Commit Graph

14 Commits

Author SHA1 Message Date
Claudio
347f58b4f3 perf(thumbs): pool NC client, smaller grid thumbs, eager owner load
Five stacked optimisations for the thumbnail hot path so the timeline
grid lands in fewer round trips and fewer bytes.

1. PhotoThumbnail: switch from 'medium' (640px) to 'small' (240px) for
   grid cells. 240px oversamples 150-200px logical cells on 2x retina
   and drops payload 5-8x. Lightbox and preview filmstrip keep 'large'
   and 'medium' respectively.

2. nextcloud_dav: pool the httpx client. A module-level AsyncClient
   with HTTP/2 + keepalive (max_connections=64, keepalive_expiry=120s)
   replaces the per-request constructor that paid a fresh TCP+TLS
   handshake on every preview fetch. Auth is per-user so it stays at
   the call site via auth=BasicAuth(...). Lifespan-managed: init in
   main.py's lifespan startup, aclose on shutdown. requirements.txt
   gains the http2 extra to pull in h2 (not currently installed).
   Same change applies to fetch_memories_info_async since it hits the
   same host.

3. PhotoThumbnail img: add decoding="async" so JPEG/WebP decode moves
   off the main thread, plus fetchPriority="low" so grid backfill
   doesn't fight UI fetches.

4. Eager-load Photo.user via joinedload from the thumb handler.
   _get_photo_with_share_fallback gains an options parameter so other
   callers stay zero-overhead; only the thumb handler asks for the
   owner join. Eliminates the second SELECT users per request.

5. Disk-fallback path picks up Cache-Control: private, max-age=86400
   in both the FileResponse and X-Accel branches so re-renders match
   the NC primary path's caching behaviour.

Net: a warm grid page should drop from ~200-400 ms median per thumb to
well under 100 ms; payload drops ~5-8x; backend sustains higher
concurrency with fewer sockets to Nextcloud and one fewer Postgres
round-trip per request.
2026-05-12 00:30:43 +02:00
e8e1adcf37 feat(auth): Authentik OIDC sign-in + Gravatar avatars
Adds optional SSO via Authentik (or any OIDC provider) alongside the
existing password flow, and pulls profile images from the provider's
`picture` claim or Gravatar so the sharing UI stops looking anonymous.
Password login stays available as a recovery path; JIT provisioning and
admin-group mapping are env-configurable.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-22 21:06:32 +02:00
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