Merge branch 'main' of https://github.com/microsoft/intelligence-toolkit

.dockerignore

@@ -19,4 +19,6 @@ coverage.xml
 .mypy_cache
 .pytest_cache
 .ruff_cache
-.venv
+.venv
+.streamlit/app_secrets.toml
+.streamlit/secrets.toml

.github/workflows/ci.yml

@@ -31,7 +31,10 @@ jobs:
       - name: Install Dependencies
         run: poetry install
 
-      - name: Tests
-        run: poetry run poe test
+      - name: Unit Tests
+        run: poetry run poe test_unit
+
+      - name: Smoke Tests
+        run: poetry run poe test_smoke
 
 

.vsts-ci.yml

@@ -70,11 +70,17 @@ stages:
               targetType: "inline"
               script: poetry install
           - task: Bash@3
-            displayName: Run Tests
+            displayName: Run Unit Tests
             inputs:
               workingDirectory: ./
               targetType: "inline"
-              script: poetry run poe test
+              script: poetry run poe test_unit
+          - task: Bash@3
+            displayName: Run Smoke Tests
+            inputs:
+              workingDirectory: ./
+              targetType: "inline"
+              script: poetry run poe test_smoke
 
       - job: buildAndPush
         displayName: BuildAndPushContainer

DEPLOYING.md

@@ -0,0 +1,130 @@
+# Deploying the app
+
+#### Recommended configuration:
+
+- *Minimum disk space*: 8GB 
+
+- *Minimum memory*: 4GB
+    - If too many users using at the same time, it might need to be higher.
+
+## Azure
+
+You can modify the code and deploy the container, or use our default container hosted on ghcr.io.
+
+### Deploying your container
+#### TODO
+
+### Using ghcr.io
+
+**Prerequisites**
+
+- Azure Account: Ensure you have an [active Azure subscription](https://azure.microsoft.com/en-us/pricing/purchase-options/azure-account?msockid=1e4bc940d7cf6738158eda91d616667e).
+
+- Terraform: Install Terraform on your local machine. You can download it from [terraform.io](https://developer.hashicorp.com/terraform/install?product_intent=terraform).
+
+- Azure CLI: Install Azure CLI. You can download it from [docs.microsoft.com](https://learn.microsoft.com/en-us/cli/azure/install-azure-cli).
+
+**Steps**
+
+1. Set Up Azure CLI
+    Login to Azure:
+
+    `az login` 
+
+    This will open a browser window for you to authenticate.
+    Set the Subscription:
+    If you have multiple subscriptions, set the one you wish to use:
+
+    `az account set --subscription "your-subscription-id"`
+
+    Create a Directory for Your Project:
+
+    ```
+    mkdir my-terraform-app  
+    cd my-terraform-app  
+    ```
+ 
+2. Use our Terraform Configuration File:
+    Download the [terraform configuration file here](https://github.com/microsoft/intelligence-toolkit/blob/main/deploy/azure/main.tf)
+
+
+    -Modify the `variables` default field to match your desired resource configuration
+
+        - az_webapp_name (change necessary. Should be unique within Global Azure)
+
+        - az_rg_name (change optional)
+
+        - location (change optional)
+
+        - az_asp_name (change optional)
+
+3. Initialize Terraform
+
+
+    `terraform init`
+    This command downloads the Azure provider and sets up your workspace.
+
+4. Create an Execution Plan
+ 
+    Plan the Deployment:
+
+    `terraform plan` 
+    This command creates an execution plan, which lets you preview the changes that Terraform will make to your infrastructure.
+
+5. Apply the Execution Plan
+
+    Deploy the Resources:
+
+    `terraform apply` 
+    Terraform will prompt you for confirmation before making any changes. Type yes and press Enter.
+
+6. Verify Deployment
+
+    Check the Resources in Azure Portal:
+    Go to the Azure Portal and verify that the resources have been created.
+
+    Check the deployed URL:
+
+`<az_webapp_name>.azurewebsites.net`
+
+## AWS
+
+Wait for step 1 to be set as complete before starting step 2. The whole process will take up to 20 minutes.
+
+1. Launch the infrastructure deploy:
+
+    - Give it a sugestive name since you'll be using it in the next step.
+
+    [![launch-stack](https://s3.amazonaws.com/cloudformation-examples/cloudformation-launch-stack.png)](https://console.aws.amazon.com/cloudformation/home?region=us-east-1#/stacks/new?stackName=itk-infra-stack&templateURL=https://s3.us-east-1.amazonaws.com/cf-templates-19n482mly1fba-us-east-1/2024-10-07T124926.165Z3xc-infrastructure.yaml)
+
+2. Launch the code deploy
+    - In VPC Configuration, you should select the resources created by the previous step: <u>VPCId, PublicSubnetAId, PublicSubnetBId, PrivateSubnetAId, PrivateSubnetBId</u>
+
+    [![launch-stack](https://s3.amazonaws.com/cloudformation-examples/cloudformation-launch-stack.png)](https://console.aws.amazon.com/cloudformation/home?region=us-east-1#/stacks/new?stackName=itk-code-stack&templateURL=https://s3.us-east-1.amazonaws.com/cf-templates-19n482mly1fba-us-east-1/2024-10-07T125858.730Zlsu-2-development.yaml)
+
+
+Once step 2 it's complete, in the output tab, you'll see the deployed URL.
+
+**Note: This code doesn't have auth, so this URL will be open to the internet.**
+
+## Environment configuration
+
+`MODE: CLOUD` to hide Settings page so users can't change configuration that would affect other users experiences.
+
+`AUTH_ENABLED: TRUE` if you would like to limit access into the app by user and password defined in a .secrets.toml file inside .streamlit:
+
+```
+[passwords]
+user_test = "user123"
+...
+```
+
+Inserting secrets.toml file to the web app:
+
+Add a value of user=password separating each user by `;` to your web app environment.
+
+`USER_CREDENTIALS="user1=pass1;user2=pass2"`
+
+
+
+

DEVELOPING.md

@@ -15,7 +15,6 @@
 
 ## Running the app
 
-
 ## GPT settings
 
 You can configure your OpenAI access when running the app via `Settings page`, or using environment variables.
@@ -104,8 +103,8 @@ See the documentation and an example of how to run the code with your data to ob
 
 ##### Recommended configuration:
 
-- *Minimum disk space*: 10GB 
-- *Minimum memory*: 8GB
+- *Minimum disk space*: 8GB 
+- *Minimum memory*: 4GB
 
 Download, install and then open docker app: https://www.docker.com/products/docker-desktop/
 
@@ -122,65 +121,28 @@ Use `cd `+ the path to the folder. For example:
 
 `cd C:\Users\user01\projects\intelligence-toolkit`
 
-Pull the latest built image:
-` docker pull ghcr.io/microsoft/intelligence-toolkit:latest` 
-
-or build it with your own code:
+Build the container:
 
 `docker build . -t intelligence-toolkit`
 
-Once the pull/build ir fininshed, run the docker container:
+Once the build is finished, run the docker container:
 
-- via shell:
+- via terminal:
 
-    `docker run  -d -p 80:80 intelligence-toolkit --name intelligence-toolkit`
+    `docker run -d --name intelligence-toolkit -p 80:80 intelligence-toolkit`
 
 Open [localhost:80](http://localhost:80)
 
-## Deploying 
-
-#### Recommended configuration:
-
-- *Minimum disk space*: 10GB 
-
-- *Minimum memory*: 8GB
-    - If too many users using at the same time, it might need to be higher.
-
-
-### Using AWS
-
-Wait for step 1 to be set as complete before starting step 2. The whole process will take up to 20 minutes.
-
-1. Launch the infrastructure deploy:
-
-    - Give it a sugestive name since you'll be using it in the next step.
-
-    [![launch-stack](https://s3.amazonaws.com/cloudformation-examples/cloudformation-launch-stack.png)](https://console.aws.amazon.com/cloudformation/home?region=us-east-1#/stacks/new?stackName=itk-infra-stack&templateURL=https://s3.us-east-1.amazonaws.com/cf-templates-19n482mly1fba-us-east-1/2024-10-07T124926.165Z3xc-infrastructure.yaml)
-
-2. Launch the code deploy
-    - In VPC Configuration, you should select the resources created by the previous step: <u>VPCId, PublicSubnetAId, PublicSubnetBId, PrivateSubnetAId, PrivateSubnetBId</u>
-
-    [![launch-stack](https://s3.amazonaws.com/cloudformation-examples/cloudformation-launch-stack.png)](https://console.aws.amazon.com/cloudformation/home?region=us-east-1#/stacks/new?stackName=itk-code-stack&templateURL=https://s3.us-east-1.amazonaws.com/cf-templates-19n482mly1fba-us-east-1/2024-10-07T125858.730Zlsu-2-development.yaml)
-
-
-Once step 2 it's complete, in the output tab, you'll see the deployed URL.
-
-**Note: This code doesn't have auth, so this URL will be open to the internet.**
-
-### Using Azure
-
-In [this tutorial](https://dev.to/keneojiteli/deploy-a-docker-app-to-app-services-on-azure-5d3h), you can learn how to create the necessary services in azure.
-
-From there, you can deploy it manually as described, or use [our YAML file](/.vsts-ci.yml) to automatically deploy to your environment. 
-
+  **Note that docker might sleep and you might need to start it again. Open Docker Desktop, in the left menu click on Container and press play on intelligence-toolkit.**
 
 # Lifecycle Scripts
 
 For Lifecycle scripts it utilizes [Poetry](https://python-poetry.org/docs#installation) and [poethepoet](https://pypi.org/project/poethepoet/) to manage build scripts.
 
 Available scripts are:
 
-- `poetry run poe test` - This will execute unit tests.
+- `poetry run poe test_unit` - This will execute unit tests on api.
+- `poetry run poe test_smoke` - This will execute smoke tests on api.
 - `poetry run poe check` - This will perform a suite of static checks across the package, including:
   - formatting
   - documentation formatting

Dockerfile

@@ -12,9 +12,8 @@ RUN curl -fsSL https://packages.microsoft.com/keys/microsoft.asc | gpg --dearmor
 ENV PATH="/root/.local/bin:$PATH"
 
 COPY . .
-RUN rm -rf .streamlit/app_secrets.toml
 RUN poetry install --only main
 
 # Run application
 EXPOSE 80
-ENTRYPOINT ["poetry", "run", "poe", "run_streamlit", "--server.port=80"]
+ENTRYPOINT ["poetry", "run", "poe", "run_streamlit", "--server.port=80", "--server.address=0.0.0.0"]

README.md

@@ -150,9 +150,9 @@ flowchart TD
 
 ### NGOs
 
-Non-profit organizations can apply for an annual Azure credit grant of up to $3,500, which can be used to set up and run an instance of the intelligence-toolkit app for your organization.
+Non-profit organizations can apply for an annual Azure credit grant of $2,000, which can be used to set up and run an instance of the intelligence-toolkit app for your organization.
 
-[Read more about eligibility and registration here](https://nonprofit.microsoft.com/en-us/getting-started)
+[Read more about eligibility and registration here](https://www.microsoft.com/en-us/nonprofits/azure)
 
 ### Getting started
 
@@ -164,10 +164,38 @@ Non-profit organizations can apply for an annual Azure credit grant of up to $3,
 
 - To pull the latest docker image with the app interface, pull it with docker.
 
-      docker pull ghcr.io/microsoft/intelligence-toolkit:latest
+  Download, install and then open docker app: https://www.docker.com/products/docker-desktop/
+
+  Then, open a terminal:
+  Windows: Search and open the app `Windows Powershell` on Windows start menu
+
+  Linux and Mac: Open `Terminal`
+
+  For any OS:
+
+  Navigate to the folder where you cloned this repo. 
+
+  Use `cd `+ the path to the folder. For example:
+
+  `cd C:\Users\user01\projects\intelligence-toolkit`
+
+  Build the container:
+
+  `docker build . -t intelligence-toolkit`
+
+  Once the build is finished, run the docker container:
+
+  - via terminal:
+
+      `docker run -d --name intelligence-toolkit -p 80:80 ghcr.io/microsoft/intelligence-toolkit:latest`
+
+  **Note that docker might sleep and you might need to start it again. Open Docker Desktop, in the left menu click on Container and press play on intelligence-toolkit.**
+
+  Open [localhost:80](http://localhost:80)
 
 
 - To start developing, see [DEVELOPING.md](./DEVELOPING.md).
+- To instructions on how to deploy, see [DEPLOYING.md](./DEPLOYING.md).
 - To learn about our contribution guidelines, see [CONTRIBUTING.md](./CONTRIBUTING.md).
 - For license details, see [LICENSE.md](./LICENSE.md).
 

app/Home.py

@@ -42,18 +42,18 @@ def main():
         page_icon=f"{path}myapp.ico",
         page_title="Intelligence Toolkit | Home",
     )
+
     load_multipage_app()
     transparency_faq, mermaid_text = get_readme_and_mermaid()
 
-    st.markdown(
-        transparency_faq
-    )
+    st.markdown(transparency_faq)
 
     mermaid.mermaid(
         code=mermaid_text,
         height=1000,
     )
 
 
+
 if __name__ == "__main__":
-    main()
+    main()