GenAI Demos with streamlit

As someone familar with python a way to create quick demos with a functional UI

Pre-req

• Azure subscription with contributor rights
• Previously deployed OpenAI / Foundry resource with gpt4o deployed.
• OPTIONAL for Websearch feature - and AI Foundry Agent deployed with Custom Webskill knowledge.

Usage

Locally

1. Create a virtual environment - Learn more about creating virtual environments with Conda.

2. Activate environment and install dependencies in requirements.txt

   pip install -r src\requirements.txt

3. Create a .env file in the root directory to configure model access. A sample.env file is provided as a template.

   Example .env file:

   # OpenAI Configuration
   OPENAI_API_ENDPOINT=https://<your-endpoint>.openai.azure.com
   OPENAI_API_KEY=<your-api-key>
   
   # AI Foundry Configuration (OPTIONAL - for WebSearch feature)
   AI_FOUNDRY_ENDPOINT=https://<your-foundry-project>.services.ai.azure.com/api/projects/<your-project-name>
   AI_FOUNDRY_AGENT_ID=asst_<your-agent-id>
   

4. Run the command below to run strealit on localhost.

python -m streamlit run src/Home.py

Deploy to cloud

1. Install AZD and clone the repo.

2. Create a main.bicepparam file from template

3. Add openai api key and endpoint from existing resource into param file.

   • Environment Name: This will be used as a prefix for the resource group that will be created to hold all Azure resources. This name should be unique within your Azure subscription.

4. Login to azd cli

   azd auth login

5. Run the following command to build a deployable copy of your application, provision the template's infrastructure to Azure and also deploy the application code to those newly provisioned resources.

   azd up

   This command will prompt you for the following information:

   • Azure Location: The Azure location where your resources will be deployed.
   • Azure Subscription: The Azure Subscription where your resources will be deployed.

   NOTE: This may take a while to complete as it executes three commands: azd package (builds a deployable copy of your application), azd provision (provisions Azure resources), and azd deploy (deploys application code). You will see a progress indicator as it packages, provisions and deploys your application.

6. If you want to change things make changes to python files and run azd deploy again to update your changes.

Notes

This uses the F1 (free) SKU for app service, which has limited CPU and RAM resources.

See the pricing calculator for details on paid SKUs replace the SKU option with a suitable choice.

Based on the this great template:

Added support for azd pipeline config, enabling creation of CI/CD pipeline for GitHub Actions. Note this is still a beta feature in AZD.