Docker

# 1. Start from an official Python image
FROM python:3.12-slim

# 2. Work inside /app in the container
WORKDIR /app

# 3. Install dependencies first (cached if requirements.txt didn't change)
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# 4. Copy the code and the trained model
COPY price_api.py price_model.joblib ./

# 5. Document the port and define the start command
EXPOSE 8000
CMD ["fastapi", "run", "price_api.py", "--port", "8000"]

venv/
__pycache__/
.env
*.ipynb
data/

# Build an image named price-api
docker build -t price-api .

# Run it: map port 8000 on your machine to 8000 in the container
docker run -p 8000:8000 price-api

# Now http://localhost:8000/docs works — from inside a container!

# Pass secrets as environment variables, never inside the image
docker run -p 8000:8000 --env-file .env price-api

services:
  api:
    build: .
    ports:
      - "8000:8000"
    env_file: .env
  ui:
    build: ./ui
    ports:
      - "8501:8501"
    environment:
      - API_URL=http://api:8000      # services find each other by name
    depends_on:
      - api

docker compose up --build