docker-compose.yml

# ─────────────────────────────────────────────────────────────
#  GTO Exercise AI — full stack: web service + CV processing service
#  Both services share a common video storage volume (single-node design).
# ─────────────────────────────────────────────────────────────
services:

  # Computer-vision engine: pose estimation, rep counting, technique analysis
  processing:
    build: ./processing-service
    container_name: gto-processing
    ports:
      - "8000:8000"
    environment:
      - MMPOSE_CONFIG=${MMPOSE_CONFIG}
      - MMPOSE_WEIGHTS=${MMPOSE_WEIGHTS}
      - HAND_WEIGHTS=${HAND_WEIGHTS}
      - BAR_WEIGHTS=${BAR_WEIGHTS}
    volumes:
      - ./processing-service/models:/app/models   # model weights (download separately)
      - ./processing-service/logs:/app/logs
      - storage:/app/storage                      # shared video storage
    networks:
      - gto
    # For GPU inference, install the NVIDIA Container Toolkit and uncomment:
    # deploy:
    #   resources:
    #     reservations:
    #       devices:
    #         - driver: nvidia
    #           count: 1
    #           capabilities: [gpu]

  # Web platform: user/trainer accounts, video upload, results
  web:
    build:
      context: ./web-service/app
    container_name: gto-web
    ports:
      - "8080:80"
    environment:
      - SECRET_KEY=${SECRET_KEY}
      - PROCESSING_URL=http://processing:8000
    volumes:
      - ./web-service/templates:/templates
      - ./web-service/db:/db
      - storage:/static/uploads                   # shared video storage
    command: >
      gunicorn --workers 4 --bind 0.0.0.0:80
      --timeout 180 --graceful-timeout 180 --keep-alive 120
      -k gevent --worker-connections 1000 app:app
    depends_on:
      - processing
    networks:
      - gto

volumes:
  storage:

networks:
  gto:
    driver: bridge