docker-compose.yml

services:
  mlflow:
    image: ghcr.io/mlflow/mlflow:v2.9.2
    ports:
      - "5000:5000"
    volumes:
      - mlflow-data:/mlflow
    command: >
      mlflow server
      --host 0.0.0.0
      --port 5000
      --backend-store-uri sqlite:///mlflow/mlflow.db
      --default-artifact-root /mlflow/artifacts
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:5000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    restart: unless-stopped

  backend:
    build:
      context: ./backend
      dockerfile: Dockerfile
    ports:
      - "8000:8000"
    environment:
      - MLFLOW_TRACKING_URI=http://mlflow:5000
      - HF_HOME=/app/cache/huggingface
      - DATABASE_URL=sqlite+aiosqlite:///./predictions.db
      - DEFAULT_MODEL=finbert-lora
      - MODEL_DIR=/app/models
      - MLRUNS_DIR=/app/mlruns
      - CORS_ORIGINS=http://localhost:3000,http://frontend:3000
    volumes:
      - hf-cache:/app/cache/huggingface
      - ./training/outputs:/app/models:ro
      - ./training/mlruns:/app/mlruns:ro
      - backend-db:/app/data
    depends_on:
      mlflow:
        condition: service_healthy
    healthcheck:
      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/api/v1/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 30s
    restart: unless-stopped

  frontend:
    build:
      context: ./frontend
      dockerfile: Dockerfile
      args:
        NEXT_PUBLIC_API_URL: http://localhost:8000
    ports:
      - "3000:3000"
    environment:
      - NEXT_PUBLIC_API_URL=http://localhost:8000
    depends_on:
      backend:
        condition: service_healthy
    healthcheck:
      test: ["CMD", "wget", "-q", "--spider", "http://localhost:3000"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 15s
    restart: unless-stopped

volumes:
  mlflow-data:
  hf-cache:
  backend-db: