FastAPI Docker Demo

A minimal example showing how to run a FastAPI application inside a Docker container. This project demonstrates containerizing a simple API with a Dockerfile and running it using uvicorn.

fastapi-docker-demo/
├── app
│   └── main.py
├── Dockerfile
└── requirements.txt

The API exposes two endpoints:

The API uses a Pydantic model to validate request data.

class Item(BaseModel):
    name: str
    price: float
    is_available: bool = True

Example request body:

{
  "name": "Laptop",
  "price": 1200.50,
  "is_available": true
}

• Docker installed
• Internet connection to pull the Python base image

Dependencies defined in requirements.txt:

fastapi
uvicorn[standard]

From the project root directory:

docker build -t fastapi-docker-demo .

docker run -p 8000:8000 fastapi-docker-demo

The API will now be available at:

http://localhost:8000

curl http://localhost:8000

Example response:

{
  "message": "FastAPI running inside Docker 🚀",
  "timestamp": "2026-03-12T10:00:00"
}

curl -X POST http://localhost:8000/items \
-H "Content-Type: application/json" \
-d '{"name":"Phone","price":699.99}'

Example response:

{
  "received_item": {
    "name": "Phone",
    "price": 699.99,
    "is_available": true
  },
  "processed_at": "2026-03-12T10:02:00"
}

FastAPI automatically generates API documentation.

Key steps in the Dockerfile:

1. Use a lightweight Python base image.
2. Set a working directory inside the container.
3. Install dependencies from requirements.txt.
4. Copy the application source code.
5. Expose port 8000.
6. Start the API server with uvicorn.

For local development:

pip install -r requirements.txt
uvicorn app.main:app --reload

Then open:

http://localhost:8000/docs

This project is for demonstration and learning purposes.