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fix(wave10): 8 P0 bugs from 7-agent prod audit
Frontend: - lib/models.ts: fix DEFAULT_MODEL_ID + FALLBACK_MODELS ids to match backend contract ('custom' instead of 'custom-yolo26-seg', etc). Old ids caused every inspect to 400 'Bilinmeyen model' which UI rendered as generic 'sunucu hatasi'. - lib/models.ts: localStorage migration for legacy ids — existing users with cached stale ids will self-heal on next read. - app/(app)/inspect/new/page.tsx: surface backend 5xx 'detail' message instead of generic 'sunucu hatasi' string. Backend: - main.py create_inspection: honor FORCE_SYNC_INFERENCE=1 env (was dead code) — async button now routes inline on worker-less HF deploy. - main.py _process_sync: restore aggregate_results(results) for N==1 (Wave 9 shortcut bypassed <inline> URL sanitizer, causing broken <img src='<inline>'> on frontend). - main.py _enqueue_async: use apply_async(retry=False) + safer exception handling — clearer 503 with 'Use sync mode' hint when worker absent. - scripts/entrypoint.sh: pre-fetch yolo11m…f297df3 -
fix(deploy): fetch weights from HF Hub when HF_MODEL_REPO is set
B2 free tier daily egress cap (1 GB) is exhausted after a handful of deploys. Hugging Face Hub gives unlimited bandwidth for public model repos with no auth + no card. New env: HF_MODEL_REPO=owner/repo (plus optional HF_MODEL_BRANCH, default 'main'). S3 path stays as fallback.6c01270 -
fix(deploy): B2/R2 require S3 SigV4 + virtual-hosted addressing
Backblaze B2 (and Cloudflare R2) return HTTP 403 Forbidden on HeadObject when boto3 uses the default s3v2 / path-style addressing. Pin signature_version='s3v4' and s3.addressing_style='virtual' on the boto3 client used by entrypoint.sh's model fetch. Fixes: ClientError 403 HeadObject when downloading damage_best.pt from s3://hasari-inspections/models/ on Render boot.430b6a9 -
v0.3.0 — multimodal vehicle damage MVP
- Backend (FastAPI + Celery + Postgres + Redis + MinIO) with JWT auth, pre-trained model registry, 4-model dropdown API - 3 custom YOLO11 models (damage/parts/severity) trained on CarDD + CarParts-Seg + Roboflow Severity - ML pipeline: parallel detection + IoU matching + part-centric output + TR cost engine - Web (Next.js 15), Desktop (Tauri 2), Mobile (RN + Expo) - Free tier deploy: Vercel + Render + Supabase + Upstash + Cloudflare R2 - Wave 1-7 hardening: detection threshold calibration, crack low-conf flag, polling resilience, thumbnail fix, multi-foto async, SEO full setup, Lighthouse 86-92 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>e327f0d