train_classifier.py's split_dataset() previously shuffled and split
individual image files, letting an augmented copy (photo_aug_2.jpeg) land
in validation while its near-duplicate source stayed in training -
inflating val accuracy with memorization rather than measuring real
generalization. Now groups by source photo (stripping _aug_N) before
shuffling and splitting 80/20.
Also records the in-progress effort to retrain the product classifier
against the full 81-class/2,493-photo foto-kemasan-v2 dataset (up from the
16 classes/118 photos the deployed model was actually trained on) - see
plans/next-enhancements.md task 2.5 and the accompanying iteration-log
entry for the real, currently-observed numbers (DINOv2 index rebuilt:
2493/2493 images; classifier training: in progress, ~32s/epoch observed).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Xsxk4ZkDQVVaLUcixDcqb5
Backend (app-pfm-ocr-v2/backend):
- Product/SKU scan feature complete: trained DINOv2 index (118 reference
photos, 16 SKU classes) and YOLO classifier (83.3% top-1 val accuracy),
fixed scripts/install-pipeline.sh (was missing ultralytics/torch), fully
browser-verified end-to-end on /scan-pfm. Mobile m-scan-pfm page cancelled
(Flutter app handles mobile; web UI is desktop-only for pipeline testing).
- Fixed a real data-loss bug: Save Ground Truth (scan-pfm and the DO-flow's
manual-label) was silently writing into the pfm-web-app container's
ephemeral filesystem instead of the host, because /sources wasn't
bind-mounted in docker-compose.yml. Added the mount, recovered an
orphaned entry.
- accounts.password is now bcrypt-hashed (bcryptjs, idempotent migration
in db/init.ts) instead of plaintext; login route compares hashes.
- /api/v1/documents/* (list, PUT, upload) now enforces real 401 auth,
matching what the Flutter client already sends. The "classic" routes
deliberately stay open — they're dev-only web UI with no login flow and
won't exist in production.
- OCR accuracy investigated end-to-end: real baseline is 95.10% overall
(target met; accuracy_report.md was stale at 75.04%, now flagged). Fixed
one genuine parser.ts bug (SO/DO field duplication in the global fallback
regex); remaining gaps are OCR/layout-model limitations, not parser bugs.
- Adopted a standalone copy of the fhanyuh/agents-settings e/n workflow
scoped to backend/ (AGENTS.md Part A/B split, SKILLS.md, plans/, docs/),
independent of the root copy which now covers Flutter only.
- next-implementation.md deleted; content folded into
backend/plans/next-enhancements.md for traceability.
Root:
- Adopted fhanyuh/agents-settings kit (AGENTS.md, SKILLS.md, plans/,
docs/feature-list.md), scoped to the Flutter app only.
- Pending documents queue now persists to Hive (lib/core/storage) instead
of memory-only, surviving an app kill mid-upload.
Removed backend_backup/ (stale Express/Prisma prototype, superseded by
pfm-web-app) and the completed plans/next-enhancement-plan.md checklist.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Reliability/PoC hardening: dedupe uploads by file_hash, surface editor sync
failures instead of a false success SnackBar with a retry-without-re-OCR path,
bound the OCR pipeline fetches with timeouts, share a single ApiClient/Dio
instance app-wide, tune capture JPEG quality, and add an opt-in
docker-compose.demo.yml for a production-mode run ahead of client demos.
Translate all Flutter-side user-facing text (screens, validators, SnackBars,
the printed delivery receipt, and shared API error messages) to Bahasa
Indonesia.
Also includes in-progress OCR parser/accuracy-tuning work from the same
session: table column/unit normalization fixes, store/customer master data,
accuracy history log, and test-image renaming/cleanup.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017eRAsLqN9Sg1b9YPz22Lxz
