Commit Graph

12 Commits

Author SHA1 Message Date
574d71371f refactor: strip AI pipeline to binary photo/other classifier
Drops face recognition, OCR, object detection, and semantic embeddings.
The sole remaining vision task is a CLIP-based binary classifier
(photography vs other); photos in "other" get needs_review=true so
screenshots, documents, memes and scans can be triaged from a new
filter pill in the UI.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-14 22:27:17 +02:00
root
5c531f11da feat: runtime feature flags, upload/download, RAW decoding
Adds Redis-backed feature flags for vision stages with admin UI toggles
and manual backfill trigger, photo upload and download routers with
frontend upload modal, and rawpy-based RAW decoding with JPEG fallback
for misnamed DNGs. Fixes pgvector serialization, is_trashed filter, and
naive-datetime bind in incremental duplicate regrouping; bumps Celery
time limits on regroup tasks beyond the 5-minute default.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-14 21:31:52 +02:00
fbeefb24a0 fix: vision tasks inherit user_id, admin owns mount root
- detect_objects, classify_content, recluster_faces now look up the
  photo's user_id and set it on created Tag rows — fixes tags being
  invisible to the owning user due to NULL user_id
- Initial admin setup creates source root at the mount root (/photos)
  instead of a subdirectory, since the admin owns the entire library
- Revert to OpenCLIP ViT-B/32 (512-d) as default embedder — SigLIP
  requires transformers version alignment not yet available in the
  Docker image. SigLIP2 code remains for future enablement.
- Add transformers to requirements for future SigLIP support

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-13 00:01:28 +02:00
348e9c3585 feat: multi-user auth with per-user media isolation
Introduce username/password authentication with admin and user roles.
Each user gets their own media directory under /photos/{username}/ with
isolated photos, folders, heaps, and tags. Admins manage users and
observe the full library from a dedicated Settings page.

Backend:
- User model with bcrypt passwords and JWT access/refresh tokens
- Auth router (login, refresh, setup, change-password, status)
- Admin router (user CRUD with last-admin protection)
- user_id FK added to photos, folders, source_roots, heaps, tags
- All data routers scoped by authenticated user
- Scanner inherits user_id from source root owner
- Thumbnails stored under user-prefixed paths for isolation
- Library endpoints accept ?scope=global for admin cross-user view
- Alembic migration 0009 with data migration for existing installs
- Defensive bootstrap.py handles fresh vs existing DB startup

Frontend:
- AuthContext with token lifecycle, auto-refresh, login/logout
- Login page, first-run setup page, auth gate in App.tsx
- Bearer token interceptor on all API requests
- User identity + logout in left sidebar
- Admin-only Settings page with Library Management and Users tabs
- UserManagement panel (add, edit role, reset password, deactivate)
- Settings shows global stats across all users for admin
- Filter bar, right sidebar, keyboard hints hidden on settings page

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-12 21:47:43 +02:00
root
07b1e5e02a feat: split celery workers, fix asyncpg-in-fork, add pipeline progress UI
Three overlapping fixes so the ingestion pipeline actually runs and the
user can see what it's doing:

Pipeline recovery
- app/database.py: use NullPool when MULITA_CELERY_WORKER=1 so each
  Celery task opens a fresh asyncpg connection on its own event loop.
  Fixes "another operation in progress" and "Future attached to a
  different loop" errors that were dropping ~every thumbnail +
  extract_metadata task on the floor.
- app/tasks/thumbs.py: initialize photo=None before the try and rollback
  on error so a transport failure in the initial SELECT doesn't raise
  UnboundLocalError in the except block and leak rows stuck in 'pending'.
- app/services/vision/bootstrap_models.py: on missing model files,
  invoke export_models automatically instead of just warning. First
  boot of a fresh install now self-heals.
- app/services/vision/export_models.py: shutil.move instead of
  Path.rename so the YOLO export survives the /app → /data/models
  cross-volume hop.
- requirements.txt: add ultralytics so export works in a stock image.

Worker topology
- docker-compose.yml: replace the single worker with worker-light
  (default/high/low queues, c=2, IO-bound) and worker-vision (vision
  queue, c=5, OMP_NUM_THREADS=1 to avoid oversubscription on 6 cores).
  Vision is pinned to ≤5 parallel inferences so ONNX doesn't each
  spawn an all-cores intra-op pool.
- .env / .env.example: CELERYD_CONCURRENCY replaced with
  CELERY_LIGHT_CONCURRENCY + CELERY_VISION_CONCURRENCY.
- Backfill queries in thumbs / scan / vision now ORDER BY taken_at
  DESC NULLS LAST so newest photos finish first — the library fills
  in top-down in the UI instead of arbitrary insertion order.

Settings visibility
- routers/library.py: new GET /maintenance/pipeline-stats returning
  done/total per stage (thumbnails, exif, gps, phash, embeddings,
  tags, ocr, faces, face clusters, duplicate groups). Worker-status
  now also reports the `vision` queue depth, which was missing.
- services/api.ts: PipelineStats / PipelineStage / ScanStatus types
  and the matching client call.
- components/dialogs/SettingsDialog.tsx:
  - new Pipeline Progress card with one progress bar per stage
  - inline scan banner (processed/total/current folder) inside the
    Library section while a scan is running
  - Tasks/min throughput computed by diffing worker processed counters
    between polls
  - Workers section calls out the vision queue and documents the
    CELERY_LIGHT/VISION_CONCURRENCY + docker compose up -d scale path

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-11 10:06:45 +02:00
fa9b21856f feat: replace face pipeline with InsightFace, add content classifier
Face detection/recognition:
- Replace YuNet + SFace with InsightFace buffalo_l (RetinaFace + ArcFace)
- 512-d ArcFace embeddings (was 128-d SFace), migration 0006 resizes column
- Remove YOLO person-bbox workaround — RetinaFace is accurate enough
- Detection threshold 0.65 cleanly separates real faces (0.72+) from
  false positives on dogs/paintings (0.56-0.61)

Content-type classification:
- CLIP zero-shot classifier using native PyTorch text encoder + ONNX
  image encoder for high-quality text-image similarity
- Categories: photograph, screenshot, document, receipt, meme, artwork
- Writes Tag(kind=content_type) per photo via photo_tags
- Margin-based confidence: top-1 vs top-2 score difference
- New ClassifierSettings in config (enabled, min_confidence)
- Wired into vision_fanout pipeline

Tested: 6 real faces from 4 photos (zero false positives), 11/13 photos
classified (8 photograph, 2 artwork, 1 meme).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-10 13:49:02 +02:00
2a6661f779 fix: model weights setup — export scripts, ORT compat, bootstrap
- Add export_models.py for OpenCLIP ViT-B/32 and YOLOv8n ONNX export
- Fix ArgMax(13) ORT ARM64 incompatibility by passing eot_indices as a
  separate ONNX input (computed outside the graph in embed.py)
- Use legacy TorchScript exporter (dynamo=False) for IR version 9 compat
- Upgrade onnxruntime to 1.18.1
- Rewrite bootstrap_models.py with clear separation of auto-downloadable
  models (YuNet, SFace) vs manually-exported ones (OpenCLIP, YOLOv8n)
- Wire bootstrap into worker CMD (runs before Celery)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-10 09:41:25 +02:00
9282a5c734 feat: add vision pipeline scaffolding with ONNX backend
Introduce the app/services/vision/ module with ABC interfaces, ONNX
Runtime backend, model registry, and per-task implementations:
- OpenCLIP ViT-B/32 embedder (image + text, 512-d)
- RapidOCR engine (PP-OCRv4 via ONNX, no PaddlePaddle)
- YOLOv8n object detector (raw ONNX, no ultralytics runtime)
- YuNet + SFace face processor (Apache 2.0, opencv_zoo, 128-d)
- DBSCAN face clustering helper

Add VisionSettings to config (mulita.yml + Pydantic), bootstrap_models.py
for first-boot weight downloads, models_data Docker volume, and ROCm
backend stub for future GPU acceleration.

No Celery tasks wired yet — models load but nothing invokes them.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-10 09:00:06 +02:00
dea04ceed9 feat: migrate to Postgres + pgvector with Alembic scaffolding
Switch the default database from SQLite to Postgres + pgvector (via
pgvector/pgvector:pg16 Docker image) to support the upcoming vision
pipeline (embeddings, OCR, object detection, face clustering).

- Add `db` service to docker-compose.yml with healthcheck
- Wire `alembic upgrade head` into backend CMD before uvicorn
- Bootstrap empty 0001_baseline revision (schema still owned by create_all)
- Guard SQLite-only PRAGMAs and inline ALTERs behind _is_sqlite flag
- Run `CREATE EXTENSION IF NOT EXISTS vector` on Postgres init
- Add asyncpg, psycopg2-binary, pgvector to requirements
- Provide docker-compose.sqlite.yml escape hatch for legacy SQLite mode

Fresh DB + rescan assumed — no SQLite→Postgres data migration.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-10 08:46:20 +02:00
733c16bf82 feat: perceptual-hash duplicate detection + grouped picker view
The Duplicates section was useless: SHA-256-only detection only caught
byte-identical files, not the actual duplicates a real library
accumulates (re-encoded JPEGs, screenshots, resized exports), and the
view was a flat date-sorted list with no grouping or actions. This
replaces the whole flow.

Detection
- New phash + duplicate_group_id columns on Photo, added via an
  idempotent ALTER TABLE pass in init_db (the project has no Alembic).
- Thumbs worker computes a 64-bit pHash from the original-resolution
  decoded frame just before the destructive thumbnail loop. Falls back
  silently — phash is nice-to-have, not a blocker for thumbnails.
- backfill_phashes Celery task fills in phashes for photos that
  predated the column, reading the existing thumb_large rather than
  re-decoding the original.
- regroup_duplicates service runs union-find over Hamming distance
  (threshold 6), persists duplicate_group_id, and maintains is_duplicate
  as derived state so existing badges/counts keep working. Chained
  after scan_all_source_roots with a 60s countdown.

API
- GET /library/duplicates/groups returns all groups with members,
  bucketed in Python from one query. Each group has a reason ("exact"
  iff every member shares a SHA-256, "similar" otherwise).
- POST /library/maintenance/{regroup-duplicates,backfill-phashes}.

Frontend
- New DuplicatesView (sectioned grid, one section per cluster) replaces
  the timeline when the user is in the duplicates section. Each section
  shows a "Keep best, discard N" button that picks the highest-pixel
  copy and reuses the existing undoable bulk-discard so Cmd+Z works.
- Manual best override: hover any non-best thumbnail and click "Keep
  this" (Crown icon, top-right) to override the auto-pick. The header
  annotates "(manual)" so it's obvious which copy will be kept.
- Keyboard nav within the duplicates view walks the flat member list,
  with ↑/↓ jumping by the measured column count and scrollIntoView on
  every move. Timeline's keyboard handler now early-returns in the
  duplicates section so the two don't fight.
- BEST pill / Keep-this button live at top-right with a ring outline so
  they don't collide visually with the cyan selection ring around a
  selected cell. Dimensions chip moved to bottom-left to free both
  right corners for the keep affordances.
- New "Duplicates" section in SettingsDialog: shows group/member counts
  and exposes both backfill + re-detect actions, sharing a query cache
  with DuplicatesView via DUPLICATE_GROUPS_QUERY_KEY.
- PhotoInfoPanel "Basic Info" section now shows the photo's full file
  path in monospace below the size/dimensions/date grid.
- New imagehash==4.3.1 dep in requirements.txt.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-09 17:19:08 +02:00
6d1b227fb9 feat: structure 2 2026-04-07 00:15:00 +02:00
46a0d7aba8 feat: structure 2026-04-06 23:30:19 +02:00