version: '3.8'

services:
  videotranscriber:
    build: .
    container_name: videotranscriber
    ports:
      - "8501:8501"
    volumes:
      # Mount your video files directory (change the left path to your actual videos folder)
      - "${VIDEO_PATH:-./videos}:/app/data/videos"
      # Mount output directory for transcripts and summaries
      - "${OUTPUT_PATH:-./outputs}:/app/data/outputs" 
      # Mount cache directory for model caching (optional, improves performance)
      - "${CACHE_PATH:-./cache}:/app/data/cache"
      # Mount a config directory if needed
      - "${CONFIG_PATH:-./config}:/app/config"
    environment:
      # Ollama configuration for host access
      - OLLAMA_API_URL=${OLLAMA_API_URL:-http://host.docker.internal:11434/api}
      # Optional: HuggingFace token for advanced features
      - HF_TOKEN=${HF_TOKEN:-}
      # GPU configuration
      - CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-}
      # Cache settings
      - TRANSFORMERS_CACHE=/app/data/cache/transformers
      - WHISPER_CACHE=/app/data/cache/whisper
    # For GPU access (uncomment if you have NVIDIA GPU and nvidia-docker)
    # deploy:
    #   resources:
    #     reservations:
    #       devices:
    #         - driver: nvidia
    #           count: 1
    #           capabilities: [gpu]
    restart: unless-stopped
    # For Linux hosts, you might prefer host networking for better Ollama access
    # network_mode: host  # Uncomment for Linux hosts
    # Use bridge networking for Windows/Mac with host.docker.internal
    networks:
      - videotranscriber-network
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8501/_stcore/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 60s

networks:
  videotranscriber-network:
    driver: bridge