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

backend/scripts/capture-scan-responses.mjs

@@ -0,0 +1,59 @@
+// One-off capture: hit /api/scan-pfm for every image in
+// sources/product-test-images-fixed/ and save the full text-level response
+// (classification all_probabilities, OCR text_lines, extracted fields,
+// possibleMatches) minus base64 image blobs to
+// sources/product_scan_fullcap.json. This lets classification re-ranking
+// experiments run offline against ground truth in seconds instead of
+// re-running the 11-minute GPU batch per iteration.
+// Usage: node scripts/capture-scan-responses.mjs [baseUrl]
+import fs from "fs";
+import path from "path";
+
+const BASE_URL = process.argv[2] || process.env.ACCURACY_BASE_URL || "http://127.0.0.1:3000";
+const FIXED_DIR = path.join("sources", "product-test-images-fixed");
+const OUT_PATH = path.join("sources", "product_scan_fullcap.json");
+
+const files = fs.readdirSync(FIXED_DIR)
+  .filter((f) => /\.(jpe?g|png|webp)$/i.test(f))
+  .sort((a, b) => parseInt(a) - parseInt(b));
+
+const results = [];
+for (const filename of files) {
+  const b64 = "data:image/jpeg;base64," +
+    fs.readFileSync(path.join(FIXED_DIR, filename), "base64");
+  process.stdout.write(`Capturing ${filename}... `);
+  try {
+    const res = await fetch(`${BASE_URL}/api/scan-pfm`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({ image_base64: b64 }),
+      signal: AbortSignal.timeout(120_000)
+    });
+    if (!res.ok) throw new Error(`HTTP ${res.status}`);
+    const d = await res.json();
+    results.push({
+      filename,
+      classification: {
+        top1_name: d.classification?.top1_name,
+        top1_confidence: d.classification?.top1_confidence,
+        method: d.classification?.method,
+        all_probabilities: (d.classification?.all_probabilities || []).slice(0, 15)
+      },
+      ocr: {
+        text_lines: d.ocr?.text_lines || [],
+        extracted_sku: d.ocr?.extracted_sku ?? null,
+        extracted_product_name: d.ocr?.extracted_product_name ?? null,
+        extracted_expired_date: d.ocr?.extracted_expired_date ?? null,
+        expired_source_line: d.ocr?.expired_source_line ?? null
+      },
+      possibleMatches: d.possibleMatches || []
+    });
+    console.log("ok");
+  } catch (err) {
+    console.log(`FAILED (${err.message})`);
+    results.push({ filename, error: err.message });
+  }
+}
+
+fs.writeFileSync(OUT_PATH, JSON.stringify(results, null, 2), "utf8");
+console.log(`\nWrote ${results.length} captures to ${OUT_PATH}`);