Simple example of usage of streamlit and FastAPI for ML model serving described on this blogpost and PyConES 2020 video.

When developing simple APIs that serve machine learning models, it can be useful to have both a backend (with API documentation) for other applications to call and a frontend for users to experiment with the functionality.

In this example, we serve an image semantic segmentation model using FastAPI for the backend service and streamlit for the frontend service. docker compose orchestrates the two services and allows communication between them.

To run the example in a machine running Docker and docker compose, run:

docker compose build
docker compose up

To visit the FastAPI documentation of the resulting service, visit http://localhost:8000/docs with a web browser.
To visit the streamlit UI, visit http://localhost:8501.

Logs can be inspected via:

docker compose logs

To modify and debug the app, development in containers can be useful (and kind of fun!).