# Video Analysis Module - Docker Compose Configuration
#
# Port Allocation (Dev AI Vision/Video: 54500-54600):
#   54500 - vLLM BusterX (deepfake detection model)
#   54600 - Video Analysis API
#
# Profiles:
#   api       - API server only (requires external vLLM)
#   api-vllm  - API + vLLM BusterX (GPU required)
#
# Required environment variables (set in deploy/.env file):
#   HF_TOKEN                      - Hugging Face token for model downloads
#   HF_CACHE_DIR                  - Cache directory for models
#   VIDEO_ANALYSIS_RUNS_DIR       - Directory for analysis artifacts
#
# Naming Convention: didiAI-{module}-{service}
#
# Network:
#   Uses the external didi-network bridge (shared with other AI modules)

networks:
  didi-network:
    external: true

services:
  # ==========================================================================
  # vLLM Server - BusterX (Deepfake Detection Model)
  # ==========================================================================
  # Vision-language model for video deepfake detection
  # Runs on GPU 1 (Qwen3.5-35B-A3B uses GPU 0 via llm-inference module)
  # BusterX is based on Qwen2.5-VL-7B (~17GB VRAM with optimizations)
  vllm-buster:
    container_name: didiAI-video-vllm-buster
    image: vllm/vllm-openai:qwen3_5
    ports:
      - "54500:54500"
    networks:
      - didi-network
    volumes:
      - ${HF_CACHE_DIR:-/cai2_ds_storage/hf_cache}:/root/.cache/huggingface
    environment:
      - HF_HOME=/root/.cache/huggingface
      - HUGGING_FACE_HUB_TOKEN=${HF_TOKEN}
      - CUDA_VISIBLE_DEVICES=1
    command: >
      --model l8cv/BusterX_plusplus
      --host 0.0.0.0
      --port 54500
      --served-model-name busterx
      --tensor-parallel-size 1
      --max-model-len 32768
      --gpu-memory-utilization 0.25
      --trust-remote-code
      --enable-prefix-caching
      --disable-log-requests
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids: ['1']
              capabilities: [gpu]
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:54500/health"]
      interval: 30s
      timeout: 10s
      retries: 10
      start_period: 600s
    restart: unless-stopped
    profiles:
      - api-vllm

  # ==========================================================================
  # Video Analysis API Server
  # ==========================================================================
  video-analysis-api:
    container_name: didiAI-video-api
    image: didiai-video-api
    build:
      context: ..
      dockerfile: deploy/Dockerfile

    ports:
      - "54600:54600"
    networks:
      - didi-network

    environment:
      # Server settings
      - VIDEO_ANALYSIS_HOST=0.0.0.0
      - VIDEO_ANALYSIS_PORT=54600
      # External URL for OpenAPI spec (REQUIRED)
      - VIDEO_ANALYSIS_EXTERNAL_URL=${VIDEO_ANALYSIS_EXTERNAL_URL}
      # vLLM connection (points to vllm-buster container)
      - VIDEO_ANALYSIS_VLLM_BASE_URL=${VIDEO_ANALYSIS_VLLM_BASE_URL:-http://didiAI-video-vllm-buster:54500}
      - VIDEO_ANALYSIS_VLLM_MODEL=${VIDEO_ANALYSIS_VLLM_MODEL:-busterx}
      - VIDEO_ANALYSIS_RUNS_DIR=${VIDEO_ANALYSIS_RUNS_DIR:-/app/runs}

      # Optional tuning
      - VIDEO_ANALYSIS_FRAMES=${VIDEO_ANALYSIS_FRAMES:-16}
      - VIDEO_ANALYSIS_MAX_SIDE=${VIDEO_ANALYSIS_MAX_SIDE:-960}
      - VIDEO_ANALYSIS_JPEG_QUALITY=${VIDEO_ANALYSIS_JPEG_QUALITY:-85}
      - VIDEO_ANALYSIS_MAX_TOKENS=${VIDEO_ANALYSIS_MAX_TOKENS:-750}
      - VIDEO_ANALYSIS_TEMPERATURE=${VIDEO_ANALYSIS_TEMPERATURE:-0.000001}
      - VIDEO_ANALYSIS_REPETITION_PENALTY=${VIDEO_ANALYSIS_REPETITION_PENALTY:-1.05}

      # Runtime config polling
      - VIDEO_ANALYSIS_DASHBOARD_URL=${VIDEO_ANALYSIS_DASHBOARD_URL:-http://didiAI-dashboard:51300}

      # Semantic analysis settings
      - VIDEO_ANALYSIS_SEMANTIC_LLM_BASE_URL=${VIDEO_ANALYSIS_SEMANTIC_LLM_BASE_URL}
      - VIDEO_ANALYSIS_SEMANTIC_AGGREGATION_MODEL=${VIDEO_ANALYSIS_SEMANTIC_AGGREGATION_MODEL}
      - VIDEO_ANALYSIS_SEMANTIC_LLM_API_KEY=${VIDEO_ANALYSIS_SEMANTIC_LLM_API_KEY}

    volumes:
      # Persist artifacts on the host (module-root runs/ folder)
      - ../runs:/app/runs

    healthcheck:
      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:54600/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 20s

    restart: unless-stopped
    profiles:
      - api
      - api-vllm