testrepo

Minimal example consisting of a small FastAPI service (backend.py) that mints an EPHEMERAL_KEY for the OpenAI realtime WebRTC API and a matching Streamlit app (app.py) that demonstrates the browser interaction.

Quick start

1. Install the dependencies
   The backend only depends on fastapi, uvicorn and openai while the app relies on streamlit:

   pip install fastapi uvicorn streamlit openai

2. Set up your credentials
   The backend will refuse to start unless the environment variable OPENAI_API_KEY is defined:

   export OPENAI_API_KEY="sk-..."  # replace with your key

3. Run the backend
   In one shell run:

   uvicorn backend:app --host 0.0.0.0 --port 8000

4. Run the Streamlit frontend
   In another terminal execute:

   streamlit run app.py --server.port 8501

   The demo page becomes available at http://localhost:8501 and will internally call http://localhost:8000/session when the "Connect & Talk" button is pressed.

Testing

Basic syntax check for both python files:

python3 -m py_compile backend.py app.py

To ensure the backend is reachable, start it as shown above and try to fetch a session:

curl http://localhost:8000/session

This should return a JSON document containing a client_secret. When running in restricted environments the call may fail with Realtime token request failed if the openai library lacks realtime functionality or the outgoing connection is blocked.

Ideas for further improvements

• Provide a requirements.txt or pyproject.toml to make the dependency versions explicit. This also allows for pip install -r requirements.txt style bootstrapping.
• Automatically read configuration from a .env file using python-dotenv to avoid the need for external environment variables.
• Add input validation and better error reporting in create_session so it becomes obvious whether the OpenAI request failed due to network restrictions or missing API capabilities.
• Ship a Dockerfile and docker-compose.yml for reproducible local runs (especially handy when the local python environment is missing some requirements).
• Include unit tests for the FastAPI route and a simple smoke test for the Streamlit component to facilitate regression testing.
• Lint/format the project via tools such as flake8, pylint, or black to keep the code quality high (CI integration would help automate this.)
• Expose the service on a configurable host/port instead of fixed values to simplify embedding into other systems.