Add patience_time and human_review Query Parameters to the Image Queries Endpoint (#79)

* use wait param to set iq patience time

* add human_review query parameter image queries endpoint

* Update spec/public-api.yaml

Co-authored-by: robotrapta <[email protected]>

* Update generated/openapi_client/api/image_queries_api.py

Co-authored-by: robotrapta <[email protected]>

---------

Co-authored-by: robotrapta <[email protected]>

Author: blaise-muhirwa

generated/docs/ImageQueriesApi.md

@@ -205,6 +205,8 @@ with 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.
+    human_review = True # bool | Allow image queries to be marked for no human review. (optional)
+    patience_time = 3.14 # float | How long to wait for a confident response. (optional)
     body = open('@path/to/image.jpeg', 'rb') # file_type |  (optional)
 
     # example passing only required values which don't have defaults set
@@ -217,7 +219,7 @@ with openapi_client.ApiClient(configuration) as api_client:
     # example passing only required values which don't have defaults set
     # and optional values
     try:
-        api_response = api_instance.submit_image_query(detector_id, body=body)
+        api_response = api_instance.submit_image_query(detector_id, human_review=human_review, patience_time=patience_time, body=body)
         pprint(api_response)
     except openapi_client.ApiException as e:
         print("Exception when calling ImageQueriesApi->submit_image_query: %s\n" % e)
@@ -229,6 +231,8 @@ with openapi_client.ApiClient(configuration) as api_client:
 Name | Type | Description  | Notes
 ------------- | ------------- | ------------- | -------------
  **detector_id** | **str**| Choose a detector by its ID. |
+ **human_review** | **bool**| Allow image queries to be marked for no human review. | [optional]
+ **patience_time** | **float**| How long to wait for a confident response. | [optional]
  **body** | **file_type**|  | [optional]
 
 ### Return type

generated/model.py

@@ -1,6 +1,6 @@
 # generated by datamodel-codegen:
 #   filename:  public-api.yaml
-#   timestamp: 2023-05-26T20:07:17+00:00
+#   timestamp: 2023-08-01T00:09:22+00:00
 
 from __future__ import annotations
 

generated/openapi_client/api/image_queries_api.py

@@ -131,6 +131,8 @@ def __init__(self, api_client=None):
             params_map={
                 "all": [
                     "detector_id",
+                    "human_review",
+                    "patience_time",
                     "body",
                 ],
                 "required": [
@@ -145,13 +147,19 @@ def __init__(self, api_client=None):
                 "allowed_values": {},
                 "openapi_types": {
                     "detector_id": (str,),
+                    "human_review": (bool,),
+                    "patience_time": (float,),
                     "body": (file_type,),
                 },
                 "attribute_map": {
                     "detector_id": "detector_id",
+                    "human_review": "human_review",
+                    "patience_time": "patience_time",
                 },
                 "location_map": {
                     "detector_id": "query",
+                    "human_review": "query",
+                    "patience_time": "query",
                     "body": "body",
                 },
                 "collection_format_map": {},
@@ -289,6 +297,8 @@ def submit_image_query(self, detector_id, **kwargs):
             detector_id (str): Choose a detector by its ID.
 
         Keyword Args:
+            human_review (bool): If set to `False` then unconfident ML predictions will not be escalated to human review.  [optional, defaults `True`]
+            patience_time (float): How long to wait for a confident response.. [optional]
             body (file_type): [optional]
             _return_http_data_only (bool): response data without head status
                 code and headers. Default is True.

spec/public-api.yaml

@@ -132,6 +132,19 @@ paths:
           type: string
         description: Choose a detector by its ID.
         required: true
+      - in: query 
+        name: human_review 
+        schema: 
+          type: boolean 
+        description: If set to `False` then unconfident ML predictions will not be escalated to human review.  (Defaults `True`) 
+        required: false 
+      - in: query 
+        name: patience_time 
+        schema: 
+          type: number 
+          format: float 
+        description: How long to wait for a confident response. 
+        required: false 
       tags:
       - image-queries
       requestBody:

src/groundlight/client.py

@@ -170,6 +170,7 @@ def submit_image_query(
         detector: Union[Detector, str],
         image: Union[str, bytes, Image.Image, BytesIO, BufferedReader, np.ndarray],
         wait: Optional[float] = None,
+        human_review: Optional[bool] = True,
     ) -> ImageQuery:
         """Evaluates an image with Groundlight.
         :param detector: the Detector object, or string id of a detector like `det_12345`
@@ -182,14 +183,17 @@ def submit_image_query(
           Any binary format must be JPEG-encoded already.  Any pixel format will get
           converted to JPEG at high quality before sending to service.
         :param wait: How long to wait (in seconds) for a confident answer.
+        :param human_review: If set to False, do not escalate for human review
         """
         if wait is None:
             wait = self.DEFAULT_WAIT
         detector_id = detector.id if isinstance(detector, Detector) else detector
 
         image_bytesio: ByteStreamWrapper = parse_supported_image_types(image)
 
-        raw_image_query = self.image_queries_api.submit_image_query(detector_id=detector_id, body=image_bytesio)
+        raw_image_query = self.image_queries_api.submit_image_query(
+            detector_id=detector_id, patience_time=wait, human_review=human_review, body=image_bytesio
+        )
         image_query = ImageQuery.parse_obj(raw_image_query.to_dict())
         if wait:
             threshold = self.get_detector(detector).confidence_threshold