FastAPI & Streamlit - Deployment & Interaction

This combination allows scientists to move from a Jupyter Notebook to a production-ready system. FastAPI handles the backend (model serving, data processing), while Streamlit provides the frontend (interactive widgets, real-time plotting).

FIRST: Verify Prerequisites

pip install fastapi uvicorn streamlit pydantic

When to Use

FastAPI:

• Serving Machine Learning models as REST APIs.
• Creating microservices for heavy scientific computations.
• Building backends that require high concurrency (async/await).
• Automatically generating API documentation (Swagger/Redoc).