feat(backend): scan-product accuracy 66.2% -> 79.7% + frozen validation benchmark
Accuracy work on the 79-image product-scan validation set (user goal: 90%): - classify_ocr_server.py: 0/90/180/270-degree expiry-date search (stops at first hit, 0-degree fallback); classification decoupled onto the upright image (rotated frames regressed DINOv2 -6pts until this); cross-line date stitching; tiled full-res OCR pass (defeats the 4000px downscale that killed small inkjet dates); VL-pipeline expiry fallback with keyword-anchored anti-hallucination guard; VL text lines merged into text_lines + VL SKU retry. Visualization endpoints removed entirely (Visual/Spotting grids - unused by frontend, 3x per-scan GPU cost). - product-scan.ts: coverage-normalized OCR-evidence re-ranking of DINOv2 top-K (tuned offline: +8/-0 on top-1 misses), re-ranked class mapped to sku_master by SKU prefix; classifier timeout 90s->240s for fallback paths. - Frozen benchmark: product-test-images-fixed/ (79 renamed images) + freeze/seed/build-undetected/capture/experiment scripts; labels trimmed to the 79 validation entries (training rows kept in .bak-with-training); 5 TRAINED-ON SKUs replaced with fresh held-out photos. - manual-label-scan page: shows last batch-test AI prediction under every field by default (new /api/product-scan-results); serves the fixed folder; fixed total hydration failure via allowedDevOrigins 127.0.0.1. - Measured (all-79, zero failures): sku/name 87.3%, expiry 64.6%, overall 79.7%. Tiles/VL-evidence/VL-SKU deployed but not yet batch-measured. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Gr6HH7JrdsXX8AARejQboM
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@@ -4,6 +4,7 @@ const nextConfig: NextConfig = {
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// Allow dev requests from any host — needed for tunnel access (ngrok, cloudflare, etc.)
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// and direct LAN/WiFi IP access from Android devices.
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allowedDevOrigins: [
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"127.0.0.1",
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"*.trycloudflare.com",
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"*.ngrok.io",
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"*.ngrok-free.app",
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