Files
food-ai/backend/internal/domain/recognition/item_enricher.go
dbastrikin 180c741424 feat: dish recognition UX, background mode, and backend bug fixes
Flutter client:
- Progress dialog: redesigned with pulsing animated icon, info hint about
  background mode, full-width Minimize button; dismiss signal via ValueNotifier
  so the dialog always closes regardless of widget lifecycle
- Background recognition: when user taps Minimize, wasMinimizedByUser flag is
  set; on completion a snackbar is shown instead of opening DishResultSheet
  directly; snackbar action opens the sheet on demand
- Fix dialog spinning forever: finally block guarantees dismissSignal=true on
  all exit paths including early returns from context.mounted checks
- Fix DishResultSheet not appearing: add ValueKey to _DailyMealsSection and
  meal card Padding so Flutter reuses elements when _TodayJobsWidget is
  inserted/removed from the SliverChildListDelegate list
- todayJobsProvider refresh: added refresh() method; called after job submit
  and on DishJobDone; all ref.read() calls guarded with context.mounted checks
- food_search_sheet: scan buttons replaced with full-width stacked OutlinedButtons
- app.dart: WidgetsBindingObserver refreshes scan providers on app resume
- L10n: added dishRecognitionHint and minimize keys to all 12 locales

Backend:
- migrations/003: ALTER TYPE recipe_source ADD VALUE 'recommendation' to fix
  22P02 error in GET /home/summary -> getRecommendations()
- item_enricher: normalizeProductCategory() validates AI-returned category
  against known slugs, falls back to "other" — fixes products_category_fkey
  FK violation during receipt recognition
- recognition prompt: enumerate valid categories so AI returns correct values

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-03-28 00:03:17 +02:00

119 lines
4.2 KiB
Go

package recognition
import (
"context"
"log/slog"
"strings"
"github.com/food-ai/backend/internal/adapters/ai"
"github.com/food-ai/backend/internal/domain/product"
)
// validProductCategories mirrors the product_categories slugs seeded in the DB.
var validProductCategories = map[string]struct{}{
"dairy": {}, "meat": {}, "produce": {}, "bakery": {},
"frozen": {}, "beverages": {}, "other": {},
}
// normalizeProductCategory returns a pointer to a valid product_categories slug.
// It lowercases and trims the AI-returned value; if it is not recognised it falls
// back to "other" rather than letting an invalid string reach the FK constraint.
func normalizeProductCategory(category string) *string {
normalized := strings.ToLower(strings.TrimSpace(category))
if _, ok := validProductCategories[normalized]; ok {
return &normalized
}
fallback := "other"
return &fallback
}
// itemEnricher matches recognized items against the product catalog,
// triggering AI classification for unknown items.
// Extracted from Handler so both the HTTP handler and the product worker pool can use it.
type itemEnricher struct {
recognizer Recognizer
productRepo ProductRepository
}
func newItemEnricher(recognizer Recognizer, productRepo ProductRepository) *itemEnricher {
return &itemEnricher{recognizer: recognizer, productRepo: productRepo}
}
// enrich matches each recognized item against the product catalog.
// Items without a match trigger a classification call and upsert into the DB.
func (enricher *itemEnricher) enrich(enrichContext context.Context, items []ai.RecognizedItem) []EnrichedItem {
result := make([]EnrichedItem, 0, len(items))
for _, item := range items {
enriched := EnrichedItem{
Name: item.Name,
Quantity: item.Quantity,
Unit: item.Unit,
Category: item.Category,
Confidence: item.Confidence,
QuantityConfidence: item.QuantityConfidence,
StorageDays: 7, // sensible default
}
catalogProduct, matchError := enricher.productRepo.FuzzyMatch(enrichContext, item.Name)
if matchError != nil {
slog.WarnContext(enrichContext, "fuzzy match product", "name", item.Name, "err", matchError)
}
if catalogProduct != nil {
productID := catalogProduct.ID
enriched.MappingID = &productID
if catalogProduct.DefaultUnit != nil {
enriched.Unit = *catalogProduct.DefaultUnit
}
if catalogProduct.StorageDays != nil {
enriched.StorageDays = *catalogProduct.StorageDays
}
if catalogProduct.Category != nil {
enriched.Category = *catalogProduct.Category
}
} else {
classification, classifyError := enricher.recognizer.ClassifyIngredient(enrichContext, item.Name)
if classifyError != nil {
slog.WarnContext(enrichContext, "classify unknown product", "name", item.Name, "err", classifyError)
} else {
saved := enricher.saveClassification(enrichContext, classification)
if saved != nil {
savedID := saved.ID
enriched.MappingID = &savedID
}
enriched.Category = classification.Category
enriched.Unit = classification.DefaultUnit
enriched.StorageDays = classification.StorageDays
}
}
result = append(result, enriched)
}
return result
}
// saveClassification upserts an AI-produced classification into the product catalog.
func (enricher *itemEnricher) saveClassification(enrichContext context.Context, classification *ai.IngredientClassification) *product.Product {
if classification == nil || classification.CanonicalName == "" {
return nil
}
catalogProduct := &product.Product{
CanonicalName: classification.CanonicalName,
Category: normalizeProductCategory(classification.Category),
DefaultUnit: strPtr(classification.DefaultUnit),
CaloriesPer100g: classification.CaloriesPer100g,
ProteinPer100g: classification.ProteinPer100g,
FatPer100g: classification.FatPer100g,
CarbsPer100g: classification.CarbsPer100g,
StorageDays: intPtr(classification.StorageDays),
}
saved, upsertError := enricher.productRepo.Upsert(enrichContext, catalogProduct)
if upsertError != nil {
slog.WarnContext(enrichContext, "upsert classified product", "name", classification.CanonicalName, "err", upsertError)
return nil
}
return saved
}