edd569d095
Sharing:
- New HeapShare and FolderShare models with read/write permissions
- Sharing API router (CRUD for heap and folder shares)
- Heap endpoints accept shared access (photo_ids, add/remove with write)
- Photo list drops user_id filter in shared context, adds owner_username
- Media serving (thumb/original/proxy) falls back to share check on 404
- ShareDialog component for managing shares from kebab menus
- HeapsPanel shows "Shared with me" section for shared heaps
- LeftSidebar shows "Shared with me" section for shared folders
- Owner badge on PhotoThumbnail for photos from other users
Auth:
- Access token default bumped to 1 year, refresh to 10 years
- Refresh token persisted in localStorage (survives page reload)
- Timer-based refresh replaced with 401 axios interceptor
Vision pipeline fixes:
- Bootstrap sets Redis ready key even on partial export failure
- Export functions run conditionally (only for actually missing models)
- _load_thumb handles multi-user path (/data/thumbs/{user_id}/{photo_id}/)
- can_access_photo_via_share uses single subquery instead of N+1 loop
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
130 lines
4.4 KiB
Python
130 lines
4.4 KiB
Python
"""
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Download vision model weights on first worker boot.
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Run as: python -m app.services.vision.bootstrap_models
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Or called from the vision worker entrypoint before Celery starts.
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Downloads are idempotent — existing files are skipped.
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For models that require export (OpenCLIP, YOLOv8n), see export_models.py.
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Those must be exported once on any machine with pip, then placed in
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the models volume before the worker starts.
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"""
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import logging
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import os
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from pathlib import Path
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from urllib.request import urlretrieve
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from app.config import settings
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logger = logging.getLogger(__name__)
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# (relative_path, url, description)
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# Models with url=None must be pre-exported via export_models.py.
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# InsightFace (RetinaFace + ArcFace) auto-downloads via the insightface
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# package on first use — no manual download entries needed.
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DOWNLOADS = []
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# Models that need manual export via export_models.py
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EXPORTS = [
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("embed/visual.onnx", "OpenCLIP ViT-B/32 visual encoder"),
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("embed/textual.onnx", "OpenCLIP ViT-B/32 textual encoder"),
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("detect/yolov8n.onnx", "YOLOv8n object detector"),
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]
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def bootstrap(models_dir: str | None = None):
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"""Ensure all model files are present. Download what we can, warn about
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files that need manual export."""
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base = Path(models_dir or settings.vision.models_dir)
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base.mkdir(parents=True, exist_ok=True)
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# Download auto-downloadable models
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for rel_path, url, desc in DOWNLOADS:
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dest = base / rel_path
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dest.parent.mkdir(parents=True, exist_ok=True)
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if dest.exists():
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logger.debug("Already exists: %s (%s)", dest, desc)
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continue
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logger.info("Downloading %s → %s", desc, dest)
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try:
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urlretrieve(url, str(dest))
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size_kb = dest.stat().st_size / 1024
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logger.info("Downloaded %s (%.0f KB)", desc, size_kb)
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except Exception as e:
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logger.error("Failed to download %s: %s", desc, e)
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if dest.exists():
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dest.unlink()
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# Check for manually-exported models
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missing = []
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for rel_path, desc in EXPORTS:
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dest = base / rel_path
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if not dest.exists():
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missing.append((rel_path, desc))
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if missing:
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logger.warning(
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"Missing %d model file(s); attempting automatic export:",
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len(missing),
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)
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for rel_path, desc in missing:
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logger.warning(" %s — %s", base / rel_path, desc)
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from app.services.vision import export_models
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missing_paths = {rel for rel, _ in missing}
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# Only run the export functions whose outputs are actually missing.
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# Each function is mapped to the file(s) it produces.
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export_map = [
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(export_models.export_openclip, ["embed/visual.onnx", "embed/textual.onnx"]),
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(export_models.export_siglip2, ["embed_siglip2/visual.onnx", "embed_siglip2/textual.onnx"]),
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(export_models.export_yolov8n, ["detect/yolov8n.onnx"]),
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]
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for export_fn, outputs in export_map:
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if not any(o in missing_paths for o in outputs):
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continue
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try:
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export_fn(base)
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except Exception as e:
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logger.error(
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"Export step %s failed: %s. "
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"Run `python -m app.services.vision.export_models "
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"--models-dir %s` manually to retry.",
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export_fn.__name__,
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e,
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base,
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)
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# Re-check what's still missing after the export pass.
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still_missing = [
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(rel_path, desc)
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for rel_path, desc in EXPORTS
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if not (base / rel_path).exists()
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]
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if still_missing:
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for rel_path, desc in still_missing:
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logger.error(" still missing: %s — %s", base / rel_path, desc)
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else:
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logger.info("All model files present in %s", base)
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else:
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logger.info("All model files present in %s", base)
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# Signal readiness via Redis so the scan pipeline knows the vision
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# worker can accept tasks.
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try:
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import redis as _redis
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r = _redis.from_url(settings.redis_url)
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r.set("mulita:vision:ready", "1")
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logger.info("Set mulita:vision:ready in Redis")
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except Exception as e:
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logger.warning("Could not set vision readiness flag in Redis: %s", e)
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO)
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bootstrap()
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