fastapi-streamlit

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).

Streamlit:

• Building interactive dashboards for data exploration.
• Creating "Apps" to demonstrate scientific results to non-technical stakeholders.
• Rapid prototyping of UIs for internal tools.
• Visualizing complex datasets with interactive sliders, maps, and charts.

Reference Documentation

• FastAPI docs: https://fastapi.tiangolo.com/
• Streamlit docs: https://docs.streamlit.io/
• Search patterns: fastapi.app, pydantic.BaseModel, st.slider, st.cache_data, st.sidebar

Core Principles

FastAPI: Type Safety and Async

FastAPI is built on Pydantic for data validation and Starlette for web capabilities. Every input is validated against Python type hints. It is one of the fastest Python frameworks thanks to async/await.

Streamlit: Execution Model

Streamlit scripts run from top to bottom every time a user interacts with a widget. It uses a "magic" caching system to prevent expensive scientific functions from re-running unnecessarily.

Quick Reference

Installation

pip install fastapi uvicorn streamlit pydantic

Standard Imports

# FastAPI
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

# Streamlit
import streamlit as st
import requests # To communicate with FastAPI

Basic Pattern - FastAPI Model Server

# main_api.py
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class ModelInput(BaseModel):
    temperature: float
    pressure: float

@app.post("/predict")
def predict(data: ModelInput):
    # Imagine a complex physical model here
    result = data.temperature * 0.5 + data.pressure * 0.2
    return {"prediction": result}

# Run with: uvicorn main_api:app --reload