feat: improved receipt recognition, batch product add, and scan UX

- Rewrite receipt OCR prompt: completes truncated names, preserves fat%
  and flavour attributes, extracts weight/volume from line, infers
  typical package sizes for solid goods with quantity_confidence field
- Add quantity_confidence to RecognizedItem, EnrichedItem, and
  ProductJobResultItem; propagate through item enricher and worker
- Replace per-item create loop with single POST /user-products/batch call
  from RecognitionConfirmScreen
- Rebuild RecognitionConfirmScreen: amber qty border for low
  quantity_confidence, tappable product name → catalog picker,
  sort items by confidence, full L10n (no hardcoded strings)
- Add timestamps (HH:mm / d MMM HH:mm) to recent scan chips
- Show close-app hint on ProductJobWatchScreen (queued + processing)
- Refresh recentProductJobsProvider on watch screen init so new job
  appears without a manual pull-to-refresh
- App-level WidgetsBindingObserver refreshes product and dish job lists
  on resume, fixing stale lists after background/foreground transitions
- Add 9 new L10n keys across all 12 locales

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/internal/adapters/openai/recognition.go

@@ -26,21 +26,41 @@ func (c *Client) RecognizeReceipt(ctx context.Context, imageBase64, mimeType, la
 	prompt := fmt.Sprintf(`You are an OCR system for grocery receipts.
 
 Analyse the receipt photo and extract a list of food products.
-For each product determine:
-- name: product name (remove article codes, extra symbols)
-- quantity: amount (number)
-- unit: unit (g, kg, ml, l, pcs, pack)
-- category: dairy | meat | produce | bakery | frozen | beverages | other
-- confidence: 0.0–1.0
 
-Skip items that are not food (household chemicals, tobacco, alcohol).
+Rules for each product:
+
+NAME (confidence):
+- Remove article codes, cashier codes (e.g. "1/72", "4607001234"), extra symbols.
+- Complete obviously truncated OCR names: "Паштет шпро." → "Паштет шпротный",
+  "Паштет с говяжьей пече" → "Паштет с говяжьей печенью".
+- Preserve meaningful product attributes: fat percentage ("3.2%%", "жирн. 9%%"),
+  flavour ("с гусиной печенью", "яблочный"), brand qualifiers ("ультрапастеризованное").
+- confidence: your certainty that the name is correct (0.0–1.0).
+
+QUANTITY + UNIT (quantity_confidence):
+- If a weight or volume is written on the receipt line (e.g. "160г", "1л", "500 мл", "0.5кг"),
+  use it as quantity+unit. quantity_confidence = 0.9–1.0.
+- If the count on the receipt is 1 and no weight/volume is stated, but the product is a
+  liquid (juice, milk, kefir, etc.) — infer 1 l and set quantity_confidence = 0.5.
+- If the count is 1 and no weight is stated, but the product is a solid packaged good
+  (pâté, spreadable cheese, sausage, butter, hard cheese, etc.) — infer a typical
+  package weight in grams (e.g. pâté 100 g, spreadable cheese 180 g, butter 200 g)
+  and set quantity_confidence = 0.35.
+- If the receipt explicitly states the quantity and unit (e.g. "2 кг", "3 шт"),
+  use them directly. quantity_confidence = 1.0.
+- Never output quantity = 1 with unit = "g" unless the receipt explicitly says "1 г".
+- unit must be one of: g, kg, ml, l, pcs, pack.
+
+CATEGORY: dairy | meat | produce | bakery | frozen | beverages | other
+
+Skip items that are not food (household chemicals, tobacco, alcohol, bags, services).
 Items with unreadable text — add to unrecognized.
 
 Return all text fields (name) in %s.
 Return ONLY valid JSON without markdown:
 {
   "items": [
-    {"name": "...", "quantity": 1, "unit": "l", "category": "dairy", "confidence": 0.95}
+    {"name": "...", "quantity": 160, "unit": "g", "category": "other", "confidence": 0.95, "quantity_confidence": 0.9}
   ],
   "unrecognized": [
     {"raw_text": "...", "price": 89.0}