services:
  mlflow-server:
    build:
      context: .
      dockerfile: Dockerfile.mlflow-server
    ports:
      - "8000:8000"
    volumes:
      # Mount the database file and artifacts directory to persist them
      - ./mlflow.db:/app/mlflow.db
      - ./mlartifacts:/app/mlartifacts
    restart: unless-stopped

  model-serve:
    build:
      context: .
      dockerfile: Dockerfile.model-serve
    ports:
      - "5001:5001"
    environment:
      # Points to the service name defined above, not localhost
      - MLFLOW_TRACKING_URI=http://mlflow-server:8000
      # You can override this at runtime: docker compose run -e MODEL_URI="..." model-serve
      - MODEL_URI=${MODEL_URI:-models:/Autoencoder_Anomaly_Detector/2}
    volumes:
      # Crucial: Since MLflow is using a local disk artifact store, 
      # the server passes local file paths back to the client. 
      # The serving container must have access to the exact same artifact paths.
      - ./mlartifacts:/app/mlartifacts
    depends_on:
      - mlflow-server
    restart: unless-stopped