Fast API Skeleton

This is a minimal FastAPI project skeleton designed for rapid development of tiny APIs, especially when serving ML models. It includes:

• A single POST /bmi endpoint to calculate Body Mass Index (BMI).
• Docker support with a simple Dockerfile.
• A Makefile to manage the Docker lifecycle (build, run, stop, logs, restart).
• Instructions for local development and testing.

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Project Structure

fast-api-skeleton/
├── app/
│ ├── main.py # FastAPI application
│ └── schemas.py # Pydantic models
├── Dockerfile # Container build instructions
├── Makefile # Docker lifecycle commands
├── requirements.txt # Python dependencies
└── README.md # Project documentation

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Requirements

• Python 3.9+
• Docker (for containerized deployment)
• (Optional) curl or HTTP client for testing

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Running Locally

1. Install dependencies

   pip install --no-cache-dir -r requirements.txt

2. Start the FastAPI server

   uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

3. Access the API docs
   Open http://localhost:8000/docs to explore and test the /bmi endpoint.

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Running with Docker

1. Build the Docker image

   docker build -t fast-api-skeleton-app .

2. Run the container

   docker run -d --name fast-api-skeleton -p 8000:80 fast-api-skeleton-app

3. View logs

   docker logs -f fast-api-skeleton

4. Stop & remove container

   make stop
   make rm

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Makefile Commands

The included Makefile provides convenient commands:

• make build
  Build the Docker image.

• make run
  Run the container (detached).

• make stop
  Stop the running container.

• make rm
  Remove the stopped container.

• make logs
  Follow container logs.

• make restart
  Rebuild and restart the container.

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BMI Endpoint

POST/bmi

• Request Body:

  {
  "name": "Alice",
  "weight": 70.0,
  "height": 1.75
  }

• Response:

  {
  "name": "Alice",
  "bmi": 22.9,
  "category": "Normal weight"
  }

This endpoint serves as a test and template for adding additional ML model inference routes.

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Extending the Skeleton

• Add more endpoints in app/main.py.
• Define new data models in app/schemas.py.
• Integrate ML inference in place of the BMI calculation.
• Update dependencies in requirements.txt as needed.

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This repository is intended as a starting point for small FastAPI projects, particularly those that will serve machine learning models. Feel free to customize!