food-ai/backend/internal/domain/recognition/item_enricher.go

package recognition

import (
	"context"
	"log/slog"

	"github.com/food-ai/backend/internal/adapters/ai"
	"github.com/food-ai/backend/internal/domain/product"
)

// 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,
			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:        strPtr(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
}