# The Car Wash REVIEW COLLECTOR — deployed on the REVIEWER's host (art-docker-station), NOT
# on a booth. Its own Komodo stack ("wash-collector" in komodo/resources.toml) points at this
# file alone, so nothing here reaches a booth and nothing of the booth stack reaches this
# host. See wiki/concepts/vision-review-outbox.md.
#
# Reachability: booths POST to /ingest over the Netbird overlay only. Bind the published
# port to the host's OVERLAY address (COLLECTOR_BIND), never 0.0.0.0 on a host that also
# has a public interface. The Netbird policy should allow booths → this host:8090 and
# nothing else on it.

services:
  collector:
    image: ${REGISTRY:-git.infra.msai.al/mca/parking_solution}/parking-collector:${TAG:-dev}
    restart: unless-stopped
    ports:
      - "${COLLECTOR_BIND:-127.0.0.1}:8090:8090"
    environment:
      # "<boothId>:<token>" pairs — one per booth, the booth's CARWASH_REVIEW_TOKEN under its
      # pseudonymous CARWASH_REVIEW_BOOTH_ID. A Komodo secret reference in the stack env.
      COLLECTOR_BOOTH_TOKENS: ${COLLECTOR_BOOTH_TOKENS:?set COLLECTOR_BOOTH_TOKENS in the stack env}
      # The single reviewer login (HTTP Basic over the overlay).
      COLLECTOR_REVIEWER_USER: ${COLLECTOR_REVIEWER_USER:-reviewer}
      COLLECTOR_REVIEWER_PASS: ${COLLECTOR_REVIEWER_PASS:?set COLLECTOR_REVIEWER_PASS in the stack env}
      LOG_LEVEL: ${LOG_LEVEL:-info}
    volumes:
      - collector-data:/data

  # Phase B trainer — a ONE-OFF JOB on this host's CPU, not a service (profile "train": it
  # only runs when asked). Reads the collector's SQLite + crops straight off the same volume
  # (read-only), writes a versioned model folder under TRAINER_OUT on the host. CPU-only
  # PyTorch: the Xeon E3-1225 v5 trains a few thousand crops in minutes (features mode) to an
  # hour (full fine-tune) — see wiki/decisions/bodytype-classifier-training.md. If a modern GPU
  # ever lands in the host, add an nvidia device reservation here; the trainer picks up CUDA.
  #
  #   docker compose -f docker-compose.collector.yml --profile train run --rm trainer inspect
  #   docker compose -f docker-compose.collector.yml --profile train run --rm trainer train --min-accuracy 0.85
  #   docker compose -f docker-compose.collector.yml --profile train run --rm trainer evaluate --model /out/<version>/bodytype.onnx
  #   docker compose -f docker-compose.collector.yml --profile train run --rm trainer publish /out/<version> --url <gitea generic package url>
  trainer:
    image: ${REGISTRY:-git.infra.msai.al/mca/parking_solution}/parking-trainer:${TAG:-dev}
    profiles: ["train"]
    environment:
      # Only `publish` needs it: a Gitea token with package:write for the model's generic package.
      TRAINER_PUBLISH_TOKEN: ${TRAINER_PUBLISH_TOKEN:-}
    volumes:
      - collector-data:/data:ro
      - ${TRAINER_OUT:-./models}:/out

volumes:
  collector-data: