services:
  candle-annotator:
    build: .
    ports:
      - "3000:3000"
    volumes:
      - candle-data:/app/data
      - ml-data:/app/ml-data
    environment:
      - NODE_ENV=production
      - INFERENCE_API_URL=http://ml-service:8001
      - INFERENCE_API_TIMEOUT=30000
      - INFERENCE_BATCH_TIMEOUT=120000
      - NEXT_PUBLIC_PREDICTIONS_ENABLED=true
    restart: unless-stopped
    depends_on:
      - ml-service
  
  ml-service:
    build: ./services/ml
    ports:
      - "8001:8001"
    volumes:
      - ml-data:/app/data
      - mlflow-data:/app/mlruns
    environment:
      - MLFLOW_TRACKING_URI=http://mlflow:5000
      - DATABASE_URL=postgresql://ml_user:ml_password@postgres:5432/ml_db
      - PYTHONUNBUFFERED=1
    restart: unless-stopped
    depends_on:
      postgres:
        condition: service_healthy
      mlflow:
        condition: service_started
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8001/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s
  
  mlflow:
    image: ghcr.io/mlflow/mlflow:v2.10.0
    ports:
      - "5000:5000"
    volumes:
      - mlflow-data:/mlflow
    command: >
      mlflow server
      --backend-store-uri /mlflow
      --default-artifact-root /mlflow/artifacts
      --host 0.0.0.0
      --port 5000
    restart: unless-stopped
  
  postgres:
    image: postgres:16-alpine
    ports:
      - "5432:5432"
    volumes:
      - postgres-data:/var/lib/postgresql/data
    environment:
      - POSTGRES_USER=ml_user
      - POSTGRES_PASSWORD=ml_password
      - POSTGRES_DB=ml_db
    restart: unless-stopped
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U ml_user -d ml_db"]
      interval: 10s
      timeout: 5s
      retries: 5

volumes:
  candle-data:
  ml-data:
  mlflow-data:
  postgres-data: