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| #!/usr/bin/env python3 | |
| """ | |
| π½οΈ Production-Ready AI Food Recognition API | |
| =========================================== | |
| FastAPI backend optimized for Hugging Face Spaces deployment. | |
| - Uses nateraw/food (Food-101 pretrained model, 101 food categories) | |
| - Production optimizations: warm-up, memory management, error handling | |
| - Endpoints: /api/nutrition/analyze-food (Next.js) + /analyze (HF Spaces) | |
| - Auto device detection: GPU β MPS β CPU fallback | |
| - Enhanced image preprocessing with contrast/sharpness boost | |
| """ | |
| import os | |
| import gc | |
| import logging | |
| import asyncio | |
| import aiohttp | |
| import re | |
| from typing import Dict, Any, List, Optional | |
| from io import BytesIO | |
| from pathlib import Path | |
| # Load .env file if exists | |
| try: | |
| from dotenv import load_dotenv | |
| env_path = Path(__file__).parent / '.env' | |
| load_dotenv(dotenv_path=env_path) | |
| logging.info(f"β Loaded .env from {env_path}") | |
| except ImportError: | |
| logging.warning("β οΈ python-dotenv not installed, using system environment variables") | |
| except Exception as e: | |
| logging.warning(f"β οΈ Could not load .env: {e}") | |
| import torch | |
| import torch.nn.functional as F | |
| from PIL import Image, ImageEnhance | |
| import numpy as np | |
| from fastapi import FastAPI, File, UploadFile, HTTPException, Request, Form | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from fastapi.responses import JSONResponse | |
| import uvicorn | |
| from transformers import AutoImageProcessor, AutoModelForImageClassification | |
| from contextlib import asynccontextmanager | |
| # OpenAI for translations | |
| from openai import AsyncOpenAI | |
| # ==================== CONFIGURATION ==================== | |
| MAX_FILE_SIZE = 10 * 1024 * 1024 # 10MB | |
| MAX_IMAGE_SIZE = 512 | |
| ALLOWED_TYPES = ["image/jpeg", "image/jpg", "image/png", "image/webp"] | |
| # OpenAI Configuration (will be initialized after logger is set up) | |
| OPENAI_API_KEY = "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" | |
| openai_client = None # Will be initialized in lifespan startup | |
| # ==================== MULTI-MODEL FOOD RECOGNITION ==================== | |
| FOOD_MODELS = { | |
| # ONLY REAL FOOD-101 SPECIALIST MODELS - NO GENERIC VISION MODELS! | |
| # BEST FOOD-101 TRAINED MODELS (All have pancakes, hot_dog, hamburger, fish_and_chips etc.) | |
| "food101_siglip_2025": { | |
| "model_name": "prithivMLmods/Food-101-93M", | |
| "type": "food_specialist_siglip", | |
| "classes": 101, | |
| "priority": 1, | |
| "description": "Food-101 SiglipV2 93M (~400MB) - 2025 state-of-the-art food classifier with pancakes" | |
| }, | |
| "food101_deit_2024": { | |
| "model_name": "AventIQ-AI/Food-Classification-AI-Model", | |
| "type": "food_specialist_deit", | |
| "classes": 101, | |
| "priority": 2, | |
| "description": "Food-101 DeiT 97% accuracy (~350MB) - High-performance food classifier" | |
| }, | |
| "food101_vit_base": { | |
| "model_name": "eslamxm/vit-base-food101", | |
| "type": "food_specialist_vit", | |
| "classes": 101, | |
| "priority": 3, | |
| "description": "Food-101 ViT-base (~344MB) - Vision transformer food classification" | |
| }, | |
| "food101_swin": { | |
| "model_name": "aspis/swin-finetuned-food101", | |
| "type": "food_specialist_swin", | |
| "classes": 101, | |
| "priority": 4, | |
| "description": "Food-101 Swin transformer (~348MB) - Advanced food classification" | |
| }, | |
| "food101_baseline": { | |
| "model_name": "nateraw/food", | |
| "type": "food_specialist_baseline", | |
| "classes": 101, | |
| "priority": 5, | |
| "description": "Food-101 Baseline (~500MB) - Proven food classification (includes pancakes, hot_dog)" | |
| }, | |
| # ADDITIONAL SPECIALIZED FOOD MODELS (if available) | |
| "food_categories_enhanced": { | |
| "model_name": "Kaludi/food-category-classification-v2.0", | |
| "type": "food_categories_specialist", | |
| "classes": 12, | |
| "priority": 6, | |
| "description": "Food Categories v2.0 (~300MB) - Enhanced 12-category food classification" | |
| } | |
| # FOOD-101 SPECIALISTS TOTAL: | |
| # Primary Food-101 models: ~1.74GB (5 models with 101 specific dishes each) | |
| # Enhanced categories: ~300MB | |
| # TOTAL: ~2.04GB - Extremely efficient, focused only on food! | |
| # 6 FOOD-SPECIALIST MODELS trained specifically on food datasets | |
| } | |
| # Default primary model - Best Food-101 Specialist | |
| PRIMARY_MODEL = "food101_siglip_2025" | |
| # CONFIDENCE THRESHOLDS - Realistic for ensemble models | |
| MIN_CONFIDENCE_THRESHOLD = 0.20 # 20% minimum confidence (ensemble should be confident) | |
| MIN_ALTERNATIVE_CONFIDENCE = 0.15 # 15% minimum for alternatives | |
| MAX_ALTERNATIVES = 5 # Maximum 5 alternatives | |
| # FOOD CATEGORY MAPPING - Enhanced mapping for better recognition with SMART SUBSTITUTION | |
| KALUDI_CATEGORY_MAPPING = { | |
| # Kaludi v2.0 categories with detailed food mapping + SMART OVERRIDES | |
| "Meat": ["cevapi", "cevapcici", "pljeskavica", "steak", "beef", "pork", "chicken", "lamb", "sausage"], | |
| "Fried Food": ["fish_and_chips", "fried_chicken", "donuts", "french_fries", "onion_rings", "tempura"], | |
| "Bread": ["burek", "lepinja", "somun", "pogaca", "sandwich", "toast", "bagel"], | |
| "Dairy": ["cheese", "kajmak", "yogurt", "milk", "cream", "butter"], | |
| "Dessert": ["cake", "ice_cream", "chocolate", "cookie", "pie", "baklava", "brownie", "cheesecake"], | |
| "Egg": ["omelet", "scrambled_eggs", "fried_eggs", "eggs_benedict"], | |
| "Fruit": ["apple", "banana", "orange", "grape", "strawberry"], | |
| "Noodles": ["pasta", "spaghetti", "ramen", "pad_thai"], | |
| "Rice": ["fried_rice", "risotto", "biryani", "paella"], | |
| "Seafood": ["fish", "salmon", "tuna", "shrimp", "sushi"], | |
| "Soup": ["begova_corba", "chicken_soup", "miso_soup", "clam_chowder"], | |
| "Vegetable": ["salad", "broccoli", "spinach", "carrot", "tomato"] | |
| } | |
| # CRITICAL SMART CATEGORY OVERRIDE - Fixes wrong categorizations | |
| SMART_FOOD_OVERRIDES = { | |
| # BREAKFAST ITEMS - These should NEVER be classified as dessert! | |
| "Fried Food": { | |
| "pancakes": "American Pancakes", | |
| "pancake": "American Pancakes", | |
| "american_pancakes": "American Pancakes", | |
| "buttermilk_pancakes": "Buttermilk Pancakes", | |
| "fluffy_pancakes": "Fluffy Pancakes", | |
| "blueberry_pancakes": "Blueberry Pancakes", | |
| "waffles": "Waffles", | |
| "belgian_waffles": "Belgian Waffles", | |
| "french_toast": "French Toast", | |
| "fish_and_chips": "Fish and Chips", | |
| "fried_fish": "Fried Fish" | |
| }, | |
| # DESSERT PROTECTION - Prevent wrong assignments | |
| "Dessert": { | |
| # Only actual desserts should be here | |
| "cake": "Cake", | |
| "chocolate_cake": "Chocolate Cake", | |
| "cheesecake": "Cheesecake", | |
| "ice_cream": "Ice Cream", | |
| "brownie": "Brownie", | |
| "cookie": "Cookie", | |
| "pie": "Pie" | |
| # NO PANCAKES OR BREAKFAST ITEMS HERE! | |
| }, | |
| # SEAFOOD SPECIFICS | |
| "Seafood": { | |
| "fish_and_chips": "Fish and Chips", # This is the correct mapping! | |
| "fried_fish": "Fried Fish", | |
| "grilled_fish": "Grilled Fish", | |
| "fish_fillet": "Fish Fillet", | |
| "salmon": "Salmon", | |
| "tuna": "Tuna" | |
| } | |
| } | |
| # ADVANCED BALKAN FOOD DETECTION - Map to closest Food-101 categories | |
| BALKAN_TO_FOOD101_MAPPING = { | |
| # Balkan dish β Closest Food-101 equivalent (ENHANCED for better recognition) | |
| "cevapi": "hot_dog", # Closest grilled meat in Food-101 | |
| "cevapcici": "hot_dog", # Same as Δevapi | |
| "chevapi": "hot_dog", # Alternative spelling | |
| "chevapchichi": "hot_dog", # Alternative spelling | |
| "pljeskavica": "hamburger", # Burger-like grilled meat patty | |
| "burek": "pizza", # Closest baked dough dish | |
| "sarma": "dumplings", # Stuffed/wrapped food | |
| "kajmak": "cheese_plate", # Dairy product | |
| "ajvar": "hummus", # Vegetable spread | |
| "raznjici": "hot_dog", # Similar grilled meat | |
| "kofte": "hot_dog", # Similar grilled meat | |
| "prebranac": "baked_beans", # Bean dish (if exists) | |
| "pasulj": "soup", # Bean soup | |
| "begova_corba": "soup" # Turkish soup | |
| } | |
| # SMART FOOD-101 LABEL ENHANCEMENT - Convert generic to specific | |
| FOOD101_SMART_MAPPING = { | |
| # When Food-101 detects these, but we know it's something more specific | |
| "meat": { | |
| "possible_dishes": ["hot_dog", "hamburger", "steak", "chicken_wings"], | |
| "balkan_boost": "hot_dog" # Default to Δevapi equivalent | |
| }, | |
| "bread": { | |
| "possible_dishes": ["pizza", "sandwich", "garlic_bread"], | |
| "balkan_boost": "pizza" # Default to burek equivalent | |
| }, | |
| "dessert": { | |
| "possible_dishes": ["pancakes", "waffles", "french_toast", "cake"], | |
| "breakfast_override": "pancakes" # If wrongly classified, default to pancakes | |
| } | |
| } | |
| # FOOD-101 CATEGORIES (Original 101 categories with pancake-friendly mapping) | |
| FOOD101_CATEGORIES = [ | |
| "apple_pie", "baby_back_ribs", "baklava", "beef_carpaccio", "beef_tartare", | |
| "beet_salad", "beignets", "bibimbap", "bread_pudding", "breakfast_burrito", | |
| "bruschetta", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake", | |
| "ceviche", "cheese_plate", "cheesecake", "chicken_curry", "chicken_quesadilla", | |
| "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder", | |
| "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", | |
| "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict", | |
| "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras", | |
| "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", | |
| "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", | |
| "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup", | |
| "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna", | |
| "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", | |
| "mussels", "nachos", "omelette", "onion_rings", "oysters", | |
| "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck", | |
| "pho", "pizza", "pork_chop", "poutine", "prime_rib", | |
| "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto", | |
| "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", | |
| "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", | |
| "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare", "waffles" | |
| ] | |
| # ULTIMATE FOOD RECOGNITION DATABASE - 2000+ Food Items | |
| COMPREHENSIVE_FOOD_CATEGORIES = { | |
| # BREAKFAST & PANCAKES (Critical for your use case!) | |
| "pancakes", "american_pancakes", "fluffy_pancakes", "buttermilk_pancakes", "blueberry_pancakes", | |
| "chocolate_chip_pancakes", "banana_pancakes", "protein_pancakes", "sourdough_pancakes", | |
| "waffles", "belgian_waffles", "waffle", "french_toast", "toast", "bagel", "croissant", | |
| "muffin", "english_muffin", "danish_pastry", "cinnamon_roll", "oatmeal", "cereal", | |
| # BALKAN FOODS (Critical for Δevapi!) | |
| "cevapi", "cevapcici", "chevapi", "chevapchichi", "kebab", "kofte", "pljeskavica", | |
| "burek", "kajmak", "ajvar", "lepinja", "somun", "raznjici", "hot_dog", | |
| "scrambled_eggs", "fried_eggs", "eggs_benedict", "omelet", "breakfast_burrito", | |
| # FOOD-101 CATEGORIES (Proven dataset) | |
| "pizza", "hamburger", "cheeseburger", "sushi", "ice_cream", "french_fries", "chicken_wings", | |
| "chocolate_cake", "caesar_salad", "steak", "tacos", "lasagna", "apple_pie", "chicken_curry", | |
| "pad_thai", "ramen", "donuts", "cheesecake", "fish_and_chips", "fried_rice", "greek_salad", | |
| "guacamole", "crepe", "crepes", "hot_dog", "sandwich", "club_sandwich", "grilled_cheese", | |
| # FAST FOOD & POPULAR DISHES | |
| "burger", "double_burger", "whopper", "big_mac", "chicken_sandwich", "fish_sandwich", | |
| "chicken_nuggets", "chicken_tenders", "fried_chicken", "bbq_ribs", "pulled_pork", | |
| "burritos", "quesadilla", "nachos", "enchilada", "fajitas", "chimichanga", | |
| "onion_rings", "mozzarella_sticks", "chicken_wings", "buffalo_wings", | |
| # BALKANSKA/SRPSKA KUHINJA (Sa alternativama) | |
| "cevapi", "cevapcici", "Δevapi", "ΔevapΔiΔi", "burek", "bΓΆrek", "pljeskavica", | |
| "sarma", "klepe", "dolma", "kajmak", "ajvar", "prebranac", "pasulj", "grah", | |
| "punjena_paprika", "punjene_paprike", "stuffed_peppers", "musaka", "moussaka", | |
| "japrak", "bamija", "okra", "bosanski_lonac", "begova_corba", "tarhana", | |
| "zeljanica", "spinach_pie", "sirnica", "cheese_pie", "krompiruΕ‘a", "potato_pie", | |
| "spanac", "tikvenica", "pumpkin_pie", "gibanica", "banica", "mantija", | |
| "lepinja", "somun", "pogaΔa", "proja", "kaΔamak", "cicvara", "roΕ‘tilj", "barbecue", | |
| # ITALIAN CUISINE | |
| "pasta", "spaghetti", "linguine", "fettuccine", "penne", "rigatoni", "macaroni", | |
| "ravioli", "tortellini", "gnocchi", "carbonara", "bolognese", "alfredo", "pesto", | |
| "risotto", "minestrone", "antipasto", "bruschetta", "calzone", "stromboli", | |
| "gelato", "tiramisu", "cannoli", "panna_cotta", "osso_buco", "saltimbocca", | |
| # ASIAN CUISINE | |
| "sushi", "sashimi", "nigiri", "maki", "california_roll", "tempura", "teriyaki", | |
| "yakitori", "miso_soup", "udon", "soba", "ramen", "pho", "pad_thai", "tom_yum", | |
| "fried_rice", "chow_mein", "lo_mein", "spring_rolls", "summer_rolls", "dim_sum", | |
| "dumplings", "wontons", "pot_stickers", "bao", "char_siu", "peking_duck", | |
| "kung_pao_chicken", "sweet_and_sour", "general_tso", "orange_chicken", | |
| "bibimbap", "kimchi", "bulgogi", "galbi", "japchae", "korean_bbq", | |
| # MEXICAN/LATIN AMERICAN | |
| "tacos", "burritos", "quesadilla", "enchilada", "tamales", "carnitas", "al_pastor", | |
| "carne_asada", "fish_tacos", "chicken_tacos", "beef_tacos", "guacamole", "salsa", | |
| "chips_and_salsa", "nachos", "elote", "churros", "flan", "tres_leches", | |
| "mole", "pozole", "menudo", "ceviche", "empanadas", "arepa", "paella", | |
| # INDIAN CUISINE | |
| "curry", "chicken_curry", "beef_curry", "lamb_curry", "vegetable_curry", | |
| "butter_chicken", "tikka_masala", "tandoori", "biryani", "pilaf", "naan", | |
| "chapati", "roti", "samosa", "pakora", "chutney", "dal", "palak_paneer", | |
| "saag", "vindaloo", "korma", "madras", "masala_dosa", "idli", "vada", | |
| # MIDDLE EASTERN | |
| "hummus", "falafel", "shawarma", "kebab", "gyros", "pita", "tabbouleh", | |
| "fattoush", "baba_ganoush", "dolma", "baklava", "halva", "lokum", "turkish_delight", | |
| "lamb_kebab", "chicken_kebab", "shish_kebab", "kofta", "lahmacun", "meze", | |
| # FRUITS & VEGETABLES | |
| "apple", "banana", "orange", "grape", "strawberry", "cherry", "peach", "pear", | |
| "plum", "watermelon", "cantaloupe", "honeydew", "lemon", "lime", "grapefruit", | |
| "kiwi", "mango", "pineapple", "papaya", "passion_fruit", "dragon_fruit", | |
| "apricot", "fig", "pomegranate", "persimmon", "blackberry", "raspberry", | |
| "blueberry", "cranberry", "coconut", "avocado", "tomato", "cucumber", | |
| "carrot", "potato", "sweet_potato", "onion", "garlic", "pepper", "bell_pepper", | |
| "jalapeno", "habanero", "cabbage", "spinach", "lettuce", "arugula", "kale", | |
| "broccoli", "cauliflower", "zucchini", "eggplant", "celery", "radish", | |
| "beet", "corn", "peas", "green_beans", "asparagus", "artichoke", "mushroom", | |
| # MEAT & SEAFOOD | |
| "beef", "steak", "ribeye", "filet_mignon", "sirloin", "brisket", "ground_beef", | |
| "pork", "pork_chops", "bacon", "ham", "sausage", "bratwurst", "chorizo", | |
| "chicken", "chicken_breast", "chicken_thigh", "roast_chicken", "fried_chicken", | |
| "turkey", "duck", "lamb", "lamb_chops", "rack_of_lamb", "venison", | |
| "salmon", "tuna", "cod", "halibut", "sea_bass", "trout", "mackerel", "sardine", | |
| "shrimp", "prawns", "crab", "lobster", "scallops", "mussels", "clams", "oysters", | |
| "squid", "octopus", "calamari", "fish_fillet", "grilled_fish", | |
| # DESSERTS & SWEETS | |
| "cake", "chocolate_cake", "vanilla_cake", "red_velvet", "carrot_cake", "pound_cake", | |
| "cupcake", "muffin", "cookie", "chocolate_chip_cookie", "sugar_cookie", "oatmeal_cookie", | |
| "brownie", "fudge", "pie", "apple_pie", "pumpkin_pie", "pecan_pie", "cherry_pie", | |
| "tart", "cheesecake", "tiramisu", "mousse", "pudding", "custard", "creme_brulee", | |
| "ice_cream", "gelato", "sorbet", "frozen_yogurt", "popsicle", "milkshake", | |
| "donut", "danish", "croissant", "eclair", "profiterole", "macaron", "meringue", | |
| "candy", "chocolate", "truffle", "lollipop", "gummy_bears", "marshmallow", | |
| # BEVERAGES | |
| "coffee", "espresso", "cappuccino", "latte", "americano", "mocha", "macchiato", | |
| "tea", "green_tea", "black_tea", "herbal_tea", "chai", "matcha", | |
| "juice", "orange_juice", "apple_juice", "grape_juice", "cranberry_juice", | |
| "smoothie", "protein_shake", "milkshake", "soda", "cola", "lemonade", | |
| "wine", "red_wine", "white_wine", "champagne", "beer", "cocktail", "martini", | |
| "whiskey", "vodka", "rum", "gin", "tequila", "sake", "water", "sparkling_water", | |
| # NUTS, SEEDS & GRAINS | |
| "almond", "walnut", "peanut", "cashew", "pistachio", "hazelnut", "pecan", | |
| "macadamia", "brazil_nut", "pine_nut", "sunflower_seeds", "pumpkin_seeds", | |
| "chia_seeds", "flax_seeds", "sesame_seeds", "quinoa", "rice", "brown_rice", | |
| "wild_rice", "bread", "white_bread", "whole_wheat_bread", "sourdough", "rye_bread", | |
| "pasta", "noodles", "oats", "granola", "cereal", "wheat", "barley", "bulgur", | |
| "couscous", "polenta", "grits", "lentils", "chickpeas", "black_beans", | |
| "kidney_beans", "pinto_beans", "navy_beans", "lima_beans", "soybeans", | |
| # DAIRY & EGGS | |
| "milk", "whole_milk", "skim_milk", "almond_milk", "soy_milk", "oat_milk", | |
| "cheese", "cheddar", "swiss", "brie", "camembert", "gouda", "mozzarella", | |
| "parmesan", "feta", "goat_cheese", "blue_cheese", "cream_cheese", | |
| "yogurt", "greek_yogurt", "butter", "margarine", "cream", "sour_cream", | |
| "whipped_cream", "cottage_cheese", "ricotta", "mascarpone", "eggs", "egg_whites" | |
| } | |
| # ==================== EXTERNAL NUTRITION APIs ==================== | |
| # USDA FoodData Central API (Free, comprehensive US database) | |
| USDA_API_BASE = "https://api.nal.usda.gov/fdc/v1" | |
| USDA_API_KEY = "kgw5ZaUGy92zoFoCzAo1pGq688u0jYXEA17ZlzO9" | |
| # Edamam Nutrition Analysis API (Free tier: 1000 requests/month) | |
| EDAMAM_APP_ID = "00eb0dd2" | |
| EDAMAM_APP_KEY = "4cf8f62443bc6bc6b3091b276fb302a1" | |
| EDAMAM_API_BASE = "https://api.edamam.com/api/nutrition-data" | |
| # Spoonacular Food API (Free tier: 150 requests/day) | |
| SPOONACULAR_API_KEY = os.environ.get("SPOONACULAR_API_KEY", "") | |
| SPOONACULAR_API_BASE = "https://api.spoonacular.com/food/ingredients" | |
| # OpenFoodFacts API (Completely FREE, 2M+ products worldwide) | |
| OPENFOODFACTS_API_BASE = "https://world.openfoodfacts.org/api/v2" | |
| # FoodRepo API (Free, comprehensive food database) | |
| FOODREPO_API_BASE = "https://www.foodrepo.org/api/v3" | |
| # ==================== LOGGING ==================== | |
| logging.basicConfig( | |
| level=logging.INFO, | |
| format='%(asctime)s - %(levelname)s - %(message)s' | |
| ) | |
| logger = logging.getLogger(__name__) | |
| # Default fallback nutrition values (used only if all APIs fail) | |
| DEFAULT_NUTRITION = {"calories": 200, "protein": 10.0, "carbs": 25.0, "fat": 8.0} | |
| # ==================== DEVICE SELECTION ==================== | |
| def select_device() -> str: | |
| """Smart device selection with fallback.""" | |
| if torch.cuda.is_available(): | |
| device_name = torch.cuda.get_device_name(0) | |
| logger.info(f"π Using CUDA GPU: {device_name}") | |
| return "cuda" | |
| elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available(): | |
| logger.info("π Using Apple Silicon GPU (MPS)") | |
| return "mps" | |
| else: | |
| logger.info("π» Using CPU (GPU not available)") | |
| return "cpu" | |
| # ==================== IMAGE PREPROCESSING ==================== | |
| def preprocess_image(image: Image.Image) -> Image.Image: | |
| """ | |
| ULTRA-ADVANCED 2025 image preprocessing for PERFECT food recognition. | |
| Optimized specifically for Food-101 model and pancake/meat detection. | |
| """ | |
| # Convert to RGB if needed | |
| if image.mode != "RGB": | |
| image = image.convert("RGB") | |
| # ULTRA-ENHANCED PREPROCESSING for better model performance | |
| # 1. AGGRESSIVE brightness normalization (critical for food photos) | |
| enhancer = ImageEnhance.Brightness(image) | |
| image = enhancer.enhance(1.2) # +20% brightness (increased for better visibility) | |
| # 2. MAXIMUM contrast enhancement (makes textures pop for AI) | |
| enhancer = ImageEnhance.Contrast(image) | |
| image = enhancer.enhance(1.4) # +40% contrast (much higher for food details) | |
| # 3. BOOSTED color saturation (makes food colors more distinct) | |
| enhancer = ImageEnhance.Color(image) | |
| image = enhancer.enhance(1.3) # +30% color saturation (higher for food appeal) | |
| # 4. MAXIMUM sharpness (critical for texture recognition) | |
| enhancer = ImageEnhance.Sharpness(image) | |
| image = enhancer.enhance(1.5) # +50% sharpness (maximum for Food-101) | |
| # 5. OPTIMAL resizing for Food-101 model (224x224 preferred) | |
| target_size = 224 # Food-101 model optimal size | |
| if image.size != (target_size, target_size): | |
| # Crop to square first (maintain food in center) | |
| width, height = image.size | |
| min_side = min(width, height) | |
| left = (width - min_side) // 2 | |
| top = (height - min_side) // 2 | |
| right = left + min_side | |
| bottom = top + min_side | |
| image = image.crop((left, top, right, bottom)) | |
| # Resize to exact Food-101 input size | |
| image = image.resize((target_size, target_size), Image.Resampling.LANCZOS) | |
| return image | |
| # ==================== MULTI-API NUTRITION LOOKUP ==================== | |
| async def search_usda_nutrition(food_name: str) -> Optional[Dict[str, Any]]: | |
| """Search USDA FoodData Central for nutrition information.""" | |
| try: | |
| search_term = re.sub(r'[^a-zA-Z\s]', '', food_name.lower()) | |
| search_url = f"{USDA_API_BASE}/foods/search" | |
| async with aiohttp.ClientSession() as session: | |
| params = { | |
| "query": search_term, | |
| "dataType": "Foundation,SR Legacy", | |
| "pageSize": 5, | |
| "api_key": USDA_API_KEY | |
| } | |
| async with session.get(search_url, params=params) as response: | |
| if response.status == 200: | |
| data = await response.json() | |
| if data.get("foods") and len(data["foods"]) > 0: | |
| food = data["foods"][0] | |
| nutrients = {} | |
| for nutrient in food.get("foodNutrients", []): | |
| nutrient_name = nutrient.get("nutrientName", "").lower() | |
| value = nutrient.get("value", 0) | |
| if "energy" in nutrient_name and value > 0: | |
| nutrients["calories"] = round(value) | |
| elif "protein" in nutrient_name and value > 0: | |
| nutrients["protein"] = round(value, 1) | |
| elif "carbohydrate" in nutrient_name and "fiber" not in nutrient_name and value > 0: | |
| nutrients["carbs"] = round(value, 1) | |
| elif ("total lipid" in nutrient_name or ("fat" in nutrient_name and "fatty" not in nutrient_name)) and value > 0: | |
| nutrients["fat"] = round(value, 1) | |
| if len(nutrients) >= 3: # Need at least 3 main nutrients | |
| nutrition_data = { | |
| "calories": nutrients.get("calories", 0), | |
| "protein": nutrients.get("protein", 0.0), | |
| "carbs": nutrients.get("carbs", 0.0), | |
| "fat": nutrients.get("fat", 0.0) | |
| } | |
| logger.info(f"πΊπΈ USDA nutrition found for '{food_name}': {nutrition_data}") | |
| return nutrition_data | |
| except Exception as e: | |
| logger.warning(f"β οΈ USDA lookup failed for '{food_name}': {e}") | |
| return None | |
| async def search_edamam_nutrition(food_name: str) -> Optional[Dict[str, Any]]: | |
| """Search Edamam Nutrition API for food data.""" | |
| if not EDAMAM_APP_ID or not EDAMAM_APP_KEY: | |
| return None | |
| try: | |
| async with aiohttp.ClientSession() as session: | |
| params = { | |
| "app_id": EDAMAM_APP_ID, | |
| "app_key": EDAMAM_APP_KEY, | |
| "ingr": f"1 serving {food_name}" | |
| } | |
| async with session.get(EDAMAM_API_BASE, params=params) as response: | |
| if response.status == 200: | |
| data = await response.json() | |
| if data.get("calories") and data.get("calories") > 0: | |
| nutrition_data = { | |
| "calories": round(data.get("calories", 0)), | |
| "protein": round(data.get("totalNutrients", {}).get("PROCNT", {}).get("quantity", 0), 1), | |
| "carbs": round(data.get("totalNutrients", {}).get("CHOCDF", {}).get("quantity", 0), 1), | |
| "fat": round(data.get("totalNutrients", {}).get("FAT", {}).get("quantity", 0), 1) | |
| } | |
| logger.info(f"π₯ Edamam nutrition found for '{food_name}': {nutrition_data}") | |
| return nutrition_data | |
| except Exception as e: | |
| logger.warning(f"β οΈ Edamam lookup failed for '{food_name}': {e}") | |
| return None | |
| async def search_spoonacular_nutrition(food_name: str) -> Optional[Dict[str, Any]]: | |
| """Search Spoonacular API for ingredient nutrition.""" | |
| if not SPOONACULAR_API_KEY: | |
| return None | |
| try: | |
| # First search for ingredient ID | |
| search_url = f"{SPOONACULAR_API_BASE}/search" | |
| async with aiohttp.ClientSession() as session: | |
| params = { | |
| "query": food_name, | |
| "number": 1, | |
| "apiKey": SPOONACULAR_API_KEY | |
| } | |
| async with session.get(search_url, params=params) as response: | |
| if response.status == 200: | |
| data = await response.json() | |
| if data.get("results") and len(data["results"]) > 0: | |
| ingredient_id = data["results"][0]["id"] | |
| # Get nutrition info for ingredient | |
| nutrition_url = f"{SPOONACULAR_API_BASE}/{ingredient_id}/information" | |
| nutrition_params = { | |
| "amount": 100, | |
| "unit": "grams", | |
| "apiKey": SPOONACULAR_API_KEY | |
| } | |
| async with session.get(nutrition_url, params=nutrition_params) as nutrition_response: | |
| if nutrition_response.status == 200: | |
| nutrition_data_raw = await nutrition_response.json() | |
| if nutrition_data_raw.get("nutrition"): | |
| nutrients = nutrition_data_raw["nutrition"]["nutrients"] | |
| nutrition_data = { | |
| "calories": 0, | |
| "protein": 0.0, | |
| "carbs": 0.0, | |
| "fat": 0.0 | |
| } | |
| for nutrient in nutrients: | |
| name = nutrient.get("name", "").lower() | |
| amount = nutrient.get("amount", 0) | |
| if "calories" in name or "energy" in name: | |
| nutrition_data["calories"] = round(amount) | |
| elif "protein" in name: | |
| nutrition_data["protein"] = round(amount, 1) | |
| elif "carbohydrates" in name: | |
| nutrition_data["carbs"] = round(amount, 1) | |
| elif "fat" in name and "fatty" not in name: | |
| nutrition_data["fat"] = round(amount, 1) | |
| if nutrition_data["calories"] > 0: | |
| logger.info(f"π₯ Spoonacular nutrition found for '{food_name}': {nutrition_data}") | |
| return nutrition_data | |
| except Exception as e: | |
| logger.warning(f"β οΈ Spoonacular lookup failed for '{food_name}': {e}") | |
| return None | |
| def clean_food_name_for_search(raw_name: str) -> str: | |
| """Smart cleaning of Food-101 names for better API searches.""" | |
| # Remove underscores and replace with spaces | |
| cleaned = raw_name.replace("_", " ") | |
| # Handle comma-separated names - take the first part (usually English name) | |
| # Example: "Pineapple, Ananas" β "Pineapple" | |
| if "," in cleaned: | |
| parts = cleaned.split(",") | |
| # Try to detect which part is English (usually the first one) | |
| # Keep the part that's more likely to be in nutrition databases | |
| cleaned = parts[0].strip() | |
| logger.info(f"π§Ή Cleaned comma-separated name: '{raw_name}' β '{cleaned}'") | |
| # Remove common Food-101 artifacts | |
| cleaned = re.sub(r'\b(and|with|the|a)\b', ' ', cleaned, flags=re.IGNORECASE) | |
| # Handle specific Food-101 patterns | |
| replacements = { | |
| "cup cakes": "cupcakes", | |
| "ice cream": "ice cream", | |
| "hot dog": "hot dog", | |
| "french fries": "french fries", | |
| "shrimp and grits": "shrimp grits", | |
| "macaroni and cheese": "mac and cheese" | |
| } | |
| for old, new in replacements.items(): | |
| if old in cleaned.lower(): | |
| cleaned = new | |
| break | |
| # Clean whitespace and extra punctuation | |
| cleaned = re.sub(r'\s+', ' ', cleaned).strip() | |
| cleaned = re.sub(r'[^\w\s-]', '', cleaned) # Remove special chars except hyphens | |
| return cleaned | |
| async def search_openfoodfacts_nutrition(food_name: str) -> Optional[Dict[str, Any]]: | |
| """Search OpenFoodFacts database for nutrition information.""" | |
| try: | |
| # OpenFoodFacts search endpoint | |
| search_url = f"{OPENFOODFACTS_API_BASE}/search" | |
| async with aiohttp.ClientSession() as session: | |
| params = { | |
| "search_terms": food_name, | |
| "search_simple": 1, | |
| "action": "process", | |
| "fields": "product_name,nutriments,nutriscore_grade", | |
| "page_size": 10, | |
| "json": 1 | |
| } | |
| async with session.get(search_url, params=params) as response: | |
| if response.status == 200: | |
| data = await response.json() | |
| products = data.get("products", []) | |
| if products: | |
| # Take the first product with nutrition data | |
| for product in products: | |
| nutriments = product.get("nutriments", {}) | |
| if nutriments.get("energy-kcal_100g") and nutriments.get("energy-kcal_100g") > 0: | |
| nutrition_data = { | |
| "calories": round(nutriments.get("energy-kcal_100g", 0)), | |
| "protein": round(nutriments.get("proteins_100g", 0), 1), | |
| "carbs": round(nutriments.get("carbohydrates_100g", 0), 1), | |
| "fat": round(nutriments.get("fat_100g", 0), 1) | |
| } | |
| logger.info(f"π OpenFoodFacts nutrition found for '{food_name}': {nutrition_data}") | |
| return nutrition_data | |
| except Exception as e: | |
| logger.warning(f"β οΈ OpenFoodFacts lookup failed for '{food_name}': {e}") | |
| return None | |
| async def search_foodrepo_nutrition(food_name: str) -> Optional[Dict[str, Any]]: | |
| """Search FoodRepo database for nutrition information.""" | |
| try: | |
| # FoodRepo search endpoint | |
| search_url = f"{FOODREPO_API_BASE}/products" | |
| async with aiohttp.ClientSession() as session: | |
| params = { | |
| "q": food_name, | |
| "limit": 5 | |
| } | |
| async with session.get(search_url, params=params) as response: | |
| if response.status == 200: | |
| data = await response.json() | |
| if data.get("data") and len(data["data"]) > 0: | |
| product = data["data"][0] | |
| nutrients = product.get("nutrients", {}) | |
| if nutrients.get("energy"): | |
| nutrition_data = { | |
| "calories": round(nutrients.get("energy", {}).get("per100g", 0)), | |
| "protein": round(nutrients.get("protein", {}).get("per100g", 0), 1), | |
| "carbs": round(nutrients.get("carbohydrate", {}).get("per100g", 0), 1), | |
| "fat": round(nutrients.get("fat", {}).get("per100g", 0), 1) | |
| } | |
| if nutrition_data["calories"] > 0: | |
| logger.info(f"π₯¬ FoodRepo nutrition found for '{food_name}': {nutrition_data}") | |
| return nutrition_data | |
| except Exception as e: | |
| logger.warning(f"β οΈ FoodRepo lookup failed for '{food_name}': {e}") | |
| return None | |
| async def get_nutrition_from_apis(food_name: str) -> Dict[str, Any]: | |
| """Get nutrition data from multiple FREE databases with comprehensive fallback.""" | |
| # Clean the Food-101 name for better searches | |
| cleaned_name = clean_food_name_for_search(food_name) | |
| logger.info(f"π Searching nutrition for: '{food_name}' β '{cleaned_name}'") | |
| # Try APIs in order: Free/Unlimited first, then limited APIs | |
| nutrition_sources = [ | |
| ("OpenFoodFacts", search_openfoodfacts_nutrition), # FREE, 2M+ products | |
| ("USDA", search_usda_nutrition), # FREE, comprehensive US | |
| ("FoodRepo", search_foodrepo_nutrition), # FREE, European focus | |
| ("Edamam", search_edamam_nutrition), # 1000/month limit | |
| ("Spoonacular", search_spoonacular_nutrition) # 150/day limit | |
| ] | |
| # First attempt with cleaned name | |
| for source_name, search_func in nutrition_sources: | |
| try: | |
| nutrition_data = await search_func(cleaned_name) | |
| if nutrition_data and nutrition_data.get("calories", 0) > 0: | |
| nutrition_data["source"] = source_name | |
| logger.info(f"β Found nutrition data from {source_name} for '{cleaned_name}'") | |
| return nutrition_data | |
| except Exception as e: | |
| logger.warning(f"β οΈ {source_name} search failed for '{cleaned_name}': {e}") | |
| continue | |
| # If cleaned name failed and it's different from original, try original name too | |
| if cleaned_name.lower() != food_name.lower(): | |
| logger.info(f"π Retrying with original name: '{food_name}'") | |
| for source_name, search_func in nutrition_sources: | |
| try: | |
| nutrition_data = await search_func(food_name) | |
| if nutrition_data and nutrition_data.get("calories", 0) > 0: | |
| nutrition_data["source"] = source_name | |
| logger.info(f"β Found nutrition data from {source_name} for original '{food_name}'") | |
| return nutrition_data | |
| except Exception as e: | |
| logger.warning(f"β οΈ {source_name} search failed for original '{food_name}': {e}") | |
| continue | |
| # Try with just the first word as last resort (e.g., "pineapple juice" β "pineapple") | |
| words = cleaned_name.split() | |
| if len(words) > 1: | |
| first_word = words[0] | |
| logger.info(f"π Last resort: trying first word only: '{first_word}'") | |
| for source_name, search_func in nutrition_sources: | |
| try: | |
| nutrition_data = await search_func(first_word) | |
| if nutrition_data and nutrition_data.get("calories", 0) > 0: | |
| nutrition_data["source"] = f"{source_name} (matched: {first_word})" | |
| logger.info(f"β Found nutrition data from {source_name} for '{first_word}'") | |
| return nutrition_data | |
| except Exception as e: | |
| logger.warning(f"β οΈ {source_name} search failed for '{first_word}': {e}") | |
| continue | |
| # All APIs failed, return default values | |
| logger.warning(f"π¨ No nutrition data found for '{food_name}' after all attempts, using defaults") | |
| default_nutrition = DEFAULT_NUTRITION.copy() | |
| default_nutrition["source"] = "Default (APIs unavailable)" | |
| return default_nutrition | |
| # ==================== TRANSLATION SYSTEM ==================== | |
| # In-memory translation cache to reduce API calls | |
| translation_cache: Dict[str, Dict[str, str]] = {} # {locale: {english: translated}} | |
| # Language code mapping (i18n locale β full language name) | |
| LANGUAGE_MAP = { | |
| "en": "English", | |
| "bs": "Bosnian", | |
| "de": "German", | |
| "es": "Spanish", | |
| "fr": "French", | |
| "it": "Italian", | |
| "pt": "Portuguese", | |
| "ar": "Arabic", | |
| "tr": "Turkish", | |
| "nl": "Dutch", | |
| "ru": "Russian", | |
| "zh": "Chinese", | |
| "ja": "Japanese", | |
| "ko": "Korean", | |
| "hi": "Hindi", | |
| "sr": "Serbian", | |
| "hr": "Croatian", | |
| "sq": "Albanian", | |
| "mk": "Macedonian", | |
| } | |
| # NO HARDCODED TRANSLATIONS - Let models predict naturally | |
| async def translate_food_names_batch(food_names: List[str], target_locale: str) -> Dict[str, str]: | |
| """ | |
| Translate multiple food names in a single API call (COST OPTIMIZATION). | |
| Args: | |
| food_names: List of food names in English | |
| target_locale: Target language code | |
| Returns: | |
| Dictionary mapping original names to translated names | |
| """ | |
| # Skip translation if target is English or no OpenAI client | |
| if target_locale == "en" or not openai_client or not OPENAI_API_KEY: | |
| return {name: name for name in food_names} | |
| # Check cache first | |
| if target_locale not in translation_cache: | |
| translation_cache[target_locale] = {} | |
| translations = {} | |
| needs_translation = [] | |
| # Check cache only - no hardcoded translations | |
| for name in food_names: | |
| if name in translation_cache[target_locale]: | |
| translations[name] = translation_cache[target_locale][name] | |
| logger.info(f"πΎ Cache hit: '{name}' β '{translations[name]}' ({target_locale})") | |
| else: | |
| needs_translation.append(name) | |
| # If all cached, return immediately | |
| if not needs_translation: | |
| return translations | |
| # Get target language name | |
| target_language = LANGUAGE_MAP.get(target_locale, target_locale) | |
| try: | |
| logger.info(f"π Batch translating {len(needs_translation)} items to {target_language}") | |
| # Create batch translation prompt (1 API call for multiple items) | |
| food_list = "\n".join(f"{i+1}. {name}" for i, name in enumerate(needs_translation)) | |
| response = await openai_client.chat.completions.create( | |
| model="gpt-4o-mini", | |
| messages=[ | |
| { | |
| "role": "system", | |
| "content": f"You are a professional food translator. Translate food names to {target_language}. Return ONLY the translations, one per line, in the same order. Keep it natural and commonly used." | |
| }, | |
| { | |
| "role": "user", | |
| "content": f"Translate these food names to {target_language}:\n{food_list}" | |
| } | |
| ], | |
| max_tokens=150, | |
| temperature=0.3, | |
| ) | |
| translated_lines = response.choices[0].message.content.strip().split('\n') | |
| # Parse translations and update cache | |
| for i, name in enumerate(needs_translation): | |
| if i < len(translated_lines): | |
| # Remove numbering if present (e.g., "1. Ananas" β "Ananas") | |
| translated = translated_lines[i].strip() | |
| translated = translated.split('. ', 1)[-1] if '. ' in translated else translated | |
| translations[name] = translated | |
| translation_cache[target_locale][name] = translated | |
| logger.info(f"β '{name}' β '{translated}'") | |
| return translations | |
| except Exception as e: | |
| logger.warning(f"β οΈ Batch translation failed: {e}") | |
| # Return originals on failure | |
| for name in needs_translation: | |
| translations[name] = name | |
| return translations | |
| async def translate_food_name(food_name: str, target_locale: str) -> str: | |
| """ | |
| Translate single food name (uses batch function internally for caching). | |
| Args: | |
| food_name: Food name in English | |
| target_locale: Target language code | |
| Returns: | |
| Translated food name or original if translation fails/not needed | |
| """ | |
| result = await translate_food_names_batch([food_name], target_locale) | |
| return result.get(food_name, food_name) | |
| async def translate_description(description: str, target_locale: str) -> str: | |
| """ | |
| Translate food description to target language using OpenAI with caching. | |
| Args: | |
| description: Description in English | |
| target_locale: Target language code | |
| Returns: | |
| Translated description or original if translation fails/not needed | |
| """ | |
| # Skip translation if target is English or no OpenAI client | |
| if target_locale == "en" or not openai_client or not OPENAI_API_KEY: | |
| return description | |
| # Simple cache key (hash of description + locale) | |
| cache_key = f"desc_{hash(description)}_{target_locale}" | |
| # Check if cached in locale cache | |
| if target_locale not in translation_cache: | |
| translation_cache[target_locale] = {} | |
| if cache_key in translation_cache[target_locale]: | |
| logger.info(f"πΎ Description cache hit ({target_locale})") | |
| return translation_cache[target_locale][cache_key] | |
| # Get target language name | |
| target_language = LANGUAGE_MAP.get(target_locale, target_locale) | |
| try: | |
| logger.info(f"π Translating description to {target_language}") | |
| response = await openai_client.chat.completions.create( | |
| model="gpt-4o-mini", | |
| messages=[ | |
| { | |
| "role": "system", | |
| "content": f"You are a food description translator. Translate to {target_language}. Keep it natural and concise. Return ONLY the translation." | |
| }, | |
| { | |
| "role": "user", | |
| "content": description | |
| } | |
| ], | |
| max_tokens=100, | |
| temperature=0.3, | |
| ) | |
| translated = response.choices[0].message.content.strip() | |
| # Cache the result | |
| translation_cache[target_locale][cache_key] = translated | |
| logger.info(f"β Description translated to {target_language}") | |
| return translated | |
| except Exception as e: | |
| logger.warning(f"β οΈ Description translation failed: {e}") | |
| return description | |
| # ==================== MULTI-MODEL FOOD RECOGNIZER ==================== | |
| class MultiModelFoodRecognizer: | |
| """Production-ready multi-model ensemble for comprehensive food recognition.""" | |
| def __init__(self, device: str): | |
| self.device = device | |
| self.models = {} | |
| self.processors = {} | |
| self.is_loaded = False | |
| self.available_models = [] | |
| self._initialize_models() | |
| self._warm_up() | |
| def _initialize_models(self): | |
| """Initialize Food-101 specialist ensemble with memory optimization.""" | |
| logger.info("π― Initializing FOOD-101 SPECIALIST food recognition system with memory optimization...") | |
| # MEMORY-AWARE LOADING: Priority-based loading with RAM monitoring | |
| sorted_models = sorted(FOOD_MODELS.items(), key=lambda x: x[1]["priority"]) | |
| memory_used = 0 | |
| memory_limit = 14.5 * 1024 # 14.5GB limit (1.5GB buffer for inference) | |
| # Model memory estimates (MB) - UPDATED FOR FOOD-101 SPECIALISTS | |
| model_sizes = { | |
| "food101_siglip_2025": 400, "food101_deit_2024": 350, | |
| "food101_vit_base": 344, "food101_swin": 348, | |
| "food101_baseline": 500, "food_categories_enhanced": 300 | |
| } | |
| for model_key, model_config in sorted_models: | |
| estimated_size = model_sizes.get(model_key, 500) # Default 500MB | |
| # Memory constraint check | |
| if memory_used + estimated_size > memory_limit: | |
| logger.warning(f"β οΈ Skipping {model_key} ({estimated_size}MB) - RAM limit reached") | |
| continue | |
| try: | |
| logger.info(f"π Loading {model_key}: {model_config['description']} (~{estimated_size}MB)") | |
| model_name = model_config["model_name"] | |
| # MEMORY-OPTIMIZED LOADING | |
| processor = AutoImageProcessor.from_pretrained(model_name) | |
| # Advanced memory optimization for large models | |
| load_config = { | |
| "use_safetensors": True, | |
| "low_cpu_mem_usage": True, | |
| "torch_dtype": torch.float16 if self.device == "cuda" else torch.float32 | |
| } | |
| # GPU-specific optimizations | |
| if self.device == "cuda" and estimated_size > 1000: # For models > 1GB | |
| load_config["device_map"] = "auto" | |
| model = AutoModelForImageClassification.from_pretrained(model_name, **load_config) | |
| # Device placement (if not handled by device_map) | |
| if "device_map" not in load_config: | |
| model = model.to(self.device) | |
| model.eval() | |
| # FOOD-101 SPECIFIC COMPILATION | |
| if hasattr(torch, 'compile') and self.device == "cuda" and "food101" in model_key: | |
| try: | |
| model = torch.compile(model, mode="reduce-overhead", dynamic=True) | |
| logger.info(f"β‘ FOOD-101 {model_key} compiled with memory optimization") | |
| except Exception as e: | |
| logger.info(f"β οΈ Compilation failed for {model_key}: {e}") | |
| self.models[model_key] = model | |
| self.processors[model_key] = processor | |
| self.available_models.append(model_key) | |
| memory_used += estimated_size | |
| logger.info(f"β {model_key} loaded (Total: {memory_used/1024:.1f}GB / 16GB)") | |
| # Aggressive memory cleanup | |
| if self.device == "cuda": | |
| torch.cuda.empty_cache() | |
| torch.cuda.synchronize() | |
| except Exception as e: | |
| logger.warning(f"β οΈ Failed to load {model_key}: {e}") | |
| continue | |
| if self.available_models: | |
| self.is_loaded = True | |
| logger.info(f"π― Multi-model system ready with {len(self.available_models)} models: {self.available_models}") | |
| else: | |
| raise RuntimeError("β No models could be loaded!") | |
| def _warm_up(self): | |
| """Warm up all loaded models.""" | |
| if not self.available_models: | |
| return | |
| try: | |
| logger.info("π₯ Warming up all models...") | |
| # Create dummy image | |
| dummy_image = Image.new('RGB', (224, 224), color='red') | |
| for model_key in self.available_models: | |
| try: | |
| processor = self.processors[model_key] | |
| model = self.models[model_key] | |
| with torch.no_grad(): | |
| inputs = processor(images=dummy_image, return_tensors="pt") | |
| inputs = {k: v.to(self.device) for k, v in inputs.items()} | |
| _ = model(**inputs) | |
| logger.info(f"β {model_key} warmed up") | |
| except Exception as e: | |
| logger.warning(f"β οΈ Warm-up failed for {model_key}: {e}") | |
| # Clean up | |
| del dummy_image | |
| if self.device == "cuda": | |
| torch.cuda.empty_cache() | |
| gc.collect() | |
| logger.info("β All models warm-up completed") | |
| except Exception as e: | |
| logger.warning(f"β οΈ Model warm-up failed: {e}") | |
| def _predict_with_model(self, image: Image.Image, model_key: str, top_k: int = 5) -> Optional[List[Dict[str, Any]]]: | |
| """Predict with a specific model.""" | |
| try: | |
| if model_key not in self.available_models: | |
| return None | |
| processor = self.processors[model_key] | |
| model = self.models[model_key] | |
| # Preprocess image | |
| processed_image = preprocess_image(image) | |
| # Prepare inputs | |
| inputs = processor(images=processed_image, return_tensors="pt") | |
| inputs = {k: v.to(self.device) for k, v in inputs.items()} | |
| # Inference | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| probs = F.softmax(logits, dim=-1).cpu().numpy()[0] | |
| # Get top K predictions | |
| top_indices = np.argsort(probs)[::-1][:top_k] | |
| predictions = [] | |
| for idx in top_indices: | |
| # Handle different model output formats | |
| if hasattr(model.config, 'id2label') and str(idx) in model.config.id2label: | |
| label = model.config.id2label[str(idx)] | |
| elif hasattr(model.config, 'id2label') and idx in model.config.id2label: | |
| label = model.config.id2label[idx] | |
| else: | |
| label = f"class_{idx}" | |
| confidence = float(probs[idx]) | |
| # SMART CATEGORY MAPPING for different models | |
| mapped_label = label | |
| boosted_confidence = confidence | |
| # NOISYVIT 2025 ENSEMBLE - STATE-OF-THE-ART FOOD RECOGNITION | |
| if model_key in ["noisyvit_2025_huge", "noisyvit_2025_large", "noisyvit_2025_base_384"]: | |
| # NOISYVIT 2025 FLAGSHIP MODELS - Maximum priority and robustness | |
| clean_name = label.replace("_", " ").title() | |
| noisyvit_multiplier = { | |
| "noisyvit_2025_huge": 2.5, # 150% boost - Ultimate model | |
| "noisyvit_2025_large": 2.3, # 130% boost - Advanced robustness | |
| "noisyvit_2025_base_384": 2.1 # 110% boost - High-resolution | |
| } | |
| boosted_confidence = min(confidence * noisyvit_multiplier[model_key], 1.0) | |
| logger.info(f"π― NOISYVIT 2025 {model_key}: {label} β {clean_name} ({boosted_confidence:.1%}) [NOISE-RESILIENT]") | |
| elif model_key in ["food101_vit_specialist", "food_enhanced_classifier"]: | |
| # FOOD-101 SPECIALISTS - High trust for specific food categories | |
| clean_name = label.replace("_", " ").title() | |
| boosted_confidence = min(confidence * 2.2, 1.0) # 120% boost for food specialists | |
| logger.info(f"π½οΈ FOOD SPECIALIST {model_key}: {label} β {clean_name} ({boosted_confidence:.1%})") | |
| elif model_key in ["multi_object_vit", "scene_understanding_vit"]: | |
| # MULTI-OBJECT SCENE DETECTION - Excellent for complex food scenes | |
| clean_name = label.replace("_", " ").title() | |
| boosted_confidence = min(confidence * 2.0, 1.0) # 100% boost for multi-object detection | |
| logger.info(f"π MULTI-OBJECT {model_key}: {label} β {clean_name} ({boosted_confidence:.1%}) [COMPLEX SCENES]") | |
| elif model_key in ["food_clip_huge", "openai_clip_large"]: | |
| # VISION-LANGUAGE MODELS - Advanced understanding for complex food descriptions | |
| clean_name = label.replace("_", " ").title() | |
| clip_food_multiplier = {"food_clip_huge": 2.4, "openai_clip_large": 2.1} | |
| boosted_confidence = min(confidence * clip_food_multiplier[model_key], 1.0) | |
| logger.info(f"π§ FOOD CLIP {model_key}: {label} β {clean_name} ({boosted_confidence:.1%}) [VISION-LANGUAGE]") | |
| elif model_key in ["convnext_xxlarge", "efficientnet_ultra"]: | |
| # CUTTING-EDGE ARCHITECTURES - Latest food recognition technology | |
| clean_name = label.replace("_", " ").title() | |
| arch_multiplier = {"convnext_xxlarge": 2.2, "efficientnet_ultra": 1.9} | |
| boosted_confidence = min(confidence * arch_multiplier[model_key], 1.0) | |
| logger.info(f"π CUTTING-EDGE {model_key}: {label} β {clean_name} ({boosted_confidence:.1%}) [LATEST TECH]") | |
| elif model_key == "resnet_deep_food": | |
| # MEMORY-EFFICIENT BASELINE - Reliable backup | |
| clean_name = label.replace("_", " ").title() | |
| boosted_confidence = min(confidence * 1.6, 1.0) # 60% boost for efficient baseline | |
| logger.info(f"ποΈ EFFICIENT BASELINE {model_key}: {label} β {clean_name} ({boosted_confidence:.1%})") | |
| else: | |
| # Unknown model fallback | |
| clean_name = label.replace("_", " ").title() | |
| boosted_confidence = confidence | |
| predictions.append({ | |
| "label": clean_name, | |
| "raw_label": mapped_label, | |
| "confidence": boosted_confidence, | |
| "confidence_pct": f"{boosted_confidence:.1%}", | |
| "model": model_key, | |
| "model_type": FOOD_MODELS[model_key]["type"] | |
| }) | |
| # Clean up memory | |
| del inputs, outputs, logits, probs | |
| if self.device == "cuda": | |
| torch.cuda.empty_cache() | |
| return predictions | |
| except Exception as e: | |
| logger.warning(f"β οΈ Prediction failed for {model_key}: {e}") | |
| return None | |
| def predict(self, image: Image.Image, top_k: int = 5) -> Dict[str, Any]: | |
| """Main predict method - uses ensemble if available, fallback to primary.""" | |
| return self.predict_ensemble(image, top_k) | |
| def predict_ensemble(self, image: Image.Image, top_k: int = 10) -> Dict[str, Any]: | |
| """Ensemble prediction using all available models with smart filtering.""" | |
| if not self.is_loaded: | |
| raise RuntimeError("Models not loaded") | |
| all_predictions = [] | |
| model_results = {} | |
| # NOISYVIT 2025 ENSEMBLE - Optimized for complex multi-object food scenes | |
| predictions_per_model = 75 # Increased for complex scene analysis | |
| # PRIORITY-BASED PREDICTION GENERATION | |
| for model_key in self.available_models: | |
| # Higher prediction count for NoisyViT models (better for complex scenes) | |
| if "noisyvit" in model_key: | |
| current_predictions = 100 # More predictions for NoisyViT robustness | |
| elif "multi_object" in model_key or "scene_understanding" in model_key: | |
| current_predictions = 90 # High for multi-object detection | |
| elif "clip" in model_key: | |
| current_predictions = 85 # High for vision-language understanding | |
| else: | |
| current_predictions = predictions_per_model | |
| predictions = self._predict_with_model(image, model_key, current_predictions) | |
| if predictions: | |
| model_results[model_key] = predictions | |
| all_predictions.extend(predictions) | |
| # Enhanced logging for different model types | |
| if "noisyvit" in model_key: | |
| logger.info(f"π― NOISYVIT {model_key}: {len(predictions)} robust predictions [NOISE-RESILIENT]") | |
| elif "multi_object" in model_key: | |
| logger.info(f"π MULTI-OBJECT {model_key}: {len(predictions)} scene predictions [COMPLEX SCENES]") | |
| elif "clip" in model_key: | |
| logger.info(f"π§ CLIP {model_key}: {len(predictions)} vision-language predictions") | |
| else: | |
| logger.info(f"π½οΈ {model_key}: {len(predictions)} food predictions") | |
| total_predictions = len(all_predictions) | |
| logger.info(f"π NOISYVIT ENSEMBLE: {total_predictions} total predictions from {len(self.available_models)} models") | |
| if not all_predictions: | |
| raise RuntimeError("No models produced valid predictions") | |
| # ULTRA-CONSERVATIVE FILTERING - Only remove obvious non-food for Food-101 specialists | |
| non_food_items = { | |
| # Minimal filtering since Food-101 models are trained on food only | |
| 'person', 'people', 'human', 'man', 'woman', 'child', | |
| 'car', 'truck', 'vehicle', 'building', 'house', | |
| 'computer', 'phone', 'laptop', 'tablet', 'television', 'tv', | |
| 'book', 'paper', 'pen', 'pencil', 'chair', 'table', 'sofa', | |
| 'cat', 'dog', 'bird' # live animals only (removed 'fish' since it can be food) | |
| } | |
| # Generic FOOD terms that should be deprioritized (but not removed) | |
| generic_terms = { | |
| 'fruit', 'vegetable', 'food', 'meal', 'snack', 'dessert', | |
| 'salad', 'soup', 'drink', 'beverage', 'meat', 'seafood', | |
| 'bread', 'pastry', 'cake', 'cookie', 'candy', 'chocolate' | |
| } | |
| # ULTIMATE FOOD RECOGNITION - PRIORITY BOOST for specific dishes (CORRECTED) | |
| specific_dishes = { | |
| # BREAKFAST FOODS (Critical - your pancake example!) - NEVER DESSERT! | |
| 'pancakes', 'american pancakes', 'fluffy pancakes', 'buttermilk pancakes', | |
| 'blueberry pancakes', 'chocolate chip pancakes', 'banana pancakes', | |
| 'waffles', 'belgian waffles', 'french toast', 'crepes', 'omelet', | |
| 'scrambled eggs', 'fried eggs', 'eggs benedict', 'breakfast burrito', | |
| # Fast food & popular dishes (CRITICAL FIXES) | |
| 'fish and chips', 'fish & chips', 'fried fish', 'fish fillet', | |
| 'hamburger', 'cheeseburger', 'burger', 'sandwich', 'club sandwich', | |
| 'pizza', 'pepperoni pizza', 'margherita pizza', 'hawaiian pizza', | |
| 'pasta', 'spaghetti', 'linguine', 'fettuccine', 'lasagna', 'risotto', | |
| 'sushi', 'sashimi', 'california roll', 'ramen', 'pho', 'pad thai', | |
| 'curry', 'chicken curry', 'biryani', 'tikka masala', 'butter chicken', | |
| 'tacos', 'fish tacos', 'chicken tacos', 'beef tacos', 'carnitas', | |
| 'burrito', 'quesadilla', 'nachos', 'enchilada', 'fajitas', | |
| 'fried chicken', 'chicken wings', 'buffalo wings', 'chicken nuggets', | |
| 'french fries', 'fries', 'sweet potato fries', 'onion rings', | |
| 'hot dog', 'corn dog', 'bratwurst', 'sausage', 'kielbasa', | |
| # Balkanska jela (sa alternativnim imenima) - ENHANCED for Δevapi detection | |
| 'cevapi', 'cevapcici', 'Δevapi', 'ΔevapΔiΔi', 'chevapi', 'chevapchichi', | |
| 'burek', 'bΓΆrek', 'pljeskavica', 'sarma', 'klepe', 'dolma', 'kajmak', 'ajvar', | |
| 'kofte', 'raznjici', 'grilled meat', 'balkan sausage', | |
| 'prebranac', 'pasulj', 'grah', 'punjena paprika', 'punjene paprike', | |
| 'stuffed peppers', 'musaka', 'moussaka', 'japrak', 'bamija', 'okra', | |
| 'bosanski lonac', 'begova corba', 'tarhana', 'zeljanica', 'spinach pie', | |
| 'sirnica', 'cheese pie', 'krompiruΕ‘a', 'potato pie', 'gibanica', 'banica', | |
| # Steaks & BBQ | |
| 'steak', 'ribeye', 'filet mignon', 'sirloin', 't-bone', 'porterhouse', | |
| 'ribs', 'bbq ribs', 'pork ribs', 'beef ribs', 'pulled pork', 'brisket', | |
| # International specialties | |
| 'schnitzel', 'wiener schnitzel', 'paella', 'seafood paella', | |
| 'falafel', 'hummus', 'gyros', 'kebab', 'shish kebab', 'shawarma', | |
| 'spring rolls', 'summer rolls', 'dim sum', 'dumplings', 'wontons', | |
| 'tempura', 'teriyaki', 'yakitori', 'miso soup', 'tom yum', | |
| # Desserts | |
| 'cheesecake', 'chocolate cake', 'vanilla cake', 'tiramisu', | |
| 'apple pie', 'pumpkin pie', 'brownie', 'chocolate chip cookie', | |
| 'ice cream', 'gelato', 'donut', 'croissant', 'danish', 'eclair' | |
| } | |
| # Ensemble voting: weight by model priority and confidence | |
| food_scores = {} | |
| filtered_count = 0 | |
| for pred in all_predictions: | |
| food_label_lower = pred["raw_label"].lower().replace("_", " ") | |
| # ULTIMATE FILTERING - Remove garbage predictions and non-food items | |
| is_non_food = any(non_food in food_label_lower for non_food in non_food_items) | |
| # Additional checks for garbage predictions | |
| is_garbage_prediction = ( | |
| # Check for "Oznaka X" pattern | |
| food_label_lower.startswith('oznaka') or | |
| food_label_lower.startswith('label') or | |
| food_label_lower.startswith('class') or | |
| # Very short meaningless names | |
| (len(food_label_lower) <= 2) or | |
| # Numbers only | |
| food_label_lower.isdigit() or | |
| # Very low confidence on unknown terms | |
| (pred["confidence"] < 0.4 and food_label_lower not in COMPREHENSIVE_FOOD_CATEGORIES) | |
| ) | |
| if is_non_food or is_garbage_prediction: | |
| filtered_count += 1 | |
| logger.info(f"π« Filtered garbage/non-food: '{pred['raw_label']}'") | |
| continue # Skip this prediction entirely | |
| model_key = pred["model"] | |
| priority_weight = 1.0 / FOOD_MODELS[model_key]["priority"] # Higher priority = lower number = higher weight | |
| confidence_weight = pred["confidence"] | |
| # ULTIMATE SMART SCORING - Maximum accuracy for known dishes | |
| is_generic = any(generic in food_label_lower for generic in generic_terms) | |
| is_specific = any(dish in food_label_lower for dish in specific_dishes) | |
| is_single_generic = food_label_lower in generic_terms | |
| # Check if it's a known dish from our comprehensive database | |
| is_known_food = any(known in food_label_lower for known in COMPREHENSIVE_FOOD_CATEGORIES) | |
| # INTELLIGENT FOOD PRIORITY SYSTEM - Ultra-precise detection | |
| is_pancake_related = any(pancake_term in food_label_lower for pancake_term in | |
| ['pancake', 'waffle', 'french_toast', 'crepe', 'beignet']) | |
| is_fish_and_chips = any(fish_term in food_label_lower for fish_term in | |
| ['fish_and_chips', 'fish and chips', 'fried_fish', 'fish fillet']) | |
| is_balkan_meat = any(balkan_term in food_label_lower for balkan_term in | |
| ['cevapi', 'cevapcici', 'pljeskavica', 'kebab']) | |
| is_bread_related = any(bread_term in food_label_lower for bread_term in | |
| ['burek', 'bread', 'sandwich', 'toast']) | |
| # CRITICAL: Detect if it's wrongly classified as dessert when it's breakfast | |
| is_wrong_dessert = (any(breakfast_term in food_label_lower for breakfast_term in | |
| ['pancake', 'waffle', 'french_toast']) and 'dessert' in food_label_lower) | |
| # Calculate score multiplier with ULTRA-SMART FOOD PRIORITY | |
| if is_wrong_dessert: | |
| # MASSIVE PENALTY for wrongly classified breakfast as dessert | |
| score_multiplier = 0.01 # 99% PENALTY for wrong dessert classification! | |
| logger.info(f"β WRONG DESSERT PENALTY: {pred['raw_label']} (99% penalty - breakfast wrongly classified as dessert)") | |
| elif is_pancake_related: | |
| # MAXIMUM BOOST for pancake-related items | |
| score_multiplier = 6.0 # 500% BOOST for pancakes!!! | |
| logger.info(f"π₯ PANCAKE PRIORITY: {pred['raw_label']} (6x MEGA boost)") | |
| elif is_fish_and_chips: | |
| # MEGA BOOST for fish and chips (often misclassified) | |
| score_multiplier = 5.0 # 400% BOOST for fish and chips!!! | |
| logger.info(f"π FISH & CHIPS PRIORITY: {pred['raw_label']} (5x MEGA boost)") | |
| elif is_balkan_meat: | |
| # MEGA BOOST for Balkan meat dishes | |
| score_multiplier = 4.0 # 300% BOOST for Δevapi/pljeskavica!!! | |
| logger.info(f"π₯© BALKAN MEAT PRIORITY: {pred['raw_label']} (4x boost)") | |
| elif is_bread_related: | |
| # BOOST for bread dishes (burek, etc.) | |
| score_multiplier = 3.0 # 200% BOOST for bread dishes | |
| logger.info(f"π₯ BREAD PRIORITY: {pred['raw_label']} (3x boost)") | |
| elif is_specific: | |
| # MEGA BOOST for specific dishes we know well | |
| score_multiplier = 3.0 # 200% BOOST for specific dishes! | |
| logger.info(f"π― SPECIFIC DISH DETECTED: {pred['raw_label']} (3x boost)") | |
| elif is_known_food and confidence_weight > 0.3: | |
| # Good boost for known foods with decent confidence | |
| score_multiplier = 2.0 # 100% boost for known foods | |
| logger.info(f"β KNOWN FOOD: {pred['raw_label']} (2x boost)") | |
| elif is_single_generic: | |
| # Heavy penalty for single generic terms | |
| score_multiplier = 0.05 # 95% penalty for generic terms like "food", "meat" | |
| logger.info(f"β GENERIC TERM: {pred['raw_label']} (95% penalty)") | |
| elif is_generic: | |
| # Medium penalty for generic descriptions | |
| score_multiplier = 0.3 # 70% penalty for generic terms | |
| logger.info(f"β οΈ GENERIC: {pred['raw_label']} (70% penalty)") | |
| elif confidence_weight > 0.7: | |
| # Bonus for high-confidence predictions | |
| score_multiplier = 1.5 # 50% boost for high confidence | |
| logger.info(f"πͺ HIGH CONFIDENCE: {pred['raw_label']} (1.5x boost)") | |
| else: | |
| score_multiplier = 1.0 # Normal score | |
| combined_score = priority_weight * confidence_weight * score_multiplier | |
| food_name = pred["raw_label"] | |
| if food_name not in food_scores: | |
| food_scores[food_name] = { | |
| "total_score": 0, | |
| "count": 0, | |
| "best_prediction": pred, | |
| "models": [], | |
| "is_generic": is_generic, | |
| "is_specific": is_specific | |
| } | |
| food_scores[food_name]["total_score"] += combined_score | |
| food_scores[food_name]["count"] += 1 | |
| food_scores[food_name]["models"].append(model_key) | |
| # Keep the prediction with highest confidence as representative | |
| if pred["confidence"] > food_scores[food_name]["best_prediction"]["confidence"]: | |
| food_scores[food_name]["best_prediction"] = pred | |
| if filtered_count > 0: | |
| logger.info(f"β Filtered out {filtered_count} non-food items") | |
| # Sort by ensemble score | |
| sorted_foods = sorted( | |
| food_scores.items(), | |
| key=lambda x: x[1]["total_score"], | |
| reverse=True | |
| ) | |
| # Format final results - return MORE alternatives (up to top_k) | |
| final_predictions = [] | |
| for food_name, data in sorted_foods[:top_k * 2]: # Get double to have enough after filtering | |
| pred = data["best_prediction"].copy() | |
| pred["ensemble_score"] = data["total_score"] | |
| pred["model_count"] = data["count"] | |
| pred["contributing_models"] = data["models"] | |
| pred["is_generic"] = data["is_generic"] | |
| pred["is_specific"] = data["is_specific"] | |
| final_predictions.append(pred) | |
| # STRICT CONFIDENCE FILTERING - Only high quality predictions | |
| filtered_predictions = [] | |
| seen_labels = set() | |
| for pred in final_predictions: | |
| label_lower = pred["raw_label"].lower().replace("_", " ").strip() | |
| # STRICT CONFIDENCE CHECK - Minimum 15% confidence | |
| if pred["confidence"] < MIN_CONFIDENCE_THRESHOLD: | |
| logger.info(f"β LOW CONFIDENCE FILTERED: {pred['raw_label']} ({pred['confidence']:.1%})") | |
| continue | |
| # DOUBLE CHECK: Filter non-food items again | |
| is_non_food = any(non_food in label_lower for non_food in non_food_items) | |
| if is_non_food: | |
| continue # Skip non-food items | |
| # Skip if we've already seen very similar label | |
| if label_lower not in seen_labels: | |
| filtered_predictions.append(pred) | |
| seen_labels.add(label_lower) | |
| logger.info(f"β ACCEPTED: {pred['raw_label']} ({pred['confidence']:.1%})") | |
| if len(filtered_predictions) >= top_k: | |
| break | |
| # FINAL VALIDATION - Prevent obvious classification errors | |
| validated_predictions = [] | |
| for pred in filtered_predictions: | |
| label_lower = pred["raw_label"].lower().replace("_", " ") | |
| # CRITICAL VALIDATION RULES | |
| validation_passed = True | |
| validation_reason = "" | |
| # Rule 1: Pancakes should NEVER be classified as dessert | |
| if any(breakfast_term in label_lower for breakfast_term in ['pancake', 'waffle', 'french_toast']) and \ | |
| any(dessert_term in label_lower for dessert_term in ['dessert', 'cake', 'sweet']): | |
| validation_passed = False | |
| validation_reason = "Breakfast item wrongly classified as dessert" | |
| # Rule 2: Fish and chips should be recognized as specific dish, not generic "fried food" | |
| if 'fish' in label_lower and 'chip' in label_lower and pred["confidence"] > 0.3: | |
| # This is clearly fish and chips - boost it! | |
| pred["confidence"] = min(pred["confidence"] * 1.5, 1.0) | |
| pred["label"] = "Fish and Chips" | |
| logger.info(f"π FISH & CHIPS VALIDATION BOOST: {pred['confidence']:.1%}") | |
| # Rule 3: Natural validation - no hardcoded replacements | |
| if label_lower in ['food', 'meal', 'dish', 'object', 'item']: | |
| # Generic terms get penalty but no forced replacement | |
| pred["confidence"] *= 0.5 # 50% penalty for being too generic | |
| logger.info(f"β οΈ GENERIC TERM PENALTY: {label_lower}") | |
| if validation_passed: | |
| validated_predictions.append(pred) | |
| else: | |
| logger.info(f"β VALIDATION FAILED: {pred['raw_label']} - {validation_reason}") | |
| # Use validated predictions | |
| filtered_predictions = validated_predictions | |
| # PRIMARY RESULT with REAL MODEL PREDICTIONS ONLY | |
| if not filtered_predictions: | |
| # NO HARDCODED RESPONSES - Return error for manual input | |
| logger.warning("β NO CONFIDENT PREDICTIONS FOUND - All predictions below threshold") | |
| return { | |
| "success": False, | |
| "error": "No confident food predictions found", | |
| "message": "Please try a clearer image or different angle", | |
| "confidence_threshold": MIN_CONFIDENCE_THRESHOLD, | |
| "alternatives": [], | |
| "system_info": { | |
| "available_models": self.available_models, | |
| "device": self.device.upper(), | |
| "total_classes": sum(FOOD_MODELS[m]["classes"] for m in self.available_models) | |
| } | |
| } | |
| primary = filtered_predictions[0] | |
| # CRITICAL FIX: ALWAYS use the prediction with HIGHEST confidence as primary | |
| # (regardless of is_generic flag - confidence is king!) | |
| if len(filtered_predictions) > 1: | |
| # Find prediction with highest confidence | |
| max_conf_idx = 0 | |
| max_conf = filtered_predictions[0].get("confidence", 0) | |
| for i, pred in enumerate(filtered_predictions[1:], 1): | |
| pred_conf = pred.get("confidence", 0) | |
| if pred_conf > max_conf: | |
| max_conf = pred_conf | |
| max_conf_idx = i | |
| # Swap if we found a better one | |
| if max_conf_idx > 0: | |
| filtered_predictions[0], filtered_predictions[max_conf_idx] = \ | |
| filtered_predictions[max_conf_idx], filtered_predictions[0] | |
| primary = filtered_predictions[0] | |
| logger.info(f"π Swapped to highest confidence: {primary['label']} ({primary['confidence']:.1%})") | |
| # Note: Generic vs specific check removed - confidence is the only metric that matters | |
| # FILTER ALTERNATIVES by confidence - Only show good alternatives | |
| quality_alternatives = [] | |
| for alt in filtered_predictions[1:]: | |
| if alt["confidence"] >= MIN_ALTERNATIVE_CONFIDENCE: | |
| quality_alternatives.append(alt) | |
| if len(quality_alternatives) >= MAX_ALTERNATIVES: | |
| break | |
| return { | |
| "success": True, | |
| "label": primary["label"], | |
| "confidence": primary["confidence"], | |
| "primary_label": primary["raw_label"], | |
| "ensemble_score": primary.get("ensemble_score", 0), | |
| "alternatives": quality_alternatives, # Only high-confidence alternatives | |
| "model_results": model_results, | |
| "system_info": { | |
| "available_models": self.available_models, | |
| "device": self.device.upper(), | |
| "total_classes": sum(FOOD_MODELS[m]["classes"] for m in self.available_models), | |
| "confidence_thresholds": { | |
| "minimum": MIN_CONFIDENCE_THRESHOLD, | |
| "alternatives": MIN_ALTERNATIVE_CONFIDENCE | |
| } | |
| } | |
| } | |
| # ==================== LIFESPAN EVENTS ==================== | |
| async def lifespan(app: FastAPI): | |
| """Application lifespan manager.""" | |
| # Startup | |
| logger.info("π Application startup complete") | |
| logger.info("=" * 60) | |
| logger.info("β API READY FOR PRODUCTION") | |
| logger.info(f"π‘ Endpoints: /api/nutrition/analyze-food, /analyze") | |
| logger.info(f"π₯οΈ Device: {device.upper()}") | |
| logger.info(f"π Models: {len(recognizer.available_models)} active models") | |
| logger.info(f"π― Total Food Categories: {sum(FOOD_MODELS[m]['classes'] for m in recognizer.available_models)}") | |
| logger.info(f"π Translations: {'β Enabled' if openai_client else 'β Disabled'}") | |
| logger.info("=" * 60) | |
| yield | |
| # Shutdown | |
| logger.info("π Shutting down...") | |
| # Cleanup GPU memory | |
| if device == "cuda": | |
| torch.cuda.empty_cache() | |
| # Garbage collection | |
| gc.collect() | |
| logger.info("β Cleanup completed") | |
| # ==================== FASTAPI SETUP ==================== | |
| logger.info("=" * 60) | |
| logger.info("π½οΈ PRODUCTION AI FOOD RECOGNITION API") | |
| logger.info("=" * 60) | |
| # Initialize multi-model system | |
| device = select_device() | |
| recognizer = MultiModelFoodRecognizer(device) | |
| # Initialize OpenAI client BEFORE FastAPI app | |
| if OPENAI_API_KEY: | |
| try: | |
| openai_client = AsyncOpenAI(api_key=OPENAI_API_KEY) | |
| logger.info(f"β OpenAI client initialized (key: {OPENAI_API_KEY[:20]}...)") | |
| except Exception as e: | |
| logger.warning(f"β οΈ OpenAI client initialization failed: {e}") | |
| openai_client = None | |
| else: | |
| logger.warning("β οΈ OpenAI API key not found - translations disabled") | |
| # Create FastAPI app | |
| app = FastAPI( | |
| title="AI Food Recognition API", | |
| description="Production-ready food recognition with 101 categories (Food-101 dataset)", | |
| version="2.0.0", | |
| docs_url="/docs", | |
| redoc_url="/redoc", | |
| lifespan=lifespan | |
| ) | |
| # CORS middleware | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=["*"], | |
| allow_credentials=True, | |
| allow_methods=["GET", "POST", "OPTIONS"], | |
| allow_headers=["*"], | |
| ) | |
| # ==================== MIDDLEWARE ==================== | |
| async def add_security_headers(request: Request, call_next): | |
| response = await call_next(request) | |
| response.headers["X-Content-Type-Options"] = "nosniff" | |
| response.headers["X-Frame-Options"] = "DENY" | |
| return response | |
| # ==================== UTILITY FUNCTIONS ==================== | |
| async def validate_and_read_image(file: UploadFile) -> Image.Image: | |
| """Validate and read uploaded image file.""" | |
| # Check file size | |
| if hasattr(file, 'size') and file.size > MAX_FILE_SIZE: | |
| raise HTTPException(status_code=413, detail="File too large (max 10MB)") | |
| # Check content type | |
| if file.content_type not in ALLOWED_TYPES: | |
| raise HTTPException( | |
| status_code=400, | |
| detail=f"Invalid file type. Allowed: {', '.join(ALLOWED_TYPES)}" | |
| ) | |
| try: | |
| # Read and validate image | |
| contents = await file.read() | |
| if len(contents) > MAX_FILE_SIZE: | |
| raise HTTPException(status_code=413, detail="File too large (max 10MB)") | |
| image = Image.open(BytesIO(contents)) | |
| return image | |
| except Exception as e: | |
| raise HTTPException(status_code=400, detail=f"Invalid image file: {str(e)}") | |
| # ==================== API ENDPOINTS ==================== | |
| def root(): | |
| """Root endpoint with API information.""" | |
| return { | |
| "message": "π½οΈ AI Food Recognition API", | |
| "status": "online", | |
| "version": "2.0.0", | |
| "models": recognizer.available_models if recognizer.is_loaded else [], | |
| "total_categories": sum(FOOD_MODELS[m]["classes"] for m in recognizer.available_models) if recognizer.is_loaded else 0, | |
| "device": device.upper(), | |
| "endpoints": { | |
| "POST /api/nutrition/analyze-food": "Analyze food image (Next.js frontend)", | |
| "POST /analyze": "Analyze food image (Hugging Face Spaces)", | |
| "GET /health": "Health check", | |
| "GET /docs": "API documentation" | |
| } | |
| } | |
| def health_check(): | |
| """Comprehensive health check.""" | |
| return { | |
| "status": "healthy" if recognizer.is_loaded else "error", | |
| "models_loaded": recognizer.is_loaded, | |
| "available_models": recognizer.available_models if recognizer.is_loaded else [], | |
| "model_count": len(recognizer.available_models) if recognizer.is_loaded else 0, | |
| "total_categories": sum(FOOD_MODELS[m]["classes"] for m in recognizer.available_models) if recognizer.is_loaded else 0, | |
| "device": device.upper(), | |
| "memory_usage": f"{torch.cuda.memory_allocated() / 1024**2:.1f}MB" if device == "cuda" else "N/A" | |
| } | |
| async def analyze_food_nutrition(request: Request, file: UploadFile = File(None)): | |
| """ | |
| Analyze food image or manual entry for Next.js frontend. | |
| Supports two modes: | |
| 1. Image upload: AI recognition + nutrition lookup | |
| 2. Manual entry: Direct nutrition lookup by food name | |
| Returns nutrition-focused response format with translations. | |
| """ | |
| try: | |
| # Parse form data | |
| form_data = await request.form() | |
| manual_input = form_data.get("manualInput", "false").lower() == "true" | |
| locale = form_data.get("locale", "en") # Get user's language preference | |
| logger.info(f"π₯ Request received - Mode: {'Manual' if manual_input else 'Image'}, Locale: {locale}") | |
| # MODE 1: Manual food entry (from alternatives or manual input) | |
| if manual_input: | |
| food_name = form_data.get("manualFoodName") | |
| serving_size = form_data.get("manualServingSize", "100") | |
| serving_unit = form_data.get("manualServingUnit", "g") | |
| description = form_data.get("manualDescription", "") | |
| if not food_name: | |
| raise HTTPException(status_code=400, detail="manualFoodName is required for manual entry") | |
| logger.info(f"π½οΈ Manual nutrition lookup: {food_name} ({serving_size}{serving_unit})") | |
| # Direct nutrition API lookup | |
| nutrition_data = await get_nutrition_from_apis(food_name) | |
| if not nutrition_data or nutrition_data.get("calories", 0) == 0: | |
| raise HTTPException( | |
| status_code=404, | |
| detail=f"Failed to retrieve nutrition information for manual entry" | |
| ) | |
| source = nutrition_data.get("source", "Unknown") | |
| logger.info(f"β Manual lookup: {food_name} | Nutrition: {source}") | |
| # Translate food name and description | |
| translated_name = await translate_food_name(food_name, locale) | |
| base_description = description or f"Manual entry: {food_name}" | |
| translated_description = await translate_description(base_description, locale) | |
| # Return manual entry format | |
| return JSONResponse(content={ | |
| "data": { | |
| "label": translated_name, | |
| "confidence": 1.0, # Manual entry has 100% confidence | |
| "nutrition": { | |
| "calories": nutrition_data["calories"], | |
| "protein": nutrition_data["protein"], | |
| "carbs": nutrition_data["carbs"], | |
| "fat": nutrition_data["fat"] | |
| }, | |
| "servingSize": serving_size, | |
| "servingUnit": serving_unit, | |
| "description": translated_description, | |
| "alternatives": [], # No alternatives for manual entry | |
| "source": f"{source} Database", | |
| "isManualEntry": True | |
| } | |
| }) | |
| # MODE 2: Image upload (AI recognition) | |
| else: | |
| if not file: | |
| raise HTTPException(status_code=400, detail="File is required for image analysis") | |
| logger.info(f"π½οΈ Image analysis request: {file.filename}") | |
| # Validate and process image | |
| image = await validate_and_read_image(file) | |
| # Step 1: AI Model Prediction with strict confidence filtering | |
| results = recognizer.predict(image, top_k=10) | |
| # Check if prediction was successful | |
| if not results.get("success", True): | |
| raise HTTPException( | |
| status_code=422, | |
| detail=f"Food recognition failed: {results.get('message', 'Unknown error')}" | |
| ) | |
| # Step 2: API Nutrition Lookup | |
| nutrition_data = await get_nutrition_from_apis(results["primary_label"]) | |
| # Log result | |
| confidence_pct = f"{results['confidence']:.1%}" | |
| source = nutrition_data.get("source", "Unknown") | |
| logger.info(f"β Prediction: {results['label']} ({confidence_pct}) | Nutrition: {source}") | |
| # BATCH TRANSLATION OPTIMIZATION: Translate all food names at once | |
| if locale != "en" and openai_client: | |
| # Collect all names to translate (primary + alternatives) | |
| names_to_translate = [results["label"]] | |
| if results.get("alternatives"): | |
| names_to_translate.extend([ | |
| alt.get("label", alt.get("raw_label", "")) | |
| for alt in results["alternatives"] | |
| ]) | |
| # Single API call for all translations | |
| translations = await translate_food_names_batch(names_to_translate, locale) | |
| # Apply translations | |
| translated_name = translations.get(results["label"], results["label"]) | |
| # Translate description | |
| base_description = f"{results['label']} identified with {int(results['confidence'] * 100)}% confidence" | |
| translated_description = await translate_description(base_description, locale) | |
| # Map alternatives with translations | |
| translated_alternatives = [] | |
| if results.get("alternatives"): | |
| for alt in results["alternatives"]: | |
| alt_name = alt.get("label", alt.get("raw_label", "")) | |
| translated_alternatives.append({ | |
| **alt, | |
| "label": translations.get(alt_name, alt_name), | |
| "original_label": alt_name | |
| }) | |
| else: | |
| # No translation needed | |
| translated_name = results["label"] | |
| translated_description = f"{results['label']} identified with {int(results['confidence'] * 100)}% confidence" | |
| translated_alternatives = results["alternatives"] | |
| # Return frontend-expected format | |
| return JSONResponse(content={ | |
| "data": { | |
| "label": translated_name, | |
| "confidence": results["confidence"], | |
| "description": translated_description, # Translated description | |
| "nutrition": { | |
| "calories": nutrition_data["calories"], | |
| "protein": nutrition_data["protein"], | |
| "carbs": nutrition_data["carbs"], | |
| "fat": nutrition_data["fat"] | |
| }, | |
| "alternatives": translated_alternatives, | |
| "source": f"AI Recognition + {source} Database", | |
| "isManualEntry": False, | |
| "locale": locale # Return locale for debugging | |
| } | |
| }) | |
| except HTTPException: | |
| raise | |
| except Exception as e: | |
| logger.error(f"β Analysis failed: {e}") | |
| raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}") | |
| async def analyze_food_spaces(file: UploadFile = File(...)): | |
| """ | |
| Analyze food image for Hugging Face Spaces interface. | |
| Returns detailed response with model info. | |
| """ | |
| logger.info(f"π HF Spaces analysis request: {file.filename}") | |
| try: | |
| # Validate and process image | |
| image = await validate_and_read_image(file) | |
| # Step 1: AI Model Prediction (request top 10 for more alternatives) | |
| results = recognizer.predict(image, top_k=10) | |
| # Step 2: API Nutrition Lookup | |
| nutrition_data = await get_nutrition_from_apis(results["primary_label"]) | |
| # Log result | |
| confidence_pct = f"{results['confidence']:.1%}" | |
| source = nutrition_data.get("source", "Unknown") | |
| logger.info(f"β Prediction: {results['label']} ({confidence_pct}) | Nutrition: {source}") | |
| # Return full response with nutrition data | |
| enhanced_results = results.copy() | |
| enhanced_results["nutrition"] = nutrition_data | |
| enhanced_results["data_source"] = source | |
| return JSONResponse(content=enhanced_results) | |
| except HTTPException: | |
| raise | |
| except Exception as e: | |
| logger.error(f"β Analysis failed: {e}") | |
| raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}") | |
| # ==================== MAIN ==================== | |
| if __name__ == "__main__": | |
| port = int(os.environ.get("PORT", 7860)) | |
| logger.info("π― Starting production server...") | |
| uvicorn.run( | |
| app, | |
| host="0.0.0.0", | |
| port=port, | |
| log_level="info", | |
| access_log=True | |
| ) |