services:
  frontend:
    build:
      context: ./frontend
      dockerfile: Dockerfile
    container_name: mulita-frontend
    ports:
      # Host port is configurable via FRONTEND_PORT in .env so multiple
      # instances / other services on the same host don't collide.
      - "${FRONTEND_PORT:-3000}:80"
    depends_on:
      - backend
    networks:
      - mulita-network
    restart: unless-stopped

  backend:
    build:
      context: ./backend
      dockerfile: Dockerfile
    container_name: mulita-backend
    ports:
      # Direct backend access on the host is rarely needed (the frontend
      # talks to it through the nginx /api proxy on the same network),
      # but it's exposed for debugging / curl. Override with BACKEND_PORT.
      - "${BACKEND_PORT:-8001}:8000"
    volumes:
      - ./mulita.yml:/app/config/mulita.yml:ro
      # The single host → container mount for your photo library. Set
      # PHOTO_DIRS in .env to your library root. Mounted :rw because file
      # operations (rename, move, empty discard pile) need to mutate the
      # filesystem; flip to :ro for a strict read-only library and the
      # write endpoints will return EROFS.
      - ${PHOTO_DIRS:-./photos}:/photos:rw
      - thumbs_data:/data/thumbs
      - proxies_data:/data/proxies
      - db_data:/data/db   # retained so the docker-compose.sqlite.yml override has somewhere to put mulita.db
    # Run Alembic migrations before starting uvicorn. On a fresh Postgres
    # the empty 0001 baseline is a no-op stamp; create_all in init_db then
    # builds the schema.
    # init_db creates all tables from models (idempotent create_all),
    # then Alembic runs migrations for existing installs. On fresh DBs
    # create_all already built the full schema, so bootstrap.py stamps
    # alembic head to skip redundant ALTER statements.
    command: sh -c "python -c 'import asyncio; from app.database import init_db; asyncio.run(init_db())' && python bootstrap.py && uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload"
    environment:
      - DATABASE_URL=postgresql+asyncpg://mulita:mulita@db:5432/mulita
      - REDIS_URL=redis://redis:6379
      - CELERY_BROKER_URL=redis://redis:6379
      - CELERY_RESULT_BACKEND=redis://redis:6379
      - PHOTO_DIRS=/photos
      - ALLOWED_ORIGINS=${ALLOWED_ORIGINS:-*}
      - SECRET_KEY=${SECRET_KEY:-mulita-dev-secret-change-me}
      - ACCESS_TOKEN_EXPIRE_MINUTES=${ACCESS_TOKEN_EXPIRE_MINUTES:-60}
      - REFRESH_TOKEN_EXPIRE_DAYS=${REFRESH_TOKEN_EXPIRE_DAYS:-30}
      - LOG_LEVEL=${LOG_LEVEL:-INFO}
      - TZ=${TZ:-UTC}
    depends_on:
      redis:
        condition: service_started
      db:
        condition: service_healthy
    networks:
      - mulita-network
    restart: unless-stopped

  # ── Celery workers ─────────────────────────────────────────────────────
  #
  # The ingestion pipeline is split across two worker services so CPU-heavy
  # vision tasks (embed / detect / OCR / faces / classify) cannot starve
  # the fast IO-bound tasks (scan / thumbnails / EXIF / phash / duplicates).
  #
  #   worker-light  listens on default,high,low  — IO-bound, cheap
  #   worker-vision listens on vision            — CPU-bound, loads ONNX
  #
  # Both share the same image, photo volume, and model cache, so there's
  # no disk duplication and model weights are loaded lazily only by
  # worker-vision. Each service has its own concurrency knob; both
  # workers ship their heartbeat to the same Redis broker so the
  # Settings > Workers panel lists them side-by-side.
  #
  # Sizing defaults target a 6-core / 16 GB host:
  #   CELERY_LIGHT_CONCURRENCY=2  (enough for parallel thumbnail + EXIF)
  #   CELERY_VISION_CONCURRENCY=5 (5 × ~2GB ONNX = ~10GB RAM, 5/6 cores)
  # Raise these in .env and run `docker compose up -d worker-light worker-vision`
  # to scale. Keep light under ~4 and vision under your physical core
  # count; more just thrashes.
  worker-light:
    build:
      context: ./backend
      dockerfile: Dockerfile
    image: mule-image-worker
    container_name: mulita-worker-light
    command: sh -c "python -m app.services.vision.bootstrap_models && celery -A app.tasks.celery worker --loglevel=${LOG_LEVEL:-info} --concurrency=${CELERY_LIGHT_CONCURRENCY:-2} -Q default,high,low -n light@%h"
    volumes:
      - ./mulita.yml:/app/config/mulita.yml:ro
      - ${PHOTO_DIRS:-./photos}:/photos:rw
      - thumbs_data:/data/thumbs
      - proxies_data:/data/proxies
      - db_data:/data/db
      - models_data:/data/models
    environment:
      - DATABASE_URL=postgresql+asyncpg://mulita:mulita@db:5432/mulita
      - REDIS_URL=redis://redis:6379
      - CELERY_BROKER_URL=redis://redis:6379
      - CELERY_RESULT_BACKEND=redis://redis:6379
      - PHOTO_DIRS=/photos
      - LOG_LEVEL=${LOG_LEVEL:-INFO}
      - TZ=${TZ:-UTC}
      # NullPool — see app/database.py for rationale.
      - MULITA_CELERY_WORKER=1
    depends_on:
      redis:
        condition: service_started
      backend:
        condition: service_started
      db:
        condition: service_healthy
    healthcheck:
      test: ["CMD-SHELL", "celery -A app.tasks.celery inspect ping -d light@$$HOSTNAME 2>/dev/null | grep -q OK"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 120s
    networks:
      - mulita-network
    restart: unless-stopped

  # Dedicated watcher worker — runs the long-lived watch_folders task
  # on its own queue so it never blocks scan/thumbnail workers.
  worker-watcher:
    build:
      context: ./backend
      dockerfile: Dockerfile
    image: mule-image-worker
    container_name: mulita-worker-watcher
    command: sh -c "celery -A app.tasks.celery worker --loglevel=${LOG_LEVEL:-info} --concurrency=1 -Q watcher -n watcher@%h"
    volumes:
      - ./mulita.yml:/app/config/mulita.yml:ro
      - ${PHOTO_DIRS:-./photos}:/photos:rw
      - db_data:/data/db
    environment:
      - DATABASE_URL=postgresql+asyncpg://mulita:mulita@db:5432/mulita
      - REDIS_URL=redis://redis:6379
      - CELERY_BROKER_URL=redis://redis:6379
      - CELERY_RESULT_BACKEND=redis://redis:6379
      - PHOTO_DIRS=/photos
      - LOG_LEVEL=${LOG_LEVEL:-INFO}
      - TZ=${TZ:-UTC}
      - MULITA_CELERY_WORKER=1
    depends_on:
      redis:
        condition: service_started
      db:
        condition: service_healthy
    networks:
      - mulita-network
    restart: unless-stopped

  worker-vision:
    build:
      context: ./backend
      dockerfile: Dockerfile
    image: mule-image-worker
    container_name: mulita-worker-vision
    command: sh -c "python -m app.services.vision.bootstrap_models && celery -A app.tasks.celery worker --loglevel=${LOG_LEVEL:-info} --concurrency=${CELERY_VISION_CONCURRENCY:-5} -Q vision -n vision@%h"
    volumes:
      - ./mulita.yml:/app/config/mulita.yml:ro
      - ${PHOTO_DIRS:-./photos}:/photos:rw
      - thumbs_data:/data/thumbs
      - proxies_data:/data/proxies
      - db_data:/data/db
      - models_data:/data/models
    environment:
      - DATABASE_URL=postgresql+asyncpg://mulita:mulita@db:5432/mulita
      - REDIS_URL=redis://redis:6379
      - CELERY_BROKER_URL=redis://redis:6379
      - CELERY_RESULT_BACKEND=redis://redis:6379
      - PHOTO_DIRS=/photos
      - LOG_LEVEL=${LOG_LEVEL:-INFO}
      - TZ=${TZ:-UTC}
      - MULITA_CELERY_WORKER=1
      # ONNX Runtime execution providers. Set to "auto" to auto-detect
      # GPU (CUDA > ROCm > OpenVINO > CPU), or explicitly:
      #   "CUDAExecutionProvider,CPUExecutionProvider"
      #   "ROCMExecutionProvider,CPUExecutionProvider"
      # Default: CPU only. To enable GPU, also uncomment the deploy
      # section below and install nvidia-container-toolkit on the host.
      - VISION_EXECUTION_PROVIDERS=${VISION_EXECUTION_PROVIDERS:-CPUExecutionProvider}
      # Pin each ONNX session to one intra-op thread so N prefork children
      # × default-all-cores doesn't oversubscribe the box. With
      # concurrency=5 and OMP=1, vision peaks at 5 busy cores, leaving
      # one for worker-light + system. These env vars cover the three
      # threading runtimes ONNX Runtime might pick up on first use.
      - OMP_NUM_THREADS=1
      - OPENBLAS_NUM_THREADS=1
      - MKL_NUM_THREADS=1
    # Uncomment for NVIDIA GPU passthrough:
    # deploy:
    #   resources:
    #     reservations:
    #       devices:
    #         - driver: nvidia
    #           count: all
    #           capabilities: [gpu]
    healthcheck:
      test: ["CMD-SHELL", "celery -A app.tasks.celery inspect ping -d vision@$$HOSTNAME 2>/dev/null | grep -q OK"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 300s
    depends_on:
      redis:
        condition: service_started
      backend:
        condition: service_started
      db:
        condition: service_healthy
    networks:
      - mulita-network
    restart: unless-stopped

  db:
    image: pgvector/pgvector:pg16
    container_name: mulita-db
    environment:
      POSTGRES_USER: mulita
      POSTGRES_PASSWORD: mulita
      POSTGRES_DB: mulita
    volumes:
      - pg_data:/var/lib/postgresql/data
    networks:
      - mulita-network
    restart: unless-stopped
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U mulita -d mulita"]
      interval: 5s
      timeout: 5s
      retries: 10

  redis:
    image: redis:7-alpine
    container_name: mulita-redis
    # Host port exposed only for local debugging; the backend / worker
    # reach Redis via the internal mulita-network on its container name.
    ports:
      - "${REDIS_PORT:-6379}:6379"
    volumes:
      - redis_data:/data
    networks:
      - mulita-network
    restart: unless-stopped
    command: redis-server --appendonly yes
    healthcheck:
      test: ["CMD", "redis-cli", "ping"]
      interval: 10s
      timeout: 5s
      retries: 5

networks:
  mulita-network:
    driver: bridge

volumes:
  thumbs_data:
  proxies_data:
  db_data:
  redis_data:
  pg_data:
  models_data: