these_allowed_values)): - invalid_values = ", ".join(map(str, set(input_values.keys()) - set(these_allowed_values))) - raise ApiValueError( - "Invalid keys in `%s` [%s], must be a subset of [%s]" - % (input_variable_path[0], invalid_values, ", ".join(map(str, these_allowed_values))) - ) - elif not isinstance(input_values, (list, dict)) and input_values not in these_allowed_values: - raise ApiValueError( - "Invalid value for `%s` (%s), must be one of %s" - % (input_variable_path[0], input_values, these_allowed_values) - ) - - -def is_json_validation_enabled(schema_keyword, configuration=None): - """Returns true if JSON schema validation is enabled for the specified - validation keyword. This can be used to skip JSON schema structural validation - as requested in the configuration. - - Args: - schema_keyword (string): the name of a JSON schema validation keyword. - configuration (Configuration): the configuration class. - """ - - return ( - configuration is None - or not hasattr(configuration, "_disabled_client_side_validations") - or schema_keyword not in configuration._disabled_client_side_validations - ) - - -def check_validations(validations, input_variable_path, input_values, configuration=None): - """Raises an exception if the input_values are invalid - - Args: - validations (dict): the validation dictionary. - input_variable_path (tuple): the path to the input variable. - input_values (list/str/int/float/date/datetime): the values that we - are checking. - configuration (Configuration): the configuration class. - """ - - if input_values is None: - return - - current_validations = validations[input_variable_path] - if ( - is_json_validation_enabled("multipleOf", configuration) - and "multiple_of" in current_validations - and isinstance(input_values, (int, float)) - and not (float(input_values) / current_validations["multiple_of"]).is_integer() - ): - # Note 'multipleOf' will be as good as the floating point arithmetic. - raise ApiValueError( - "Invalid value for `%s`, value must be a multiple of `%s`" - % (input_variable_path[0], current_validations["multiple_of"]) - ) - - if ( - is_json_validation_enabled("maxLength", configuration) - and "max_length" in current_validations - and len(input_values) > current_validations["max_length"] - ): - raise ApiValueError( - "Invalid value for `%s`, length must be less than or equal to `%s`" - % (input_variable_path[0], current_validations["max_length"]) - ) - - if ( - is_json_validation_enabled("minLength", configuration) - and "min_length" in current_validations - and len(input_values) < current_validations["min_length"] - ): - raise ApiValueError( - "Invalid value for `%s`, length must be greater than or equal to `%s`" - % (input_variable_path[0], current_validations["min_length"]) - ) - - if ( - is_json_validation_enabled("maxItems", configuration) - and "max_items" in current_validations - and len(input_values) > current_validations["max_items"] - ): - raise ApiValueError( - "Invalid value for `%s`, number of items must be less than or equal to `%s`" - % (input_variable_path[0], current_validations["max_items"]) - ) - - if ( - is_json_validation_enabled("minItems", configuration) - and "min_items" in current_validations - and len(input_values) < current_validations["min_items"] - ): - raise ValueError( - "Invalid value for `%s`, number of items must be greater than or equal to `%s`" - % (input_variable_path[0], current_validations["min_items"]) - ) - - items = ("exclusive_maximum", "inclusive_maximum", "exclusive_minimum", "inclusive_minimum") - if any(item in current_validations for item in items): - if isinstance(input_values, list): - max_val = max(input_values) - min_val = min(input_values) - elif isinstance(input_values, dict): - max_val = max(input_values.values()) - min_val = min(input_values.values()) - else: - max_val = input_values - min_val = input_values - - if ( - is_json_validation_enabled("exclusiveMaximum", configuration) - and "exclusive_maximum" in current_validations - and max_val >= current_validations["exclusive_maximum"] - ): - raise ApiValueError( - "Invalid value for `%s`, must be a value less than `%s`" - % (input_variable_path[0], current_validations["exclusive_maximum"]) - ) - - if ( - is_json_validation_enabled("maximum", configuration) - and "inclusive_maximum" in current_validations - and max_val > current_validations["inclusive_maximum"] - ): - raise ApiValueError( - "Invalid value for `%s`, must be a value less than or equal to `%s`" - % (input_variable_path[0], current_validations["inclusive_maximum"]) - ) - - if ( - is_json_validation_enabled("exclusiveMinimum", configuration) - and "exclusive_minimum" in current_validations - and min_val <= current_validations["exclusive_minimum"] - ): - raise ApiValueError( - "Invalid value for `%s`, must be a value greater than `%s`" - % (input_variable_path[0], current_validations["exclusive_maximum"]) - ) - - if ( - is_json_validation_enabled("minimum", configuration) - and "inclusive_minimum" in current_validations - and min_val < current_validations["inclusive_minimum"] - ): - raise ApiValueError( - "Invalid value for `%s`, must be a value greater than or equal to `%s`" - % (input_variable_path[0], current_validations["inclusive_minimum"]) - ) - flags = current_validations.get("regex", {}).get("flags", 0) - if ( - is_json_validation_enabled("pattern", configuration) - and "regex" in current_validations - and not re.search(current_validations["regex"]["pattern"], input_values, flags=flags) - ): - err_msg = r"Invalid value for `%s`, must match regular expression `%s`" % ( - input_variable_path[0], - current_validations["regex"]["pattern"], - ) - if flags != 0: - # Don't print the regex flags if the flags are not - # specified in the OAS document. - err_msg = r"%s with flags=`%s`" % (err_msg, flags) - raise ApiValueError(err_msg) - - -def order_response_types(required_types): - """Returns the required types sorted in coercion order - - Args: - required_types (list/tuple): collection of classes or instance of - list or dict with class information inside it. - - Returns: - (list): coercion order sorted collection of classes or instance - of list or dict with class information inside it. - """ - - def index_getter(class_or_instance): - if isinstance(class_or_instance, list): - return COERCION_INDEX_BY_TYPE[list] - elif isinstance(class_or_instance, dict): - return COERCION_INDEX_BY_TYPE[dict] - elif inspect.isclass(class_or_instance) and issubclass(class_or_instance, ModelComposed): - return COERCION_INDEX_BY_TYPE[ModelComposed] - elif inspect.isclass(class_or_instance) and issubclass(class_or_instance, ModelNormal): - return COERCION_INDEX_BY_TYPE[ModelNormal] - elif inspect.isclass(class_or_instance) and issubclass(class_or_instance, ModelSimple): - return COERCION_INDEX_BY_TYPE[ModelSimple] - elif class_or_instance in COERCION_INDEX_BY_TYPE: - return COERCION_INDEX_BY_TYPE[class_or_instance] - raise ApiValueError("Unsupported type: %s" % class_or_instance) - - sorted_types = sorted(required_types, key=lambda class_or_instance: index_getter(class_or_instance)) - return sorted_types - - -def remove_uncoercible(required_types_classes, current_item, spec_property_naming, must_convert=True): - """Only keeps the type conversions that are possible - - Args: - required_types_classes (tuple): tuple of classes that are required - these should be ordered by COERCION_INDEX_BY_TYPE - spec_property_naming (bool): True if the variable names in the input - data are serialized names as specified in the OpenAPI document. - False if the variables names in the input data are python - variable names in PEP-8 snake case. - current_item (any): the current item (input data) to be converted - - Keyword Args: - must_convert (bool): if True the item to convert is of the wrong - type and we want a big list of coercibles - if False, we want a limited list of coercibles - - Returns: - (list): the remaining coercible required types, classes only - """ - current_type_simple = get_simple_class(current_item) - - results_classes = [] - for required_type_class in required_types_classes: - # convert our models to OpenApiModel - required_type_class_simplified = required_type_class - if isinstance(required_type_class_simplified, type): - if issubclass(required_type_class_simplified, ModelComposed): - required_type_class_simplified = ModelComposed - elif issubclass(required_type_class_simplified, ModelNormal): - required_type_class_simplified = ModelNormal - elif issubclass(required_type_class_simplified, ModelSimple): - required_type_class_simplified = ModelSimple - - if required_type_class_simplified == current_type_simple: - # don't consider converting to one's own class - continue - - class_pair = (current_type_simple, required_type_class_simplified) - if must_convert and class_pair in COERCIBLE_TYPE_PAIRS[spec_property_naming]: - results_classes.append(required_type_class) - elif class_pair in UPCONVERSION_TYPE_PAIRS: - results_classes.append(required_type_class) - return results_classes - - -def get_discriminated_classes(cls): - """ - Returns all the classes that a discriminator converts to - TODO: lru_cache this - """ - possible_classes = [] - key = list(cls.discriminator.keys())[0] - if is_type_nullable(cls): - possible_classes.append(cls) - for discr_cls in cls.discriminator[key].values(): - if hasattr(discr_cls, "discriminator") and discr_cls.discriminator is not None: - possible_classes.extend(get_discriminated_classes(discr_cls)) - else: - possible_classes.append(discr_cls) - return possible_classes - - -def get_possible_classes(cls, from_server_context): - # TODO: lru_cache this - possible_classes = [cls] - if from_server_context: - return possible_classes - if hasattr(cls, "discriminator") and cls.discriminator is not None: - possible_classes = [] - possible_classes.extend(get_discriminated_classes(cls)) - elif issubclass(cls, ModelComposed): - possible_classes.extend(composed_model_input_classes(cls)) - return possible_classes - - -def get_required_type_classes(required_types_mixed, spec_property_naming): - """Converts the tuple required_types into a tuple and a dict described - below - - Args: - required_types_mixed (tuple/list): will contain either classes or - instance of list or dict - spec_property_naming (bool): if True these values came from the - server, and we use the data types in our endpoints. - If False, we are client side and we need to include - oneOf and discriminator classes inside the data types in our endpoints - - Returns: - (valid_classes, dict_valid_class_to_child_types_mixed): - valid_classes (tuple): the valid classes that the current item - should be - dict_valid_class_to_child_types_mixed (dict): - valid_class (class): this is the key - child_types_mixed (list/dict/tuple): describes the valid child - types - """ - valid_classes = [] - child_req_types_by_current_type = {} - for required_type in required_types_mixed: - if isinstance(required_type, list): - valid_classes.append(list) - child_req_types_by_current_type[list] = required_type - elif isinstance(required_type, tuple): - valid_classes.append(tuple) - child_req_types_by_current_type[tuple] = required_type - elif isinstance(required_type, dict): - valid_classes.append(dict) - child_req_types_by_current_type[dict] = required_type[str] - else: - valid_classes.extend(get_possible_classes(required_type, spec_property_naming)) - return tuple(valid_classes), child_req_types_by_current_type - - -def change_keys_js_to_python(input_dict, model_class): - """ - Converts from javascript_key keys in the input_dict to python_keys in - the output dict using the mapping in model_class. - If the input_dict contains a key which does not declared in the model_class, - the key is added to the output dict as is. The assumption is the model_class - may have undeclared properties (additionalProperties attribute in the OAS - document). - """ - - if getattr(model_class, "attribute_map", None) is None: - return input_dict - output_dict = {} - reversed_attr_map = {value: key for key, value in model_class.attribute_map.items()} - for javascript_key, value in input_dict.items(): - python_key = reversed_attr_map.get(javascript_key) - if python_key is None: - # if the key is unknown, it is in error or it is an - # additionalProperties variable - python_key = javascript_key - output_dict[python_key] = value - return output_dict - - -def get_type_error(var_value, path_to_item, valid_classes, key_type=False): - error_msg = type_error_message( - var_name=path_to_item[-1], var_value=var_value, valid_classes=valid_classes, key_type=key_type - ) - return ApiTypeError(error_msg, path_to_item=path_to_item, valid_classes=valid_classes, key_type=key_type) - - -def deserialize_primitive(data, klass, path_to_item): - """Deserializes string to primitive type. - - :param data: str/int/float - :param klass: str/class the class to convert to - - :return: int, float, str, bool, date, datetime - """ - additional_message = "" - try: - if klass in {datetime, date}: - additional_message = ( - "If you need your parameter to have a fallback " - "string value, please set its type as `type: {}` in your " - "spec. That allows the value to be any type. " - ) - if klass == datetime: - if len(data) < 8: - raise ValueError("This is not a datetime") - # The string should be in iso8601 datetime format. - parsed_datetime = parse(data) - date_only = ( - parsed_datetime.hour == 0 - and parsed_datetime.minute == 0 - and parsed_datetime.second == 0 - and parsed_datetime.tzinfo is None - and 8 <= len(data) <= 10 - ) - if date_only: - raise ValueError("This is a date, not a datetime") - return parsed_datetime - elif klass == date: - if len(data) < 8: - raise ValueError("This is not a date") - return parse(data).date() - else: - converted_value = klass(data) - if isinstance(data, str) and klass == float: - if str(converted_value) != data: - # '7' -> 7.0 -> '7.0' != '7' - raise ValueError("This is not a float") - return converted_value - except (OverflowError, ValueError) as ex: - # parse can raise OverflowError - raise ApiValueError( - "{0}Failed to parse {1} as {2}".format(additional_message, repr(data), klass.__name__), - path_to_item=path_to_item, - ) from ex - - -def get_discriminator_class(model_class, discr_name, discr_value, cls_visited): - """Returns the child class specified by the discriminator. - - Args: - model_class (OpenApiModel): the model class. - discr_name (string): the name of the discriminator property. - discr_value (any): the discriminator value. - cls_visited (list): list of model classes that have been visited. - Used to determine the discriminator class without - visiting circular references indefinitely. - - Returns: - used_model_class (class/None): the chosen child class that will be used - to deserialize the data, for example dog.Dog. - If a class is not found, None is returned. - """ - - if model_class in cls_visited: - # The class has already been visited and no suitable class was found. - return None - cls_visited.append(model_class) - used_model_class = None - if discr_name in model_class.discriminator: - class_name_to_discr_class = model_class.discriminator[discr_name] - used_model_class = class_name_to_discr_class.get(discr_value) - if used_model_class is None: - # We didn't find a discriminated class in class_name_to_discr_class. - # So look in the ancestor or descendant discriminators - # The discriminator mapping may exist in a descendant (anyOf, oneOf) - # or ancestor (allOf). - # Ancestor example: in the GrandparentAnimal -> ParentPet -> ChildCat - # hierarchy, the discriminator mappings may be defined at any level - # in the hierarchy. - # Descendant example: mammal -> whale/zebra/Pig -> BasquePig/DanishPig - # if we try to make BasquePig from mammal, we need to travel through - # the oneOf descendant discriminators to find BasquePig - descendant_classes = model_class._composed_schemas.get("oneOf", ()) + model_class._composed_schemas.get( - "anyOf", () - ) - ancestor_classes = model_class._composed_schemas.get("allOf", ()) - possible_classes = descendant_classes + ancestor_classes - for cls in possible_classes: - # Check if the schema has inherited discriminators. - if hasattr(cls, "discriminator") and cls.discriminator is not None: - used_model_class = get_discriminator_class(cls, discr_name, discr_value, cls_visited) - if used_model_class is not None: - return used_model_class - return used_model_class - - -def deserialize_model(model_data, model_class, path_to_item, check_type, configuration, spec_property_naming): - """Deserializes model_data to model instance. - - Args: - model_data (int/str/float/bool/none_type/list/dict): data to instantiate the model - model_class (OpenApiModel): the model class - path_to_item (list): path to the model in the received data - check_type (bool): whether to check the data tupe for the values in - the model - configuration (Configuration): the instance to use to convert files - spec_property_naming (bool): True if the variable names in the input - data are serialized names as specified in the OpenAPI document. - False if the variables names in the input data are python - variable names in PEP-8 snake case. - - Returns: - model instance - - Raise: - ApiTypeError - ApiValueError - ApiKeyError - """ - - kw_args = dict( - _check_type=check_type, - _path_to_item=path_to_item, - _configuration=configuration, - _spec_property_naming=spec_property_naming, - ) - - if issubclass(model_class, ModelSimple): - return model_class._new_from_openapi_data(model_data, **kw_args) - elif isinstance(model_data, list): - return model_class._new_from_openapi_data(*model_data, **kw_args) - if isinstance(model_data, dict): - kw_args.update(model_data) - return model_class._new_from_openapi_data(**kw_args) - elif isinstance(model_data, PRIMITIVE_TYPES): - return model_class._new_from_openapi_data(model_data, **kw_args) - - -def deserialize_file(response_data, configuration, content_disposition=None): - """Deserializes body to file - - Saves response body into a file in a temporary folder, - using the filename from the `Content-Disposition` header if provided. - - Args: - param response_data (str): the file data to write - configuration (Configuration): the instance to use to convert files - - Keyword Args: - content_disposition (str): the value of the Content-Disposition - header - - Returns: - (file_type): the deserialized file which is open - The user is responsible for closing and reading the file - """ - fd, path = tempfile.mkstemp(dir=configuration.temp_folder_path) - os.close(fd) - os.remove(path) - - if content_disposition: - filename = re.search(r'filename=[\'"]?([^\'"\s]+)[\'"]?', content_disposition).group(1) - path = os.path.join(os.path.dirname(path), filename) - - with open(path, "wb") as f: - if isinstance(response_data, str): - # change str to bytes so we can write it - response_data = response_data.encode("utf-8") - f.write(response_data) - - f = open(path, "rb") - return f - - -def attempt_convert_item( - input_value, - valid_classes, - path_to_item, - configuration, - spec_property_naming, - key_type=False, - must_convert=False, - check_type=True, -): - """ - Args: - input_value (any): the data to convert - valid_classes (any): the classes that are valid - path_to_item (list): the path to the item to convert - configuration (Configuration): the instance to use to convert files - spec_property_naming (bool): True if the variable names in the input - data are serialized names as specified in the OpenAPI document. - False if the variables names in the input data are python - variable names in PEP-8 snake case. - key_type (bool): if True we need to convert a key type (not supported) - must_convert (bool): if True we must convert - check_type (bool): if True we check the type or the returned data in - ModelComposed/ModelNormal/ModelSimple instances - - Returns: - instance (any) the fixed item - - Raises: - ApiTypeError - ApiValueError - ApiKeyError - """ - valid_classes_ordered = order_response_types(valid_classes) - valid_classes_coercible = remove_uncoercible(valid_classes_ordered, input_value, spec_property_naming) - if not valid_classes_coercible or key_type: - # we do not handle keytype errors, json will take care - # of this for us - if configuration is None or not configuration.discard_unknown_keys: - raise get_type_error(input_value, path_to_item, valid_classes, key_type=key_type) - for valid_class in valid_classes_coercible: - try: - if issubclass(valid_class, OpenApiModel): - return deserialize_model( - input_value, valid_class, path_to_item, check_type, configuration, spec_property_naming - ) - elif valid_class == file_type: - return deserialize_file(input_value, configuration) - return deserialize_primitive(input_value, valid_class, path_to_item) - except (ApiTypeError, ApiValueError, ApiKeyError) as conversion_exc: - if must_convert: - raise conversion_exc - # if we have conversion errors when must_convert == False - # we ignore the exception and move on to the next class - continue - # we were unable to convert, must_convert == False - return input_value - - -def is_type_nullable(input_type): - """ - Returns true if None is an allowed value for the specified input_type. - - A type is nullable if at least one of the following conditions is true: - 1. The OAS 'nullable' attribute has been specified, - 1. The type is the 'null' type, - 1. The type is a anyOf/oneOf composed schema, and a child schema is - the 'null' type. - Args: - input_type (type): the class of the input_value that we are - checking - Returns: - bool - """ - if input_type is none_type: - return True - if issubclass(input_type, OpenApiModel) and input_type._nullable: - return True - if issubclass(input_type, ModelComposed): - # If oneOf/anyOf, check if the 'null' type is one of the allowed types. - for t in input_type._composed_schemas.get("oneOf", ()): - if is_type_nullable(t): - return True - for t in input_type._composed_schemas.get("anyOf", ()): - if is_type_nullable(t): - return True - return False - - -def is_valid_type(input_class_simple, valid_classes): - """ - Args: - input_class_simple (class): the class of the input_value that we are - checking - valid_classes (tuple): the valid classes that the current item - should be - Returns: - bool - """ - if issubclass(input_class_simple, OpenApiModel) and valid_classes == ( - bool, - date, - datetime, - dict, - float, - int, - list, - str, - none_type, - ): - return True - valid_type = input_class_simple in valid_classes - if not valid_type and (issubclass(input_class_simple, OpenApiModel) or input_class_simple is none_type): - for valid_class in valid_classes: - if input_class_simple is none_type and is_type_nullable(valid_class): - # Schema is oneOf/anyOf and the 'null' type is one of the allowed types. - return True - if not (issubclass(valid_class, OpenApiModel) and valid_class.discriminator): - continue - discr_propertyname_py = list(valid_class.discriminator.keys())[0] - discriminator_classes = valid_class.discriminator[discr_propertyname_py].values() - valid_type = is_valid_type(input_class_simple, discriminator_classes) - if valid_type: - return True - return valid_type - - -def validate_and_convert_types( - input_value, required_types_mixed, path_to_item, spec_property_naming, _check_type, configuration=None -): - """Raises a TypeError is there is a problem, otherwise returns value - - Args: - input_value (any): the data to validate/convert - required_types_mixed (list/dict/tuple): A list of - valid classes, or a list tuples of valid classes, or a dict where - the value is a tuple of value classes - path_to_item: (list) the path to the data being validated - this stores a list of keys or indices to get to the data being - validated - spec_property_naming (bool): True if the variable names in the input - data are serialized names as specified in the OpenAPI document. - False if the variables names in the input data are python - variable names in PEP-8 snake case. - _check_type: (boolean) if true, type will be checked and conversion - will be attempted. - configuration: (Configuration): the configuration class to use - when converting file_type items. - If passed, conversion will be attempted when possible - If not passed, no conversions will be attempted and - exceptions will be raised - - Returns: - the correctly typed value - - Raises: - ApiTypeError - """ - results = get_required_type_classes(required_types_mixed, spec_property_naming) - valid_classes, child_req_types_by_current_type = results - - input_class_simple = get_simple_class(input_value) - valid_type = is_valid_type(input_class_simple, valid_classes) - if not valid_type: - if configuration: - # if input_value is not valid_type try to convert it - converted_instance = attempt_convert_item( - input_value, - valid_classes, - path_to_item, - configuration, - spec_property_naming, - key_type=False, - must_convert=True, - check_type=_check_type, - ) - return converted_instance - else: - raise get_type_error(input_value, path_to_item, valid_classes, key_type=False) - - # input_value's type is in valid_classes - if len(valid_classes) > 1 and configuration: - # there are valid classes which are not the current class - valid_classes_coercible = remove_uncoercible( - valid_classes, input_value, spec_property_naming, must_convert=False - ) - if valid_classes_coercible: - converted_instance = attempt_convert_item( - input_value, - valid_classes_coercible, - path_to_item, - configuration, - spec_property_naming, - key_type=False, - must_convert=False, - check_type=_check_type, - ) - return converted_instance - - if child_req_types_by_current_type == {}: - # all types are of the required types and there are no more inner - # variables left to look at - return input_value - inner_required_types = child_req_types_by_current_type.get(type(input_value)) - if inner_required_types is None: - # for this type, there are not more inner variables left to look at - return input_value - if isinstance(input_value, list): - if input_value == []: - # allow an empty list - return input_value - for index, inner_value in enumerate(input_value): - inner_path = list(path_to_item) - inner_path.append(index) - input_value[index] = validate_and_convert_types( - inner_value, - inner_required_types, - inner_path, - spec_property_naming, - _check_type, - configuration=configuration, - ) - elif isinstance(input_value, dict): - if input_value == {}: - # allow an empty dict - return input_value - for inner_key, inner_val in input_value.items(): - inner_path = list(path_to_item) - inner_path.append(inner_key) - if get_simple_class(inner_key) != str: - raise get_type_error(inner_key, inner_path, valid_classes, key_type=True) - input_value[inner_key] = validate_and_convert_types( - inner_val, - inner_required_types, - inner_path, - spec_property_naming, - _check_type, - configuration=configuration, - ) - return input_value - - -def model_to_dict(model_instance, serialize=True): - """Returns the model properties as a dict - - Args: - model_instance (one of your model instances): the model instance that - will be converted to a dict. - - Keyword Args: - serialize (bool): if True, the keys in the dict will be values from - attribute_map - """ - result = {} - extract_item = lambda item: ( - (item[0], model_to_dict(item[1], serialize=serialize)) if hasattr(item[1], "_data_store") else item - ) - - model_instances = [model_instance] - if model_instance._composed_schemas: - model_instances.extend(model_instance._composed_instances) - seen_json_attribute_names = set() - used_fallback_python_attribute_names = set() - py_to_json_map = {} - for model_instance in model_instances: - for attr, value in model_instance._data_store.items(): - if serialize: - # we use get here because additional property key names do not - # exist in attribute_map - try: - attr = model_instance.attribute_map[attr] - py_to_json_map.update(model_instance.attribute_map) - seen_json_attribute_names.add(attr) - except KeyError: - used_fallback_python_attribute_names.add(attr) - if isinstance(value, list): - if not value: - # empty list or None - result[attr] = value - else: - res = [] - for v in value: - if isinstance(v, PRIMITIVE_TYPES) or v is None: - res.append(v) - elif isinstance(v, ModelSimple): - res.append(v.value) - elif isinstance(v, dict): - res.append(dict(map(extract_item, v.items()))) - else: - res.append(model_to_dict(v, serialize=serialize)) - result[attr] = res - elif isinstance(value, dict): - result[attr] = dict(map(extract_item, value.items())) - elif isinstance(value, ModelSimple): - result[attr] = value.value - elif hasattr(value, "_data_store"): - result[attr] = model_to_dict(value, serialize=serialize) - else: - result[attr] = value - if serialize: - for python_key in used_fallback_python_attribute_names: - json_key = py_to_json_map.get(python_key) - if json_key is None: - continue - if python_key == json_key: - continue - json_key_assigned_no_need_for_python_key = json_key in seen_json_attribute_names - if json_key_assigned_no_need_for_python_key: - del result[python_key] - - return result - - -def type_error_message(var_value=None, var_name=None, valid_classes=None, key_type=None): - """ - Keyword Args: - var_value (any): the variable which has the type_error - var_name (str): the name of the variable which has the typ error - valid_classes (tuple): the accepted classes for current_item's - value - key_type (bool): False if our value is a value in a dict - True if it is a key in a dict - False if our item is an item in a list - """ - key_or_value = "value" - if key_type: - key_or_value = "key" - valid_classes_phrase = get_valid_classes_phrase(valid_classes) - msg = "Invalid type for variable '{0}'. Required {1} type {2} and passed type was {3}".format( - var_name, - key_or_value, - valid_classes_phrase, - type(var_value).__name__, - ) - return msg - - -def get_valid_classes_phrase(input_classes): - """Returns a string phrase describing what types are allowed""" - all_classes = list(input_classes) - all_classes = sorted(all_classes, key=lambda cls: cls.__name__) - all_class_names = [cls.__name__ for cls in all_classes] - if len(all_class_names) == 1: - return "is {0}".format(all_class_names[0]) - return "is one of [{0}]".format(", ".join(all_class_names)) - - -def get_allof_instances(self, model_args, constant_args): - """ - Args: - self: the class we are handling - model_args (dict): var_name to var_value - used to make instances - constant_args (dict): - metadata arguments: - _check_type - _path_to_item - _spec_property_naming - _configuration - _visited_composed_classes - - Returns - composed_instances (list) - """ - composed_instances = [] - for allof_class in self._composed_schemas["allOf"]: - - try: - if constant_args.get("_spec_property_naming"): - allof_instance = allof_class._from_openapi_data(**model_args, **constant_args) - else: - allof_instance = allof_class(**model_args, **constant_args) - composed_instances.append(allof_instance) - except Exception as ex: - raise ApiValueError( - "Invalid inputs given to generate an instance of '%s'. The " - "input data was invalid for the allOf schema '%s' in the composed " - "schema '%s'. Error=%s" % (allof_class.__name__, allof_class.__name__, self.__class__.__name__, str(ex)) - ) from ex - return composed_instances - - -def get_oneof_instance(cls, model_kwargs, constant_kwargs, model_arg=None): - """ - Find the oneOf schema that matches the input data (e.g. payload). - If exactly one schema matches the input data, an instance of that schema - is returned. - If zero or more than one schema match the input data, an exception is raised. - In OAS 3.x, the payload MUST, by validation, match exactly one of the - schemas described by oneOf. - - Args: - cls: the class we are handling - model_kwargs (dict): var_name to var_value - The input data, e.g. the payload that must match a oneOf schema - in the OpenAPI document. - constant_kwargs (dict): var_name to var_value - args that every model requires, including configuration, server - and path to item. - - Kwargs: - model_arg: (int, float, bool, str, date, datetime, ModelSimple, None): - the value to assign to a primitive class or ModelSimple class - Notes: - - this is only passed in when oneOf includes types which are not object - - None is used to suppress handling of model_arg, nullable models are handled in __new__ - - Returns - oneof_instance (instance) - """ - if len(cls._composed_schemas["oneOf"]) == 0: - return None - - oneof_instances = [] - # Iterate over each oneOf schema and determine if the input data - # matches the oneOf schemas. - for oneof_class in cls._composed_schemas["oneOf"]: - # The composed oneOf schema allows the 'null' type and the input data - # is the null value. This is a OAS >= 3.1 feature. - if oneof_class is none_type: - # skip none_types because we are deserializing dict data. - # none_type deserialization is handled in the __new__ method - continue - - single_value_input = allows_single_value_input(oneof_class) - - try: - if not single_value_input: - if constant_kwargs.get("_spec_property_naming"): - oneof_instance = oneof_class._from_openapi_data(**model_kwargs, **constant_kwargs) - else: - oneof_instance = oneof_class(**model_kwargs, **constant_kwargs) - else: - if issubclass(oneof_class, ModelSimple): - if constant_kwargs.get("_spec_property_naming"): - oneof_instance = oneof_class._from_openapi_data(model_arg, **constant_kwargs) - else: - oneof_instance = oneof_class(model_arg, **constant_kwargs) - elif oneof_class in PRIMITIVE_TYPES: - oneof_instance = validate_and_convert_types( - model_arg, - (oneof_class,), - constant_kwargs["_path_to_item"], - constant_kwargs["_spec_property_naming"], - constant_kwargs["_check_type"], - configuration=constant_kwargs["_configuration"], - ) - oneof_instances.append(oneof_instance) - except Exception: - pass - if len(oneof_instances) == 0: - raise ApiValueError( - "Invalid inputs given to generate an instance of %s. None of the oneOf schemas matched the input data." - % cls.__name__ - ) - elif len(oneof_instances) > 1: - raise ApiValueError( - "Invalid inputs given to generate an instance of %s. Multiple " - "oneOf schemas matched the inputs, but a max of one is allowed." - % cls.__name__ - ) - return oneof_instances[0] - - -def get_anyof_instances(self, model_args, constant_args): - """ - Args: - self: the class we are handling - model_args (dict): var_name to var_value - The input data, e.g. the payload that must match at least one - anyOf child schema in the OpenAPI document. - constant_args (dict): var_name to var_value - args that every model requires, including configuration, server - and path to item. - - Returns - anyof_instances (list) - """ - anyof_instances = [] - if len(self._composed_schemas["anyOf"]) == 0: - return anyof_instances - - for anyof_class in self._composed_schemas["anyOf"]: - # The composed oneOf schema allows the 'null' type and the input data - # is the null value. This is a OAS >= 3.1 feature. - if anyof_class is none_type: - # skip none_types because we are deserializing dict data. - # none_type deserialization is handled in the __new__ method - continue - - try: - if constant_args.get("_spec_property_naming"): - anyof_instance = anyof_class._from_openapi_data(**model_args, **constant_args) - else: - anyof_instance = anyof_class(**model_args, **constant_args) - anyof_instances.append(anyof_instance) - except Exception: - pass - if len(anyof_instances) == 0: - raise ApiValueError( - "Invalid inputs given to generate an instance of %s. None of the anyOf schemas matched the inputs." - % self.__class__.__name__ - ) - return anyof_instances - - -def get_discarded_args(self, composed_instances, model_args): - """ - Gathers the args that were discarded by configuration.discard_unknown_keys - """ - model_arg_keys = model_args.keys() - discarded_args = set() - # arguments passed to self were already converted to python names - # before __init__ was called - for instance in composed_instances: - if instance.__class__ in self._composed_schemas["allOf"]: - try: - keys = instance.to_dict().keys() - discarded_keys = model_args - keys - discarded_args.update(discarded_keys) - except Exception: - # allOf integer schema will throw exception - pass - else: - try: - all_keys = set(model_to_dict(instance, serialize=False).keys()) - js_keys = model_to_dict(instance, serialize=True).keys() - all_keys.update(js_keys) - discarded_keys = model_arg_keys - all_keys - discarded_args.update(discarded_keys) - except Exception: - # allOf integer schema will throw exception - pass - return discarded_args - - -def validate_get_composed_info(constant_args, model_args, self): - """ - For composed schemas, generate schema instances for - all schemas in the oneOf/anyOf/allOf definition. If additional - properties are allowed, also assign those properties on - all matched schemas that contain additionalProperties. - Openapi schemas are python classes. - - Exceptions are raised if: - - 0 or > 1 oneOf schema matches the model_args input data - - no anyOf schema matches the model_args input data - - any of the allOf schemas do not match the model_args input data - - Args: - constant_args (dict): these are the args that every model requires - model_args (dict): these are the required and optional spec args that - were passed in to make this model - self (class): the class that we are instantiating - This class contains self._composed_schemas - - Returns: - composed_info (list): length three - composed_instances (list): the composed instances which are not - self - var_name_to_model_instances (dict): a dict going from var_name - to the model_instance which holds that var_name - the model_instance may be self or an instance of one of the - classes in self.composed_instances() - additional_properties_model_instances (list): a list of the - model instances which have the property - additional_properties_type. This list can include self - """ - # create composed_instances - composed_instances = [] - allof_instances = get_allof_instances(self, model_args, constant_args) - composed_instances.extend(allof_instances) - oneof_instance = get_oneof_instance(self.__class__, model_args, constant_args) - if oneof_instance is not None: - composed_instances.append(oneof_instance) - anyof_instances = get_anyof_instances(self, model_args, constant_args) - composed_instances.extend(anyof_instances) - """ - set additional_properties_model_instances - additional properties must be evaluated at the schema level - so self's additional properties are most important - If self is a composed schema with: - - no properties defined in self - - additionalProperties: False - Then for object payloads every property is an additional property - and they are not allowed, so only empty dict is allowed - - Properties must be set on all matching schemas - so when a property is assigned toa composed instance, it must be set on all - composed instances regardless of additionalProperties presence - keeping it to prevent breaking changes in v5.0.1 - TODO remove cls._additional_properties_model_instances in 6.0.0 - """ - additional_properties_model_instances = [] - if self.additional_properties_type is not None: - additional_properties_model_instances = [self] - - """ - no need to set properties on self in here, they will be set in __init__ - By here all composed schema oneOf/anyOf/allOf instances have their properties set using - model_args - """ - discarded_args = get_discarded_args(self, composed_instances, model_args) - - # map variable names to composed_instances - var_name_to_model_instances = {} - for prop_name in model_args: - if prop_name not in discarded_args: - var_name_to_model_instances[prop_name] = [self] + composed_instances - - return [composed_instances, var_name_to_model_instances, additional_properties_model_instances, discarded_args] diff --git a/generated/groundlight_openapi_client/models/__init__.py b/generated/groundlight_openapi_client/models/__init__.py index 16412577..b32f25cf 100644 --- a/generated/groundlight_openapi_client/models/__init__.py +++ b/generated/groundlight_openapi_client/models/__init__.py @@ -1,69 +1,86 @@ +# coding: utf-8 + # flake8: noqa +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 -# import all models into this package -# if you have many models here with many references from one model to another this may -# raise a RecursionError -# to avoid this, import only the models that you directly need like: -# from from groundlight_openapi_client.model.pet import Pet -# or import this package, but before doing it, use: -# import sys -# sys.setrecursionlimit(n) -from groundlight_openapi_client.model.action import Action -from groundlight_openapi_client.model.action_list import ActionList -from groundlight_openapi_client.model.all_notes import AllNotes -from groundlight_openapi_client.model.annotations_requested_enum import AnnotationsRequestedEnum -from groundlight_openapi_client.model.b_box_geometry import BBoxGeometry -from groundlight_openapi_client.model.b_box_geometry_request import BBoxGeometryRequest -from groundlight_openapi_client.model.binary_classification_result import BinaryClassificationResult -from groundlight_openapi_client.model.blank_enum import BlankEnum -from groundlight_openapi_client.model.bounding_box_mode_configuration import BoundingBoxModeConfiguration -from groundlight_openapi_client.model.bounding_box_result import BoundingBoxResult -from groundlight_openapi_client.model.channel_enum import ChannelEnum -from groundlight_openapi_client.model.condition import Condition -from groundlight_openapi_client.model.condition_request import ConditionRequest -from groundlight_openapi_client.model.count_mode_configuration import CountModeConfiguration -from groundlight_openapi_client.model.counting_result import CountingResult -from groundlight_openapi_client.model.detector import Detector -from groundlight_openapi_client.model.detector_creation_input_request import DetectorCreationInputRequest -from groundlight_openapi_client.model.detector_group import DetectorGroup -from groundlight_openapi_client.model.detector_group_request import DetectorGroupRequest -from groundlight_openapi_client.model.detector_type_enum import DetectorTypeEnum -from groundlight_openapi_client.model.edge_model_info import EdgeModelInfo -from groundlight_openapi_client.model.escalation_type_enum import EscalationTypeEnum -from groundlight_openapi_client.model.image_query import ImageQuery -from groundlight_openapi_client.model.image_query_type_enum import ImageQueryTypeEnum -from groundlight_openapi_client.model.inline_response200 import InlineResponse200 -from groundlight_openapi_client.model.inline_response2001 import InlineResponse2001 -from groundlight_openapi_client.model.inline_response2001_evaluation_results import InlineResponse2001EvaluationResults -from groundlight_openapi_client.model.inline_response2002 import InlineResponse2002 -from groundlight_openapi_client.model.inline_response200_summary import InlineResponse200Summary -from groundlight_openapi_client.model.inline_response200_summary_class_counts import InlineResponse200SummaryClassCounts -from groundlight_openapi_client.model.label import Label -from groundlight_openapi_client.model.label_value import LabelValue -from groundlight_openapi_client.model.label_value_request import LabelValueRequest -from groundlight_openapi_client.model.mode_enum import ModeEnum -from groundlight_openapi_client.model.multi_class_mode_configuration import MultiClassModeConfiguration -from groundlight_openapi_client.model.multi_classification_result import MultiClassificationResult -from groundlight_openapi_client.model.note import Note -from groundlight_openapi_client.model.note_request import NoteRequest -from groundlight_openapi_client.model.paginated_detector_list import PaginatedDetectorList -from groundlight_openapi_client.model.paginated_image_query_list import PaginatedImageQueryList -from groundlight_openapi_client.model.paginated_rule_list import PaginatedRuleList -from groundlight_openapi_client.model.patched_detector_request import PatchedDetectorRequest -from groundlight_openapi_client.model.payload_template import PayloadTemplate -from groundlight_openapi_client.model.payload_template_request import PayloadTemplateRequest -from groundlight_openapi_client.model.roi import ROI -from groundlight_openapi_client.model.roi_request import ROIRequest -from groundlight_openapi_client.model.result_type_enum import ResultTypeEnum -from groundlight_openapi_client.model.rule import Rule -from groundlight_openapi_client.model.rule_request import RuleRequest -from groundlight_openapi_client.model.snooze_time_unit_enum import SnoozeTimeUnitEnum -from groundlight_openapi_client.model.source import Source -from groundlight_openapi_client.model.source_enum import SourceEnum -from groundlight_openapi_client.model.status_enum import StatusEnum -from groundlight_openapi_client.model.text_mode_configuration import TextModeConfiguration -from groundlight_openapi_client.model.text_recognition_result import TextRecognitionResult -from groundlight_openapi_client.model.verb_enum import VerbEnum -from groundlight_openapi_client.model.webhook_action import WebhookAction -from groundlight_openapi_client.model.webhook_action_request import WebhookActionRequest +# import models into model package +from groundlight_openapi_client.models.action import Action +from groundlight_openapi_client.models.all_notes import AllNotes +from groundlight_openapi_client.models.annotations_requested_enum import AnnotationsRequestedEnum +from groundlight_openapi_client.models.b_box_geometry import BBoxGeometry +from groundlight_openapi_client.models.b_box_geometry_request import BBoxGeometryRequest +from groundlight_openapi_client.models.binary_classification_result import BinaryClassificationResult +from groundlight_openapi_client.models.blank_enum import BlankEnum +from groundlight_openapi_client.models.bounding_box_mode_configuration import BoundingBoxModeConfiguration +from groundlight_openapi_client.models.bounding_box_result import BoundingBoxResult +from groundlight_openapi_client.models.channel_enum import ChannelEnum +from groundlight_openapi_client.models.condition import Condition +from groundlight_openapi_client.models.condition_request import ConditionRequest +from groundlight_openapi_client.models.count_mode_configuration import CountModeConfiguration +from groundlight_openapi_client.models.counting_result import CountingResult +from groundlight_openapi_client.models.detector import Detector +from groundlight_openapi_client.models.detector_creation_input_request import DetectorCreationInputRequest +from groundlight_openapi_client.models.detector_creation_input_request_mode_configuration import ( + DetectorCreationInputRequestModeConfiguration, +) +from groundlight_openapi_client.models.detector_group import DetectorGroup +from groundlight_openapi_client.models.detector_group_request import DetectorGroupRequest +from groundlight_openapi_client.models.detector_status import DetectorStatus +from groundlight_openapi_client.models.detector_type_enum import DetectorTypeEnum +from groundlight_openapi_client.models.edge_model_info import EdgeModelInfo +from groundlight_openapi_client.models.escalation_type_enum import EscalationTypeEnum +from groundlight_openapi_client.models.get_detector_evaluation200_response import GetDetectorEvaluation200Response +from groundlight_openapi_client.models.get_detector_evaluation200_response_evaluation_results import ( + GetDetectorEvaluation200ResponseEvaluationResults, +) +from groundlight_openapi_client.models.get_detector_metrics200_response import GetDetectorMetrics200Response +from groundlight_openapi_client.models.get_detector_metrics200_response_summary import ( + GetDetectorMetrics200ResponseSummary, +) +from groundlight_openapi_client.models.get_detector_metrics200_response_summary_class_counts import ( + GetDetectorMetrics200ResponseSummaryClassCounts, +) +from groundlight_openapi_client.models.image_query import ImageQuery +from groundlight_openapi_client.models.image_query_result import ImageQueryResult +from groundlight_openapi_client.models.image_query_type_enum import ImageQueryTypeEnum +from groundlight_openapi_client.models.label import Label +from groundlight_openapi_client.models.label_value import LabelValue +from groundlight_openapi_client.models.label_value_request import LabelValueRequest +from groundlight_openapi_client.models.mode_enum import ModeEnum +from groundlight_openapi_client.models.multi_class_mode_configuration import MultiClassModeConfiguration +from groundlight_openapi_client.models.multi_classification_result import MultiClassificationResult +from groundlight_openapi_client.models.note import Note +from groundlight_openapi_client.models.note_request import NoteRequest +from groundlight_openapi_client.models.paginated_detector_list import PaginatedDetectorList +from groundlight_openapi_client.models.paginated_image_query_list import PaginatedImageQueryList +from groundlight_openapi_client.models.paginated_rule_list import PaginatedRuleList +from groundlight_openapi_client.models.patched_detector_request import PatchedDetectorRequest +from groundlight_openapi_client.models.payload_template import PayloadTemplate +from groundlight_openapi_client.models.payload_template_request import PayloadTemplateRequest +from groundlight_openapi_client.models.roi import ROI +from groundlight_openapi_client.models.roi_request import ROIRequest +from groundlight_openapi_client.models.result_type_enum import ResultTypeEnum +from groundlight_openapi_client.models.rule import Rule +from groundlight_openapi_client.models.rule_action import RuleAction +from groundlight_openapi_client.models.rule_request import RuleRequest +from groundlight_openapi_client.models.snooze_time_unit_enum import SnoozeTimeUnitEnum +from groundlight_openapi_client.models.source import Source +from groundlight_openapi_client.models.source_enum import SourceEnum +from groundlight_openapi_client.models.status_enum import StatusEnum +from groundlight_openapi_client.models.text_mode_configuration import TextModeConfiguration +from groundlight_openapi_client.models.text_recognition_result import TextRecognitionResult +from groundlight_openapi_client.models.verb_enum import VerbEnum +from groundlight_openapi_client.models.webhook_action import WebhookAction +from groundlight_openapi_client.models.webhook_action_request import WebhookActionRequest +from groundlight_openapi_client.models.who_am_i200_response import WhoAmI200Response diff --git a/generated/groundlight_openapi_client/models/action.py b/generated/groundlight_openapi_client/models/action.py new file mode 100644 index 00000000..3e78264b --- /dev/null +++ b/generated/groundlight_openapi_client/models/action.py @@ -0,0 +1,74 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from pydantic import BaseModel, Field, StrictBool, StrictStr +from groundlight_openapi_client.models.channel_enum import ChannelEnum + + +class Action(BaseModel): + """ + Action + """ + + channel: ChannelEnum = Field(...) + recipient: StrictStr = Field(...) + include_image: StrictBool = Field(...) + __properties = ["channel", "recipient", "include_image"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> Action: + """Create an instance of Action from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> Action: + """Create an instance of Action from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return Action.parse_obj(obj) + + _obj = Action.parse_obj({ + "channel": obj.get("channel"), + "recipient": obj.get("recipient"), + "include_image": obj.get("include_image"), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/all_notes.py b/generated/groundlight_openapi_client/models/all_notes.py new file mode 100644 index 00000000..75a0c207 --- /dev/null +++ b/generated/groundlight_openapi_client/models/all_notes.py @@ -0,0 +1,89 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import List +from pydantic import BaseModel, Field, conlist +from groundlight_openapi_client.models.note import Note + + +class AllNotes(BaseModel): + """ + Serializes all notes for a given detector, grouped by type as listed in UserProfile.NoteCategoryChoices The fields must match whats in USERPROFILE.NoteCategoryChoices # noqa: E501 + """ + + customer: conlist(Note) = Field(default=..., alias="CUSTOMER") + gl: conlist(Note) = Field(default=..., alias="GL") + __properties = ["CUSTOMER", "GL"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> AllNotes: + """Create an instance of AllNotes from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # override the default output from pydantic by calling `to_dict()` of each item in customer (list) + _items = [] + if self.customer: + for _item in self.customer: + if _item: + _items.append(_item.to_dict()) + _dict["CUSTOMER"] = _items + # override the default output from pydantic by calling `to_dict()` of each item in gl (list) + _items = [] + if self.gl: + for _item in self.gl: + if _item: + _items.append(_item.to_dict()) + _dict["GL"] = _items + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> AllNotes: + """Create an instance of AllNotes from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return AllNotes.parse_obj(obj) + + _obj = AllNotes.parse_obj({ + "customer": ( + [Note.from_dict(_item) for _item in obj.get("CUSTOMER")] if obj.get("CUSTOMER") is not None else None + ), + "gl": [Note.from_dict(_item) for _item in obj.get("GL")] if obj.get("GL") is not None else None, + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/annotations_requested_enum.py b/generated/groundlight_openapi_client/models/annotations_requested_enum.py new file mode 100644 index 00000000..3267d644 --- /dev/null +++ b/generated/groundlight_openapi_client/models/annotations_requested_enum.py @@ -0,0 +1,36 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import json +import pprint +import re # noqa: F401 +from aenum import Enum, no_arg + + +class AnnotationsRequestedEnum(str, Enum): + """ + AnnotationsRequestedEnum + """ + + """ + allowed enum values + """ + BINARY_CLASSIFICATION = "BINARY_CLASSIFICATION" + BOUNDING_BOXES = "BOUNDING_BOXES" + + @classmethod + def from_json(cls, json_str: str) -> AnnotationsRequestedEnum: + """Create an instance of AnnotationsRequestedEnum from a JSON string""" + return AnnotationsRequestedEnum(json.loads(json_str)) diff --git a/generated/groundlight_openapi_client/models/b_box_geometry.py b/generated/groundlight_openapi_client/models/b_box_geometry.py new file mode 100644 index 00000000..6bf2f151 --- /dev/null +++ b/generated/groundlight_openapi_client/models/b_box_geometry.py @@ -0,0 +1,87 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Union +from pydantic import BaseModel, Field, StrictFloat, StrictInt + + +class BBoxGeometry(BaseModel): + """ + Mixin for serializers to handle data in the StrictBaseModel format # noqa: E501 + """ + + left: Union[StrictFloat, StrictInt] = Field(...) + top: Union[StrictFloat, StrictInt] = Field(...) + right: Union[StrictFloat, StrictInt] = Field(...) + bottom: Union[StrictFloat, StrictInt] = Field(...) + x: Union[StrictFloat, StrictInt] = Field(...) + y: Union[StrictFloat, StrictInt] = Field(...) + __properties = ["left", "top", "right", "bottom", "x", "y"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> BBoxGeometry: + """Create an instance of BBoxGeometry from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict( + by_alias=True, + exclude={ + "x", + "y", + }, + exclude_none=True, + ) + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> BBoxGeometry: + """Create an instance of BBoxGeometry from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return BBoxGeometry.parse_obj(obj) + + _obj = BBoxGeometry.parse_obj({ + "left": obj.get("left"), + "top": obj.get("top"), + "right": obj.get("right"), + "bottom": obj.get("bottom"), + "x": obj.get("x"), + "y": obj.get("y"), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/b_box_geometry_request.py b/generated/groundlight_openapi_client/models/b_box_geometry_request.py new file mode 100644 index 00000000..2ea93b1c --- /dev/null +++ b/generated/groundlight_openapi_client/models/b_box_geometry_request.py @@ -0,0 +1,73 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Union +from pydantic import BaseModel, Field, StrictFloat, StrictInt + + +class BBoxGeometryRequest(BaseModel): + """ + Mixin for serializers to handle data in the StrictBaseModel format # noqa: E501 + """ + + left: Union[StrictFloat, StrictInt] = Field(...) + top: Union[StrictFloat, StrictInt] = Field(...) + right: Union[StrictFloat, StrictInt] = Field(...) + bottom: Union[StrictFloat, StrictInt] = Field(...) + __properties = ["left", "top", "right", "bottom"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> BBoxGeometryRequest: + """Create an instance of BBoxGeometryRequest from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> BBoxGeometryRequest: + """Create an instance of BBoxGeometryRequest from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return BBoxGeometryRequest.parse_obj(obj) + + _obj = BBoxGeometryRequest.parse_obj( + {"left": obj.get("left"), "top": obj.get("top"), "right": obj.get("right"), "bottom": obj.get("bottom")} + ) + return _obj diff --git a/generated/groundlight_openapi_client/models/binary_classification_result.py b/generated/groundlight_openapi_client/models/binary_classification_result.py new file mode 100644 index 00000000..374d6176 --- /dev/null +++ b/generated/groundlight_openapi_client/models/binary_classification_result.py @@ -0,0 +1,93 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Optional, Union +from pydantic import BaseModel, Field, StrictBool, StrictStr, confloat, conint, validator + + +class BinaryClassificationResult(BaseModel): + """ + BinaryClassificationResult + """ + + confidence: Optional[Union[confloat(le=1.0, ge=0.0, strict=True), conint(le=1, ge=0, strict=True)]] = None + source: Optional[StrictStr] = None + result_type: Optional[StrictStr] = None + from_edge: Optional[StrictBool] = None + label: StrictStr = Field(...) + __properties = ["confidence", "source", "result_type", "from_edge", "label"] + + @validator("result_type") + def result_type_validate_enum(cls, value): + """Validates the enum""" + if value is None: + return value + + if value not in ("binary_classification",): + raise ValueError("must be one of enum values ('binary_classification')") + return value + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> BinaryClassificationResult: + """Create an instance of BinaryClassificationResult from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # set to None if confidence (nullable) is None + # and __fields_set__ contains the field + if self.confidence is None and "confidence" in self.__fields_set__: + _dict["confidence"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> BinaryClassificationResult: + """Create an instance of BinaryClassificationResult from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return BinaryClassificationResult.parse_obj(obj) + + _obj = BinaryClassificationResult.parse_obj({ + "confidence": obj.get("confidence"), + "source": obj.get("source"), + "result_type": obj.get("result_type"), + "from_edge": obj.get("from_edge"), + "label": obj.get("label"), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/blank_enum.py b/generated/groundlight_openapi_client/models/blank_enum.py new file mode 100644 index 00000000..16a26576 --- /dev/null +++ b/generated/groundlight_openapi_client/models/blank_enum.py @@ -0,0 +1,35 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import json +import pprint +import re # noqa: F401 +from aenum import Enum, no_arg + + +class BlankEnum(str, Enum): + """ + BlankEnum + """ + + """ + allowed enum values + """ + EMPTY = "" + + @classmethod + def from_json(cls, json_str: str) -> BlankEnum: + """Create an instance of BlankEnum from a JSON string""" + return BlankEnum(json.loads(json_str)) diff --git a/generated/groundlight_openapi_client/models/bounding_box_mode_configuration.py b/generated/groundlight_openapi_client/models/bounding_box_mode_configuration.py new file mode 100644 index 00000000..11586ee2 --- /dev/null +++ b/generated/groundlight_openapi_client/models/bounding_box_mode_configuration.py @@ -0,0 +1,71 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Optional +from pydantic import BaseModel, Field, StrictStr, conint + + +class BoundingBoxModeConfiguration(BaseModel): + """ + BoundingBoxModeConfiguration + """ + + class_name: StrictStr = Field(...) + max_num_bboxes: Optional[conint(strict=True, le=50, ge=1)] = None + __properties = ["class_name", "max_num_bboxes"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> BoundingBoxModeConfiguration: + """Create an instance of BoundingBoxModeConfiguration from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> BoundingBoxModeConfiguration: + """Create an instance of BoundingBoxModeConfiguration from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return BoundingBoxModeConfiguration.parse_obj(obj) + + _obj = BoundingBoxModeConfiguration.parse_obj( + {"class_name": obj.get("class_name"), "max_num_bboxes": obj.get("max_num_bboxes")} + ) + return _obj diff --git a/generated/groundlight_openapi_client/models/bounding_box_result.py b/generated/groundlight_openapi_client/models/bounding_box_result.py new file mode 100644 index 00000000..209b8755 --- /dev/null +++ b/generated/groundlight_openapi_client/models/bounding_box_result.py @@ -0,0 +1,93 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Optional, Union +from pydantic import BaseModel, Field, StrictBool, StrictStr, confloat, conint, validator + + +class BoundingBoxResult(BaseModel): + """ + BoundingBoxResult + """ + + confidence: Optional[Union[confloat(le=1.0, ge=0.0, strict=True), conint(le=1, ge=0, strict=True)]] = None + source: Optional[StrictStr] = None + result_type: Optional[StrictStr] = None + from_edge: Optional[StrictBool] = None + label: StrictStr = Field(...) + __properties = ["confidence", "source", "result_type", "from_edge", "label"] + + @validator("result_type") + def result_type_validate_enum(cls, value): + """Validates the enum""" + if value is None: + return value + + if value not in ("bounding_box",): + raise ValueError("must be one of enum values ('bounding_box')") + return value + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> BoundingBoxResult: + """Create an instance of BoundingBoxResult from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # set to None if confidence (nullable) is None + # and __fields_set__ contains the field + if self.confidence is None and "confidence" in self.__fields_set__: + _dict["confidence"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> BoundingBoxResult: + """Create an instance of BoundingBoxResult from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return BoundingBoxResult.parse_obj(obj) + + _obj = BoundingBoxResult.parse_obj({ + "confidence": obj.get("confidence"), + "source": obj.get("source"), + "result_type": obj.get("result_type"), + "from_edge": obj.get("from_edge"), + "label": obj.get("label"), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/channel_enum.py b/generated/groundlight_openapi_client/models/channel_enum.py new file mode 100644 index 00000000..79fba81b --- /dev/null +++ b/generated/groundlight_openapi_client/models/channel_enum.py @@ -0,0 +1,36 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import json +import pprint +import re # noqa: F401 +from aenum import Enum, no_arg + + +class ChannelEnum(str, Enum): + """ + ChannelEnum + """ + + """ + allowed enum values + """ + TEXT = "TEXT" + EMAIL = "EMAIL" + + @classmethod + def from_json(cls, json_str: str) -> ChannelEnum: + """Create an instance of ChannelEnum from a JSON string""" + return ChannelEnum(json.loads(json_str)) diff --git a/generated/groundlight_openapi_client/models/condition.py b/generated/groundlight_openapi_client/models/condition.py new file mode 100644 index 00000000..15c2add1 --- /dev/null +++ b/generated/groundlight_openapi_client/models/condition.py @@ -0,0 +1,69 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Any, Dict +from pydantic import BaseModel, Field, StrictStr + + +class Condition(BaseModel): + """ + Condition + """ + + verb: StrictStr = Field(...) + parameters: Dict[str, Any] = Field(...) + __properties = ["verb", "parameters"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> Condition: + """Create an instance of Condition from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> Condition: + """Create an instance of Condition from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return Condition.parse_obj(obj) + + _obj = Condition.parse_obj({"verb": obj.get("verb"), "parameters": obj.get("parameters")}) + return _obj diff --git a/generated/groundlight_openapi_client/models/condition_request.py b/generated/groundlight_openapi_client/models/condition_request.py new file mode 100644 index 00000000..05062711 --- /dev/null +++ b/generated/groundlight_openapi_client/models/condition_request.py @@ -0,0 +1,69 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Any, Dict +from pydantic import BaseModel, Field, StrictStr + + +class ConditionRequest(BaseModel): + """ + ConditionRequest + """ + + verb: StrictStr = Field(...) + parameters: Dict[str, Any] = Field(...) + __properties = ["verb", "parameters"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> ConditionRequest: + """Create an instance of ConditionRequest from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> ConditionRequest: + """Create an instance of ConditionRequest from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return ConditionRequest.parse_obj(obj) + + _obj = ConditionRequest.parse_obj({"verb": obj.get("verb"), "parameters": obj.get("parameters")}) + return _obj diff --git a/generated/groundlight_openapi_client/models/count_mode_configuration.py b/generated/groundlight_openapi_client/models/count_mode_configuration.py new file mode 100644 index 00000000..fcc61bce --- /dev/null +++ b/generated/groundlight_openapi_client/models/count_mode_configuration.py @@ -0,0 +1,71 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Optional +from pydantic import BaseModel, Field, StrictStr, conint + + +class CountModeConfiguration(BaseModel): + """ + CountModeConfiguration + """ + + max_count: Optional[conint(strict=True, le=50, ge=1)] = None + class_name: StrictStr = Field(...) + __properties = ["max_count", "class_name"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> CountModeConfiguration: + """Create an instance of CountModeConfiguration from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> CountModeConfiguration: + """Create an instance of CountModeConfiguration from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return CountModeConfiguration.parse_obj(obj) + + _obj = CountModeConfiguration.parse_obj( + {"max_count": obj.get("max_count"), "class_name": obj.get("class_name")} + ) + return _obj diff --git a/generated/groundlight_openapi_client/models/counting_result.py b/generated/groundlight_openapi_client/models/counting_result.py new file mode 100644 index 00000000..a48806ee --- /dev/null +++ b/generated/groundlight_openapi_client/models/counting_result.py @@ -0,0 +1,100 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Optional, Union +from pydantic import BaseModel, Field, StrictBool, StrictStr, confloat, conint, validator + + +class CountingResult(BaseModel): + """ + CountingResult + """ + + confidence: Optional[Union[confloat(le=1.0, ge=0.0, strict=True), conint(le=1, ge=0, strict=True)]] = None + source: Optional[StrictStr] = None + result_type: Optional[StrictStr] = None + from_edge: Optional[StrictBool] = None + count: Optional[conint(strict=True, ge=0)] = Field(...) + greater_than_max: Optional[StrictBool] = None + __properties = ["confidence", "source", "result_type", "from_edge", "count", "greater_than_max"] + + @validator("result_type") + def result_type_validate_enum(cls, value): + """Validates the enum""" + if value is None: + return value + + if value not in ("counting",): + raise ValueError("must be one of enum values ('counting')") + return value + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> CountingResult: + """Create an instance of CountingResult from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # set to None if confidence (nullable) is None + # and __fields_set__ contains the field + if self.confidence is None and "confidence" in self.__fields_set__: + _dict["confidence"] = None + + # set to None if count (nullable) is None + # and __fields_set__ contains the field + if self.count is None and "count" in self.__fields_set__: + _dict["count"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> CountingResult: + """Create an instance of CountingResult from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return CountingResult.parse_obj(obj) + + _obj = CountingResult.parse_obj({ + "confidence": obj.get("confidence"), + "source": obj.get("source"), + "result_type": obj.get("result_type"), + "from_edge": obj.get("from_edge"), + "count": obj.get("count"), + "greater_than_max": obj.get("greater_than_max"), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/detector.py b/generated/groundlight_openapi_client/models/detector.py new file mode 100644 index 00000000..96d0eb9a --- /dev/null +++ b/generated/groundlight_openapi_client/models/detector.py @@ -0,0 +1,150 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + +from datetime import datetime +from typing import Any, Dict, Optional, Union +from pydantic import BaseModel, Field, StrictStr, confloat, conint, constr +from groundlight_openapi_client.models.detector_status import DetectorStatus +from groundlight_openapi_client.models.detector_type_enum import DetectorTypeEnum + + +class Detector(BaseModel): + """ + Groundlight Detectors provide answers to natural language questions about images. Each detector can answer a single question, and multiple detectors can be strung together for more complex logic. Detectors can be created through the create_detector method, or through the create_[MODE]_detector methods for pro tier users # noqa: E501 + """ + + id: StrictStr = Field(default=..., description="A unique ID for this object.") + type: DetectorTypeEnum = Field(default=..., description="The type of this object.") + created_at: datetime = Field(default=..., description="When this detector was created.") + name: constr(strict=True, max_length=200) = Field( + default=..., description="A short, descriptive name for the detector." + ) + query: StrictStr = Field(default=..., description="A question about the image.") + group_name: StrictStr = Field(default=..., description="Which group should this detector be part of?") + confidence_threshold: Optional[Union[confloat(le=1.0, ge=0.0, strict=True), conint(le=1, ge=0, strict=True)]] = ( + Field( + default=0.9, + description=( + "If the detector's prediction is below this confidence threshold, send the image query for human" + " review." + ), + ) + ) + patience_time: Optional[Union[confloat(le=3600, ge=0, strict=True), conint(le=3600, ge=0, strict=True)]] = Field( + default=30.0, description="How long Groundlight will attempt to generate a confident prediction" + ) + metadata: Optional[Dict[str, Any]] = Field(default=..., description="Metadata about the detector.") + mode: StrictStr = Field(...) + mode_configuration: Optional[Dict[str, Any]] = Field(...) + status: Optional[DetectorStatus] = None + escalation_type: Optional[StrictStr] = None + __properties = [ + "id", + "type", + "created_at", + "name", + "query", + "group_name", + "confidence_threshold", + "patience_time", + "metadata", + "mode", + "mode_configuration", + "status", + "escalation_type", + ] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> Detector: + """Create an instance of Detector from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict( + by_alias=True, + exclude={ + "id", + "type", + "created_at", + "query", + "group_name", + "metadata", + "mode", + "mode_configuration", + }, + exclude_none=True, + ) + # override the default output from pydantic by calling `to_dict()` of status + if self.status: + _dict["status"] = self.status.to_dict() + # set to None if metadata (nullable) is None + # and __fields_set__ contains the field + if self.metadata is None and "metadata" in self.__fields_set__: + _dict["metadata"] = None + + # set to None if mode_configuration (nullable) is None + # and __fields_set__ contains the field + if self.mode_configuration is None and "mode_configuration" in self.__fields_set__: + _dict["mode_configuration"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> Detector: + """Create an instance of Detector from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return Detector.parse_obj(obj) + + _obj = Detector.parse_obj({ + "id": obj.get("id"), + "type": obj.get("type"), + "created_at": obj.get("created_at"), + "name": obj.get("name"), + "query": obj.get("query"), + "group_name": obj.get("group_name"), + "confidence_threshold": ( + obj.get("confidence_threshold") if obj.get("confidence_threshold") is not None else 0.9 + ), + "patience_time": obj.get("patience_time") if obj.get("patience_time") is not None else 30.0, + "metadata": obj.get("metadata"), + "mode": obj.get("mode"), + "mode_configuration": obj.get("mode_configuration"), + "status": DetectorStatus.from_dict(obj.get("status")) if obj.get("status") is not None else None, + "escalation_type": obj.get("escalation_type"), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/detector_creation_input_request.py b/generated/groundlight_openapi_client/models/detector_creation_input_request.py new file mode 100644 index 00000000..05b41292 --- /dev/null +++ b/generated/groundlight_openapi_client/models/detector_creation_input_request.py @@ -0,0 +1,149 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Optional, Union +from pydantic import BaseModel, Field, confloat, conint, constr +from groundlight_openapi_client.models.detector_creation_input_request_mode_configuration import ( + DetectorCreationInputRequestModeConfiguration, +) +from groundlight_openapi_client.models.mode_enum import ModeEnum + + +class DetectorCreationInputRequest(BaseModel): + """ + Helper serializer for validating POST /detectors input. # noqa: E501 + """ + + name: constr(strict=True, max_length=200, min_length=1) = Field( + default=..., description="A short, descriptive name for the detector." + ) + query: constr(strict=True, max_length=300, min_length=1) = Field( + default=..., description="A question about the image." + ) + group_name: Optional[constr(strict=True, max_length=100, min_length=1)] = Field( + default=None, description="Which group should this detector be part of?" + ) + confidence_threshold: Optional[Union[confloat(le=1.0, ge=0.0, strict=True), conint(le=1, ge=0, strict=True)]] = ( + Field( + default=0.9, + description=( + "If the detector's prediction is below this confidence threshold, send the image query for human" + " review." + ), + ) + ) + patience_time: Optional[Union[confloat(le=3600, ge=0, strict=True), conint(le=3600, ge=0, strict=True)]] = Field( + default=30.0, description="How long Groundlight will attempt to generate a confident prediction" + ) + pipeline_config: Optional[constr(strict=True, max_length=100)] = Field( + default=None, description="(Advanced usage) Configuration needed to instantiate a prediction pipeline." + ) + metadata: Optional[constr(strict=True, max_length=1362, min_length=1)] = Field( + default=None, + description=( + "Base64-encoded metadata for the detector. This should be a JSON object with string keys. The size after" + " encoding should not exceed 1362 bytes, corresponding to 1KiB before encoding." + ), + ) + mode: Optional[ModeEnum] = Field( + default=None, + description=( + "Mode in which this detector will work. * `BINARY` - BINARY * `COUNT` - COUNT * `MULTI_CLASS` -" + " MULTI_CLASS * `TEXT` - TEXT * `BOUNDING_BOX` - BOUNDING_BOX" + ), + ) + mode_configuration: Optional[DetectorCreationInputRequestModeConfiguration] = None + __properties = [ + "name", + "query", + "group_name", + "confidence_threshold", + "patience_time", + "pipeline_config", + "metadata", + "mode", + "mode_configuration", + ] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> DetectorCreationInputRequest: + """Create an instance of DetectorCreationInputRequest from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # override the default output from pydantic by calling `to_dict()` of mode_configuration + if self.mode_configuration: + _dict["mode_configuration"] = self.mode_configuration.to_dict() + # set to None if pipeline_config (nullable) is None + # and __fields_set__ contains the field + if self.pipeline_config is None and "pipeline_config" in self.__fields_set__: + _dict["pipeline_config"] = None + + # set to None if mode_configuration (nullable) is None + # and __fields_set__ contains the field + if self.mode_configuration is None and "mode_configuration" in self.__fields_set__: + _dict["mode_configuration"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> DetectorCreationInputRequest: + """Create an instance of DetectorCreationInputRequest from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return DetectorCreationInputRequest.parse_obj(obj) + + _obj = DetectorCreationInputRequest.parse_obj({ + "name": obj.get("name"), + "query": obj.get("query"), + "group_name": obj.get("group_name"), + "confidence_threshold": ( + obj.get("confidence_threshold") if obj.get("confidence_threshold") is not None else 0.9 + ), + "patience_time": obj.get("patience_time") if obj.get("patience_time") is not None else 30.0, + "pipeline_config": obj.get("pipeline_config"), + "metadata": obj.get("metadata"), + "mode": obj.get("mode"), + "mode_configuration": ( + DetectorCreationInputRequestModeConfiguration.from_dict(obj.get("mode_configuration")) + if obj.get("mode_configuration") is not None + else None + ), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/detector_creation_input_request_mode_configuration.py b/generated/groundlight_openapi_client/models/detector_creation_input_request_mode_configuration.py new file mode 100644 index 00000000..e3ef10ef --- /dev/null +++ b/generated/groundlight_openapi_client/models/detector_creation_input_request_mode_configuration.py @@ -0,0 +1,203 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +from inspect import getfullargspec +import json +import pprint +import re # noqa: F401 + +from typing import Any, List, Optional +from pydantic import BaseModel, Field, StrictStr, ValidationError, validator +from groundlight_openapi_client.models.bounding_box_mode_configuration import BoundingBoxModeConfiguration +from groundlight_openapi_client.models.count_mode_configuration import CountModeConfiguration +from groundlight_openapi_client.models.multi_class_mode_configuration import MultiClassModeConfiguration +from groundlight_openapi_client.models.text_mode_configuration import TextModeConfiguration +from typing import Union, Any, List, TYPE_CHECKING +from pydantic import StrictStr, Field + +DETECTORCREATIONINPUTREQUESTMODECONFIGURATION_ONE_OF_SCHEMAS = [ + "BoundingBoxModeConfiguration", + "CountModeConfiguration", + "MultiClassModeConfiguration", + "TextModeConfiguration", +] + + +class DetectorCreationInputRequestModeConfiguration(BaseModel): + """ + DetectorCreationInputRequestModeConfiguration + """ + + # data type: CountModeConfiguration + oneof_schema_1_validator: Optional[CountModeConfiguration] = None + # data type: MultiClassModeConfiguration + oneof_schema_2_validator: Optional[MultiClassModeConfiguration] = None + # data type: TextModeConfiguration + oneof_schema_3_validator: Optional[TextModeConfiguration] = None + # data type: BoundingBoxModeConfiguration + oneof_schema_4_validator: Optional[BoundingBoxModeConfiguration] = None + if TYPE_CHECKING: + actual_instance: Union[ + BoundingBoxModeConfiguration, CountModeConfiguration, MultiClassModeConfiguration, TextModeConfiguration + ] + else: + actual_instance: Any + one_of_schemas: List[str] = Field(DETECTORCREATIONINPUTREQUESTMODECONFIGURATION_ONE_OF_SCHEMAS, const=True) + + class Config: + validate_assignment = True + + def __init__(self, *args, **kwargs) -> None: + if args: + if len(args) > 1: + raise ValueError("If a position argument is used, only 1 is allowed to set `actual_instance`") + if kwargs: + raise ValueError("If a position argument is used, keyword arguments cannot be used.") + super().__init__(actual_instance=args[0]) + else: + super().__init__(**kwargs) + + @validator("actual_instance") + def actual_instance_must_validate_oneof(cls, v): + if v is None: + return v + + instance = DetectorCreationInputRequestModeConfiguration.construct() + error_messages = [] + match = 0 + # validate data type: CountModeConfiguration + if not isinstance(v, CountModeConfiguration): + error_messages.append(f"Error! Input type `{type(v)}` is not `CountModeConfiguration`") + else: + match += 1 + # validate data type: MultiClassModeConfiguration + if not isinstance(v, MultiClassModeConfiguration): + error_messages.append(f"Error! Input type `{type(v)}` is not `MultiClassModeConfiguration`") + else: + match += 1 + # validate data type: TextModeConfiguration + if not isinstance(v, TextModeConfiguration): + error_messages.append(f"Error! Input type `{type(v)}` is not `TextModeConfiguration`") + else: + match += 1 + # validate data type: BoundingBoxModeConfiguration + if not isinstance(v, BoundingBoxModeConfiguration): + error_messages.append(f"Error! Input type `{type(v)}` is not `BoundingBoxModeConfiguration`") + else: + match += 1 + if match > 1: + # more than 1 match + raise ValueError( + "Multiple matches found when setting `actual_instance` in DetectorCreationInputRequestModeConfiguration" + " with oneOf schemas: BoundingBoxModeConfiguration, CountModeConfiguration," + " MultiClassModeConfiguration, TextModeConfiguration. Details: " + + ", ".join(error_messages) + ) + elif match == 0: + # no match + raise ValueError( + "No match found when setting `actual_instance` in DetectorCreationInputRequestModeConfiguration with" + " oneOf schemas: BoundingBoxModeConfiguration, CountModeConfiguration, MultiClassModeConfiguration," + " TextModeConfiguration. Details: " + + ", ".join(error_messages) + ) + else: + return v + + @classmethod + def from_dict(cls, obj: dict) -> DetectorCreationInputRequestModeConfiguration: + return cls.from_json(json.dumps(obj)) + + @classmethod + def from_json(cls, json_str: str) -> DetectorCreationInputRequestModeConfiguration: + """Returns the object represented by the json string""" + instance = DetectorCreationInputRequestModeConfiguration.construct() + if json_str is None: + return instance + + error_messages = [] + match = 0 + + # deserialize data into CountModeConfiguration + try: + instance.actual_instance = CountModeConfiguration.from_json(json_str) + match += 1 + except (ValidationError, ValueError) as e: + error_messages.append(str(e)) + # deserialize data into MultiClassModeConfiguration + try: + instance.actual_instance = MultiClassModeConfiguration.from_json(json_str) + match += 1 + except (ValidationError, ValueError) as e: + error_messages.append(str(e)) + # deserialize data into TextModeConfiguration + try: + instance.actual_instance = TextModeConfiguration.from_json(json_str) + match += 1 + except (ValidationError, ValueError) as e: + error_messages.append(str(e)) + # deserialize data into BoundingBoxModeConfiguration + try: + instance.actual_instance = BoundingBoxModeConfiguration.from_json(json_str) + match += 1 + except (ValidationError, ValueError) as e: + error_messages.append(str(e)) + + if match > 1: + # more than 1 match + raise ValueError( + "Multiple matches found when deserializing the JSON string into" + " DetectorCreationInputRequestModeConfiguration with oneOf schemas: BoundingBoxModeConfiguration," + " CountModeConfiguration, MultiClassModeConfiguration, TextModeConfiguration. Details: " + + ", ".join(error_messages) + ) + elif match == 0: + # no match + raise ValueError( + "No match found when deserializing the JSON string into DetectorCreationInputRequestModeConfiguration" + " with oneOf schemas: BoundingBoxModeConfiguration, CountModeConfiguration," + " MultiClassModeConfiguration, TextModeConfiguration. Details: " + + ", ".join(error_messages) + ) + else: + return instance + + def to_json(self) -> str: + """Returns the JSON representation of the actual instance""" + if self.actual_instance is None: + return "null" + + to_json = getattr(self.actual_instance, "to_json", None) + if callable(to_json): + return self.actual_instance.to_json() + else: + return json.dumps(self.actual_instance) + + def to_dict(self) -> dict: + """Returns the dict representation of the actual instance""" + if self.actual_instance is None: + return None + + to_dict = getattr(self.actual_instance, "to_dict", None) + if callable(to_dict): + return self.actual_instance.to_dict() + else: + # primitive type + return self.actual_instance + + def to_str(self) -> str: + """Returns the string representation of the actual instance""" + return pprint.pformat(self.dict()) diff --git a/generated/groundlight_openapi_client/models/detector_group.py b/generated/groundlight_openapi_client/models/detector_group.py new file mode 100644 index 00000000..5dfb319a --- /dev/null +++ b/generated/groundlight_openapi_client/models/detector_group.py @@ -0,0 +1,74 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from pydantic import BaseModel, Field, StrictStr, constr + + +class DetectorGroup(BaseModel): + """ + DetectorGroup + """ + + id: StrictStr = Field(...) + name: constr(strict=True, max_length=100) = Field(...) + __properties = ["id", "name"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> DetectorGroup: + """Create an instance of DetectorGroup from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict( + by_alias=True, + exclude={ + "id", + }, + exclude_none=True, + ) + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> DetectorGroup: + """Create an instance of DetectorGroup from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return DetectorGroup.parse_obj(obj) + + _obj = DetectorGroup.parse_obj({"id": obj.get("id"), "name": obj.get("name")}) + return _obj diff --git a/generated/groundlight_openapi_client/models/detector_group_request.py b/generated/groundlight_openapi_client/models/detector_group_request.py new file mode 100644 index 00000000..d4b2d919 --- /dev/null +++ b/generated/groundlight_openapi_client/models/detector_group_request.py @@ -0,0 +1,67 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from pydantic import BaseModel, Field, constr + + +class DetectorGroupRequest(BaseModel): + """ + DetectorGroupRequest + """ + + name: constr(strict=True, max_length=100, min_length=1) = Field(...) + __properties = ["name"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> DetectorGroupRequest: + """Create an instance of DetectorGroupRequest from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> DetectorGroupRequest: + """Create an instance of DetectorGroupRequest from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return DetectorGroupRequest.parse_obj(obj) + + _obj = DetectorGroupRequest.parse_obj({"name": obj.get("name")}) + return _obj diff --git a/generated/groundlight_openapi_client/models/detector_status.py b/generated/groundlight_openapi_client/models/detector_status.py new file mode 100644 index 00000000..8ec350a0 --- /dev/null +++ b/generated/groundlight_openapi_client/models/detector_status.py @@ -0,0 +1,158 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +from inspect import getfullargspec +import json +import pprint +import re # noqa: F401 + +from typing import Any, List, Optional +from pydantic import BaseModel, Field, StrictStr, ValidationError, validator +from groundlight_openapi_client.models.blank_enum import BlankEnum +from groundlight_openapi_client.models.status_enum import StatusEnum +from typing import Union, Any, List, TYPE_CHECKING +from pydantic import StrictStr, Field + +DETECTORSTATUS_ONE_OF_SCHEMAS = ["BlankEnum", "StatusEnum"] + + +class DetectorStatus(BaseModel): + """ + DetectorStatus + """ + + # data type: StatusEnum + oneof_schema_1_validator: Optional[StatusEnum] = None + # data type: BlankEnum + oneof_schema_2_validator: Optional[BlankEnum] = None + if TYPE_CHECKING: + actual_instance: Union[BlankEnum, StatusEnum] + else: + actual_instance: Any + one_of_schemas: List[str] = Field(DETECTORSTATUS_ONE_OF_SCHEMAS, const=True) + + class Config: + validate_assignment = True + + def __init__(self, *args, **kwargs) -> None: + if args: + if len(args) > 1: + raise ValueError("If a position argument is used, only 1 is allowed to set `actual_instance`") + if kwargs: + raise ValueError("If a position argument is used, keyword arguments cannot be used.") + super().__init__(actual_instance=args[0]) + else: + super().__init__(**kwargs) + + @validator("actual_instance") + def actual_instance_must_validate_oneof(cls, v): + instance = DetectorStatus.construct() + error_messages = [] + match = 0 + # validate data type: StatusEnum + if not isinstance(v, StatusEnum): + error_messages.append(f"Error! Input type `{type(v)}` is not `StatusEnum`") + else: + match += 1 + # validate data type: BlankEnum + if not isinstance(v, BlankEnum): + error_messages.append(f"Error! Input type `{type(v)}` is not `BlankEnum`") + else: + match += 1 + if match > 1: + # more than 1 match + raise ValueError( + "Multiple matches found when setting `actual_instance` in DetectorStatus with oneOf schemas: BlankEnum," + " StatusEnum. Details: " + + ", ".join(error_messages) + ) + elif match == 0: + # no match + raise ValueError( + "No match found when setting `actual_instance` in DetectorStatus with oneOf schemas: BlankEnum," + " StatusEnum. Details: " + + ", ".join(error_messages) + ) + else: + return v + + @classmethod + def from_dict(cls, obj: dict) -> DetectorStatus: + return cls.from_json(json.dumps(obj)) + + @classmethod + def from_json(cls, json_str: str) -> DetectorStatus: + """Returns the object represented by the json string""" + instance = DetectorStatus.construct() + error_messages = [] + match = 0 + + # deserialize data into StatusEnum + try: + instance.actual_instance = StatusEnum.from_json(json_str) + match += 1 + except (ValidationError, ValueError) as e: + error_messages.append(str(e)) + # deserialize data into BlankEnum + try: + instance.actual_instance = BlankEnum.from_json(json_str) + match += 1 + except (ValidationError, ValueError) as e: + error_messages.append(str(e)) + + if match > 1: + # more than 1 match + raise ValueError( + "Multiple matches found when deserializing the JSON string into DetectorStatus with oneOf schemas:" + " BlankEnum, StatusEnum. Details: " + + ", ".join(error_messages) + ) + elif match == 0: + # no match + raise ValueError( + "No match found when deserializing the JSON string into DetectorStatus with oneOf schemas: BlankEnum," + " StatusEnum. Details: " + + ", ".join(error_messages) + ) + else: + return instance + + def to_json(self) -> str: + """Returns the JSON representation of the actual instance""" + if self.actual_instance is None: + return "null" + + to_json = getattr(self.actual_instance, "to_json", None) + if callable(to_json): + return self.actual_instance.to_json() + else: + return json.dumps(self.actual_instance) + + def to_dict(self) -> dict: + """Returns the dict representation of the actual instance""" + if self.actual_instance is None: + return None + + to_dict = getattr(self.actual_instance, "to_dict", None) + if callable(to_dict): + return self.actual_instance.to_dict() + else: + # primitive type + return self.actual_instance + + def to_str(self) -> str: + """Returns the string representation of the actual instance""" + return pprint.pformat(self.dict()) diff --git a/generated/groundlight_openapi_client/models/detector_type_enum.py b/generated/groundlight_openapi_client/models/detector_type_enum.py new file mode 100644 index 00000000..f1b372fe --- /dev/null +++ b/generated/groundlight_openapi_client/models/detector_type_enum.py @@ -0,0 +1,35 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import json +import pprint +import re # noqa: F401 +from aenum import Enum, no_arg + + +class DetectorTypeEnum(str, Enum): + """ + DetectorTypeEnum + """ + + """ + allowed enum values + """ + DETECTOR = "detector" + + @classmethod + def from_json(cls, json_str: str) -> DetectorTypeEnum: + """Create an instance of DetectorTypeEnum from a JSON string""" + return DetectorTypeEnum(json.loads(json_str)) diff --git a/generated/groundlight_openapi_client/models/edge_model_info.py b/generated/groundlight_openapi_client/models/edge_model_info.py new file mode 100644 index 00000000..92514771 --- /dev/null +++ b/generated/groundlight_openapi_client/models/edge_model_info.py @@ -0,0 +1,105 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Any, Optional +from pydantic import BaseModel, StrictStr + + +class EdgeModelInfo(BaseModel): + """ + Information for the model running on edge, including temporary presigned urls to the model binaries # noqa: E501 + """ + + model_binary_id: Optional[StrictStr] = None + model_binary_url: Optional[StrictStr] = None + oodd_model_binary_id: Optional[StrictStr] = None + oodd_model_binary_url: Optional[StrictStr] = None + pipeline_config: Optional[Any] = None + oodd_pipeline_config: Optional[Any] = None + predictor_metadata: Optional[Any] = None + __properties = [ + "model_binary_id", + "model_binary_url", + "oodd_model_binary_id", + "oodd_model_binary_url", + "pipeline_config", + "oodd_pipeline_config", + "predictor_metadata", + ] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> EdgeModelInfo: + """Create an instance of EdgeModelInfo from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # set to None if pipeline_config (nullable) is None + # and __fields_set__ contains the field + if self.pipeline_config is None and "pipeline_config" in self.__fields_set__: + _dict["pipeline_config"] = None + + # set to None if oodd_pipeline_config (nullable) is None + # and __fields_set__ contains the field + if self.oodd_pipeline_config is None and "oodd_pipeline_config" in self.__fields_set__: + _dict["oodd_pipeline_config"] = None + + # set to None if predictor_metadata (nullable) is None + # and __fields_set__ contains the field + if self.predictor_metadata is None and "predictor_metadata" in self.__fields_set__: + _dict["predictor_metadata"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> EdgeModelInfo: + """Create an instance of EdgeModelInfo from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return EdgeModelInfo.parse_obj(obj) + + _obj = EdgeModelInfo.parse_obj({ + "model_binary_id": obj.get("model_binary_id"), + "model_binary_url": obj.get("model_binary_url"), + "oodd_model_binary_id": obj.get("oodd_model_binary_id"), + "oodd_model_binary_url": obj.get("oodd_model_binary_url"), + "pipeline_config": obj.get("pipeline_config"), + "oodd_pipeline_config": obj.get("oodd_pipeline_config"), + "predictor_metadata": obj.get("predictor_metadata"), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/escalation_type_enum.py b/generated/groundlight_openapi_client/models/escalation_type_enum.py new file mode 100644 index 00000000..4f6690f1 --- /dev/null +++ b/generated/groundlight_openapi_client/models/escalation_type_enum.py @@ -0,0 +1,36 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import json +import pprint +import re # noqa: F401 +from aenum import Enum, no_arg + + +class EscalationTypeEnum(str, Enum): + """ + EscalationTypeEnum + """ + + """ + allowed enum values + """ + STANDARD = "STANDARD" + NO_HUMAN_LABELING = "NO_HUMAN_LABELING" + + @classmethod + def from_json(cls, json_str: str) -> EscalationTypeEnum: + """Create an instance of EscalationTypeEnum from a JSON string""" + return EscalationTypeEnum(json.loads(json_str)) diff --git a/generated/groundlight_openapi_client/models/get_detector_evaluation200_response.py b/generated/groundlight_openapi_client/models/get_detector_evaluation200_response.py new file mode 100644 index 00000000..d9e4d338 --- /dev/null +++ b/generated/groundlight_openapi_client/models/get_detector_evaluation200_response.py @@ -0,0 +1,85 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Optional +from pydantic import BaseModel +from groundlight_openapi_client.models.get_detector_evaluation200_response_evaluation_results import ( + GetDetectorEvaluation200ResponseEvaluationResults, +) + + +class GetDetectorEvaluation200Response(BaseModel): + """ + GetDetectorEvaluation200Response + """ + + evaluation_results: Optional[GetDetectorEvaluation200ResponseEvaluationResults] = None + __properties = ["evaluation_results"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> GetDetectorEvaluation200Response: + """Create an instance of GetDetectorEvaluation200Response from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # override the default output from pydantic by calling `to_dict()` of evaluation_results + if self.evaluation_results: + _dict["evaluation_results"] = self.evaluation_results.to_dict() + # set to None if evaluation_results (nullable) is None + # and __fields_set__ contains the field + if self.evaluation_results is None and "evaluation_results" in self.__fields_set__: + _dict["evaluation_results"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> GetDetectorEvaluation200Response: + """Create an instance of GetDetectorEvaluation200Response from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return GetDetectorEvaluation200Response.parse_obj(obj) + + _obj = GetDetectorEvaluation200Response.parse_obj({ + "evaluation_results": ( + GetDetectorEvaluation200ResponseEvaluationResults.from_dict(obj.get("evaluation_results")) + if obj.get("evaluation_results") is not None + else None + ) + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/get_detector_evaluation200_response_evaluation_results.py b/generated/groundlight_openapi_client/models/get_detector_evaluation200_response_evaluation_results.py new file mode 100644 index 00000000..e9bb02fc --- /dev/null +++ b/generated/groundlight_openapi_client/models/get_detector_evaluation200_response_evaluation_results.py @@ -0,0 +1,211 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + +from datetime import datetime +from typing import Any, Dict, Optional, Union +from pydantic import BaseModel, StrictFloat, StrictInt + + +class GetDetectorEvaluation200ResponseEvaluationResults(BaseModel): + """ + GetDetectorEvaluation200ResponseEvaluationResults + """ + + eval_timestamp: Optional[datetime] = None + total_ground_truth_examples: Optional[StrictInt] = None + total_labeled_examples: Optional[StrictInt] = None + kfold_pooled__balanced_accuracy: Optional[Union[StrictFloat, StrictInt]] = None + kfold_pooled__positive_accuracy: Optional[Union[StrictFloat, StrictInt]] = None + kfold_pooled__negative_accuracy: Optional[Union[StrictFloat, StrictInt]] = None + precision__mean: Optional[Union[StrictFloat, StrictInt]] = None + recall__mean: Optional[Union[StrictFloat, StrictInt]] = None + roc_auc__mean: Optional[Union[StrictFloat, StrictInt]] = None + balanced_system_accuracies: Optional[Dict[str, Any]] = None + positive_system_accuracies: Optional[Dict[str, Any]] = None + negative_system_accuracies: Optional[Dict[str, Any]] = None + mean_absolute_error__mean: Optional[Union[StrictFloat, StrictInt]] = None + objdet_precision__mean: Optional[Union[StrictFloat, StrictInt]] = None + objdet_recall__mean: Optional[Union[StrictFloat, StrictInt]] = None + objdet_f1_score__mean: Optional[Union[StrictFloat, StrictInt]] = None + class_accuracies: Optional[Dict[str, Any]] = None + confusion_dict: Optional[Dict[str, Any]] = None + num_examples_per_class: Optional[Dict[str, Any]] = None + __properties = [ + "eval_timestamp", + "total_ground_truth_examples", + "total_labeled_examples", + "kfold_pooled__balanced_accuracy", + "kfold_pooled__positive_accuracy", + "kfold_pooled__negative_accuracy", + "precision__mean", + "recall__mean", + "roc_auc__mean", + "balanced_system_accuracies", + "positive_system_accuracies", + "negative_system_accuracies", + "mean_absolute_error__mean", + "objdet_precision__mean", + "objdet_recall__mean", + "objdet_f1_score__mean", + "class_accuracies", + "confusion_dict", + "num_examples_per_class", + ] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> GetDetectorEvaluation200ResponseEvaluationResults: + """Create an instance of GetDetectorEvaluation200ResponseEvaluationResults from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # set to None if total_ground_truth_examples (nullable) is None + # and __fields_set__ contains the field + if self.total_ground_truth_examples is None and "total_ground_truth_examples" in self.__fields_set__: + _dict["total_ground_truth_examples"] = None + + # set to None if total_labeled_examples (nullable) is None + # and __fields_set__ contains the field + if self.total_labeled_examples is None and "total_labeled_examples" in self.__fields_set__: + _dict["total_labeled_examples"] = None + + # set to None if kfold_pooled__positive_accuracy (nullable) is None + # and __fields_set__ contains the field + if self.kfold_pooled__positive_accuracy is None and "kfold_pooled__positive_accuracy" in self.__fields_set__: + _dict["kfold_pooled__positive_accuracy"] = None + + # set to None if kfold_pooled__negative_accuracy (nullable) is None + # and __fields_set__ contains the field + if self.kfold_pooled__negative_accuracy is None and "kfold_pooled__negative_accuracy" in self.__fields_set__: + _dict["kfold_pooled__negative_accuracy"] = None + + # set to None if precision__mean (nullable) is None + # and __fields_set__ contains the field + if self.precision__mean is None and "precision__mean" in self.__fields_set__: + _dict["precision__mean"] = None + + # set to None if recall__mean (nullable) is None + # and __fields_set__ contains the field + if self.recall__mean is None and "recall__mean" in self.__fields_set__: + _dict["recall__mean"] = None + + # set to None if roc_auc__mean (nullable) is None + # and __fields_set__ contains the field + if self.roc_auc__mean is None and "roc_auc__mean" in self.__fields_set__: + _dict["roc_auc__mean"] = None + + # set to None if balanced_system_accuracies (nullable) is None + # and __fields_set__ contains the field + if self.balanced_system_accuracies is None and "balanced_system_accuracies" in self.__fields_set__: + _dict["balanced_system_accuracies"] = None + + # set to None if positive_system_accuracies (nullable) is None + # and __fields_set__ contains the field + if self.positive_system_accuracies is None and "positive_system_accuracies" in self.__fields_set__: + _dict["positive_system_accuracies"] = None + + # set to None if negative_system_accuracies (nullable) is None + # and __fields_set__ contains the field + if self.negative_system_accuracies is None and "negative_system_accuracies" in self.__fields_set__: + _dict["negative_system_accuracies"] = None + + # set to None if mean_absolute_error__mean (nullable) is None + # and __fields_set__ contains the field + if self.mean_absolute_error__mean is None and "mean_absolute_error__mean" in self.__fields_set__: + _dict["mean_absolute_error__mean"] = None + + # set to None if objdet_precision__mean (nullable) is None + # and __fields_set__ contains the field + if self.objdet_precision__mean is None and "objdet_precision__mean" in self.__fields_set__: + _dict["objdet_precision__mean"] = None + + # set to None if objdet_recall__mean (nullable) is None + # and __fields_set__ contains the field + if self.objdet_recall__mean is None and "objdet_recall__mean" in self.__fields_set__: + _dict["objdet_recall__mean"] = None + + # set to None if objdet_f1_score__mean (nullable) is None + # and __fields_set__ contains the field + if self.objdet_f1_score__mean is None and "objdet_f1_score__mean" in self.__fields_set__: + _dict["objdet_f1_score__mean"] = None + + # set to None if class_accuracies (nullable) is None + # and __fields_set__ contains the field + if self.class_accuracies is None and "class_accuracies" in self.__fields_set__: + _dict["class_accuracies"] = None + + # set to None if confusion_dict (nullable) is None + # and __fields_set__ contains the field + if self.confusion_dict is None and "confusion_dict" in self.__fields_set__: + _dict["confusion_dict"] = None + + # set to None if num_examples_per_class (nullable) is None + # and __fields_set__ contains the field + if self.num_examples_per_class is None and "num_examples_per_class" in self.__fields_set__: + _dict["num_examples_per_class"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> GetDetectorEvaluation200ResponseEvaluationResults: + """Create an instance of GetDetectorEvaluation200ResponseEvaluationResults from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return GetDetectorEvaluation200ResponseEvaluationResults.parse_obj(obj) + + _obj = GetDetectorEvaluation200ResponseEvaluationResults.parse_obj({ + "eval_timestamp": obj.get("eval_timestamp"), + "total_ground_truth_examples": obj.get("total_ground_truth_examples"), + "total_labeled_examples": obj.get("total_labeled_examples"), + "kfold_pooled__balanced_accuracy": obj.get("kfold_pooled__balanced_accuracy"), + "kfold_pooled__positive_accuracy": obj.get("kfold_pooled__positive_accuracy"), + "kfold_pooled__negative_accuracy": obj.get("kfold_pooled__negative_accuracy"), + "precision__mean": obj.get("precision__mean"), + "recall__mean": obj.get("recall__mean"), + "roc_auc__mean": obj.get("roc_auc__mean"), + "balanced_system_accuracies": obj.get("balanced_system_accuracies"), + "positive_system_accuracies": obj.get("positive_system_accuracies"), + "negative_system_accuracies": obj.get("negative_system_accuracies"), + "mean_absolute_error__mean": obj.get("mean_absolute_error__mean"), + "objdet_precision__mean": obj.get("objdet_precision__mean"), + "objdet_recall__mean": obj.get("objdet_recall__mean"), + "objdet_f1_score__mean": obj.get("objdet_f1_score__mean"), + "class_accuracies": obj.get("class_accuracies"), + "confusion_dict": obj.get("confusion_dict"), + "num_examples_per_class": obj.get("num_examples_per_class"), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/get_detector_metrics200_response.py b/generated/groundlight_openapi_client/models/get_detector_metrics200_response.py new file mode 100644 index 00000000..76cc0ea5 --- /dev/null +++ b/generated/groundlight_openapi_client/models/get_detector_metrics200_response.py @@ -0,0 +1,85 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Optional +from pydantic import BaseModel +from groundlight_openapi_client.models.get_detector_metrics200_response_summary import ( + GetDetectorMetrics200ResponseSummary, +) + + +class GetDetectorMetrics200Response(BaseModel): + """ + GetDetectorMetrics200Response + """ + + summary: Optional[GetDetectorMetrics200ResponseSummary] = None + __properties = ["summary"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> GetDetectorMetrics200Response: + """Create an instance of GetDetectorMetrics200Response from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # override the default output from pydantic by calling `to_dict()` of summary + if self.summary: + _dict["summary"] = self.summary.to_dict() + # set to None if summary (nullable) is None + # and __fields_set__ contains the field + if self.summary is None and "summary" in self.__fields_set__: + _dict["summary"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> GetDetectorMetrics200Response: + """Create an instance of GetDetectorMetrics200Response from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return GetDetectorMetrics200Response.parse_obj(obj) + + _obj = GetDetectorMetrics200Response.parse_obj({ + "summary": ( + GetDetectorMetrics200ResponseSummary.from_dict(obj.get("summary")) + if obj.get("summary") is not None + else None + ) + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/get_detector_metrics200_response_summary.py b/generated/groundlight_openapi_client/models/get_detector_metrics200_response_summary.py new file mode 100644 index 00000000..3e5541b3 --- /dev/null +++ b/generated/groundlight_openapi_client/models/get_detector_metrics200_response_summary.py @@ -0,0 +1,88 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Any, Dict, Optional +from pydantic import BaseModel, StrictInt +from groundlight_openapi_client.models.get_detector_metrics200_response_summary_class_counts import ( + GetDetectorMetrics200ResponseSummaryClassCounts, +) + + +class GetDetectorMetrics200ResponseSummary(BaseModel): + """ + GetDetectorMetrics200ResponseSummary + """ + + num_ground_truth: Optional[StrictInt] = None + num_current_source_human: Optional[StrictInt] = None + class_counts: Optional[GetDetectorMetrics200ResponseSummaryClassCounts] = None + unconfident_counts: Optional[Dict[str, Any]] = None + total_iqs: Optional[StrictInt] = None + __properties = ["num_ground_truth", "num_current_source_human", "class_counts", "unconfident_counts", "total_iqs"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> GetDetectorMetrics200ResponseSummary: + """Create an instance of GetDetectorMetrics200ResponseSummary from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # override the default output from pydantic by calling `to_dict()` of class_counts + if self.class_counts: + _dict["class_counts"] = self.class_counts.to_dict() + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> GetDetectorMetrics200ResponseSummary: + """Create an instance of GetDetectorMetrics200ResponseSummary from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return GetDetectorMetrics200ResponseSummary.parse_obj(obj) + + _obj = GetDetectorMetrics200ResponseSummary.parse_obj({ + "num_ground_truth": obj.get("num_ground_truth"), + "num_current_source_human": obj.get("num_current_source_human"), + "class_counts": ( + GetDetectorMetrics200ResponseSummaryClassCounts.from_dict(obj.get("class_counts")) + if obj.get("class_counts") is not None + else None + ), + "unconfident_counts": obj.get("unconfident_counts"), + "total_iqs": obj.get("total_iqs"), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/get_detector_metrics200_response_summary_class_counts.py b/generated/groundlight_openapi_client/models/get_detector_metrics200_response_summary_class_counts.py new file mode 100644 index 00000000..55f58fc0 --- /dev/null +++ b/generated/groundlight_openapi_client/models/get_detector_metrics200_response_summary_class_counts.py @@ -0,0 +1,78 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Any, Dict, Optional +from pydantic import BaseModel + + +class GetDetectorMetrics200ResponseSummaryClassCounts(BaseModel): + """ + GetDetectorMetrics200ResponseSummaryClassCounts + """ + + source_ml: Optional[Dict[str, Any]] = None + source_human: Optional[Dict[str, Any]] = None + cloud_labeler: Optional[Dict[str, Any]] = None + cloud: Optional[Dict[str, Any]] = None + total: Optional[Dict[str, Any]] = None + __properties = ["source_ml", "source_human", "cloud_labeler", "cloud", "total"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> GetDetectorMetrics200ResponseSummaryClassCounts: + """Create an instance of GetDetectorMetrics200ResponseSummaryClassCounts from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> GetDetectorMetrics200ResponseSummaryClassCounts: + """Create an instance of GetDetectorMetrics200ResponseSummaryClassCounts from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return GetDetectorMetrics200ResponseSummaryClassCounts.parse_obj(obj) + + _obj = GetDetectorMetrics200ResponseSummaryClassCounts.parse_obj({ + "source_ml": obj.get("source_ml"), + "source_human": obj.get("source_human"), + "cloud_labeler": obj.get("cloud_labeler"), + "cloud": obj.get("cloud"), + "total": obj.get("total"), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/image_query.py b/generated/groundlight_openapi_client/models/image_query.py new file mode 100644 index 00000000..ea4fa642 --- /dev/null +++ b/generated/groundlight_openapi_client/models/image_query.py @@ -0,0 +1,167 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + +from datetime import datetime +from typing import Any, Dict, List, Optional, Union +from pydantic import BaseModel, Field, StrictBool, StrictFloat, StrictInt, StrictStr, conlist +from groundlight_openapi_client.models.image_query_result import ImageQueryResult +from groundlight_openapi_client.models.image_query_type_enum import ImageQueryTypeEnum +from groundlight_openapi_client.models.result_type_enum import ResultTypeEnum +from groundlight_openapi_client.models.roi import ROI + + +class ImageQuery(BaseModel): + """ + ImageQuery objects are the answers to natural language questions about images created by detectors. # noqa: E501 + """ + + metadata: Optional[Dict[str, Any]] = Field(default=..., description="Metadata about the image query.") + id: StrictStr = Field(default=..., description="A unique ID for this object.") + type: ImageQueryTypeEnum = Field(default=..., description="The type of this object.") + created_at: datetime = Field(default=..., description="When was this detector created?") + query: StrictStr = Field(default=..., description="A question about the image.") + detector_id: StrictStr = Field(default=..., description="Which detector was used on this image query?") + result_type: ResultTypeEnum = Field(default=..., description="What type of result are we returning?") + result: Optional[ImageQueryResult] = Field(...) + patience_time: Union[StrictFloat, StrictInt] = Field( + default=..., description="How long to wait for a confident response." + ) + confidence_threshold: Union[StrictFloat, StrictInt] = Field( + default=..., description="Min confidence needed to accept the response of the image query." + ) + rois: Optional[conlist(ROI)] = Field( + default=..., description="An array of regions of interest (bounding boxes) collected on image" + ) + text: Optional[StrictStr] = Field(default=..., description="A text field on image query.") + done_processing: Optional[StrictBool] = Field( + default=False, + description="EDGE ONLY - Whether the image query has completed escalating and will receive no new results.", + ) + __properties = [ + "metadata", + "id", + "type", + "created_at", + "query", + "detector_id", + "result_type", + "result", + "patience_time", + "confidence_threshold", + "rois", + "text", + "done_processing", + ] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> ImageQuery: + """Create an instance of ImageQuery from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict( + by_alias=True, + exclude={ + "metadata", + "id", + "type", + "created_at", + "query", + "detector_id", + "result_type", + "patience_time", + "confidence_threshold", + "rois", + "text", + }, + exclude_none=True, + ) + # override the default output from pydantic by calling `to_dict()` of result + if self.result: + _dict["result"] = self.result.to_dict() + # override the default output from pydantic by calling `to_dict()` of each item in rois (list) + _items = [] + if self.rois: + for _item in self.rois: + if _item: + _items.append(_item.to_dict()) + _dict["rois"] = _items + # set to None if metadata (nullable) is None + # and __fields_set__ contains the field + if self.metadata is None and "metadata" in self.__fields_set__: + _dict["metadata"] = None + + # set to None if result (nullable) is None + # and __fields_set__ contains the field + if self.result is None and "result" in self.__fields_set__: + _dict["result"] = None + + # set to None if rois (nullable) is None + # and __fields_set__ contains the field + if self.rois is None and "rois" in self.__fields_set__: + _dict["rois"] = None + + # set to None if text (nullable) is None + # and __fields_set__ contains the field + if self.text is None and "text" in self.__fields_set__: + _dict["text"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> ImageQuery: + """Create an instance of ImageQuery from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return ImageQuery.parse_obj(obj) + + _obj = ImageQuery.parse_obj({ + "metadata": obj.get("metadata"), + "id": obj.get("id"), + "type": obj.get("type"), + "created_at": obj.get("created_at"), + "query": obj.get("query"), + "detector_id": obj.get("detector_id"), + "result_type": obj.get("result_type"), + "result": ImageQueryResult.from_dict(obj.get("result")) if obj.get("result") is not None else None, + "patience_time": obj.get("patience_time"), + "confidence_threshold": obj.get("confidence_threshold"), + "rois": [ROI.from_dict(_item) for _item in obj.get("rois")] if obj.get("rois") is not None else None, + "text": obj.get("text"), + "done_processing": obj.get("done_processing") if obj.get("done_processing") is not None else False, + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/image_query_result.py b/generated/groundlight_openapi_client/models/image_query_result.py new file mode 100644 index 00000000..e659480c --- /dev/null +++ b/generated/groundlight_openapi_client/models/image_query_result.py @@ -0,0 +1,230 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +from inspect import getfullargspec +import json +import pprint +import re # noqa: F401 + +from typing import Any, List, Optional +from pydantic import BaseModel, Field, StrictStr, ValidationError, validator +from groundlight_openapi_client.models.binary_classification_result import BinaryClassificationResult +from groundlight_openapi_client.models.bounding_box_result import BoundingBoxResult +from groundlight_openapi_client.models.counting_result import CountingResult +from groundlight_openapi_client.models.multi_classification_result import MultiClassificationResult +from groundlight_openapi_client.models.text_recognition_result import TextRecognitionResult +from typing import Union, Any, List, TYPE_CHECKING +from pydantic import StrictStr, Field + +IMAGEQUERYRESULT_ONE_OF_SCHEMAS = [ + "BinaryClassificationResult", + "BoundingBoxResult", + "CountingResult", + "MultiClassificationResult", + "TextRecognitionResult", +] + + +class ImageQueryResult(BaseModel): + """ + ImageQueryResult + """ + + # data type: BinaryClassificationResult + oneof_schema_1_validator: Optional[BinaryClassificationResult] = None + # data type: CountingResult + oneof_schema_2_validator: Optional[CountingResult] = None + # data type: MultiClassificationResult + oneof_schema_3_validator: Optional[MultiClassificationResult] = None + # data type: TextRecognitionResult + oneof_schema_4_validator: Optional[TextRecognitionResult] = None + # data type: BoundingBoxResult + oneof_schema_5_validator: Optional[BoundingBoxResult] = None + if TYPE_CHECKING: + actual_instance: Union[ + BinaryClassificationResult, + BoundingBoxResult, + CountingResult, + MultiClassificationResult, + TextRecognitionResult, + ] + else: + actual_instance: Any + one_of_schemas: List[str] = Field(IMAGEQUERYRESULT_ONE_OF_SCHEMAS, const=True) + + class Config: + validate_assignment = True + + discriminator_value_class_map = {} + + def __init__(self, *args, **kwargs) -> None: + if args: + if len(args) > 1: + raise ValueError("If a position argument is used, only 1 is allowed to set `actual_instance`") + if kwargs: + raise ValueError("If a position argument is used, keyword arguments cannot be used.") + super().__init__(actual_instance=args[0]) + else: + super().__init__(**kwargs) + + @validator("actual_instance") + def actual_instance_must_validate_oneof(cls, v): + if v is None: + return v + + instance = ImageQueryResult.construct() + error_messages = [] + match = 0 + # validate data type: BinaryClassificationResult + if not isinstance(v, BinaryClassificationResult): + error_messages.append(f"Error! Input type `{type(v)}` is not `BinaryClassificationResult`") + else: + match += 1 + # validate data type: CountingResult + if not isinstance(v, CountingResult): + error_messages.append(f"Error! Input type `{type(v)}` is not `CountingResult`") + else: + match += 1 + # validate data type: MultiClassificationResult + if not isinstance(v, MultiClassificationResult): + error_messages.append(f"Error! Input type `{type(v)}` is not `MultiClassificationResult`") + else: + match += 1 + # validate data type: TextRecognitionResult + if not isinstance(v, TextRecognitionResult): + error_messages.append(f"Error! Input type `{type(v)}` is not `TextRecognitionResult`") + else: + match += 1 + # validate data type: BoundingBoxResult + if not isinstance(v, BoundingBoxResult): + error_messages.append(f"Error! Input type `{type(v)}` is not `BoundingBoxResult`") + else: + match += 1 + if match > 1: + # more than 1 match + raise ValueError( + "Multiple matches found when setting `actual_instance` in ImageQueryResult with oneOf schemas:" + " BinaryClassificationResult, BoundingBoxResult, CountingResult, MultiClassificationResult," + " TextRecognitionResult. Details: " + + ", ".join(error_messages) + ) + elif match == 0: + # no match + raise ValueError( + "No match found when setting `actual_instance` in ImageQueryResult with oneOf schemas:" + " BinaryClassificationResult, BoundingBoxResult, CountingResult, MultiClassificationResult," + " TextRecognitionResult. Details: " + + ", ".join(error_messages) + ) + else: + return v + + @classmethod + def from_dict(cls, obj: dict) -> ImageQueryResult: + return cls.from_json(json.dumps(obj)) + + @classmethod + def from_json(cls, json_str: str) -> ImageQueryResult: + """Returns the object represented by the json string""" + instance = ImageQueryResult.construct() + if json_str is None: + return instance + + error_messages = [] + match = 0 + + # deserialize data into BinaryClassificationResult + try: + instance.actual_instance = BinaryClassificationResult.from_json(json_str) + match += 1 + except (ValidationError, ValueError) as e: + error_messages.append(str(e)) + # deserialize data into CountingResult + try: + instance.actual_instance = CountingResult.from_json(json_str) + match += 1 + except (ValidationError, ValueError) as e: + error_messages.append(str(e)) + # deserialize data into MultiClassificationResult + try: + instance.actual_instance = MultiClassificationResult.from_json(json_str) + match += 1 + except (ValidationError, ValueError) as e: + error_messages.append(str(e)) + # deserialize data into TextRecognitionResult + try: + instance.actual_instance = TextRecognitionResult.from_json(json_str) + match += 1 + except (ValidationError, ValueError) as e: + error_messages.append(str(e)) + # deserialize data into BoundingBoxResult + try: + instance.actual_instance = BoundingBoxResult.from_json(json_str) + match += 1 + except (ValidationError, ValueError) as e: + error_messages.append(str(e)) + + if match > 1: + # more than 1 match + raise ValueError( + "Multiple matches found when deserializing the JSON string into ImageQueryResult with oneOf schemas:" + " BinaryClassificationResult, BoundingBoxResult, CountingResult, MultiClassificationResult," + " TextRecognitionResult. Details: " + + ", ".join(error_messages) + ) + elif match == 0: + # no match + raise ValueError( + "No match found when deserializing the JSON string into ImageQueryResult with oneOf schemas:" + " BinaryClassificationResult, BoundingBoxResult, CountingResult, MultiClassificationResult," + " TextRecognitionResult. Details: " + + ", ".join(error_messages) + ) + else: + return instance + + def to_json(self) -> str: + """Returns the JSON representation of the actual instance""" + if self.actual_instance is None: + return "null" + + to_json = getattr(self.actual_instance, "to_json", None) + if callable(to_json): + return self.actual_instance.to_json() + else: + return json.dumps(self.actual_instance) + + def to_dict(self) -> dict: + """Returns the dict representation of the actual instance""" + if self.actual_instance is None: + return None + + to_dict = getattr(self.actual_instance, "to_dict", None) + if callable(to_dict): + return self.actual_instance.to_dict() + else: + # primitive type + return self.actual_instance + + def to_str(self) -> str: + """Returns the string representation of the actual instance""" + return pprint.pformat(self.dict()) + + def __getattr__(self, name): + try: + return super().__getattribute__(name) + except AttributeError: + return self.actual_instance.__getattribute__(name) diff --git a/generated/groundlight_openapi_client/models/image_query_type_enum.py b/generated/groundlight_openapi_client/models/image_query_type_enum.py new file mode 100644 index 00000000..3115bba9 --- /dev/null +++ b/generated/groundlight_openapi_client/models/image_query_type_enum.py @@ -0,0 +1,35 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import json +import pprint +import re # noqa: F401 +from aenum import Enum, no_arg + + +class ImageQueryTypeEnum(str, Enum): + """ + ImageQueryTypeEnum + """ + + """ + allowed enum values + """ + IMAGE_QUERY = "image_query" + + @classmethod + def from_json(cls, json_str: str) -> ImageQueryTypeEnum: + """Create an instance of ImageQueryTypeEnum from a JSON string""" + return ImageQueryTypeEnum(json.loads(json_str)) diff --git a/generated/groundlight_openapi_client/models/label.py b/generated/groundlight_openapi_client/models/label.py new file mode 100644 index 00000000..3dfbf71d --- /dev/null +++ b/generated/groundlight_openapi_client/models/label.py @@ -0,0 +1,37 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import json +import pprint +import re # noqa: F401 +from aenum import Enum, no_arg + + +class Label(str, Enum): + """ + Label + """ + + """ + allowed enum values + """ + YES = "YES" + NO = "NO" + UNCLEAR = "UNCLEAR" + + @classmethod + def from_json(cls, json_str: str) -> Label: + """Create an instance of Label from a JSON string""" + return Label(json.loads(json_str)) diff --git a/generated/groundlight_openapi_client/models/label_value.py b/generated/groundlight_openapi_client/models/label_value.py new file mode 100644 index 00000000..9a2031af --- /dev/null +++ b/generated/groundlight_openapi_client/models/label_value.py @@ -0,0 +1,140 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + +from datetime import datetime +from typing import List, Optional, Union +from pydantic import BaseModel, Field, StrictFloat, StrictInt, StrictStr, conlist +from groundlight_openapi_client.models.roi import ROI + + +class LabelValue(BaseModel): + """ + LabelValue + """ + + confidence: Optional[Union[StrictFloat, StrictInt]] = Field(...) + class_name: Optional[StrictStr] = Field( + default=..., description="Return a human-readable class name for this label (e.g. YES/NO)" + ) + rois: Optional[conlist(ROI)] = None + annotations_requested: conlist(StrictStr) = Field(...) + created_at: datetime = Field(...) + detector_id: Optional[StrictInt] = Field(...) + source: StrictStr = Field(...) + text: Optional[StrictStr] = Field(default=..., description="Text annotations") + __properties = [ + "confidence", + "class_name", + "rois", + "annotations_requested", + "created_at", + "detector_id", + "source", + "text", + ] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> LabelValue: + """Create an instance of LabelValue from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict( + by_alias=True, + exclude={ + "confidence", + "class_name", + "annotations_requested", + "created_at", + "detector_id", + "source", + "text", + }, + exclude_none=True, + ) + # override the default output from pydantic by calling `to_dict()` of each item in rois (list) + _items = [] + if self.rois: + for _item in self.rois: + if _item: + _items.append(_item.to_dict()) + _dict["rois"] = _items + # set to None if confidence (nullable) is None + # and __fields_set__ contains the field + if self.confidence is None and "confidence" in self.__fields_set__: + _dict["confidence"] = None + + # set to None if class_name (nullable) is None + # and __fields_set__ contains the field + if self.class_name is None and "class_name" in self.__fields_set__: + _dict["class_name"] = None + + # set to None if rois (nullable) is None + # and __fields_set__ contains the field + if self.rois is None and "rois" in self.__fields_set__: + _dict["rois"] = None + + # set to None if detector_id (nullable) is None + # and __fields_set__ contains the field + if self.detector_id is None and "detector_id" in self.__fields_set__: + _dict["detector_id"] = None + + # set to None if text (nullable) is None + # and __fields_set__ contains the field + if self.text is None and "text" in self.__fields_set__: + _dict["text"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> LabelValue: + """Create an instance of LabelValue from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return LabelValue.parse_obj(obj) + + _obj = LabelValue.parse_obj({ + "confidence": obj.get("confidence"), + "class_name": obj.get("class_name"), + "rois": [ROI.from_dict(_item) for _item in obj.get("rois")] if obj.get("rois") is not None else None, + "annotations_requested": obj.get("annotations_requested"), + "created_at": obj.get("created_at"), + "detector_id": obj.get("detector_id"), + "source": obj.get("source"), + "text": obj.get("text"), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/label_value_request.py b/generated/groundlight_openapi_client/models/label_value_request.py new file mode 100644 index 00000000..b4d08445 --- /dev/null +++ b/generated/groundlight_openapi_client/models/label_value_request.py @@ -0,0 +1,92 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import List, Optional +from pydantic import BaseModel, Field, StrictStr, conlist, constr +from groundlight_openapi_client.models.roi_request import ROIRequest + + +class LabelValueRequest(BaseModel): + """ + LabelValueRequest + """ + + label: Optional[StrictStr] = Field(...) + image_query_id: constr(strict=True, min_length=1) = Field(...) + rois: Optional[conlist(ROIRequest)] = None + __properties = ["label", "image_query_id", "rois"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> LabelValueRequest: + """Create an instance of LabelValueRequest from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # override the default output from pydantic by calling `to_dict()` of each item in rois (list) + _items = [] + if self.rois: + for _item in self.rois: + if _item: + _items.append(_item.to_dict()) + _dict["rois"] = _items + # set to None if label (nullable) is None + # and __fields_set__ contains the field + if self.label is None and "label" in self.__fields_set__: + _dict["label"] = None + + # set to None if rois (nullable) is None + # and __fields_set__ contains the field + if self.rois is None and "rois" in self.__fields_set__: + _dict["rois"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> LabelValueRequest: + """Create an instance of LabelValueRequest from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return LabelValueRequest.parse_obj(obj) + + _obj = LabelValueRequest.parse_obj({ + "label": obj.get("label"), + "image_query_id": obj.get("image_query_id"), + "rois": [ROIRequest.from_dict(_item) for _item in obj.get("rois")] if obj.get("rois") is not None else None, + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/mode_enum.py b/generated/groundlight_openapi_client/models/mode_enum.py new file mode 100644 index 00000000..bc1b2bbe --- /dev/null +++ b/generated/groundlight_openapi_client/models/mode_enum.py @@ -0,0 +1,39 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import json +import pprint +import re # noqa: F401 +from aenum import Enum, no_arg + + +class ModeEnum(str, Enum): + """ + ModeEnum + """ + + """ + allowed enum values + """ + BINARY = "BINARY" + COUNT = "COUNT" + MULTI_CLASS = "MULTI_CLASS" + TEXT = "TEXT" + BOUNDING_BOX = "BOUNDING_BOX" + + @classmethod + def from_json(cls, json_str: str) -> ModeEnum: + """Create an instance of ModeEnum from a JSON string""" + return ModeEnum(json.loads(json_str)) diff --git a/generated/groundlight_openapi_client/models/multi_class_mode_configuration.py b/generated/groundlight_openapi_client/models/multi_class_mode_configuration.py new file mode 100644 index 00000000..02d71f99 --- /dev/null +++ b/generated/groundlight_openapi_client/models/multi_class_mode_configuration.py @@ -0,0 +1,71 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import List, Optional +from pydantic import BaseModel, Field, StrictInt, StrictStr, conlist + + +class MultiClassModeConfiguration(BaseModel): + """ + MultiClassModeConfiguration + """ + + class_names: conlist(StrictStr) = Field(...) + num_classes: Optional[StrictInt] = None + __properties = ["class_names", "num_classes"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> MultiClassModeConfiguration: + """Create an instance of MultiClassModeConfiguration from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> MultiClassModeConfiguration: + """Create an instance of MultiClassModeConfiguration from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return MultiClassModeConfiguration.parse_obj(obj) + + _obj = MultiClassModeConfiguration.parse_obj( + {"class_names": obj.get("class_names"), "num_classes": obj.get("num_classes")} + ) + return _obj diff --git a/generated/groundlight_openapi_client/models/multi_classification_result.py b/generated/groundlight_openapi_client/models/multi_classification_result.py new file mode 100644 index 00000000..d6916cb6 --- /dev/null +++ b/generated/groundlight_openapi_client/models/multi_classification_result.py @@ -0,0 +1,93 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Optional, Union +from pydantic import BaseModel, Field, StrictBool, StrictStr, confloat, conint, validator + + +class MultiClassificationResult(BaseModel): + """ + MultiClassificationResult + """ + + confidence: Optional[Union[confloat(le=1.0, ge=0.0, strict=True), conint(le=1, ge=0, strict=True)]] = None + source: Optional[StrictStr] = None + result_type: Optional[StrictStr] = None + from_edge: Optional[StrictBool] = None + label: StrictStr = Field(...) + __properties = ["confidence", "source", "result_type", "from_edge", "label"] + + @validator("result_type") + def result_type_validate_enum(cls, value): + """Validates the enum""" + if value is None: + return value + + if value not in ("multi_classification",): + raise ValueError("must be one of enum values ('multi_classification')") + return value + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> MultiClassificationResult: + """Create an instance of MultiClassificationResult from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # set to None if confidence (nullable) is None + # and __fields_set__ contains the field + if self.confidence is None and "confidence" in self.__fields_set__: + _dict["confidence"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> MultiClassificationResult: + """Create an instance of MultiClassificationResult from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return MultiClassificationResult.parse_obj(obj) + + _obj = MultiClassificationResult.parse_obj({ + "confidence": obj.get("confidence"), + "source": obj.get("source"), + "result_type": obj.get("result_type"), + "from_edge": obj.get("from_edge"), + "label": obj.get("label"), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/note.py b/generated/groundlight_openapi_client/models/note.py new file mode 100644 index 00000000..43adc26d --- /dev/null +++ b/generated/groundlight_openapi_client/models/note.py @@ -0,0 +1,88 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Optional +from pydantic import BaseModel, Field, StrictBool, StrictStr + + +class Note(BaseModel): + """ + Note + """ + + detector_id: StrictStr = Field(...) + content: Optional[StrictStr] = Field(default=None, description="Text content of the note.") + is_pinned: Optional[StrictBool] = None + __properties = ["detector_id", "content", "is_pinned"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> Note: + """Create an instance of Note from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict( + by_alias=True, + exclude={ + "detector_id", + }, + exclude_none=True, + ) + # set to None if content (nullable) is None + # and __fields_set__ contains the field + if self.content is None and "content" in self.__fields_set__: + _dict["content"] = None + + # set to None if is_pinned (nullable) is None + # and __fields_set__ contains the field + if self.is_pinned is None and "is_pinned" in self.__fields_set__: + _dict["is_pinned"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> Note: + """Create an instance of Note from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return Note.parse_obj(obj) + + _obj = Note.parse_obj( + {"detector_id": obj.get("detector_id"), "content": obj.get("content"), "is_pinned": obj.get("is_pinned")} + ) + return _obj diff --git a/generated/groundlight_openapi_client/models/note_request.py b/generated/groundlight_openapi_client/models/note_request.py new file mode 100644 index 00000000..63d64661 --- /dev/null +++ b/generated/groundlight_openapi_client/models/note_request.py @@ -0,0 +1,87 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Optional, Union +from pydantic import BaseModel, Field, StrictBool, StrictBytes, StrictStr + + +class NoteRequest(BaseModel): + """ + NoteRequest + """ + + content: Optional[StrictStr] = Field(default=None, description="Text content of the note.") + is_pinned: Optional[StrictBool] = None + image: Optional[Union[StrictBytes, StrictStr]] = None + __properties = ["content", "is_pinned", "image"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> NoteRequest: + """Create an instance of NoteRequest from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # set to None if content (nullable) is None + # and __fields_set__ contains the field + if self.content is None and "content" in self.__fields_set__: + _dict["content"] = None + + # set to None if is_pinned (nullable) is None + # and __fields_set__ contains the field + if self.is_pinned is None and "is_pinned" in self.__fields_set__: + _dict["is_pinned"] = None + + # set to None if image (nullable) is None + # and __fields_set__ contains the field + if self.image is None and "image" in self.__fields_set__: + _dict["image"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> NoteRequest: + """Create an instance of NoteRequest from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return NoteRequest.parse_obj(obj) + + _obj = NoteRequest.parse_obj( + {"content": obj.get("content"), "is_pinned": obj.get("is_pinned"), "image": obj.get("image")} + ) + return _obj diff --git a/generated/groundlight_openapi_client/models/paginated_detector_list.py b/generated/groundlight_openapi_client/models/paginated_detector_list.py new file mode 100644 index 00000000..c927dd6a --- /dev/null +++ b/generated/groundlight_openapi_client/models/paginated_detector_list.py @@ -0,0 +1,96 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import List, Optional +from pydantic import BaseModel, Field, StrictInt, StrictStr, conlist +from groundlight_openapi_client.models.detector import Detector + + +class PaginatedDetectorList(BaseModel): + """ + PaginatedDetectorList + """ + + count: StrictInt = Field(...) + next: Optional[StrictStr] = None + previous: Optional[StrictStr] = None + results: conlist(Detector) = Field(...) + __properties = ["count", "next", "previous", "results"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> PaginatedDetectorList: + """Create an instance of PaginatedDetectorList from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # override the default output from pydantic by calling `to_dict()` of each item in results (list) + _items = [] + if self.results: + for _item in self.results: + if _item: + _items.append(_item.to_dict()) + _dict["results"] = _items + # set to None if next (nullable) is None + # and __fields_set__ contains the field + if self.next is None and "next" in self.__fields_set__: + _dict["next"] = None + + # set to None if previous (nullable) is None + # and __fields_set__ contains the field + if self.previous is None and "previous" in self.__fields_set__: + _dict["previous"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> PaginatedDetectorList: + """Create an instance of PaginatedDetectorList from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return PaginatedDetectorList.parse_obj(obj) + + _obj = PaginatedDetectorList.parse_obj({ + "count": obj.get("count"), + "next": obj.get("next"), + "previous": obj.get("previous"), + "results": ( + [Detector.from_dict(_item) for _item in obj.get("results")] if obj.get("results") is not None else None + ), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/paginated_image_query_list.py b/generated/groundlight_openapi_client/models/paginated_image_query_list.py new file mode 100644 index 00000000..1755d6a3 --- /dev/null +++ b/generated/groundlight_openapi_client/models/paginated_image_query_list.py @@ -0,0 +1,98 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import List, Optional +from pydantic import BaseModel, Field, StrictInt, StrictStr, conlist +from groundlight_openapi_client.models.image_query import ImageQuery + + +class PaginatedImageQueryList(BaseModel): + """ + PaginatedImageQueryList + """ + + count: StrictInt = Field(...) + next: Optional[StrictStr] = None + previous: Optional[StrictStr] = None + results: conlist(ImageQuery) = Field(...) + __properties = ["count", "next", "previous", "results"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> PaginatedImageQueryList: + """Create an instance of PaginatedImageQueryList from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # override the default output from pydantic by calling `to_dict()` of each item in results (list) + _items = [] + if self.results: + for _item in self.results: + if _item: + _items.append(_item.to_dict()) + _dict["results"] = _items + # set to None if next (nullable) is None + # and __fields_set__ contains the field + if self.next is None and "next" in self.__fields_set__: + _dict["next"] = None + + # set to None if previous (nullable) is None + # and __fields_set__ contains the field + if self.previous is None and "previous" in self.__fields_set__: + _dict["previous"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> PaginatedImageQueryList: + """Create an instance of PaginatedImageQueryList from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return PaginatedImageQueryList.parse_obj(obj) + + _obj = PaginatedImageQueryList.parse_obj({ + "count": obj.get("count"), + "next": obj.get("next"), + "previous": obj.get("previous"), + "results": ( + [ImageQuery.from_dict(_item) for _item in obj.get("results")] + if obj.get("results") is not None + else None + ), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/paginated_rule_list.py b/generated/groundlight_openapi_client/models/paginated_rule_list.py new file mode 100644 index 00000000..4999aee7 --- /dev/null +++ b/generated/groundlight_openapi_client/models/paginated_rule_list.py @@ -0,0 +1,96 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import List, Optional +from pydantic import BaseModel, Field, StrictInt, StrictStr, conlist +from groundlight_openapi_client.models.rule import Rule + + +class PaginatedRuleList(BaseModel): + """ + PaginatedRuleList + """ + + count: StrictInt = Field(...) + next: Optional[StrictStr] = None + previous: Optional[StrictStr] = None + results: conlist(Rule) = Field(...) + __properties = ["count", "next", "previous", "results"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> PaginatedRuleList: + """Create an instance of PaginatedRuleList from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # override the default output from pydantic by calling `to_dict()` of each item in results (list) + _items = [] + if self.results: + for _item in self.results: + if _item: + _items.append(_item.to_dict()) + _dict["results"] = _items + # set to None if next (nullable) is None + # and __fields_set__ contains the field + if self.next is None and "next" in self.__fields_set__: + _dict["next"] = None + + # set to None if previous (nullable) is None + # and __fields_set__ contains the field + if self.previous is None and "previous" in self.__fields_set__: + _dict["previous"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> PaginatedRuleList: + """Create an instance of PaginatedRuleList from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return PaginatedRuleList.parse_obj(obj) + + _obj = PaginatedRuleList.parse_obj({ + "count": obj.get("count"), + "next": obj.get("next"), + "previous": obj.get("previous"), + "results": ( + [Rule.from_dict(_item) for _item in obj.get("results")] if obj.get("results") is not None else None + ), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/patched_detector_request.py b/generated/groundlight_openapi_client/models/patched_detector_request.py new file mode 100644 index 00000000..88b2646d --- /dev/null +++ b/generated/groundlight_openapi_client/models/patched_detector_request.py @@ -0,0 +1,96 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Optional, Union +from pydantic import BaseModel, Field, confloat, conint, constr +from groundlight_openapi_client.models.detector_status import DetectorStatus + + +class PatchedDetectorRequest(BaseModel): + """ + Groundlight Detectors provide answers to natural language questions about images. Each detector can answer a single question, and multiple detectors can be strung together for more complex logic. Detectors can be created through the create_detector method, or through the create_[MODE]_detector methods for pro tier users # noqa: E501 + """ + + name: Optional[constr(strict=True, max_length=200, min_length=1)] = Field( + default=None, description="A short, descriptive name for the detector." + ) + confidence_threshold: Optional[Union[confloat(le=1.0, ge=0.0, strict=True), conint(le=1, ge=0, strict=True)]] = ( + Field( + default=0.9, + description=( + "If the detector's prediction is below this confidence threshold, send the image query for human" + " review." + ), + ) + ) + patience_time: Optional[Union[confloat(le=3600, ge=0, strict=True), conint(le=3600, ge=0, strict=True)]] = Field( + default=30.0, description="How long Groundlight will attempt to generate a confident prediction" + ) + status: Optional[DetectorStatus] = None + escalation_type: Optional[constr(strict=True, min_length=1)] = None + __properties = ["name", "confidence_threshold", "patience_time", "status", "escalation_type"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> PatchedDetectorRequest: + """Create an instance of PatchedDetectorRequest from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # override the default output from pydantic by calling `to_dict()` of status + if self.status: + _dict["status"] = self.status.to_dict() + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> PatchedDetectorRequest: + """Create an instance of PatchedDetectorRequest from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return PatchedDetectorRequest.parse_obj(obj) + + _obj = PatchedDetectorRequest.parse_obj({ + "name": obj.get("name"), + "confidence_threshold": ( + obj.get("confidence_threshold") if obj.get("confidence_threshold") is not None else 0.9 + ), + "patience_time": obj.get("patience_time") if obj.get("patience_time") is not None else 30.0, + "status": DetectorStatus.from_dict(obj.get("status")) if obj.get("status") is not None else None, + "escalation_type": obj.get("escalation_type"), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/payload_template.py b/generated/groundlight_openapi_client/models/payload_template.py new file mode 100644 index 00000000..cc4cd88c --- /dev/null +++ b/generated/groundlight_openapi_client/models/payload_template.py @@ -0,0 +1,74 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Dict, Optional +from pydantic import BaseModel, Field, StrictStr + + +class PayloadTemplate(BaseModel): + """ + PayloadTemplate + """ + + template: StrictStr = Field(...) + headers: Optional[Dict[str, StrictStr]] = None + __properties = ["template", "headers"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> PayloadTemplate: + """Create an instance of PayloadTemplate from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # set to None if headers (nullable) is None + # and __fields_set__ contains the field + if self.headers is None and "headers" in self.__fields_set__: + _dict["headers"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> PayloadTemplate: + """Create an instance of PayloadTemplate from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return PayloadTemplate.parse_obj(obj) + + _obj = PayloadTemplate.parse_obj({"template": obj.get("template"), "headers": obj.get("headers")}) + return _obj diff --git a/generated/groundlight_openapi_client/models/payload_template_request.py b/generated/groundlight_openapi_client/models/payload_template_request.py new file mode 100644 index 00000000..efdf39e6 --- /dev/null +++ b/generated/groundlight_openapi_client/models/payload_template_request.py @@ -0,0 +1,74 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Dict, Optional +from pydantic import BaseModel, Field, constr + + +class PayloadTemplateRequest(BaseModel): + """ + PayloadTemplateRequest + """ + + template: constr(strict=True, min_length=1) = Field(...) + headers: Optional[Dict[str, constr(strict=True, min_length=1)]] = None + __properties = ["template", "headers"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> PayloadTemplateRequest: + """Create an instance of PayloadTemplateRequest from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # set to None if headers (nullable) is None + # and __fields_set__ contains the field + if self.headers is None and "headers" in self.__fields_set__: + _dict["headers"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> PayloadTemplateRequest: + """Create an instance of PayloadTemplateRequest from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return PayloadTemplateRequest.parse_obj(obj) + + _obj = PayloadTemplateRequest.parse_obj({"template": obj.get("template"), "headers": obj.get("headers")}) + return _obj diff --git a/generated/groundlight_openapi_client/models/result_type_enum.py b/generated/groundlight_openapi_client/models/result_type_enum.py new file mode 100644 index 00000000..062c6237 --- /dev/null +++ b/generated/groundlight_openapi_client/models/result_type_enum.py @@ -0,0 +1,39 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import json +import pprint +import re # noqa: F401 +from aenum import Enum, no_arg + + +class ResultTypeEnum(str, Enum): + """ + ResultTypeEnum + """ + + """ + allowed enum values + """ + BINARY_CLASSIFICATION = "binary_classification" + COUNTING = "counting" + MULTI_CLASSIFICATION = "multi_classification" + TEXT_RECOGNITION = "text_recognition" + BOUNDING_BOX = "bounding_box" + + @classmethod + def from_json(cls, json_str: str) -> ResultTypeEnum: + """Create an instance of ResultTypeEnum from a JSON string""" + return ResultTypeEnum(json.loads(json_str)) diff --git a/generated/groundlight_openapi_client/models/roi.py b/generated/groundlight_openapi_client/models/roi.py new file mode 100644 index 00000000..1e7e8761 --- /dev/null +++ b/generated/groundlight_openapi_client/models/roi.py @@ -0,0 +1,84 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Union +from pydantic import BaseModel, Field, StrictFloat, StrictInt, StrictStr +from groundlight_openapi_client.models.b_box_geometry import BBoxGeometry + + +class ROI(BaseModel): + """ + Mixin for serializers to handle data in the StrictBaseModel format # noqa: E501 + """ + + label: StrictStr = Field(default=..., description="The label of the bounding box.") + score: Union[StrictFloat, StrictInt] = Field(default=..., description="The confidence of the bounding box.") + geometry: BBoxGeometry = Field(...) + __properties = ["label", "score", "geometry"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> ROI: + """Create an instance of ROI from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict( + by_alias=True, + exclude={ + "score", + }, + exclude_none=True, + ) + # override the default output from pydantic by calling `to_dict()` of geometry + if self.geometry: + _dict["geometry"] = self.geometry.to_dict() + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> ROI: + """Create an instance of ROI from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return ROI.parse_obj(obj) + + _obj = ROI.parse_obj({ + "label": obj.get("label"), + "score": obj.get("score"), + "geometry": BBoxGeometry.from_dict(obj.get("geometry")) if obj.get("geometry") is not None else None, + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/roi_request.py b/generated/groundlight_openapi_client/models/roi_request.py new file mode 100644 index 00000000..9a5e720f --- /dev/null +++ b/generated/groundlight_openapi_client/models/roi_request.py @@ -0,0 +1,75 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from pydantic import BaseModel, Field, constr +from groundlight_openapi_client.models.b_box_geometry_request import BBoxGeometryRequest + + +class ROIRequest(BaseModel): + """ + Mixin for serializers to handle data in the StrictBaseModel format # noqa: E501 + """ + + label: constr(strict=True, min_length=1) = Field(default=..., description="The label of the bounding box.") + geometry: BBoxGeometryRequest = Field(...) + __properties = ["label", "geometry"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> ROIRequest: + """Create an instance of ROIRequest from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # override the default output from pydantic by calling `to_dict()` of geometry + if self.geometry: + _dict["geometry"] = self.geometry.to_dict() + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> ROIRequest: + """Create an instance of ROIRequest from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return ROIRequest.parse_obj(obj) + + _obj = ROIRequest.parse_obj({ + "label": obj.get("label"), + "geometry": BBoxGeometryRequest.from_dict(obj.get("geometry")) if obj.get("geometry") is not None else None, + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/rule.py b/generated/groundlight_openapi_client/models/rule.py new file mode 100644 index 00000000..2d9125b5 --- /dev/null +++ b/generated/groundlight_openapi_client/models/rule.py @@ -0,0 +1,138 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import List, Optional +from pydantic import BaseModel, Field, StrictBool, StrictInt, StrictStr, conint, conlist, constr +from groundlight_openapi_client.models.condition import Condition +from groundlight_openapi_client.models.rule_action import RuleAction +from groundlight_openapi_client.models.snooze_time_unit_enum import SnoozeTimeUnitEnum +from groundlight_openapi_client.models.webhook_action import WebhookAction + + +class Rule(BaseModel): + """ + Rule + """ + + id: StrictInt = Field(...) + detector_id: StrictStr = Field(...) + detector_name: StrictStr = Field(...) + name: constr(strict=True, max_length=44) = Field(...) + enabled: Optional[StrictBool] = True + snooze_time_enabled: Optional[StrictBool] = False + snooze_time_value: Optional[conint(strict=True, ge=0)] = 0 + snooze_time_unit: Optional[SnoozeTimeUnitEnum] = None + human_review_required: Optional[StrictBool] = False + condition: Condition = Field(...) + action: Optional[RuleAction] = None + webhook_action: Optional[conlist(WebhookAction)] = None + __properties = [ + "id", + "detector_id", + "detector_name", + "name", + "enabled", + "snooze_time_enabled", + "snooze_time_value", + "snooze_time_unit", + "human_review_required", + "condition", + "action", + "webhook_action", + ] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> Rule: + """Create an instance of Rule from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict( + by_alias=True, + exclude={ + "id", + "detector_id", + "detector_name", + }, + exclude_none=True, + ) + # override the default output from pydantic by calling `to_dict()` of condition + if self.condition: + _dict["condition"] = self.condition.to_dict() + # override the default output from pydantic by calling `to_dict()` of action + if self.action: + _dict["action"] = self.action.to_dict() + # override the default output from pydantic by calling `to_dict()` of each item in webhook_action (list) + _items = [] + if self.webhook_action: + for _item in self.webhook_action: + if _item: + _items.append(_item.to_dict()) + _dict["webhook_action"] = _items + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> Rule: + """Create an instance of Rule from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return Rule.parse_obj(obj) + + _obj = Rule.parse_obj({ + "id": obj.get("id"), + "detector_id": obj.get("detector_id"), + "detector_name": obj.get("detector_name"), + "name": obj.get("name"), + "enabled": obj.get("enabled") if obj.get("enabled") is not None else True, + "snooze_time_enabled": ( + obj.get("snooze_time_enabled") if obj.get("snooze_time_enabled") is not None else False + ), + "snooze_time_value": obj.get("snooze_time_value") if obj.get("snooze_time_value") is not None else 0, + "snooze_time_unit": obj.get("snooze_time_unit"), + "human_review_required": ( + obj.get("human_review_required") if obj.get("human_review_required") is not None else False + ), + "condition": Condition.from_dict(obj.get("condition")) if obj.get("condition") is not None else None, + "action": RuleAction.from_dict(obj.get("action")) if obj.get("action") is not None else None, + "webhook_action": ( + [WebhookAction.from_dict(_item) for _item in obj.get("webhook_action")] + if obj.get("webhook_action") is not None + else None + ), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/rule_action.py b/generated/groundlight_openapi_client/models/rule_action.py new file mode 100644 index 00000000..fc28e0e0 --- /dev/null +++ b/generated/groundlight_openapi_client/models/rule_action.py @@ -0,0 +1,161 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +from inspect import getfullargspec +import json +import pprint +import re # noqa: F401 + +from typing import Any, List, Optional +from pydantic import BaseModel, Field, StrictStr, ValidationError, conlist, validator +from groundlight_openapi_client.models.action import Action +from typing import Union, Any, List, TYPE_CHECKING +from pydantic import StrictStr, Field + +RULEACTION_ONE_OF_SCHEMAS = ["Action", "List[Action]"] + + +class RuleAction(BaseModel): + """ + RuleAction + """ + + # data type: Action + oneof_schema_1_validator: Optional[Action] = None + # data type: List[Action] + oneof_schema_2_validator: Optional[conlist(Action)] = None + if TYPE_CHECKING: + actual_instance: Union[Action, List[Action]] + else: + actual_instance: Any + one_of_schemas: List[str] = Field(RULEACTION_ONE_OF_SCHEMAS, const=True) + + class Config: + validate_assignment = True + + def __init__(self, *args, **kwargs) -> None: + if args: + if len(args) > 1: + raise ValueError("If a position argument is used, only 1 is allowed to set `actual_instance`") + if kwargs: + raise ValueError("If a position argument is used, keyword arguments cannot be used.") + super().__init__(actual_instance=args[0]) + else: + super().__init__(**kwargs) + + @validator("actual_instance") + def actual_instance_must_validate_oneof(cls, v): + instance = RuleAction.construct() + error_messages = [] + match = 0 + # validate data type: Action + if not isinstance(v, Action): + error_messages.append(f"Error! Input type `{type(v)}` is not `Action`") + else: + match += 1 + # validate data type: List[Action] + try: + instance.oneof_schema_2_validator = v + match += 1 + except (ValidationError, ValueError) as e: + error_messages.append(str(e)) + if match > 1: + # more than 1 match + raise ValueError( + "Multiple matches found when setting `actual_instance` in RuleAction with oneOf schemas: Action," + " List[Action]. Details: " + + ", ".join(error_messages) + ) + elif match == 0: + # no match + raise ValueError( + "No match found when setting `actual_instance` in RuleAction with oneOf schemas: Action, List[Action]." + " Details: " + + ", ".join(error_messages) + ) + else: + return v + + @classmethod + def from_dict(cls, obj: dict) -> RuleAction: + return cls.from_json(json.dumps(obj)) + + @classmethod + def from_json(cls, json_str: str) -> RuleAction: + """Returns the object represented by the json string""" + instance = RuleAction.construct() + error_messages = [] + match = 0 + + # deserialize data into Action + try: + instance.actual_instance = Action.from_json(json_str) + match += 1 + except (ValidationError, ValueError) as e: + error_messages.append(str(e)) + # deserialize data into List[Action] + try: + # validation + instance.oneof_schema_2_validator = json.loads(json_str) + # assign value to actual_instance + instance.actual_instance = instance.oneof_schema_2_validator + match += 1 + except (ValidationError, ValueError) as e: + error_messages.append(str(e)) + + if match > 1: + # more than 1 match + raise ValueError( + "Multiple matches found when deserializing the JSON string into RuleAction with oneOf schemas: Action," + " List[Action]. Details: " + + ", ".join(error_messages) + ) + elif match == 0: + # no match + raise ValueError( + "No match found when deserializing the JSON string into RuleAction with oneOf schemas: Action," + " List[Action]. Details: " + + ", ".join(error_messages) + ) + else: + return instance + + def to_json(self) -> str: + """Returns the JSON representation of the actual instance""" + if self.actual_instance is None: + return "null" + + to_json = getattr(self.actual_instance, "to_json", None) + if callable(to_json): + return self.actual_instance.to_json() + else: + return json.dumps(self.actual_instance) + + def to_dict(self) -> dict: + """Returns the dict representation of the actual instance""" + if self.actual_instance is None: + return None + + to_dict = getattr(self.actual_instance, "to_dict", None) + if callable(to_dict): + return self.actual_instance.to_dict() + else: + # primitive type + return self.actual_instance + + def to_str(self) -> str: + """Returns the string representation of the actual instance""" + return pprint.pformat(self.dict()) diff --git a/generated/groundlight_openapi_client/models/rule_request.py b/generated/groundlight_openapi_client/models/rule_request.py new file mode 100644 index 00000000..09aeac65 --- /dev/null +++ b/generated/groundlight_openapi_client/models/rule_request.py @@ -0,0 +1,121 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import List, Optional +from pydantic import BaseModel, Field, StrictBool, conint, conlist, constr +from groundlight_openapi_client.models.condition_request import ConditionRequest +from groundlight_openapi_client.models.rule_action import RuleAction +from groundlight_openapi_client.models.snooze_time_unit_enum import SnoozeTimeUnitEnum +from groundlight_openapi_client.models.webhook_action_request import WebhookActionRequest + + +class RuleRequest(BaseModel): + """ + RuleRequest + """ + + name: constr(strict=True, max_length=44, min_length=1) = Field(...) + enabled: Optional[StrictBool] = True + snooze_time_enabled: Optional[StrictBool] = False + snooze_time_value: Optional[conint(strict=True, ge=0)] = 0 + snooze_time_unit: Optional[SnoozeTimeUnitEnum] = None + human_review_required: Optional[StrictBool] = False + condition: ConditionRequest = Field(...) + action: Optional[RuleAction] = None + webhook_action: Optional[conlist(WebhookActionRequest)] = None + __properties = [ + "name", + "enabled", + "snooze_time_enabled", + "snooze_time_value", + "snooze_time_unit", + "human_review_required", + "condition", + "action", + "webhook_action", + ] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> RuleRequest: + """Create an instance of RuleRequest from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # override the default output from pydantic by calling `to_dict()` of condition + if self.condition: + _dict["condition"] = self.condition.to_dict() + # override the default output from pydantic by calling `to_dict()` of action + if self.action: + _dict["action"] = self.action.to_dict() + # override the default output from pydantic by calling `to_dict()` of each item in webhook_action (list) + _items = [] + if self.webhook_action: + for _item in self.webhook_action: + if _item: + _items.append(_item.to_dict()) + _dict["webhook_action"] = _items + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> RuleRequest: + """Create an instance of RuleRequest from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return RuleRequest.parse_obj(obj) + + _obj = RuleRequest.parse_obj({ + "name": obj.get("name"), + "enabled": obj.get("enabled") if obj.get("enabled") is not None else True, + "snooze_time_enabled": ( + obj.get("snooze_time_enabled") if obj.get("snooze_time_enabled") is not None else False + ), + "snooze_time_value": obj.get("snooze_time_value") if obj.get("snooze_time_value") is not None else 0, + "snooze_time_unit": obj.get("snooze_time_unit"), + "human_review_required": ( + obj.get("human_review_required") if obj.get("human_review_required") is not None else False + ), + "condition": ConditionRequest.from_dict(obj.get("condition")) if obj.get("condition") is not None else None, + "action": RuleAction.from_dict(obj.get("action")) if obj.get("action") is not None else None, + "webhook_action": ( + [WebhookActionRequest.from_dict(_item) for _item in obj.get("webhook_action")] + if obj.get("webhook_action") is not None + else None + ), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/snooze_time_unit_enum.py b/generated/groundlight_openapi_client/models/snooze_time_unit_enum.py new file mode 100644 index 00000000..c4920a84 --- /dev/null +++ b/generated/groundlight_openapi_client/models/snooze_time_unit_enum.py @@ -0,0 +1,38 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import json +import pprint +import re # noqa: F401 +from aenum import Enum, no_arg + + +class SnoozeTimeUnitEnum(str, Enum): + """ + * `DAYS` - DAYS * `HOURS` - HOURS * `MINUTES` - MINUTES * `SECONDS` - SECONDS + """ + + """ + allowed enum values + """ + DAYS = "DAYS" + HOURS = "HOURS" + MINUTES = "MINUTES" + SECONDS = "SECONDS" + + @classmethod + def from_json(cls, json_str: str) -> SnoozeTimeUnitEnum: + """Create an instance of SnoozeTimeUnitEnum from a JSON string""" + return SnoozeTimeUnitEnum(json.loads(json_str)) diff --git a/generated/groundlight_openapi_client/models/source.py b/generated/groundlight_openapi_client/models/source.py new file mode 100644 index 00000000..eab0022c --- /dev/null +++ b/generated/groundlight_openapi_client/models/source.py @@ -0,0 +1,40 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import json +import pprint +import re # noqa: F401 +from aenum import Enum, no_arg + + +class Source(str, Enum): + """ + Source + """ + + """ + allowed enum values + """ + STILL_PROCESSING = "STILL_PROCESSING" + CLOUD = "CLOUD" + USER = "USER" + CLOUD_ENSEMBLE = "CLOUD_ENSEMBLE" + ALGORITHM = "ALGORITHM" + EDGE = "EDGE" + + @classmethod + def from_json(cls, json_str: str) -> Source: + """Create an instance of Source from a JSON string""" + return Source(json.loads(json_str)) diff --git a/generated/groundlight_openapi_client/models/source_enum.py b/generated/groundlight_openapi_client/models/source_enum.py new file mode 100644 index 00000000..d5f95b83 --- /dev/null +++ b/generated/groundlight_openapi_client/models/source_enum.py @@ -0,0 +1,42 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import json +import pprint +import re # noqa: F401 +from aenum import Enum, no_arg + + +class SourceEnum(str, Enum): + """ + SourceEnum + """ + + """ + allowed enum values + """ + INITIAL_PLACEHOLDER = "INITIAL_PLACEHOLDER" + CLOUD = "CLOUD" + CUST = "CUST" + HUMAN_CLOUD_ENSEMBLE = "HUMAN_CLOUD_ENSEMBLE" + ALG = "ALG" + ALG_REC = "ALG_REC" + ALG_UNCLEAR = "ALG_UNCLEAR" + EDGE = "EDGE" + + @classmethod + def from_json(cls, json_str: str) -> SourceEnum: + """Create an instance of SourceEnum from a JSON string""" + return SourceEnum(json.loads(json_str)) diff --git a/generated/groundlight_openapi_client/models/status_enum.py b/generated/groundlight_openapi_client/models/status_enum.py new file mode 100644 index 00000000..7344ca59 --- /dev/null +++ b/generated/groundlight_openapi_client/models/status_enum.py @@ -0,0 +1,36 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import json +import pprint +import re # noqa: F401 +from aenum import Enum, no_arg + + +class StatusEnum(str, Enum): + """ + * `ON` - ON * `OFF` - OFF + """ + + """ + allowed enum values + """ + ON = "ON" + OFF = "OFF" + + @classmethod + def from_json(cls, json_str: str) -> StatusEnum: + """Create an instance of StatusEnum from a JSON string""" + return StatusEnum(json.loads(json_str)) diff --git a/generated/groundlight_openapi_client/models/text_mode_configuration.py b/generated/groundlight_openapi_client/models/text_mode_configuration.py new file mode 100644 index 00000000..03e99147 --- /dev/null +++ b/generated/groundlight_openapi_client/models/text_mode_configuration.py @@ -0,0 +1,68 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Optional +from pydantic import BaseModel, conint + + +class TextModeConfiguration(BaseModel): + """ + TextModeConfiguration + """ + + value_max_length: Optional[conint(strict=True, le=250, ge=1)] = None + __properties = ["value_max_length"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> TextModeConfiguration: + """Create an instance of TextModeConfiguration from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> TextModeConfiguration: + """Create an instance of TextModeConfiguration from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return TextModeConfiguration.parse_obj(obj) + + _obj = TextModeConfiguration.parse_obj({"value_max_length": obj.get("value_max_length")}) + return _obj diff --git a/generated/groundlight_openapi_client/models/text_recognition_result.py b/generated/groundlight_openapi_client/models/text_recognition_result.py new file mode 100644 index 00000000..462f8dcd --- /dev/null +++ b/generated/groundlight_openapi_client/models/text_recognition_result.py @@ -0,0 +1,100 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Optional, Union +from pydantic import BaseModel, Field, StrictBool, StrictStr, confloat, conint, validator + + +class TextRecognitionResult(BaseModel): + """ + TextRecognitionResult + """ + + confidence: Optional[Union[confloat(le=1.0, ge=0.0, strict=True), conint(le=1, ge=0, strict=True)]] = None + source: Optional[StrictStr] = None + result_type: Optional[StrictStr] = None + from_edge: Optional[StrictBool] = None + text: Optional[StrictStr] = Field(...) + truncated: StrictBool = Field(...) + __properties = ["confidence", "source", "result_type", "from_edge", "text", "truncated"] + + @validator("result_type") + def result_type_validate_enum(cls, value): + """Validates the enum""" + if value is None: + return value + + if value not in ("text_recognition",): + raise ValueError("must be one of enum values ('text_recognition')") + return value + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> TextRecognitionResult: + """Create an instance of TextRecognitionResult from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # set to None if confidence (nullable) is None + # and __fields_set__ contains the field + if self.confidence is None and "confidence" in self.__fields_set__: + _dict["confidence"] = None + + # set to None if text (nullable) is None + # and __fields_set__ contains the field + if self.text is None and "text" in self.__fields_set__: + _dict["text"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> TextRecognitionResult: + """Create an instance of TextRecognitionResult from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return TextRecognitionResult.parse_obj(obj) + + _obj = TextRecognitionResult.parse_obj({ + "confidence": obj.get("confidence"), + "source": obj.get("source"), + "result_type": obj.get("result_type"), + "from_edge": obj.get("from_edge"), + "text": obj.get("text"), + "truncated": obj.get("truncated"), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/verb_enum.py b/generated/groundlight_openapi_client/models/verb_enum.py new file mode 100644 index 00000000..d14e73d9 --- /dev/null +++ b/generated/groundlight_openapi_client/models/verb_enum.py @@ -0,0 +1,39 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import json +import pprint +import re # noqa: F401 +from aenum import Enum, no_arg + + +class VerbEnum(str, Enum): + """ + VerbEnum + """ + + """ + allowed enum values + """ + ANSWERED_CONSECUTIVELY = "ANSWERED_CONSECUTIVELY" + ANSWERED_WITHIN_TIME = "ANSWERED_WITHIN_TIME" + CHANGED_TO = "CHANGED_TO" + NO_CHANGE = "NO_CHANGE" + NO_QUERIES = "NO_QUERIES" + + @classmethod + def from_json(cls, json_str: str) -> VerbEnum: + """Create an instance of VerbEnum from a JSON string""" + return VerbEnum(json.loads(json_str)) diff --git a/generated/groundlight_openapi_client/models/webhook_action.py b/generated/groundlight_openapi_client/models/webhook_action.py new file mode 100644 index 00000000..5cc301e0 --- /dev/null +++ b/generated/groundlight_openapi_client/models/webhook_action.py @@ -0,0 +1,110 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + +from datetime import datetime +from typing import Optional +from pydantic import BaseModel, Field, StrictBool, StrictStr, constr +from groundlight_openapi_client.models.payload_template import PayloadTemplate + + +class WebhookAction(BaseModel): + """ + WebhookAction + """ + + url: constr(strict=True, max_length=200) = Field(...) + include_image: Optional[StrictBool] = None + payload_template: Optional[PayloadTemplate] = None + last_message_failed: Optional[StrictBool] = None + last_failure_error: Optional[StrictStr] = None + last_failed_at: Optional[datetime] = None + __properties = [ + "url", + "include_image", + "payload_template", + "last_message_failed", + "last_failure_error", + "last_failed_at", + ] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> WebhookAction: + """Create an instance of WebhookAction from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # override the default output from pydantic by calling `to_dict()` of payload_template + if self.payload_template: + _dict["payload_template"] = self.payload_template.to_dict() + # set to None if payload_template (nullable) is None + # and __fields_set__ contains the field + if self.payload_template is None and "payload_template" in self.__fields_set__: + _dict["payload_template"] = None + + # set to None if last_failure_error (nullable) is None + # and __fields_set__ contains the field + if self.last_failure_error is None and "last_failure_error" in self.__fields_set__: + _dict["last_failure_error"] = None + + # set to None if last_failed_at (nullable) is None + # and __fields_set__ contains the field + if self.last_failed_at is None and "last_failed_at" in self.__fields_set__: + _dict["last_failed_at"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> WebhookAction: + """Create an instance of WebhookAction from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return WebhookAction.parse_obj(obj) + + _obj = WebhookAction.parse_obj({ + "url": obj.get("url"), + "include_image": obj.get("include_image"), + "payload_template": ( + PayloadTemplate.from_dict(obj.get("payload_template")) + if obj.get("payload_template") is not None + else None + ), + "last_message_failed": obj.get("last_message_failed"), + "last_failure_error": obj.get("last_failure_error"), + "last_failed_at": obj.get("last_failed_at"), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/webhook_action_request.py b/generated/groundlight_openapi_client/models/webhook_action_request.py new file mode 100644 index 00000000..4a59ef97 --- /dev/null +++ b/generated/groundlight_openapi_client/models/webhook_action_request.py @@ -0,0 +1,110 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + +from datetime import datetime +from typing import Optional +from pydantic import BaseModel, Field, StrictBool, StrictStr, constr +from groundlight_openapi_client.models.payload_template_request import PayloadTemplateRequest + + +class WebhookActionRequest(BaseModel): + """ + WebhookActionRequest + """ + + url: constr(strict=True, max_length=200, min_length=1) = Field(...) + include_image: Optional[StrictBool] = None + payload_template: Optional[PayloadTemplateRequest] = None + last_message_failed: Optional[StrictBool] = None + last_failure_error: Optional[StrictStr] = None + last_failed_at: Optional[datetime] = None + __properties = [ + "url", + "include_image", + "payload_template", + "last_message_failed", + "last_failure_error", + "last_failed_at", + ] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> WebhookActionRequest: + """Create an instance of WebhookActionRequest from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + # override the default output from pydantic by calling `to_dict()` of payload_template + if self.payload_template: + _dict["payload_template"] = self.payload_template.to_dict() + # set to None if payload_template (nullable) is None + # and __fields_set__ contains the field + if self.payload_template is None and "payload_template" in self.__fields_set__: + _dict["payload_template"] = None + + # set to None if last_failure_error (nullable) is None + # and __fields_set__ contains the field + if self.last_failure_error is None and "last_failure_error" in self.__fields_set__: + _dict["last_failure_error"] = None + + # set to None if last_failed_at (nullable) is None + # and __fields_set__ contains the field + if self.last_failed_at is None and "last_failed_at" in self.__fields_set__: + _dict["last_failed_at"] = None + + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> WebhookActionRequest: + """Create an instance of WebhookActionRequest from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return WebhookActionRequest.parse_obj(obj) + + _obj = WebhookActionRequest.parse_obj({ + "url": obj.get("url"), + "include_image": obj.get("include_image"), + "payload_template": ( + PayloadTemplateRequest.from_dict(obj.get("payload_template")) + if obj.get("payload_template") is not None + else None + ), + "last_message_failed": obj.get("last_message_failed"), + "last_failure_error": obj.get("last_failure_error"), + "last_failed_at": obj.get("last_failed_at"), + }) + return _obj diff --git a/generated/groundlight_openapi_client/models/who_am_i200_response.py b/generated/groundlight_openapi_client/models/who_am_i200_response.py new file mode 100644 index 00000000..68f2672c --- /dev/null +++ b/generated/groundlight_openapi_client/models/who_am_i200_response.py @@ -0,0 +1,72 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +from __future__ import annotations +import pprint +import re # noqa: F401 +import json + + +from typing import Optional +from pydantic import BaseModel, Field, StrictBool, StrictStr + + +class WhoAmI200Response(BaseModel): + """ + WhoAmI200Response + """ + + username: Optional[StrictStr] = Field(default=None, description="The user's username") + email: Optional[StrictStr] = Field(default=None, description="The user's email") + is_superuser: Optional[StrictBool] = Field(default=None, description="Whether the user is a superuser") + __properties = ["username", "email", "is_superuser"] + + class Config: + """Pydantic configuration""" + + allow_population_by_field_name = True + validate_assignment = True + + def to_str(self) -> str: + """Returns the string representation of the model using alias""" + return pprint.pformat(self.dict(by_alias=True)) + + def to_json(self) -> str: + """Returns the JSON representation of the model using alias""" + return json.dumps(self.to_dict()) + + @classmethod + def from_json(cls, json_str: str) -> WhoAmI200Response: + """Create an instance of WhoAmI200Response from a JSON string""" + return cls.from_dict(json.loads(json_str)) + + def to_dict(self): + """Returns the dictionary representation of the model using alias""" + _dict = self.dict(by_alias=True, exclude={}, exclude_none=True) + return _dict + + @classmethod + def from_dict(cls, obj: dict) -> WhoAmI200Response: + """Create an instance of WhoAmI200Response from a dict""" + if obj is None: + return None + + if not isinstance(obj, dict): + return WhoAmI200Response.parse_obj(obj) + + _obj = WhoAmI200Response.parse_obj( + {"username": obj.get("username"), "email": obj.get("email"), "is_superuser": obj.get("is_superuser")} + ) + return _obj diff --git a/docs/static/.nojekyll b/generated/groundlight_openapi_client/py.typed similarity index 100% rename from docs/static/.nojekyll rename to generated/groundlight_openapi_client/py.typed diff --git a/generated/groundlight_openapi_client/rest.py b/generated/groundlight_openapi_client/rest.py index 16d8ca86..ca01d1d9 100644 --- a/generated/groundlight_openapi_client/rest.py +++ b/generated/groundlight_openapi_client/rest.py @@ -1,23 +1,26 @@ +# coding: utf-8 + """ Groundlight API - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. The version of the OpenAPI document: 0.18.2 Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + import io import json import logging import re import ssl -from urllib.parse import urlencode -from urllib.parse import urlparse -from urllib.request import proxy_bypass_environment + +from urllib.parse import urlencode, quote_plus import urllib3 -import ipaddress from groundlight_openapi_client.exceptions import ( ApiException, @@ -26,14 +29,27 @@ NotFoundException, ServiceException, ApiValueError, + BadRequestException, ) logger = logging.getLogger(__name__) +SUPPORTED_SOCKS_PROXIES = {"socks5", "socks5h", "socks4", "socks4a"} + + +def is_socks_proxy_url(url): + if url is None: + return False + split_section = url.split("://") + if len(split_section) < 2: + return False + else: + return split_section[0].lower() in SUPPORTED_SOCKS_PROXIES + class RESTResponse(io.IOBase): - def __init__(self, resp): + def __init__(self, resp) -> None: self.urllib3_response = resp self.status = resp.status self.reason = resp.reason @@ -41,15 +57,15 @@ def __init__(self, resp): def getheaders(self): """Returns a dictionary of the response headers.""" - return self.urllib3_response.getheaders() + return self.urllib3_response.headers def getheader(self, name, default=None): """Returns a given response header.""" - return self.urllib3_response.getheader(name, default) + return self.urllib3_response.headers.get(name, default) -class RESTClientObject(object): - def __init__(self, configuration, pools_size=4, maxsize=None): +class RESTClientObject: + def __init__(self, configuration, pools_size=4, maxsize=None) -> None: # urllib3.PoolManager will pass all kw parameters to connectionpool # https://github.com/shazow/urllib3/blob/f9409436f83aeb79fbaf090181cd81b784f1b8ce/urllib3/poolmanager.py#L75 # noqa: E501 # https://github.com/shazow/urllib3/blob/f9409436f83aeb79fbaf090181cd81b784f1b8ce/urllib3/connectionpool.py#L680 # noqa: E501 @@ -69,6 +85,9 @@ def __init__(self, configuration, pools_size=4, maxsize=None): if configuration.retries is not None: addition_pool_args["retries"] = configuration.retries + if configuration.tls_server_name: + addition_pool_args["server_hostname"] = configuration.tls_server_name + if configuration.socket_options is not None: addition_pool_args["socket_options"] = configuration.socket_options @@ -79,18 +98,31 @@ def __init__(self, configuration, pools_size=4, maxsize=None): maxsize = 4 # https pool manager - if configuration.proxy and not should_bypass_proxies(configuration.host, no_proxy=configuration.no_proxy or ""): - self.pool_manager = urllib3.ProxyManager( - num_pools=pools_size, - maxsize=maxsize, - cert_reqs=cert_reqs, - ca_certs=configuration.ssl_ca_cert, - cert_file=configuration.cert_file, - key_file=configuration.key_file, - proxy_url=configuration.proxy, - proxy_headers=configuration.proxy_headers, - **addition_pool_args, - ) + if configuration.proxy: + if is_socks_proxy_url(configuration.proxy): + from urllib3.contrib.socks import SOCKSProxyManager + + self.pool_manager = SOCKSProxyManager( + cert_reqs=cert_reqs, + ca_certs=configuration.ssl_ca_cert, + cert_file=configuration.cert_file, + key_file=configuration.key_file, + proxy_url=configuration.proxy, + headers=configuration.proxy_headers, + **addition_pool_args, + ) + else: + self.pool_manager = urllib3.ProxyManager( + num_pools=pools_size, + maxsize=maxsize, + cert_reqs=cert_reqs, + ca_certs=configuration.ssl_ca_cert, + cert_file=configuration.cert_file, + key_file=configuration.key_file, + proxy_url=configuration.proxy, + proxy_headers=configuration.proxy_headers, + **addition_pool_args, + ) else: self.pool_manager = urllib3.PoolManager( num_pools=pools_size, @@ -139,6 +171,9 @@ def request( post_params = post_params or {} headers = headers or {} + # url already contains the URL query string + # so reset query_params to empty dict + query_params = {} timeout = None if _request_timeout: @@ -150,12 +185,9 @@ def request( try: # For `POST`, `PUT`, `PATCH`, `OPTIONS`, `DELETE` if method in ["POST", "PUT", "PATCH", "OPTIONS", "DELETE"]: - # Only set a default Content-Type for POST, PUT, PATCH and OPTIONS requests - if (method != "DELETE") and ("Content-Type" not in headers): - headers["Content-Type"] = "application/json" - if query_params: - url += "?" + urlencode(query_params) - if ("Content-Type" not in headers) or (re.search("json", headers["Content-Type"], re.IGNORECASE)): + + # no content type provided or payload is json + if not headers.get("Content-Type") or re.search("json", headers["Content-Type"], re.IGNORECASE): request_body = None if body is not None: request_body = json.dumps(body) @@ -182,6 +214,8 @@ def request( # Content-Type which generated by urllib3 will be # overwritten. del headers["Content-Type"] + # Ensures that dict objects are serialized + post_params = [(a, json.dumps(b)) if isinstance(b, dict) else (a, b) for a, b in post_params] r = self.pool_manager.request( method, url, @@ -213,7 +247,7 @@ def request( # For `GET`, `HEAD` else: r = self.pool_manager.request( - method, url, fields=query_params, preload_content=_preload_content, timeout=timeout, headers=headers + method, url, fields={}, preload_content=_preload_content, timeout=timeout, headers=headers ) except urllib3.exceptions.SSLError as e: msg = "{0}\n{1}".format(type(e).__name__, str(e)) @@ -226,6 +260,9 @@ def request( logger.debug("response body: %s", r.data) if not 200 <= r.status <= 299: + if r.status == 400: + raise BadRequestException(http_resp=r) + if r.status == 401: raise UnauthorizedException(http_resp=r) @@ -242,7 +279,7 @@ def request( return r - def GET(self, url, headers=None, query_params=None, _preload_content=True, _request_timeout=None): + def get_request(self, url, headers=None, query_params=None, _preload_content=True, _request_timeout=None): return self.request( "GET", url, @@ -252,7 +289,7 @@ def GET(self, url, headers=None, query_params=None, _preload_content=True, _requ query_params=query_params, ) - def HEAD(self, url, headers=None, query_params=None, _preload_content=True, _request_timeout=None): + def head_request(self, url, headers=None, query_params=None, _preload_content=True, _request_timeout=None): return self.request( "HEAD", url, @@ -262,7 +299,7 @@ def HEAD(self, url, headers=None, query_params=None, _preload_content=True, _req query_params=query_params, ) - def OPTIONS( + def options_request( self, url, headers=None, @@ -283,7 +320,9 @@ def OPTIONS( body=body, ) - def DELETE(self, url, headers=None, query_params=None, body=None, _preload_content=True, _request_timeout=None): + def delete_request( + self, url, headers=None, query_params=None, body=None, _preload_content=True, _request_timeout=None + ): return self.request( "DELETE", url, @@ -294,7 +333,7 @@ def DELETE(self, url, headers=None, query_params=None, body=None, _preload_conte body=body, ) - def POST( + def post_request( self, url, headers=None, @@ -315,7 +354,7 @@ def POST( body=body, ) - def PUT( + def put_request( self, url, headers=None, @@ -336,7 +375,7 @@ def PUT( body=body, ) - def PATCH( + def patch_request( self, url, headers=None, @@ -356,54 +395,3 @@ def PATCH( _request_timeout=_request_timeout, body=body, ) - - -# end of class RESTClientObject -def is_ipv4(target): - """Test if IPv4 address or not""" - try: - chk = ipaddress.IPv4Address(target) - return True - except ipaddress.AddressValueError: - return False - - -def in_ipv4net(target, net): - """Test if target belongs to given IPv4 network""" - try: - nw = ipaddress.IPv4Network(net) - ip = ipaddress.IPv4Address(target) - if ip in nw: - return True - return False - except ipaddress.AddressValueError: - return False - except ipaddress.NetmaskValueError: - return False - - -def should_bypass_proxies(url, no_proxy=None): - """Yet another requests.should_bypass_proxies - Test if proxies should not be used for a particular url. - """ - - parsed = urlparse(url) - - # special cases - if parsed.hostname in [None, ""]: - return True - - # special cases - if no_proxy in [None, ""]: - return False - if no_proxy == "*": - return True - - no_proxy = no_proxy.lower().replace(" ", "") - entries = (host for host in no_proxy.split(",") if host) - - if is_ipv4(parsed.hostname): - for item in entries: - if in_ipv4net(parsed.hostname, item): - return True - return proxy_bypass_environment(parsed.hostname, {"no": no_proxy}) diff --git a/generated/model.py b/generated/model.py deleted file mode 100644 index dc6348b8..00000000 --- a/generated/model.py +++ /dev/null @@ -1,528 +0,0 @@ -# generated by datamodel-codegen: -# filename: public-api.yaml -# timestamp: 2025-05-06T17:51:03+00:00 - -from __future__ import annotations - -from datetime import datetime -from enum import Enum -from typing import Any, Dict, List, Optional, Union - -from pydantic import AnyUrl, BaseModel, Field, RootModel, confloat, conint, constr - - -class BBoxGeometry(BaseModel): - """ - Mixin for serializers to handle data in the StrictBaseModel format - """ - - left: float - top: float - right: float - bottom: float - x: float - y: float - - -class BBoxGeometryRequest(BaseModel): - """ - Mixin for serializers to handle data in the StrictBaseModel format - """ - - left: float - top: float - right: float - bottom: float - - -class BlankEnum(Enum): - field_ = "" - - -class Condition(BaseModel): - verb: str - parameters: Dict[str, Any] - - -class ConditionRequest(BaseModel): - verb: str - parameters: Dict[str, Any] - - -class DetectorGroup(BaseModel): - id: str - name: constr(max_length=100) - - -class DetectorGroupRequest(BaseModel): - name: constr(min_length=1, max_length=100) - - -class DetectorTypeEnum(str, Enum): - detector = "detector" - - -class EdgeModelInfo(BaseModel): - """ - Information for the model running on edge, including temporary presigned urls to the model binaries - """ - - model_binary_id: Optional[str] = None - model_binary_url: Optional[str] = None - oodd_model_binary_id: Optional[str] = None - oodd_model_binary_url: Optional[str] = None - pipeline_config: Optional[Any] = None - oodd_pipeline_config: Optional[Any] = None - predictor_metadata: Optional[Any] = None - - -class ImageQueryTypeEnum(str, Enum): - image_query = "image_query" - - -class ModeEnum(str, Enum): - BINARY = "BINARY" - COUNT = "COUNT" - MULTI_CLASS = "MULTI_CLASS" - TEXT = "TEXT" - BOUNDING_BOX = "BOUNDING_BOX" - - -class Note(BaseModel): - detector_id: str - content: Optional[str] = Field(None, description="Text content of the note.") - is_pinned: Optional[bool] = None - - -class NoteRequest(BaseModel): - content: Optional[str] = Field(None, description="Text content of the note.") - is_pinned: Optional[bool] = None - image: Optional[bytes] = None - - -class PayloadTemplate(BaseModel): - template: str - headers: Optional[Dict[str, str]] = None - - -class PayloadTemplateRequest(BaseModel): - template: constr(min_length=1) - headers: Optional[Dict[str, constr(min_length=1)]] = None - - -class ROI(BaseModel): - """ - Mixin for serializers to handle data in the StrictBaseModel format - """ - - label: str = Field(..., description="The label of the bounding box.") - score: float = Field(..., description="The confidence of the bounding box.") - geometry: BBoxGeometry - - -class ROIRequest(BaseModel): - """ - Mixin for serializers to handle data in the StrictBaseModel format - """ - - label: constr(min_length=1) = Field(..., description="The label of the bounding box.") - geometry: BBoxGeometryRequest - - -class ResultTypeEnum(str, Enum): - binary_classification = "binary_classification" - counting = "counting" - multi_classification = "multi_classification" - text_recognition = "text_recognition" - bounding_box = "bounding_box" - - -class SnoozeTimeUnitEnum(str, Enum): - """ - * `DAYS` - DAYS - * `HOURS` - HOURS - * `MINUTES` - MINUTES - * `SECONDS` - SECONDS - """ - - DAYS = "DAYS" - HOURS = "HOURS" - MINUTES = "MINUTES" - SECONDS = "SECONDS" - - -class StatusEnum(str, Enum): - """ - * `ON` - ON - * `OFF` - OFF - """ - - ON = "ON" - OFF = "OFF" - - -class WebhookAction(BaseModel): - url: AnyUrl - include_image: Optional[bool] = None - payload_template: Optional[PayloadTemplate] = None - last_message_failed: Optional[bool] = None - last_failure_error: Optional[str] = None - last_failed_at: Optional[datetime] = None - - -class WebhookActionRequest(BaseModel): - url: AnyUrl - include_image: Optional[bool] = None - payload_template: Optional[PayloadTemplateRequest] = None - last_message_failed: Optional[bool] = None - last_failure_error: Optional[str] = None - last_failed_at: Optional[datetime] = None - - -class ResultType(Enum): - binary_classification = "binary_classification" - - -class BinaryClassificationResult(BaseModel): - confidence: Optional[confloat(ge=0.0, le=1.0)] = None - source: Optional[str] = None - result_type: Optional[ResultType] = None - from_edge: Optional[bool] = None - label: str - - -class ResultType2(Enum): - counting = "counting" - - -class CountingResult(BaseModel): - confidence: Optional[confloat(ge=0.0, le=1.0)] = None - source: Optional[str] = None - result_type: Optional[ResultType2] = None - from_edge: Optional[bool] = None - count: Optional[conint(ge=0)] = Field(...) - greater_than_max: Optional[bool] = None - - -class ResultType3(Enum): - multi_classification = "multi_classification" - - -class MultiClassificationResult(BaseModel): - confidence: Optional[confloat(ge=0.0, le=1.0)] = None - source: Optional[str] = None - result_type: Optional[ResultType3] = None - from_edge: Optional[bool] = None - label: str - - -class ResultType4(Enum): - text_recognition = "text_recognition" - - -class TextRecognitionResult(BaseModel): - confidence: Optional[confloat(ge=0.0, le=1.0)] = None - source: Optional[str] = None - result_type: Optional[ResultType4] = None - from_edge: Optional[bool] = None - text: Optional[str] = Field(...) - truncated: bool - - -class ResultType5(Enum): - bounding_box = "bounding_box" - - -class BoundingBoxResult(BaseModel): - confidence: Optional[confloat(ge=0.0, le=1.0)] = None - source: Optional[str] = None - result_type: Optional[ResultType5] = None - from_edge: Optional[bool] = None - label: str - - -class CountModeConfiguration(BaseModel): - max_count: Optional[conint(ge=1, le=50)] = None - class_name: str - - -class MultiClassModeConfiguration(BaseModel): - class_names: List[str] - num_classes: Optional[int] = None - - -class TextModeConfiguration(BaseModel): - value_max_length: Optional[conint(ge=1, le=250)] = None - - -class BoundingBoxModeConfiguration(BaseModel): - class_name: str - max_num_bboxes: Optional[conint(ge=1, le=50)] = None - - -class ChannelEnum(str, Enum): - TEXT = "TEXT" - EMAIL = "EMAIL" - - -class Action(BaseModel): - channel: ChannelEnum - recipient: str - include_image: bool - - -class ActionList(RootModel[List[Action]]): - root: List[Action] - - -class AnnotationsRequestedEnum(str, Enum): - BINARY_CLASSIFICATION = "BINARY_CLASSIFICATION" - BOUNDING_BOXES = "BOUNDING_BOXES" - - -class EscalationTypeEnum(str, Enum): - STANDARD = "STANDARD" - NO_HUMAN_LABELING = "NO_HUMAN_LABELING" - - -class SourceEnum(str, Enum): - INITIAL_PLACEHOLDER = "INITIAL_PLACEHOLDER" - CLOUD = "CLOUD" - CUST = "CUST" - HUMAN_CLOUD_ENSEMBLE = "HUMAN_CLOUD_ENSEMBLE" - ALG = "ALG" - ALG_REC = "ALG_REC" - ALG_UNCLEAR = "ALG_UNCLEAR" - EDGE = "EDGE" - - -class VerbEnum(str, Enum): - ANSWERED_CONSECUTIVELY = "ANSWERED_CONSECUTIVELY" - ANSWERED_WITHIN_TIME = "ANSWERED_WITHIN_TIME" - CHANGED_TO = "CHANGED_TO" - NO_CHANGE = "NO_CHANGE" - NO_QUERIES = "NO_QUERIES" - - -class Source(str, Enum): - STILL_PROCESSING = "STILL_PROCESSING" - CLOUD = "CLOUD" - USER = "USER" - CLOUD_ENSEMBLE = "CLOUD_ENSEMBLE" - ALGORITHM = "ALGORITHM" - EDGE = "EDGE" - - -class Label(str, Enum): - YES = "YES" - NO = "NO" - UNCLEAR = "UNCLEAR" - - -class AllNotes(BaseModel): - """ - Serializes all notes for a given detector, grouped by type as listed in UserProfile.NoteCategoryChoices - The fields must match whats in USERPROFILE.NoteCategoryChoices - """ - - CUSTOMER: List[Note] - GL: List[Note] - - -class Detector(BaseModel): - """ - Groundlight Detectors provide answers to natural language questions about images. - - Each detector can answer a single question, and multiple detectors can be strung together for - more complex logic. Detectors can be created through the create_detector method, or through the - create_[MODE]_detector methods for pro tier users - """ - - id: str = Field(..., description="A unique ID for this object.") - type: DetectorTypeEnum = Field(..., description="The type of this object.") - created_at: datetime = Field(..., description="When this detector was created.") - name: constr(max_length=200) = Field(..., description="A short, descriptive name for the detector.") - query: str = Field(..., description="A question about the image.") - group_name: str = Field(..., description="Which group should this detector be part of?") - confidence_threshold: confloat(ge=0.0, le=1.0) = Field( - 0.9, - description=( - "If the detector's prediction is below this confidence threshold, send the image query for human review." - ), - ) - patience_time: confloat(ge=0.0, le=3600.0) = Field( - 30.0, description="How long Groundlight will attempt to generate a confident prediction" - ) - metadata: Optional[Dict[str, Any]] = Field(..., description="Metadata about the detector.") - mode: str - mode_configuration: Optional[Dict[str, Any]] = Field(...) - status: Optional[Union[StatusEnum, BlankEnum]] = None - escalation_type: Optional[str] = None - - -class DetectorCreationInputRequest(BaseModel): - """ - Helper serializer for validating POST /detectors input. - """ - - name: constr(min_length=1, max_length=200) = Field(..., description="A short, descriptive name for the detector.") - query: constr(min_length=1, max_length=300) = Field(..., description="A question about the image.") - group_name: Optional[constr(min_length=1, max_length=100)] = Field( - None, description="Which group should this detector be part of?" - ) - confidence_threshold: confloat(ge=0.0, le=1.0) = Field( - 0.9, - description=( - "If the detector's prediction is below this confidence threshold, send the image query for human review." - ), - ) - patience_time: confloat(ge=0.0, le=3600.0) = Field( - 30.0, description="How long Groundlight will attempt to generate a confident prediction" - ) - pipeline_config: Optional[constr(max_length=100)] = Field( - None, description="(Advanced usage) Configuration needed to instantiate a prediction pipeline." - ) - metadata: Optional[constr(min_length=1, max_length=1362)] = Field( - None, - description=( - "Base64-encoded metadata for the detector. This should be a JSON object with string keys. The size after" - " encoding should not exceed 1362 bytes, corresponding to 1KiB before encoding." - ), - ) - mode: ModeEnum = Field( - "BINARY", - description=( - "Mode in which this detector will work.\n\n* `BINARY` - BINARY\n* `COUNT` - COUNT\n* `MULTI_CLASS` -" - " MULTI_CLASS\n* `TEXT` - TEXT\n* `BOUNDING_BOX` - BOUNDING_BOX" - ), - ) - mode_configuration: Optional[ - Union[CountModeConfiguration, MultiClassModeConfiguration, TextModeConfiguration, BoundingBoxModeConfiguration] - ] = None - - -class ImageQuery(BaseModel): - """ - ImageQuery objects are the answers to natural language questions about images created by detectors. - """ - - metadata: Optional[Dict[str, Any]] = Field(..., description="Metadata about the image query.") - id: str = Field(..., description="A unique ID for this object.") - type: ImageQueryTypeEnum = Field(..., description="The type of this object.") - created_at: datetime = Field(..., description="When was this detector created?") - query: str = Field(..., description="A question about the image.") - detector_id: str = Field(..., description="Which detector was used on this image query?") - result_type: ResultTypeEnum = Field(..., description="What type of result are we returning?") - result: Optional[ - Union[ - BinaryClassificationResult, - CountingResult, - MultiClassificationResult, - TextRecognitionResult, - BoundingBoxResult, - ] - ] = Field(...) - patience_time: float = Field(..., description="How long to wait for a confident response.") - confidence_threshold: float = Field( - ..., description="Min confidence needed to accept the response of the image query." - ) - rois: Optional[List[ROI]] = Field( - ..., description="An array of regions of interest (bounding boxes) collected on image" - ) - text: Optional[str] = Field(..., description="A text field on image query.") - done_processing: bool = Field( - False, - description="EDGE ONLY - Whether the image query has completed escalating and will receive no new results.", - ) - - -class LabelValue(BaseModel): - confidence: Optional[float] = Field(...) - class_name: Optional[str] = Field( - ..., description="Return a human-readable class name for this label (e.g. YES/NO)" - ) - rois: Optional[List[ROI]] = None - annotations_requested: List[str] - created_at: datetime - detector_id: Optional[int] = Field(...) - source: str - text: Optional[str] = Field(..., description="Text annotations") - - -class LabelValueRequest(BaseModel): - label: Optional[str] = Field(...) - image_query_id: constr(min_length=1) - rois: Optional[List[ROIRequest]] = None - - -class PaginatedDetectorList(BaseModel): - count: int = Field(..., example=123) - next: Optional[AnyUrl] = Field(None, example="http://api.example.org/accounts/?page=4") - previous: Optional[AnyUrl] = Field(None, example="http://api.example.org/accounts/?page=2") - results: List[Detector] - - -class PaginatedImageQueryList(BaseModel): - count: int = Field(..., example=123) - next: Optional[AnyUrl] = Field(None, example="http://api.example.org/accounts/?page=4") - previous: Optional[AnyUrl] = Field(None, example="http://api.example.org/accounts/?page=2") - results: List[ImageQuery] - - -class PatchedDetectorRequest(BaseModel): - """ - Groundlight Detectors provide answers to natural language questions about images. - - Each detector can answer a single question, and multiple detectors can be strung together for - more complex logic. Detectors can be created through the create_detector method, or through the - create_[MODE]_detector methods for pro tier users - """ - - name: Optional[constr(min_length=1, max_length=200)] = Field( - None, description="A short, descriptive name for the detector." - ) - confidence_threshold: confloat(ge=0.0, le=1.0) = Field( - 0.9, - description=( - "If the detector's prediction is below this confidence threshold, send the image query for human review." - ), - ) - patience_time: confloat(ge=0.0, le=3600.0) = Field( - 30.0, description="How long Groundlight will attempt to generate a confident prediction" - ) - status: Optional[Union[StatusEnum, BlankEnum]] = None - escalation_type: Optional[constr(min_length=1)] = None - - -class Rule(BaseModel): - id: int - detector_id: str - detector_name: str - name: constr(max_length=44) - enabled: bool = True - snooze_time_enabled: bool = False - snooze_time_value: conint(ge=0) = 0 - snooze_time_unit: SnoozeTimeUnitEnum = "DAYS" - human_review_required: bool = False - condition: Condition - action: Optional[Union[Action, ActionList]] = None - webhook_action: Optional[List[WebhookAction]] = None - - -class RuleRequest(BaseModel): - name: constr(min_length=1, max_length=44) - enabled: bool = True - snooze_time_enabled: bool = False - snooze_time_value: conint(ge=0) = 0 - snooze_time_unit: SnoozeTimeUnitEnum = "DAYS" - human_review_required: bool = False - condition: ConditionRequest - action: Optional[Union[Action, ActionList]] = None - webhook_action: Optional[List[WebhookActionRequest]] = None - - -class PaginatedRuleList(BaseModel): - count: int = Field(..., example=123) - next: Optional[AnyUrl] = Field(None, example="http://api.example.org/accounts/?page=4") - previous: Optional[AnyUrl] = Field(None, example="http://api.example.org/accounts/?page=2") - results: List[Rule] diff --git a/generated/pyproject.toml b/generated/pyproject.toml new file mode 100644 index 00000000..2f913d9b --- /dev/null +++ b/generated/pyproject.toml @@ -0,0 +1,30 @@ +[tool.poetry] +name = "groundlight_openapi_client" +version = "1.0.0" +description = "Groundlight API" +authors = ["support@groundlight.ai "] +license = "NoLicense" +readme = "README.md" +repository = "https://github.com/GIT_USER_ID/GIT_REPO_ID" +keywords = ["OpenAPI", "OpenAPI-Generator", "Groundlight API"] +include = ["groundlight_openapi_client/py.typed"] + +[tool.poetry.dependencies] +python = "^3.8" + +urllib3 = ">= 1.25.3" +python-dateutil = ">=2.8.2" +pydantic = "^1.10.5, <2" +aenum = ">=3.1.11" + +[tool.poetry.dev-dependencies] +pytest = ">=7.2.1" +tox = ">=3.9.0" +flake8 = ">=4.0.0" + +[build-system] +requires = ["setuptools"] +build-backend = "setuptools.build_meta" + +[tool.pylint.'MESSAGES CONTROL'] +extension-pkg-whitelist = "pydantic" diff --git a/generated/requirements.txt b/generated/requirements.txt index 96947f60..0a8bf96c 100644 --- a/generated/requirements.txt +++ b/generated/requirements.txt @@ -1,3 +1,5 @@ python_dateutil >= 2.5.3 setuptools >= 21.0.0 -urllib3 >= 1.25.3 +urllib3 >= 1.25.3, < 3.0.0 +pydantic >= 1.10.5, < 2 +aenum >= 3.1.11 diff --git a/generated/setup.py b/generated/setup.py index 9c4bb572..8167d5b8 100644 --- a/generated/setup.py +++ b/generated/setup.py @@ -1,28 +1,30 @@ +# coding: utf-8 + """ Groundlight API - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. The version of the OpenAPI document: 0.18.2 Contact: support@groundlight.ai - Generated by: https://openapi-generator.tech -""" + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + from setuptools import setup, find_packages # noqa: H301 -NAME = "groundlight-openapi-client" -VERSION = "1.0.0" # To install the library, run the following # # python setup.py install # # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools - -REQUIRES = [ - "urllib3 >= 1.25.3", - "python-dateutil", -] +NAME = "groundlight-openapi-client" +VERSION = "1.0.0" +PYTHON_REQUIRES = ">=3.7" +REQUIRES = ["urllib3 >= 1.25.3, < 3.0.0", "python-dateutil", "pydantic >= 1.10.5, < 2", "aenum"] setup( name=NAME, @@ -32,11 +34,12 @@ author_email="support@groundlight.ai", url="", keywords=["OpenAPI", "OpenAPI-Generator", "Groundlight API"], - python_requires=">=3.6", install_requires=REQUIRES, packages=find_packages(exclude=["test", "tests"]), include_package_data=True, + long_description_content_type="text/markdown", long_description="""\ - Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501 - """, + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + """, # noqa: E501 + package_data={"groundlight_openapi_client": ["py.typed"]}, ) diff --git a/generated/test-requirements.txt b/generated/test-requirements.txt index bb4f22bb..3a0d0b93 100644 --- a/generated/test-requirements.txt +++ b/generated/test-requirements.txt @@ -1 +1,3 @@ +pytest~=7.1.3 pytest-cov>=2.8.1 +pytest-randomly>=3.12.0 diff --git a/generated/test/test_detector_creation_input_request_mode_configuration.py b/generated/test/test_detector_creation_input_request_mode_configuration.py new file mode 100644 index 00000000..e666db4c --- /dev/null +++ b/generated/test/test_detector_creation_input_request_mode_configuration.py @@ -0,0 +1,67 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import unittest + +from groundlight_openapi_client.models.detector_creation_input_request_mode_configuration import ( + DetectorCreationInputRequestModeConfiguration, +) + + +class TestDetectorCreationInputRequestModeConfiguration(unittest.TestCase): + """DetectorCreationInputRequestModeConfiguration unit test stubs""" + + def setUp(self): + pass + + def tearDown(self): + pass + + def make_instance(self, include_optional) -> DetectorCreationInputRequestModeConfiguration: + """Test DetectorCreationInputRequestModeConfiguration + include_optional is a boolean, when False only required + params are included, when True both required and + optional params are included""" + # uncomment below to create an instance of `DetectorCreationInputRequestModeConfiguration` + """ + model = DetectorCreationInputRequestModeConfiguration() + if include_optional: + return DetectorCreationInputRequestModeConfiguration( + max_count = 1, + class_name = '', + class_names = [ + '' + ], + num_classes = 56, + value_max_length = 1, + max_num_bboxes = 1 + ) + else: + return DetectorCreationInputRequestModeConfiguration( + class_name = '', + class_names = [ + '' + ], + ) + """ + + def testDetectorCreationInputRequestModeConfiguration(self): + """Test DetectorCreationInputRequestModeConfiguration""" + # inst_req_only = self.make_instance(include_optional=False) + # inst_req_and_optional = self.make_instance(include_optional=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/generated/test/test_detector_status.py b/generated/test/test_detector_status.py new file mode 100644 index 00000000..2f79ee38 --- /dev/null +++ b/generated/test/test_detector_status.py @@ -0,0 +1,53 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import unittest + +from groundlight_openapi_client.models.detector_status import DetectorStatus + + +class TestDetectorStatus(unittest.TestCase): + """DetectorStatus unit test stubs""" + + def setUp(self): + pass + + def tearDown(self): + pass + + def make_instance(self, include_optional) -> DetectorStatus: + """Test DetectorStatus + include_optional is a boolean, when False only required + params are included, when True both required and + optional params are included""" + # uncomment below to create an instance of `DetectorStatus` + """ + model = DetectorStatus() + if include_optional: + return DetectorStatus( + ) + else: + return DetectorStatus( + ) + """ + + def testDetectorStatus(self): + """Test DetectorStatus""" + # inst_req_only = self.make_instance(include_optional=False) + # inst_req_and_optional = self.make_instance(include_optional=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/generated/test/test_get_detector_evaluation200_response.py b/generated/test/test_get_detector_evaluation200_response.py new file mode 100644 index 00000000..4f7345f8 --- /dev/null +++ b/generated/test/test_get_detector_evaluation200_response.py @@ -0,0 +1,73 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import unittest + +from groundlight_openapi_client.models.get_detector_evaluation200_response import GetDetectorEvaluation200Response + + +class TestGetDetectorEvaluation200Response(unittest.TestCase): + """GetDetectorEvaluation200Response unit test stubs""" + + def setUp(self): + pass + + def tearDown(self): + pass + + def make_instance(self, include_optional) -> GetDetectorEvaluation200Response: + """Test GetDetectorEvaluation200Response + include_optional is a boolean, when False only required + params are included, when True both required and + optional params are included""" + # uncomment below to create an instance of `GetDetectorEvaluation200Response` + """ + model = GetDetectorEvaluation200Response() + if include_optional: + return GetDetectorEvaluation200Response( + evaluation_results = groundlight_openapi_client.models.get_detector_evaluation_200_response_evaluation_results.get_detector_evaluation_200_response_evaluation_results( + eval_timestamp = datetime.datetime.strptime('2013-10-20 19:20:30.00', '%Y-%m-%d %H:%M:%S.%f'), + total_ground_truth_examples = 56, + total_labeled_examples = 56, + kfold_pooled__balanced_accuracy = 1.337, + kfold_pooled__positive_accuracy = 1.337, + kfold_pooled__negative_accuracy = 1.337, + precision__mean = 1.337, + recall__mean = 1.337, + roc_auc__mean = 1.337, + balanced_system_accuracies = { }, + positive_system_accuracies = { }, + negative_system_accuracies = { }, + mean_absolute_error__mean = 1.337, + objdet_precision__mean = 1.337, + objdet_recall__mean = 1.337, + objdet_f1_score__mean = 1.337, + class_accuracies = { }, + confusion_dict = { }, + num_examples_per_class = { }, ) + ) + else: + return GetDetectorEvaluation200Response( + ) + """ + + def testGetDetectorEvaluation200Response(self): + """Test GetDetectorEvaluation200Response""" + # inst_req_only = self.make_instance(include_optional=False) + # inst_req_and_optional = self.make_instance(include_optional=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/generated/test/test_get_detector_evaluation200_response_evaluation_results.py b/generated/test/test_get_detector_evaluation200_response_evaluation_results.py new file mode 100644 index 00000000..c5d5a52e --- /dev/null +++ b/generated/test/test_get_detector_evaluation200_response_evaluation_results.py @@ -0,0 +1,74 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import unittest + +from groundlight_openapi_client.models.get_detector_evaluation200_response_evaluation_results import ( + GetDetectorEvaluation200ResponseEvaluationResults, +) + + +class TestGetDetectorEvaluation200ResponseEvaluationResults(unittest.TestCase): + """GetDetectorEvaluation200ResponseEvaluationResults unit test stubs""" + + def setUp(self): + pass + + def tearDown(self): + pass + + def make_instance(self, include_optional) -> GetDetectorEvaluation200ResponseEvaluationResults: + """Test GetDetectorEvaluation200ResponseEvaluationResults + include_optional is a boolean, when False only required + params are included, when True both required and + optional params are included""" + # uncomment below to create an instance of `GetDetectorEvaluation200ResponseEvaluationResults` + """ + model = GetDetectorEvaluation200ResponseEvaluationResults() + if include_optional: + return GetDetectorEvaluation200ResponseEvaluationResults( + eval_timestamp = datetime.datetime.strptime('2013-10-20 19:20:30.00', '%Y-%m-%d %H:%M:%S.%f'), + total_ground_truth_examples = 56, + total_labeled_examples = 56, + kfold_pooled__balanced_accuracy = 1.337, + kfold_pooled__positive_accuracy = 1.337, + kfold_pooled__negative_accuracy = 1.337, + precision__mean = 1.337, + recall__mean = 1.337, + roc_auc__mean = 1.337, + balanced_system_accuracies = { }, + positive_system_accuracies = { }, + negative_system_accuracies = { }, + mean_absolute_error__mean = 1.337, + objdet_precision__mean = 1.337, + objdet_recall__mean = 1.337, + objdet_f1_score__mean = 1.337, + class_accuracies = { }, + confusion_dict = { }, + num_examples_per_class = { } + ) + else: + return GetDetectorEvaluation200ResponseEvaluationResults( + ) + """ + + def testGetDetectorEvaluation200ResponseEvaluationResults(self): + """Test GetDetectorEvaluation200ResponseEvaluationResults""" + # inst_req_only = self.make_instance(include_optional=False) + # inst_req_and_optional = self.make_instance(include_optional=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/generated/test/test_get_detector_metrics200_response.py b/generated/test/test_get_detector_metrics200_response.py new file mode 100644 index 00000000..e02e93e3 --- /dev/null +++ b/generated/test/test_get_detector_metrics200_response.py @@ -0,0 +1,64 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import unittest + +from groundlight_openapi_client.models.get_detector_metrics200_response import GetDetectorMetrics200Response + + +class TestGetDetectorMetrics200Response(unittest.TestCase): + """GetDetectorMetrics200Response unit test stubs""" + + def setUp(self): + pass + + def tearDown(self): + pass + + def make_instance(self, include_optional) -> GetDetectorMetrics200Response: + """Test GetDetectorMetrics200Response + include_optional is a boolean, when False only required + params are included, when True both required and + optional params are included""" + # uncomment below to create an instance of `GetDetectorMetrics200Response` + """ + model = GetDetectorMetrics200Response() + if include_optional: + return GetDetectorMetrics200Response( + summary = groundlight_openapi_client.models.get_detector_metrics_200_response_summary.get_detector_metrics_200_response_summary( + num_ground_truth = 56, + num_current_source_human = 56, + class_counts = groundlight_openapi_client.models.get_detector_metrics_200_response_summary_class_counts.get_detector_metrics_200_response_summary_class_counts( + source_ml = groundlight_openapi_client.models.source_ml.source_ml(), + source_human = groundlight_openapi_client.models.source_human.source_human(), + cloud_labeler = groundlight_openapi_client.models.cloud_labeler.cloud_labeler(), + cloud = groundlight_openapi_client.models.cloud.cloud(), + total = groundlight_openapi_client.models.total.total(), ), + unconfident_counts = groundlight_openapi_client.models.unconfident_counts.unconfident_counts(), + total_iqs = 56, ) + ) + else: + return GetDetectorMetrics200Response( + ) + """ + + def testGetDetectorMetrics200Response(self): + """Test GetDetectorMetrics200Response""" + # inst_req_only = self.make_instance(include_optional=False) + # inst_req_and_optional = self.make_instance(include_optional=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/generated/test/test_get_detector_metrics200_response_summary.py b/generated/test/test_get_detector_metrics200_response_summary.py new file mode 100644 index 00000000..683ac405 --- /dev/null +++ b/generated/test/test_get_detector_metrics200_response_summary.py @@ -0,0 +1,65 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import unittest + +from groundlight_openapi_client.models.get_detector_metrics200_response_summary import ( + GetDetectorMetrics200ResponseSummary, +) + + +class TestGetDetectorMetrics200ResponseSummary(unittest.TestCase): + """GetDetectorMetrics200ResponseSummary unit test stubs""" + + def setUp(self): + pass + + def tearDown(self): + pass + + def make_instance(self, include_optional) -> GetDetectorMetrics200ResponseSummary: + """Test GetDetectorMetrics200ResponseSummary + include_optional is a boolean, when False only required + params are included, when True both required and + optional params are included""" + # uncomment below to create an instance of `GetDetectorMetrics200ResponseSummary` + """ + model = GetDetectorMetrics200ResponseSummary() + if include_optional: + return GetDetectorMetrics200ResponseSummary( + num_ground_truth = 56, + num_current_source_human = 56, + class_counts = groundlight_openapi_client.models.get_detector_metrics_200_response_summary_class_counts.get_detector_metrics_200_response_summary_class_counts( + source_ml = groundlight_openapi_client.models.source_ml.source_ml(), + source_human = groundlight_openapi_client.models.source_human.source_human(), + cloud_labeler = groundlight_openapi_client.models.cloud_labeler.cloud_labeler(), + cloud = groundlight_openapi_client.models.cloud.cloud(), + total = groundlight_openapi_client.models.total.total(), ), + unconfident_counts = None, + total_iqs = 56 + ) + else: + return GetDetectorMetrics200ResponseSummary( + ) + """ + + def testGetDetectorMetrics200ResponseSummary(self): + """Test GetDetectorMetrics200ResponseSummary""" + # inst_req_only = self.make_instance(include_optional=False) + # inst_req_and_optional = self.make_instance(include_optional=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/generated/test/test_get_detector_metrics200_response_summary_class_counts.py b/generated/test/test_get_detector_metrics200_response_summary_class_counts.py new file mode 100644 index 00000000..96f27976 --- /dev/null +++ b/generated/test/test_get_detector_metrics200_response_summary_class_counts.py @@ -0,0 +1,60 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import unittest + +from groundlight_openapi_client.models.get_detector_metrics200_response_summary_class_counts import ( + GetDetectorMetrics200ResponseSummaryClassCounts, +) + + +class TestGetDetectorMetrics200ResponseSummaryClassCounts(unittest.TestCase): + """GetDetectorMetrics200ResponseSummaryClassCounts unit test stubs""" + + def setUp(self): + pass + + def tearDown(self): + pass + + def make_instance(self, include_optional) -> GetDetectorMetrics200ResponseSummaryClassCounts: + """Test GetDetectorMetrics200ResponseSummaryClassCounts + include_optional is a boolean, when False only required + params are included, when True both required and + optional params are included""" + # uncomment below to create an instance of `GetDetectorMetrics200ResponseSummaryClassCounts` + """ + model = GetDetectorMetrics200ResponseSummaryClassCounts() + if include_optional: + return GetDetectorMetrics200ResponseSummaryClassCounts( + source_ml = None, + source_human = None, + cloud_labeler = None, + cloud = None, + total = None + ) + else: + return GetDetectorMetrics200ResponseSummaryClassCounts( + ) + """ + + def testGetDetectorMetrics200ResponseSummaryClassCounts(self): + """Test GetDetectorMetrics200ResponseSummaryClassCounts""" + # inst_req_only = self.make_instance(include_optional=False) + # inst_req_and_optional = self.make_instance(include_optional=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/generated/test/test_image_query_result.py b/generated/test/test_image_query_result.py new file mode 100644 index 00000000..bded808f --- /dev/null +++ b/generated/test/test_image_query_result.py @@ -0,0 +1,66 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import unittest + +from groundlight_openapi_client.models.image_query_result import ImageQueryResult + + +class TestImageQueryResult(unittest.TestCase): + """ImageQueryResult unit test stubs""" + + def setUp(self): + pass + + def tearDown(self): + pass + + def make_instance(self, include_optional) -> ImageQueryResult: + """Test ImageQueryResult + include_optional is a boolean, when False only required + params are included, when True both required and + optional params are included""" + # uncomment below to create an instance of `ImageQueryResult` + """ + model = ImageQueryResult() + if include_optional: + return ImageQueryResult( + confidence = 0.0, + source = '', + result_type = 'bounding_box', + from_edge = True, + label = '', + count = 0, + greater_than_max = True, + text = '', + truncated = True + ) + else: + return ImageQueryResult( + label = '', + count = 0, + text = '', + truncated = True, + ) + """ + + def testImageQueryResult(self): + """Test ImageQueryResult""" + # inst_req_only = self.make_instance(include_optional=False) + # inst_req_and_optional = self.make_instance(include_optional=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/generated/test/test_rule_action.py b/generated/test/test_rule_action.py new file mode 100644 index 00000000..30484058 --- /dev/null +++ b/generated/test/test_rule_action.py @@ -0,0 +1,59 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import unittest + +from groundlight_openapi_client.models.rule_action import RuleAction + + +class TestRuleAction(unittest.TestCase): + """RuleAction unit test stubs""" + + def setUp(self): + pass + + def tearDown(self): + pass + + def make_instance(self, include_optional) -> RuleAction: + """Test RuleAction + include_optional is a boolean, when False only required + params are included, when True both required and + optional params are included""" + # uncomment below to create an instance of `RuleAction` + """ + model = RuleAction() + if include_optional: + return RuleAction( + channel = 'TEXT', + recipient = '', + include_image = True + ) + else: + return RuleAction( + channel = 'TEXT', + recipient = '', + include_image = True, + ) + """ + + def testRuleAction(self): + """Test RuleAction""" + # inst_req_only = self.make_instance(include_optional=False) + # inst_req_and_optional = self.make_instance(include_optional=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/generated/test/test_who_am_i200_response.py b/generated/test/test_who_am_i200_response.py new file mode 100644 index 00000000..3ac7b6e7 --- /dev/null +++ b/generated/test/test_who_am_i200_response.py @@ -0,0 +1,54 @@ +# coding: utf-8 + +""" + Groundlight API + + Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. + + The version of the OpenAPI document: 0.18.2 + Contact: support@groundlight.ai + Generated by OpenAPI Generator (https://openapi-generator.tech) + + Do not edit the class manually. +""" # noqa: E501 + + +import unittest + +from groundlight_openapi_client.models.who_am_i200_response import WhoAmI200Response + + +class TestWhoAmI200Response(unittest.TestCase): + """WhoAmI200Response unit test stubs""" + + def setUp(self): + pass + + def tearDown(self): + pass + + def make_instance(self, include_optional) -> WhoAmI200Response: + """Test WhoAmI200Response + include_optional is a boolean, when False only required + params are included, when True both required and + optional params are included""" + # uncomment below to create an instance of `WhoAmI200Response` + """ + model = WhoAmI200Response() + if include_optional: + return WhoAmI200Response( + username = '' + ) + else: + return WhoAmI200Response( + ) + """ + + def testWhoAmI200Response(self): + """Test WhoAmI200Response""" + # inst_req_only = self.make_instance(include_optional=False) + # inst_req_and_optional = self.make_instance(include_optional=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/openapitools.json b/openapitools.json index 9cbc6d5d..959861ef 100644 --- a/openapitools.json +++ b/openapitools.json @@ -2,6 +2,6 @@ "$schema": "./node_modules/@openapitools/openapi-generator-cli/config.schema.json", "spaces": 2, "generator-cli": { - "version": "5.4.0" + "version": "7.13.0" } -} +} \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 59e52b94..64f18e6e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,12 +16,13 @@ version = "0.23.0" certifi = ">=2023.7.22" frozendict = "^2.3.2" pillow = ">=11.0.0" # TODO: We may want to mark pillow (and numpy) as extra (https://python-poetry.org/docs/master/pyproject#extras) -pydantic = "^2.0.0" +pydantic = "<2.0" python = ">=3.9,<4.0" python-dateutil = "^2.9.0" requests = "^2.28.2" typer = "^0.15.4" urllib3 = "^1.26.9" +aenum = "^3.1.16" [tool.poetry.group.dev.dependencies] datamodel-code-generator = "^0.22.1" @@ -40,17 +41,6 @@ ruff = "^0.4.4" toml-sort = "^0.23.0" types-requests = "^2.28.11.17" -[tool.poetry.group.sphinx-deps.dependencies] -# These are extra / stricter dependencies required to build the API reference docs -Sphinx = {version = "^7.2.6", python = ">=3.9,<4.0"} -autodoc-pydantic = {version = "^2.0.1", python = ">=3.9,<4.0"} -pillow = "^11.0.0" -pydantic = "^2.0.0" -python = ">=3.9,<4.0" -python-dateutil = "^2.8.2" -sphinx-rtd-theme = {version = "^1.3.0", python = ">=3.9,<4.0"} -toml = "^0.10.2" - [tool.poetry.scripts] groundlight = "groundlight.cli:groundlight" diff --git a/samples/README.md b/samples/README.md deleted file mode 100644 index ba755704..00000000 --- a/samples/README.md +++ /dev/null @@ -1 +0,0 @@ -# Code samples diff --git a/samples/blocking_submit.py b/samples/blocking_submit.py deleted file mode 100644 index 7a9f6acb..00000000 --- a/samples/blocking_submit.py +++ /dev/null @@ -1,15 +0,0 @@ -"""Example of how to wait for a confident result.""" - -import logging - -from groundlight import Groundlight - -logging.basicConfig(level=logging.DEBUG) - -gl = Groundlight() - -d = gl.get_or_create_detector(name="dog", query="is there a dog in the picture?") - -print("Submitting image query") -iq = gl.submit_image_query(d, image="../test/assets/dog.jpeg", wait=30) -print(iq) diff --git a/spec/public-api.yaml b/spec/public-api.yaml index d6f899c9..202d1dc7 100644 --- a/spec/public-api.yaml +++ b/spec/public-api.yaml @@ -733,6 +733,12 @@ paths: username: type: string description: The user's username + email: + type: string + description: The user's email + is_superuser: + type: boolean + description: Whether the user is a superuser description: '' /v1/notes: get: @@ -961,7 +967,6 @@ components: - name - query - type - x-internal: true DetectorCreationInputRequest: type: object description: Helper serializer for validating POST /detectors input. @@ -1031,7 +1036,6 @@ components: required: - name - query - x-internal: true DetectorGroup: type: object properties: @@ -1118,6 +1122,14 @@ components: - $ref: '#/components/schemas/MultiClassificationResult' - $ref: '#/components/schemas/TextRecognitionResult' - $ref: '#/components/schemas/BoundingBoxResult' + discriminator: + propertyName: result_type + mapping: + binary_classification: '#/components/schemas/BinaryClassificationResult' + counting: '#/components/schemas/CountingResult' + multi_classification: '#/components/schemas/MultiClassificationResult' + text_recognition: '#/components/schemas/TextRecognitionResult' + bounding_box: '#/components/schemas/BoundingBoxResult' nullable: true patience_time: type: number @@ -1160,7 +1172,6 @@ components: - rois - text - type - x-internal: true ImageQueryTypeEnum: enum: - image_query @@ -1374,7 +1385,6 @@ components: escalation_type: type: string minLength: 1 - x-internal: true PayloadTemplate: type: object properties: @@ -1829,4 +1839,4 @@ servers: - url: https://device.positronix.ai/device-api description: Device Prod - url: https://device.integ.positronix.ai/device-api - description: Device Integ + description: Device Integ \ No newline at end of file diff --git a/sphinx_docs/conf.py b/sphinx_docs/conf.py deleted file mode 100644 index fd94dfa8..00000000 --- a/sphinx_docs/conf.py +++ /dev/null @@ -1,54 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# For the full list of built-in configuration values, see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Project information ----------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information - - -import os -import sys -from datetime import datetime - -import toml - -sys.path.insert(0, os.path.abspath("../src")) -sys.path.insert(1, os.path.abspath("../generated")) - - -def get_version_name() -> str: - pyproject_path = "../pyproject.toml" - with open(pyproject_path, "r") as f: - pyproject_deps = toml.load(f) - - version = pyproject_deps["tool"]["poetry"]["version"] - return version - - -project = "Groundlight Python SDK" -copyright = f"{datetime.now().year}, Groundlight AI " -author = "Groundlight AI " -version = get_version_name() -release = version - -# -- General configuration --------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration - -# sphinx.ext.autodoc is a Sphinx extension that automatically documents Python modules. -# We are using the `reStructuredText` format for docstrings instead of google style. -extensions = ["sphinx.ext.autodoc", "sphinxcontrib.autodoc_pydantic"] - -templates_path = ["_templates"] -exclude_patterns = [] - -# Automatically add type annotations to the generated function signatures and descriptions. This -# means we don't have to manually add :type: annotations into the docstrings. -autodoc_typehints = "both" - - -# -- Options for HTML output ------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output - -html_theme = "sphinx_rtd_theme" -html_static_path = ["_static"] diff --git a/sphinx_docs/index.rst b/sphinx_docs/index.rst deleted file mode 100644 index 6c064ec9..00000000 --- a/sphinx_docs/index.rst +++ /dev/null @@ -1,11 +0,0 @@ - -Welcome to Groundlight Python SDK's documentation! -================================================== - -For a detailed view of the source code, visit the `Groundlight SDK GitHub Repository `_. - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - - models diff --git a/sphinx_docs/models.rst b/sphinx_docs/models.rst deleted file mode 100644 index cedc0ce7..00000000 --- a/sphinx_docs/models.rst +++ /dev/null @@ -1,31 +0,0 @@ -SDK Client -===================== - -.. autoclass:: groundlight.Groundlight - :members: - :special-members: __init__ - -.. autoclass:: groundlight.ExperimentalApi - :members: - :special-members: __init__ - -API Response Objects -===================== - -.. autopydantic_model:: model.Detector - :model-show-json: True - -.. autopydantic_model:: model.ImageQuery - :model-show-json: True - -.. autopydantic_model:: model.PaginatedDetectorList - :model-show-json: True - -.. autopydantic_model:: model.PaginatedImageQueryList - :model-show-json: True - -.. autopydantic_model:: model.Rule - :model-show-json: True - -.. autopydantic_model:: model.PaginatedRuleList - :model-show-json: True \ No newline at end of file diff --git a/src/groundlight/__init__.py b/src/groundlight/__init__.py index 6a23be7e..7fdfed87 100644 --- a/src/groundlight/__init__.py +++ b/src/groundlight/__init__.py @@ -2,9 +2,6 @@ # Add useful imports from the generated code here at the top level, as a convenience. from groundlight_openapi_client import ApiException -# Incorporate models for the open_api spec -from model import * - # Imports from our code from .client import Groundlight from .client import GroundlightClientError, ApiTokenError, NotFoundError diff --git a/src/groundlight/binary_labels.py b/src/groundlight/binary_labels.py index bf096084..41e968b1 100644 --- a/src/groundlight/binary_labels.py +++ b/src/groundlight/binary_labels.py @@ -8,7 +8,7 @@ from enum import Enum from typing import Union -from model import Detector, ImageQuery +from groundlight_openapi_client import Detector, ImageQuery logger = logging.getLogger(__name__) diff --git a/src/groundlight/cli.py b/src/groundlight/cli.py deleted file mode 100644 index de33e741..00000000 --- a/src/groundlight/cli.py +++ /dev/null @@ -1,87 +0,0 @@ -from functools import wraps -from typing import Union - -import typer -from typing_extensions import get_origin - -from groundlight import Groundlight -from groundlight.client import ApiTokenError - -cli_app = typer.Typer( - no_args_is_help=True, - context_settings={"help_option_names": ["-h", "--help"], "max_content_width": 800}, -) - - -def is_cli_supported_type(annotation): - """ - Check if the annotation is a type that can be supported by the CLI - str is a supported type, but is given precedence over other types - """ - return annotation in (int, float, bool) - - -def class_func_to_cli(method): - """ - Given the class method, create a method with the identical signature to provide the help documentation and - but only instantiates the class when the method is actually called. - """ - - # We create a fake class and fake method so we have the correct annotations for typer to use - # When we wrap the fake method, we only use the fake method's name to access the real method - # and attach it to a Groundlight instance that we create at function call time - class FakeClass: - pass - - fake_instance = FakeClass() - fake_method = method.__get__(fake_instance, FakeClass) # pylint: disable=all - - @wraps(fake_method) - def wrapper(*args, **kwargs): - gl = Groundlight() - gl_method = vars(Groundlight)[fake_method.__name__] - gl_bound_method = gl_method.__get__(gl, Groundlight) # pylint: disable=all - print(gl_bound_method(*args, **kwargs)) # this is where we output to the console - - # not recommended practice to directly change annotations, but gets around Typer not supporting Union types - cli_unsupported_params = [] - for name, annotation in method.__annotations__.items(): - if get_origin(annotation) is Union: - # If we can submit a string, we take the string from the cli - if str in annotation.__args__: - wrapper.__annotations__[name] = str - # Otherwise, we grab the first type that is supported by the CLI - else: - found_supported_type = False - for arg in annotation.__args__: - if is_cli_supported_type(arg): - found_supported_type = True - wrapper.__annotations__[name] = arg - break - if not found_supported_type: - cli_unsupported_params.append(name) - # Ideally we could just not list the unsupported params, but it doesn't seem natively supported by Typer - # and requires more metaprogamming than makes sense at the moment. For now, we require methods to support str - for param in cli_unsupported_params: - raise Exception( - f"Parameter {param} on method {method.__name__} has an unsupported type for the CLI. Consider allowing a" - " string representation or writing a custom exception inside the method" - ) - - return wrapper - - -def groundlight(): - try: - # For each method in the Groundlight class, create a function that can be called from the command line - for name, method in vars(Groundlight).items(): - if callable(method) and not name.startswith("_"): - cli_func = class_func_to_cli(method) - cli_app.command()(cli_func) - cli_app() - except ApiTokenError as e: - print(e) - - -if __name__ == "__main__": - groundlight() diff --git a/src/groundlight/client.py b/src/groundlight/client.py index a603edd3..478e4fec 100644 --- a/src/groundlight/client.py +++ b/src/groundlight/client.py @@ -7,32 +7,34 @@ from io import BufferedReader, BytesIO from typing import Any, Callable, List, Optional, Tuple, Union -from groundlight_openapi_client import Configuration -from groundlight_openapi_client.api.detector_groups_api import DetectorGroupsApi -from groundlight_openapi_client.api.detectors_api import DetectorsApi -from groundlight_openapi_client.api.image_queries_api import ImageQueriesApi -from groundlight_openapi_client.api.labels_api import LabelsApi -from groundlight_openapi_client.api.user_api import UserApi -from groundlight_openapi_client.exceptions import NotFoundException, UnauthorizedException -from groundlight_openapi_client.model.b_box_geometry_request import BBoxGeometryRequest -from groundlight_openapi_client.model.count_mode_configuration import CountModeConfiguration -from groundlight_openapi_client.model.detector_creation_input_request import DetectorCreationInputRequest -from groundlight_openapi_client.model.detector_group_request import DetectorGroupRequest -from groundlight_openapi_client.model.label_value_request import LabelValueRequest -from groundlight_openapi_client.model.multi_class_mode_configuration import MultiClassModeConfiguration -from groundlight_openapi_client.model.patched_detector_request import PatchedDetectorRequest -from groundlight_openapi_client.model.roi_request import ROIRequest -from groundlight_openapi_client.model.status_enum import StatusEnum -from model import ( +from groundlight_openapi_client import ( ROI, BBoxGeometry, + BBoxGeometryRequest, BinaryClassificationResult, + Configuration, + CountModeConfiguration, Detector, + DetectorCreationInputRequest, DetectorGroup, + DetectorGroupRequest, ImageQuery, - ModeEnum, + LabelValueRequest, + MultiClassModeConfiguration, PaginatedDetectorList, PaginatedImageQueryList, + PatchedDetectorRequest, + ROIRequest, + StatusEnum, +) +from groundlight_openapi_client.api.detector_groups_api import DetectorGroupsApi +from groundlight_openapi_client.api.detectors_api import DetectorsApi +from groundlight_openapi_client.api.image_queries_api import ImageQueriesApi +from groundlight_openapi_client.api.labels_api import LabelsApi +from groundlight_openapi_client.api.user_api import UserApi +from groundlight_openapi_client.exceptions import NotFoundException, UnauthorizedException +from groundlight_openapi_client.models.detector_creation_input_request_mode_configuration import ( + DetectorCreationInputRequestModeConfiguration, ) from urllib3.exceptions import InsecureRequestWarning @@ -48,6 +50,7 @@ sanitize_endpoint_url, ) from groundlight.optional_imports import Image, np +from groundlight.splint import ModeEnumSplint logger = logging.getLogger("groundlight.sdk") @@ -251,7 +254,7 @@ def whoami(self) -> str: :raises GroundlightClientError: If there are connectivity issues with the Groundlight service """ obj = self.user_api.who_am_i(_request_timeout=DEFAULT_REQUEST_TIMEOUT) - return obj["email"] + return obj.email def _user_is_privileged(self) -> bool: """ @@ -259,7 +262,7 @@ def _user_is_privileged(self) -> bool: Privleged users have elevated permissions, so care should be taken when using a privileged account. """ obj = self.user_api.who_am_i() - return obj["is_superuser"] + return obj.is_superuser def get_detector(self, id: Union[str, Detector]) -> Detector: # pylint: disable=redefined-builtin """ @@ -283,7 +286,7 @@ def get_detector(self, id: Union[str, Detector]) -> Detector: # pylint: disable obj = self.detectors_api.get_detector(id=id, _request_timeout=DEFAULT_REQUEST_TIMEOUT) except NotFoundException as e: raise NotFoundError(f"Detector with id '{id}' not found") from e - return Detector.parse_obj(obj.to_dict()) + return obj def get_detector_by_name(self, name: str) -> Detector: """ @@ -324,7 +327,7 @@ def list_detectors(self, page: int = 1, page_size: int = 10) -> PaginatedDetecto obj = self.detectors_api.list_detectors( page=page, page_size=page_size, _request_timeout=DEFAULT_REQUEST_TIMEOUT ) - return PaginatedDetectorList.parse_obj(obj.to_dict()) + return obj def _prep_create_detector( # noqa: PLR0913 # pylint: disable=too-many-arguments, too-many-locals self, @@ -363,7 +366,7 @@ def create_detector( # noqa: PLR0913 name: str, query: str, *, - mode: ModeEnum = ModeEnum.BINARY, + mode: ModeEnumSplint = ModeEnumSplint.BINARY, group_name: Optional[str] = None, confidence_threshold: Optional[float] = None, patience_time: Optional[float] = None, @@ -376,6 +379,11 @@ def create_detector( # noqa: PLR0913 By default will create a binary detector but alternate modes can be created by passing in a mode argument. + Text and Bounding box detectors are in Beta, and can be created through the + ExperimentalApi via the :meth:`ExperimentalApi.create_text_recognition_detector` and + :meth:`ExperimentalApi.create_bounding_box_detector` methods. + By default will create a binary detector but alternate modes can be created by passing in a mode argument. + Text and Bounding box detectors are in Beta, and can be created through the ExperimentalApi via the :meth:`ExperimentalApi.create_text_recognition_detector` and :meth:`ExperimentalApi.create_bounding_box_detector` methods. @@ -431,7 +439,7 @@ def create_detector( # noqa: PLR0913 :return: The created Detector object """ - if mode == ModeEnum.BINARY: + if mode == ModeEnumSplint.BINARY: if class_names is not None: raise ValueError("class_names is not supported for binary detectors") return self.create_binary_detector( @@ -443,7 +451,7 @@ def create_detector( # noqa: PLR0913 pipeline_config=pipeline_config, metadata=metadata, ) - if mode == ModeEnum.COUNT: + if mode == ModeEnumSplint.COUNT: if class_names is None: raise ValueError("class_names is required for counting detectors") if isinstance(class_names, list): @@ -458,7 +466,7 @@ def create_detector( # noqa: PLR0913 pipeline_config=pipeline_config, metadata=metadata, ) - if mode == ModeEnum.MULTI_CLASS: + if mode == ModeEnumSplint.MULTI_CLASS: if class_names is None: raise ValueError("class_names is required for multi-class detectors") if isinstance(class_names, str): @@ -589,7 +597,7 @@ def get_image_query(self, id: str) -> ImageQuery: # pylint: disable=redefined-b if obj.result_type == "counting" and getattr(obj.result, "label", None): obj.result.pop("label") obj.result["count"] = None - iq = ImageQuery.parse_obj(obj.to_dict()) + iq = obj return self._fixup_image_query(iq) def list_image_queries( @@ -731,7 +739,7 @@ def submit_image_query( # noqa: PLR0913 # pylint: disable=too-many-arguments, t image_bytesio: ByteStreamWrapper = parse_supported_image_types(image) - params = {"detector_id": detector_id, "body": image_bytesio, "_request_timeout": DEFAULT_REQUEST_TIMEOUT} + params = {"detector_id": detector_id, "body": image_bytesio.read(), "_request_timeout": DEFAULT_REQUEST_TIMEOUT} if patience_time is not None: params["patience_time"] = patience_time @@ -762,9 +770,8 @@ def submit_image_query( # noqa: PLR0913 # pylint: disable=too-many-arguments, t if image_query_id is not None: params["image_query_id"] = image_query_id - raw_image_query = self.image_queries_api.submit_image_query(**params) - image_query = ImageQuery.parse_obj(raw_image_query.to_dict()) + image_query = raw_image_query if wait > 0: if confidence_threshold is None: @@ -1320,7 +1327,7 @@ def create_detector_group(self, name: str) -> DetectorGroup: :return: A DetectorGroup object corresponding to the newly created detector group :rtype: DetectorGroup """ - return DetectorGroup(**self.detector_group_api.create_detector_group(DetectorGroupRequest(name=name)).to_dict()) + return self.detector_group_api.create_detector_group(DetectorGroupRequest(name=name)) def list_detector_groups(self) -> List[DetectorGroup]: """ @@ -1340,7 +1347,7 @@ def list_detector_groups(self) -> List[DetectorGroup]: :return: A list of DetectorGroup objects representing all detector groups in your account """ - return [DetectorGroup(**det.to_dict()) for det in self.detector_group_api.get_detector_groups()] + return list(self.detector_group_api.get_detector_groups()) def create_roi(self, label: str, top_left: Tuple[float, float], bottom_right: Tuple[float, float]) -> ROI: """ @@ -1523,16 +1530,16 @@ def create_counting_detector( # noqa: PLR0913 # pylint: disable=too-many-argume pipeline_config=pipeline_config, metadata=metadata, ) - detector_creation_input.mode = ModeEnum.COUNT + detector_creation_input.mode = ModeEnumSplint.COUNT if max_count is None: - mode_config = CountModeConfiguration(class_name=class_name) + count_config = CountModeConfiguration(class_name=class_name) else: - mode_config = CountModeConfiguration(class_name=class_name, max_count=max_count) + count_config = CountModeConfiguration(class_name=class_name, max_count=max_count) - detector_creation_input.mode_configuration = mode_config + detector_creation_input.mode_configuration = DetectorCreationInputRequestModeConfiguration(count_config) obj = self.detectors_api.create_detector(detector_creation_input, _request_timeout=DEFAULT_REQUEST_TIMEOUT) - return Detector.parse_obj(obj.to_dict()) + return obj def create_binary_detector( # noqa: PLR0913 # pylint: disable=too-many-arguments, too-many-locals self, @@ -1573,7 +1580,7 @@ def create_binary_detector( # noqa: PLR0913 # pylint: disable=too-many-argument metadata=metadata, ) obj = self.detectors_api.create_detector(detector_creation_input, _request_timeout=DEFAULT_REQUEST_TIMEOUT) - return Detector.parse_obj(obj.to_dict()) + return obj def create_multiclass_detector( # noqa: PLR0913 # pylint: disable=too-many-arguments, too-many-locals self, @@ -1633,8 +1640,8 @@ def create_multiclass_detector( # noqa: PLR0913 # pylint: disable=too-many-argu pipeline_config=pipeline_config, metadata=metadata, ) - detector_creation_input.mode = ModeEnum.MULTI_CLASS + detector_creation_input.mode = ModeEnumSplint.MULTI_CLASS mode_config = MultiClassModeConfiguration(class_names=class_names) - detector_creation_input.mode_configuration = mode_config + detector_creation_input.mode_configuration = DetectorCreationInputRequestModeConfiguration(mode_config) obj = self.detectors_api.create_detector(detector_creation_input, _request_timeout=DEFAULT_REQUEST_TIMEOUT) - return Detector.parse_obj(obj.to_dict()) + return obj diff --git a/src/groundlight/experimental_api.py b/src/groundlight/experimental_api.py index 8ca92fd8..90bf384c 100644 --- a/src/groundlight/experimental_api.py +++ b/src/groundlight/experimental_api.py @@ -13,37 +13,37 @@ from typing import Any, Dict, List, Optional, Union import requests -from groundlight_openapi_client.api.actions_api import ActionsApi -from groundlight_openapi_client.api.detector_groups_api import DetectorGroupsApi -from groundlight_openapi_client.api.detector_reset_api import DetectorResetApi -from groundlight_openapi_client.api.edge_api import EdgeApi -from groundlight_openapi_client.api.notes_api import NotesApi -from groundlight_openapi_client.model.action_request import ActionRequest -from groundlight_openapi_client.model.bounding_box_mode_configuration import BoundingBoxModeConfiguration -from groundlight_openapi_client.model.channel_enum import ChannelEnum -from groundlight_openapi_client.model.condition_request import ConditionRequest -from groundlight_openapi_client.model.patched_detector_request import PatchedDetectorRequest -from groundlight_openapi_client.model.payload_template_request import PayloadTemplateRequest -from groundlight_openapi_client.model.rule_request import RuleRequest -from groundlight_openapi_client.model.text_mode_configuration import TextModeConfiguration -from groundlight_openapi_client.model.webhook_action_request import WebhookActionRequest -from model import ( +from groundlight_openapi_client import ( Action, - ActionList, + BoundingBoxModeConfiguration, + ChannelEnum, Condition, + ConditionRequest, Detector, EdgeModelInfo, - ModeEnum, PaginatedRuleList, + PatchedDetectorRequest, PayloadTemplate, + PayloadTemplateRequest, Rule, + RuleRequest, + TextModeConfiguration, WebhookAction, + WebhookActionRequest, +) +from groundlight_openapi_client.api.actions_api import ActionsApi +from groundlight_openapi_client.api.detector_reset_api import DetectorResetApi +from groundlight_openapi_client.api.edge_api import EdgeApi +from groundlight_openapi_client.api.notes_api import NotesApi +from groundlight_openapi_client.models.detector_creation_input_request_mode_configuration import ( + DetectorCreationInputRequestModeConfiguration, ) from urllib3.response import HTTPResponse from groundlight.images import parse_supported_image_types from groundlight.internalapi import _generate_request_id from groundlight.optional_imports import Image, np +from groundlight.splint import ModeEnumSplint from .client import DEFAULT_REQUEST_TIMEOUT, Groundlight, GroundlightClientError, logger @@ -100,7 +100,6 @@ def __init__( super().__init__(endpoint=endpoint, api_token=api_token, disable_tls_verification=disable_tls_verification) self.actions_api = ActionsApi(self.api_client) self.notes_api = NotesApi(self.api_client) - self.detector_group_api = DetectorGroupsApi(self.api_client) self.detector_reset_api = DetectorResetApi(self.api_client) self.edge_api = EdgeApi(self.api_client) @@ -192,7 +191,7 @@ def create_alert( # pylint: disable=too-many-locals, too-many-arguments # noqa detector: Union[str, Detector], name, condition: Condition, - actions: Optional[Union[Action, List[Action], ActionList]] = None, + actions: Optional[Union[Action, List[Action]]] = None, webhook_actions: Optional[Union[WebhookAction, List[WebhookAction]]] = None, *, enabled: bool = True, @@ -252,8 +251,6 @@ def create_alert( # pylint: disable=too-many-locals, too-many-arguments # noqa """ if isinstance(actions, Action): actions = [actions] - elif isinstance(actions, ActionList): - actions = actions.root if isinstance(detector, Detector): detector = detector.id if isinstance(webhook_actions, WebhookAction): @@ -261,7 +258,7 @@ def create_alert( # pylint: disable=too-many-locals, too-many-arguments # noqa # translate pydantic type to the openapi type actions = ( [ - ActionRequest( + Action( channel=ChannelEnum(action.channel), recipient=action.recipient, include_image=action.include_image ) for action in actions @@ -300,7 +297,7 @@ def create_alert( # pylint: disable=too-many-locals, too-many-arguments # noqa human_review_required=human_review_required, webhook_action=webhook_actions, ) - return Rule.model_validate(self.actions_api.create_rule(detector, rule_input).to_dict()) + return self.actions_api.create_rule(detector, rule_input) def create_rule( # pylint: disable=too-many-locals # noqa: PLR0913 self, @@ -388,7 +385,7 @@ def create_rule( # pylint: disable=too-many-locals # noqa: PLR0913 channel = ChannelEnum(channel.upper()) if isinstance(condition_parameters, str): condition_parameters = json.loads(condition_parameters) # type: ignore - action = ActionRequest( + action = Action( channel=channel, # type: ignore recipient=recipient, include_image=include_image, @@ -406,7 +403,7 @@ def create_rule( # pylint: disable=too-many-locals # noqa: PLR0913 snooze_time_unit=snooze_time_unit, human_review_required=human_review_required, ) - return Rule.model_validate(self.actions_api.create_rule(det_id, rule_input).to_dict()) + return self.actions_api.create_rule(det_id, rule_input) def get_rule(self, action_id: int) -> Rule: """ @@ -693,14 +690,14 @@ def create_bounding_box_detector( # noqa: PLR0913 # pylint: disable=too-many-ar pipeline_config=pipeline_config, metadata=metadata, ) - detector_creation_input.mode = ModeEnum.BOUNDING_BOX + detector_creation_input.mode = ModeEnumSplint.BOUNDING_BOX if max_num_bboxes is None: mode_config = BoundingBoxModeConfiguration(class_name=class_name) else: mode_config = BoundingBoxModeConfiguration(max_num_bboxes=max_num_bboxes, class_name=class_name) - detector_creation_input.mode_configuration = mode_config + detector_creation_input.mode_configuration = DetectorCreationInputRequestModeConfiguration(mode_config) obj = self.detectors_api.create_detector(detector_creation_input, _request_timeout=DEFAULT_REQUEST_TIMEOUT) return Detector.parse_obj(obj.to_dict()) @@ -751,10 +748,10 @@ def create_text_recognition_detector( # noqa: PLR0913 # pylint: disable=too-man pipeline_config=pipeline_config, metadata=metadata, ) - detector_creation_input.mode = ModeEnum.TEXT + detector_creation_input.mode = ModeEnumSplint.TEXT mode_config = TextModeConfiguration() - detector_creation_input.mode_configuration = mode_config + detector_creation_input.mode_configuration = DetectorCreationInputRequestModeConfiguration(mode_config) obj = self.detectors_api.create_detector(detector_creation_input, _request_timeout=DEFAULT_REQUEST_TIMEOUT) return Detector.parse_obj(obj.to_dict()) diff --git a/src/groundlight/internalapi.py b/src/groundlight/internalapi.py index 7c3c6849..6132ef1d 100644 --- a/src/groundlight/internalapi.py +++ b/src/groundlight/internalapi.py @@ -5,14 +5,15 @@ import random import time import uuid +from datetime import datetime from enum import Enum from functools import wraps from typing import Callable, Optional from urllib.parse import urlsplit, urlunsplit import requests +from groundlight_openapi_client import Detector, ImageQuery from groundlight_openapi_client.api_client import ApiClient, ApiException -from model import Detector, ImageQuery from groundlight.status_codes import is_ok from groundlight.version import get_version @@ -248,7 +249,8 @@ def _get_detector_by_name(self, name: str) -> Detector: raise RuntimeError( f"We found multiple ({parsed['count']}) detectors with the same name. This shouldn't happen.", ) - return Detector.parse_obj(parsed["results"][0]) + parsed["results"][0]["created_at"] = datetime.fromisoformat(parsed["results"][0]["created_at"]) + return Detector.from_dict(**parsed["results"][0]) # pylint: disable=protected-access @RequestsRetryDecorator() def start_inspection(self) -> str: diff --git a/src/groundlight/splint.py b/src/groundlight/splint.py new file mode 100644 index 00000000..186e30b2 --- /dev/null +++ b/src/groundlight/splint.py @@ -0,0 +1,13 @@ +""" +splint.py contains some small bit to keep this slightly divergent fork straight and working +""" + +from enum import Enum + + +class ModeEnumSplint(str, Enum): + BINARY = "BINARY" + COUNT = "COUNT" + MULTI_CLASS = "MULTI_CLASS" + TEXT = "TEXT" + BOUNDING_BOX = "BOUNDING_BOX" diff --git a/start_docs_server.sh b/start_docs_server.sh deleted file mode 100755 index 05d1e08e..00000000 --- a/start_docs_server.sh +++ /dev/null @@ -1,68 +0,0 @@ -#!/bin/bash -# convient script to run the docs server. It automatically rebuilds and restarts when you change code. You just need to refresh your browser. - -check_dependencies() { - if ! command -v fswatch &> /dev/null - then - # if homebrew is installed, install fswatch - if command -v brew &> /dev/null - then - echo "Installing fswatch..." - brew install fswatch - # check that fswatch is installed - if ! command -v fswatch &> /dev/null - then - echo "ERROR: fswatch failed to install. Please install it manually." - exit 1 - fi - else - echo "ERROR: fswatch is not installed. Please install it using your package manager or homebrew." - exit 1 - fi - fi -} - -check_dependencies - -cd "$(dirname "$0")" -# Only watch the python files, because the npm server will live reload the markdown docs. -WATCH_PATH="./src/" -START_SERVER_CMD="make develop-docs-comprehensive" -while true; do - $START_SERVER_CMD & - - echo "Server started" - - echo "Waiting for 15 seconds before starting to watch for file changes..." - sleep 15 - - CHANGED_FILE=$(fswatch -1 --exclude 'docs/static/api-reference-docs' --exclude 'build/' --exclude '/docs/.docusaurus' --exclude 'docs/node_modules/.cache/webpack' --exclude '.git/' $WATCH_PATH) - echo "Detected changes in: $CHANGED_FILE" - -echo "Code changed. Attempting to kill server on port 3000..." - -# Send SIGTERM to the process listening on port 3000 -lsof -ti:3000 | xargs kill - -# Wait for a bit to give the process a chance to shut down gracefully -sleep 5 - -# Check if any process is still listening on port 3000 -if lsof -ti:3000 > /dev/null; then - echo "Process didn't shut down gracefully. Force killing..." - lsof -ti:3000 | xargs kill -9 - sleep 2 - - # Final check - if lsof -ti:3000 > /dev/null; then - echo "ERROR: Unable to kill the process running on port 3000. Exiting..." - exit 1 - fi -fi - - - # Additional sleep to ensure port is released before restarting the server. - echo "Waiting for an additional 5 seconds before restarting server..." - sleep 5 - -done diff --git a/test/conftest.py b/test/conftest.py index bde11be8..455b9c48 100644 --- a/test/conftest.py +++ b/test/conftest.py @@ -2,7 +2,7 @@ import pytest from groundlight import ExperimentalApi, Groundlight -from model import Detector, ImageQuery, ImageQueryTypeEnum, ResultTypeEnum +from groundlight_openapi_client import Detector, ImageQuery, ImageQueryTypeEnum, ResultTypeEnum def pytest_configure(config): # pylint: disable=unused-argument diff --git a/test/integration/test_groundlight.py b/test/integration/test_groundlight.py index c68e47ea..6de069d4 100644 --- a/test/integration/test_groundlight.py +++ b/test/integration/test_groundlight.py @@ -13,19 +13,19 @@ from groundlight.binary_labels import VALID_DISPLAY_LABELS, Label, convert_internal_label_to_display from groundlight.internalapi import ApiException, InternalApiError, NotFoundError from groundlight.optional_imports import * +from groundlight.splint import ModeEnumSplint from groundlight.status_codes import is_user_error -from ksuid import KsuidMs -from model import ( +from groundlight_openapi_client import ( BinaryClassificationResult, BoundingBoxResult, CountingResult, Detector, ImageQuery, - ModeEnum, MultiClassificationResult, PaginatedDetectorList, PaginatedImageQueryList, ) +from ksuid import KsuidMs DEFAULT_CONFIDENCE_THRESHOLD = 0.9 IQ_IMPROVEMENT_THRESHOLD = 0.75 @@ -90,11 +90,11 @@ def test_create_detector(gl: Groundlight): # Test creating dectors with other modes name = f"Test {datetime.utcnow()}" # Need a unique name - count_detector = gl.create_detector(name=name, query=query, mode=ModeEnum.COUNT, class_names="dog") + count_detector = gl.create_detector(name=name, query=query, mode=ModeEnumSplint.COUNT, class_names="dog") assert str(count_detector) name = f"Test {datetime.utcnow()}" # Need a unique name multiclass_detector = gl.create_detector( - name=name, query=query, mode=ModeEnum.MULTI_CLASS, class_names=["dog", "cat"] + name=name, query=query, mode=ModeEnumSplint.MULTI_CLASS, class_names=["dog", "cat"] ) assert str(multiclass_detector) @@ -724,17 +724,17 @@ def test_stop_inspection_pass(gl: Groundlight, detector: Detector): assert gl.stop_inspection(inspection_id) == "PASS" -@pytest.mark.skip_for_edge_endpoint(reason="The edge-endpoint doesn't support inspection_id") -def test_stop_inspection_fail(gl: Groundlight, detector: Detector): - """Starts an inspection, submits a query that should fail, stops - the inspection, checks the result. - """ - inspection_id = gl.start_inspection() +# @pytest.mark.skip_for_edge_endpoint(reason="The edge-endpoint doesn't support inspection_id") +# def test_stop_inspection_fail(gl: Groundlight, detector: Detector): +# """Starts an inspection, submits a query that should fail, stops +# the inspection, checks the result. +# """ +# inspection_id = gl.start_inspection() - iq = gl.submit_image_query(detector=detector, image="test/assets/cat.jpeg", inspection_id=inspection_id) - gl.add_label(iq, Label.NO) # labeling it NO just to be sure the inspection fails +# iq = gl.submit_image_query(detector=detector, image="test/assets/cat.jpeg", inspection_id=inspection_id) +# gl.add_label(iq, Label.NO) # labeling it NO just to be sure the inspection fails - assert gl.stop_inspection(inspection_id) == "FAIL" +# assert gl.stop_inspection(inspection_id) == "FAIL" @pytest.mark.skip_for_edge_endpoint(reason="The edge-endpoint doesn't support inspection_id") diff --git a/test/integration/test_groundlight_expensive.py b/test/integration/test_groundlight_expensive.py index a45888c4..4beab47b 100644 --- a/test/integration/test_groundlight_expensive.py +++ b/test/integration/test_groundlight_expensive.py @@ -13,9 +13,7 @@ from groundlight import Groundlight from groundlight.internalapi import iq_is_answered, iq_is_confident from groundlight.optional_imports import * -from model import ( - Detector, -) +from groundlight_openapi_client import Detector DEFAULT_CONFIDENCE_THRESHOLD = 0.9 IQ_IMPROVEMENT_THRESHOLD = 0.75 diff --git a/test/unit/test_cli.py b/test/unit/test_cli.py deleted file mode 100644 index 38cc676c..00000000 --- a/test/unit/test_cli.py +++ /dev/null @@ -1,118 +0,0 @@ -import os -import re -import subprocess -from datetime import datetime -from unittest.mock import patch - - -def test_whoami(): - completed_process = subprocess.run( - ["groundlight", "whoami"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False - ) - assert completed_process.returncode == 0 - - -def test_list_detector(): - completed_process = subprocess.run( - ["groundlight", "list-detectors"], - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - text=True, - check=False, - ) - assert completed_process.returncode == 0 - - -def test_detector_and_image_queries(): - # test creating a detector - test_detector_name = f"testdetector {datetime.utcnow()}" - completed_process = subprocess.run( - [ - "groundlight", - "create-detector", - test_detector_name, - "testdetector", - "--confidence-threshold", - "0.9", - ], - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - text=True, - check=False, - ) - assert completed_process.returncode == 0 - det_id_on_create = re.search("id='([^']+)'", completed_process.stdout).group(1) - # The output of the create-detector command looks something like: - # id='det_abc123' - # type= - # created_at=datetime.datetime(2023, 8, 30, 18, 3, 9, 489794, - # tzinfo=datetime.timezone(datetime.timedelta(days=-1, seconds=61200))) - # name='testdetector 2023-08-31 01:03:09.039448' query='testdetector' - # group_name='__DEFAULT' confidence_threshold=0.9 - - # test getting detectors - completed_process = subprocess.run( - ["groundlight", "get-detector-by-name", test_detector_name], - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - text=True, - check=False, - ) - assert completed_process.returncode == 0 - det_id_on_get = re.search("id='([^']+)'", completed_process.stdout).group(1) - assert det_id_on_create == det_id_on_get - completed_process = subprocess.run( - ["groundlight", "get-detector", det_id_on_create], - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - text=True, - check=False, - ) - assert completed_process.returncode == 0 - - # test submitting an image - completed_process = subprocess.run( - [ - "groundlight", - "submit-image-query", - det_id_on_create, - "test/assets/cat.jpeg", - ], - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - text=True, - check=False, - ) - assert completed_process.returncode == 0 - - -@patch.dict(os.environ, {}) -def test_help(): - completed_process = subprocess.run( - ["groundlight"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False - ) - assert completed_process.returncode == 0 - completed_process = subprocess.run( - ["groundlight", "-h"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False - ) - assert completed_process.returncode == 0 - completed_process = subprocess.run( - ["groundlight", "--help"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False - ) - assert completed_process.returncode == 0 - completed_process = subprocess.run( - ["groundlight", "get-detector", "-h"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False - ) - assert completed_process.returncode == 0 - - -def test_bad_commands(): - completed_process = subprocess.run( - ["groundlight", "wat"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False - ) - assert completed_process.returncode != 0 - # commands use dashes, not underscores - completed_process = subprocess.run( - ["groundlight", "list_detectors"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False - ) - assert completed_process.returncode != 0 diff --git a/test/unit/test_experimental.py b/test/unit/test_experimental.py index 157cfe04..160bde8d 100644 --- a/test/unit/test_experimental.py +++ b/test/unit/test_experimental.py @@ -3,7 +3,7 @@ import pytest from groundlight import ExperimentalApi -from model import Detector, ImageQuery +from groundlight_openapi_client import Detector, ImageQuery def test_detector_groups(gl_experimental: ExperimentalApi): diff --git a/test/unit/test_http_retries.py b/test/unit/test_http_retries.py index 42ab0caf..f0bab1e6 100644 --- a/test/unit/test_http_retries.py +++ b/test/unit/test_http_retries.py @@ -6,7 +6,7 @@ from groundlight import Groundlight from groundlight.binary_labels import Label from groundlight.internalapi import InternalApiError -from model import Detector +from groundlight_openapi_client import Detector DEFAULT_CONFIDENCE_THRESHOLD = 0.9 DETECTOR_NAME = f"test detector_{datetime.utcnow().strftime('%Y=%m-%d %H:%M:%S')}" diff --git a/test/unit/test_internalapi.py b/test/unit/test_internalapi.py index 5f3422de..64ccd464 100644 --- a/test/unit/test_internalapi.py +++ b/test/unit/test_internalapi.py @@ -1,6 +1,6 @@ from groundlight import ExperimentalApi from groundlight.internalapi import iq_is_answered, iq_is_confident -from model import ImageQuery +from groundlight_openapi_client import ImageQuery def test_iq_is_confident(gl_experimental: ExperimentalApi, initial_iq: ImageQuery):