refactor: restructure internal/ into adapters/, infra/, and app layers
- internal/gemini/ → internal/adapters/openai/ (renamed package to openai) - internal/pexels/ → internal/adapters/pexels/ - internal/config/ → internal/infra/config/ - internal/database/ → internal/infra/database/ - internal/locale/ → internal/infra/locale/ - internal/middleware/ → internal/infra/middleware/ - internal/server/ → internal/infra/server/ All import paths and call sites updated accordingly. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
dbastrikin
229
backend/internal/adapters/openai/recognition.go
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229
backend/internal/adapters/openai/recognition.go
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package openai
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import (
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"encoding/json"
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"fmt"
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"strings"
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"context"
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)
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// RecognizedItem is a food item identified in an image.
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type RecognizedItem struct {
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Name string `json:"name"`
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Quantity float64 `json:"quantity"`
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Unit string `json:"unit"`
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Category string `json:"category"`
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Confidence float64 `json:"confidence"`
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}
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// UnrecognizedItem is text from a receipt that could not be identified as food.
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type UnrecognizedItem struct {
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RawText string `json:"raw_text"`
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Price float64 `json:"price,omitempty"`
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}
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// ReceiptResult is the full result of receipt OCR.
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type ReceiptResult struct {
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Items []RecognizedItem `json:"items"`
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Unrecognized []UnrecognizedItem `json:"unrecognized"`
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}
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// DishResult is the result of dish recognition.
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type DishResult struct {
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DishName string `json:"dish_name"`
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WeightGrams int `json:"weight_grams"`
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Calories float64 `json:"calories"`
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ProteinG float64 `json:"protein_g"`
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FatG float64 `json:"fat_g"`
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CarbsG float64 `json:"carbs_g"`
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Confidence float64 `json:"confidence"`
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SimilarDishes []string `json:"similar_dishes"`
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}
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// IngredientTranslation holds the localized name and aliases for one language.
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type IngredientTranslation struct {
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Lang string `json:"lang"`
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Name string `json:"name"`
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Aliases []string `json:"aliases"`
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}
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// IngredientClassification is the AI-produced classification of an unknown food item.
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type IngredientClassification struct {
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CanonicalName string `json:"canonical_name"`
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Aliases []string `json:"aliases"` // English aliases
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Translations []IngredientTranslation `json:"translations"` // other languages
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Category string `json:"category"`
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DefaultUnit string `json:"default_unit"`
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CaloriesPer100g *float64 `json:"calories_per_100g"`
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ProteinPer100g *float64 `json:"protein_per_100g"`
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FatPer100g *float64 `json:"fat_per_100g"`
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CarbsPer100g *float64 `json:"carbs_per_100g"`
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StorageDays int `json:"storage_days"`
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}
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// RecognizeReceipt uses the vision model to extract food items from a receipt photo.
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func (c *Client) RecognizeReceipt(ctx context.Context, imageBase64, mimeType string) (*ReceiptResult, error) {
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prompt := `Ты — OCR-система для чеков из продуктовых магазинов.
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Проанализируй фото чека и извлеки список продуктов питания.
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Для каждого продукта определи:
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- name: название на русском языке (убери артикулы, коды, лишние символы)
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- quantity: количество (число)
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- unit: единица (г, кг, мл, л, шт, уп)
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- category: dairy | meat | produce | bakery | frozen | beverages | other
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- confidence: 0.0–1.0
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Позиции, которые не являются едой (бытовая химия, табак, алкоголь) — пропусти.
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Позиции с нечитаемым текстом — добавь в unrecognized.
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Верни ТОЛЬКО валидный JSON без markdown:
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{
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"items": [
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{"name": "Молоко 2.5%", "quantity": 1, "unit": "л", "category": "dairy", "confidence": 0.95}
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],
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"unrecognized": [
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{"raw_text": "ТОВ АРТИК 1ШТ", "price": 89.0}
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]
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}`
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text, err := c.generateVisionContent(ctx, prompt, imageBase64, mimeType)
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if err != nil {
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return nil, fmt.Errorf("recognize receipt: %w", err)
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}
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var result ReceiptResult
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if err := parseJSON(text, &result); err != nil {
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return nil, fmt.Errorf("parse receipt result: %w", err)
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}
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if result.Items == nil {
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result.Items = []RecognizedItem{}
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}
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if result.Unrecognized == nil {
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result.Unrecognized = []UnrecognizedItem{}
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}
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return &result, nil
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}
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// RecognizeProducts uses the vision model to identify food items in a photo (fridge, shelf, etc.).
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func (c *Client) RecognizeProducts(ctx context.Context, imageBase64, mimeType string) ([]RecognizedItem, error) {
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prompt := `Ты — система распознавания продуктов питания.
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Посмотри на фото и определи все видимые продукты питания.
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Для каждого продукта оцени:
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- name: название на русском языке
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- quantity: приблизительное количество (число)
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- unit: единица (г, кг, мл, л, шт)
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- category: dairy | meat | produce | bakery | frozen | beverages | other
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- confidence: 0.0–1.0
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Только продукты питания. Пустые упаковки и несъедобные предметы — пропусти.
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Верни ТОЛЬКО валидный JSON без markdown:
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{
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"items": [
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{"name": "Яйца", "quantity": 10, "unit": "шт", "category": "dairy", "confidence": 0.9}
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]
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}`
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text, err := c.generateVisionContent(ctx, prompt, imageBase64, mimeType)
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if err != nil {
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return nil, fmt.Errorf("recognize products: %w", err)
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}
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var result struct {
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Items []RecognizedItem `json:"items"`
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}
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if err := parseJSON(text, &result); err != nil {
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return nil, fmt.Errorf("parse products result: %w", err)
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}
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if result.Items == nil {
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return []RecognizedItem{}, nil
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}
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return result.Items, nil
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}
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// RecognizeDish uses the vision model to identify a dish and estimate its nutritional content.
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func (c *Client) RecognizeDish(ctx context.Context, imageBase64, mimeType string) (*DishResult, error) {
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prompt := `Ты — диетолог и кулинарный эксперт.
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Посмотри на фото блюда и определи:
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- dish_name: название блюда на русском языке
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- weight_grams: приблизительный вес порции в граммах
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- calories: калорийность порции (приблизительно)
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- protein_g, fat_g, carbs_g: БЖУ на порцию
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- confidence: 0.0–1.0
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- similar_dishes: до 3 похожих блюд (для поиска рецептов)
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Верни ТОЛЬКО валидный JSON без markdown:
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{
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"dish_name": "Паста Карбонара",
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"weight_grams": 350,
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"calories": 520,
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"protein_g": 22,
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"fat_g": 26,
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"carbs_g": 48,
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"confidence": 0.85,
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"similar_dishes": ["Паста с беконом", "Спагетти"]
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}`
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text, err := c.generateVisionContent(ctx, prompt, imageBase64, mimeType)
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if err != nil {
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return nil, fmt.Errorf("recognize dish: %w", err)
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}
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var result DishResult
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if err := parseJSON(text, &result); err != nil {
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return nil, fmt.Errorf("parse dish result: %w", err)
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}
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if result.SimilarDishes == nil {
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result.SimilarDishes = []string{}
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}
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return &result, nil
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}
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// ClassifyIngredient uses the text model to classify an unknown food item
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// and build an ingredient_mappings record for it.
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func (c *Client) ClassifyIngredient(ctx context.Context, name string) (*IngredientClassification, error) {
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prompt := fmt.Sprintf(`Classify the food product: "%s".
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Return ONLY valid JSON without markdown:
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{
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"canonical_name": "turkey_breast",
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"aliases": ["turkey breast"],
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"translations": [
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{"lang": "ru", "name": "грудка индейки", "aliases": ["грудка индейки", "филе индейки"]}
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],
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"category": "meat",
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"default_unit": "g",
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"calories_per_100g": 135,
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"protein_per_100g": 29,
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"fat_per_100g": 1,
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"carbs_per_100g": 0,
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"storage_days": 3
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}`, name)
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messages := []map[string]string{
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{"role": "user", "content": prompt},
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}
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text, err := c.generateContent(ctx, messages)
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if err != nil {
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return nil, fmt.Errorf("classify ingredient: %w", err)
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}
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var result IngredientClassification
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if err := parseJSON(text, &result); err != nil {
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return nil, fmt.Errorf("parse classification: %w", err)
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}
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return &result, nil
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}
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// parseJSON strips optional markdown fences and unmarshals JSON.
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func parseJSON(text string, dst any) error {
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text = strings.TrimSpace(text)
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if strings.HasPrefix(text, "```") {
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text = strings.TrimPrefix(text, "```json")
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text = strings.TrimPrefix(text, "```")
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text = strings.TrimSuffix(text, "```")
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text = strings.TrimSpace(text)
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}
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return json.Unmarshal([]byte(text), dst)
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}
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