versatile

.gitattributes

@@ -33,7 +33,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-nutri/images/*.jpg filter=lfs diff=lfs merge=lfs -text
-images/*.jpg filter=lfs diff=lfs merge=lfs -text
-images/*.png filter=lfs diff=lfs merge=lfs -text
-images/*.avif filter=lfs diff=lfs merge=lfs -text

app.py

@@ -2,8 +2,7 @@ import os
 import base64
 from io import BytesIO
 from PIL import Image
-from fastapi import FastAPI, HTTPException, Security
-from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 from transformers import pipeline
 from ultralytics import YOLO
@@ -13,37 +12,30 @@ import threading
 import logging
 import requests
 import asyncio
-import math
 
+# ==============================
 # Logging
+# ==============================
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+# ==============================
 # Models
+# ==============================
 food_classifier = pipeline("image-classification", model="nateraw/food")
 yolo_model = YOLO("yolov8n.pt")
 
 # USDA API config
 USDA_API_URL = "https://api.nal.usda.gov/fdc/v1/foods/search"
-USDA_API_KEY = os.getenv("USDA_API_KEY", "DEMO_KEY")  # Replace with your USDA API key
+USDA_API_KEY = os.getenv("USDA_API_KEY", "qktfia6caeuBSww2A5SYns8NaLlE2OuozHaEASzw")
 
 # FastAPI app
 app = FastAPI()
-security = HTTPBearer()
-
-# Debug endpoint to verify secrets
-@app.get("/debug_secrets")
-async def debug_secrets():
-    return {
-        "HUGGINGFACE_API_TOKEN": bool(os.getenv("HUGGINGFACE_API_TOKEN")),
-        "USDA_API_KEY": bool(os.getenv("USDA_API_KEY"))
-    }
 
 # Request schema
 class ImageRequest(BaseModel):
-    image: str
-    portion_size: float | None = None
-    reference_object_size: float | None = None
+    image: str  # Base64 image
+    portion_size: float = 100.0
 
 # Decode base64 image
 def decode_base64_image(base64_string):
@@ -55,109 +47,9 @@ def decode_base64_image(base64_string):
         logger.error(f"Image decoding failed: {str(e)}")
         raise HTTPException(status_code=400, detail="Invalid base64 image")
 
-# Estimate portion size based on bounding box area
-def estimate_portion_size(yolo_results, img_width, img_height, reference_object_size=None, food_labels=None, container_labels=None, top_food=None):
-    try:
-        food_area_pixels = 0
-        scaling_factor = None
-
-        # Default plate area (25 cm diameter -> ~490 cm²)
-        default_ref_area_cm2 = math.pi * (25 / 2) ** 2  # Area of a 25 cm diameter plate
-
-        # Validate reference_object_size
-        if reference_object_size is not None and reference_object_size <= 0:
-            logger.warning(f"Invalid reference_object_size ({reference_object_size} cm), using default plate size")
-            reference_object_size = None
-
-        # Check for reference object (e.g., plate, bowl)
-        if reference_object_size and container_labels:
-            for result in yolo_results:
-                for box, cls in zip(result.boxes.xyxy, result.boxes.cls):
-                    label = result.names[int(cls)]
-                    if label in container_labels:
-                        x1, y1, x2, y2 = map(int, box)
-                        container_area_pixels = (x2 - x1) * (y2 - y1)
-                        if container_area_pixels > 0:
-                            ref_area_cm2 = math.pi * (reference_object_size / 2) ** 2
-                            scaling_factor = ref_area_cm2 / container_area_pixels
-                            logger.info(f"Container detected: {label}, area={container_area_pixels} pixels, scaling_factor={scaling_factor}")
-                            break
-
-        # If no reference object or size, use default scaling
-        if not scaling_factor:
-            image_area_pixels = img_width * img_height
-            scaling_factor = default_ref_area_cm2 / (image_area_pixels * 0.5)
-            logger.info(f"No reference object, using default scaling: image_area={image_area_pixels} pixels, scaling_factor={scaling_factor}")
-
-        # Calculate food area
-        for result in yolo_results:
-            for box, cls in zip(result.boxes.xyxy, result.boxes.cls):
-                label = result.names[int(cls)]
-                if label in food_labels:
-                    x1, y1, x2, y2 = map(int, box)
-                    food_area_pixels += (x2 - x1) * (y2 - y1)
-
-        if food_area_pixels == 0:
-            logger.warning("No food detected for portion size estimation")
-            return 100  # Default portion size in grams
-
-        # Convert pixel area to cm²
-        food_area_cm2 = food_area_pixels * scaling_factor
-        logger.info(f"Food area: {food_area_pixels} pixels, {food_area_cm2} cm²")
-
-        # Adjust density for specific foods (e.g., akpu/fufu ~0.8 g/cm³)
-        density = 0.8 if top_food in ["akpu", "fufu"] else 1.0
-        portion_size_grams = food_area_cm2 * density
-
-        # Cap portion size to realistic range (50-500 g)
-        portion_size_grams = max(min(portion_size_grams, 500), 50)
-        logger.info(f"Estimated portion size: {portion_size_grams} grams (density={density} g/cm³)")
-
-        return portion_size_grams
-    except Exception as e:
-        logger.error(f"Portion size estimation failed: {str(e)}")
-        return 100  # Fallback to default portion size
-
 # Crop image to food or container
 def crop_image_to_food(img, yolo_results, 
-                       food_labels=[
-                           "chicken_curry", "pizza", "salad", "lasagna", "risotto",
-                           "akpu", "fufu", "egusi_soup", "jollof_rice", "pounded_yam",
-                           "banga_soup", "bitterleaf_soup", "edikaikong_soup", "okra_soup",
-                           "ogbono_soup", "nkwobi", "moin_moin", "puff_puff", "suya",
-                           "abacha", "amala", "ewa_agoyin", "ofada_stew", "pepper_soup",
-                           "kimchi", "bibimbap", "bulgogi", "japchae", "tteokbokki",
-                           "samgyeopsal", "kimchi_jjigae", "doenjang_jjigae", "sundubu_jjigae",
-                           "galbi", "kimbap", "jajangmyeon", "naengmyeon", "dakgalbi",
-                           "haemul_pajeon", "samgyetang", "bossam", "seolleongtang",
-                           "mandu", "yangnyeom_chicken", "gamjatang", "jokbal", "budae_jjigae",
-                           "haemul_tang", "dongtae_jjigae", "kongguksu", "mul_naengmyeon",
-                           "tteokguk", "miyeokguk",
-                           "sushi", "ramen", "udon", "tempura", "sashimi", "onigiri",
-                           "yakitori", "miso_soup", "okonomiyaki", "takoyaki",
-                           "donburi", "gyudon", "katsu_curry", "soba", "tonkatsu",
-                           "shabu_shabu", "natto", "unagi", "chawanmushi", "tamagoyaki",
-                           "yakisoba", "omurice", "kare_raisu", "oyakodon", "gyoza",
-                           "fried_rice", "dumplings", "mapo_tofu", "kung_pao_chicken",
-                           "sweet_sour_pork", "chow_mein", "spring_rolls", "peking_duck",
-                           "dim_sum", "hot_pot", "xiaolongbao", "char_siu", "wonton_soup",
-                           "egg_foo_young", "beef_broccoli", "szechuan_chicken", "lo_mein",
-                           "hunan_pork", "crispy_duck", "ma_la_tang", "dan_dan_noodles",
-                           "zha_jiang_mian", "lion_head_meatballs",
-                           "pad_thai", "tom_yum", "green_curry", "red_curry", "som_tam",
-                           "massaman_curry", "khao_soi", "pad_kra_pao", "tom_kha_gai",
-                           "larb", "panang_curry", "pad_see_ew", "khao_man_gai",
-                           "nam_tok", "gaeng_som", "khao_pad", "mango_sticky_rice",
-                           "satay_chicken", "thai_fried_rice", "tod_man_pla", "kuay_teow",
-                           "butter_chicken", "biryani", "paneer_tikka", "dal", "naan",
-                           "rogan_josh", "palak_paneer", "samosa", "chole", "tandoori_chicken",
-                           "aloo_gobi", "vindaloo", "dosa", "idli", "vada",
-                           "rajma", "pav_bhaji", "korma", "malai_kofta", "jalebi",
-                           "paratha", "bhindi_masala", "chicken_tikka_masala",
-                           "pho", "banh_mi", "laksa", "nasi_goreng", "rendang", "satay",
-                           "adobo", "sinigang", "hainan_chicken_rice", "char_kway_teow",
-                           "lechon", "soto_ayam", "bubur_ayam", "nasi_lemak", "mee_goreng"
-                       ], 
+                       food_labels=["chicken_curry", "pizza", "salad", "lasagna", "risotto"], 
                        container_labels=["bowl", "plate", "dish"]):
     try:
         for result in yolo_results:
@@ -177,19 +69,15 @@ def crop_image_to_food(img, yolo_results,
         logger.error(f"Cropping failed: {str(e)}")
         return img, False
 
-# Calculate nutrients using USDA API with fallback for Nigerian foods
+# Calculate nutrients
 def calculate_nutrients(food_items, portion_size):
     nutrients = {"protein": 0, "carbs": 0, "fat": 0, "fiber": 0, "sodium": 0}
     micronutrients = {"vitamin_c": 0, "calcium": 0, "iron": 0}
-    calories = 0
     top_food = max(food_items, key=food_items.get, default=None)
     if not top_food:
-        logger.warning("No food items detected")
-        return nutrients, micronutrients, calories
+        return nutrients, micronutrients, 0
 
     query_food = top_food.replace("_", " ")
-    if query_food == "akpu":
-        query_food = "fufu"  # Map akpu to fufu for USDA API
     try:
         response = requests.get(USDA_API_URL, params={
             "api_key": USDA_API_KEY,
@@ -199,24 +87,7 @@ def calculate_nutrients(food_items, portion_size):
         response.raise_for_status()
         data = response.json()
         if not data.get("foods"):
-            logger.warning(f"No USDA data found for {query_food}")
-            # Fallback for Nigerian foods like akpu/fufu
-            if query_food == "fufu":
-                nutrients = {
-                    "protein": 1.1 * (portion_size / 100),
-                    "carbs": 38.1 * (portion_size / 100),
-                    "fat": 0.2 * (portion_size / 100),
-                    "fiber": 1.6 * (portion_size / 100),
-                    "sodium": 0.02 * (portion_size / 100),
-                }
-                micronutrients = {
-                    "vitamin_c": 20.6 * (portion_size / 100),
-                    "calcium": 12 * (portion_size / 100),
-                    "iron": 0.7 * (portion_size / 100),
-                }
-                calories = (nutrients["protein"] * 4) + (nutrients["carbs"] * 4) + (nutrients["fat"] * 9)
-                return nutrients, micronutrients, round(calories, 2)
-            return nutrients, micronutrients, calories
+            return nutrients, micronutrients, 0
 
         food_data = data["foods"][0]
         food_nutrients = {n["nutrientName"]: n["value"] for n in food_data["foodNutrients"]}
@@ -226,7 +97,7 @@ def calculate_nutrients(food_items, portion_size):
             "carbs": food_nutrients.get("Carbohydrate, by difference", 0) * (portion_size / 100),
             "fat": food_nutrients.get("Total lipid (fat)", 0) * (portion_size / 100),
             "fiber": food_nutrients.get("Fiber, total dietary", 0) * (portion_size / 100),
-            "sodium": food_nutrients.get("Sodium, Na", 0) * (portion_size / 100) / 1000,
+            "sodium": food_nutrients.get("Sodium, Na", 0) * (portion_size / 100),
         }
         micronutrients = {
             "vitamin_c": food_nutrients.get("Vitamin C, total ascorbic acid", 0) * (portion_size / 100),
@@ -234,146 +105,30 @@ def calculate_nutrients(food_items, portion_size):
             "iron": food_nutrients.get("Iron, Fe", 0) * (portion_size / 100),
         }
         calories = (nutrients["protein"] * 4) + (nutrients["carbs"] * 4) + (nutrients["fat"] * 9)
+        return nutrients, micronutrients, calories
     except Exception as e:
         logger.error(f"USDA API request failed: {str(e)}")
-        # Fallback for Nigerian foods if API fails
-        if query_food == "fufu":
-            nutrients = {
-                "protein": 1.1 * (portion_size / 100),
-                "carbs": 38.1 * (portion_size / 100),
-                "fat": 0.2 * (portion_size / 100),
-                "fiber": 1.6 * (portion_size / 100),
-                "sodium": 0.02 * (portion_size / 100),
-            }
-            micronutrients = {
-                "vitamin_c": 20.6 * (portion_size / 100),
-                "calcium": 12 * (portion_size / 100),
-                "iron": 0.7 * (portion_size / 100),
-            }
-            calories = (nutrients["protein"] * 4) + (nutrients["carbs"] * 4) + (nutrients["fat"] * 9)
-        else:
-            raise HTTPException(status_code=500, detail=f"Failed to fetch nutrient data: {str(e)}")
-
-    return nutrients, micronutrients, round(calories, 2)
+        return nutrients, micronutrients, 0
 
+# ==============================
 # FastAPI endpoint
+# ==============================
 @app.post("/analyze_food")
-async def analyze_food(request: ImageRequest, credentials: HTTPAuthorizationCredentials = Security(security)):
+async def analyze_food(request: ImageRequest):
     try:
-        logger.info(f"Received request: portion_size={request.portion_size}, reference_object_size={request.reference_object_size}")
-        
-        # Validate API token
-        expected_token = os.getenv("HUGGINGFACE_API_TOKEN")
-        if not expected_token:
-            logger.error("Hugging Face API token not configured")
-            raise HTTPException(status_code=500, detail="Hugging Face API token not configured")
-        if credentials.credentials != expected_token:
-            raise HTTPException(status_code=401, detail="Invalid API token")
-
         img = decode_base64_image(request.image)
         yolo_results = yolo_model(img)
-
-        # Food classification to get top_food for density adjustment
         cropped_img, was_cropped = crop_image_to_food(img, yolo_results)
+
+        # Food classification
         food_results = food_classifier(cropped_img)
         food_items = {r["label"]: r["score"] for r in food_results if r["score"] >= 0.3}
-        top_food = max(food_items, key=food_items.get, default=None)
-
-        # Estimate portion size if not provided
-        food_labels = [
-            "chicken_curry", "pizza", "salad", "lasagna", "risotto",
-            "akpu", "fufu", "egusi_soup", "jollof_rice", "pounded_yam",
-            "banga_soup", "bitterleaf_soup", "edikaikong_soup", "okra_soup",
-            "ogbono_soup", "nkwobi", "moin_moin", "puff_puff", "suya",
-            "abacha", "amala", "ewa_agoyin", "ofada_stew", "pepper_soup",
-            "kimchi", "bibimbap", "bulgogi", "japchae", "tteokbokki",
-            "samgyeopsal", "kimchi_jjigae", "doenjang_jjigae", "sundubu_jjigae",
-            "galbi", "kimbap", "jajangmyeon", "naengmyeon", "dakgalbi",
-            "haemul_pajeon", "samgyetang", "bossam", "seolleongtang",
-            "mandu", "yangnyeom_chicken", "gamjatang", "jokbal", "budae_jjigae",
-            "haemul_tang", "dongtae_jjigae", "kongguksu", "mul_naengmyeon",
-            "tteokguk", "miyeokguk",
-            "sushi", "ramen", "udon", "tempura", "sashimi", "onigiri",
-            "yakitori", "miso_soup", "okonomiyaki", "takoyaki",
-            "donburi", "gyudon", "katsu_curry", "soba", "tonkatsu",
-            "shabu_shabu", "natto", "unagi", "chawanmushi", "tamagoyaki",
-            "yakisoba", "omurice", "kare_raisu", "oyakodon", "gyoza",
-            "fried_rice", "dumplings", "mapo_tofu", "kung_pao_chicken",
-            "sweet_sour_pork", "chow_mein", "spring_rolls", "peking_duck",
-            "dim_sum", "hot_pot", "xiaolongbao", "char_siu", "wonton_soup",
-            "egg_foo_young", "beef_broccoli", "szechuan_chicken", "lo_mein",
-            "hunan_pork", "crispy_duck", "ma_la_tang", "dan_dan_noodles",
-            "zha_jiang_mian", "lion_head_meatballs",
-            "pad_thai", "tom_yum", "green_curry", "red_curry", "som_tam",
-            "massaman_curry", "khao_soi", "pad_kra_pao", "tom_kha_gai",
-            "larb", "panang_curry", "pad_see_ew", "khao_man_gai",
-            "nam_tok", "gaeng_som", "khao_pad", "mango_sticky_rice",
-            "satay_chicken", "thai_fried_rice", "tod_man_pla", "kuay_teow",
-            "butter_chicken", "biryani", "paneer_tikka", "dal", "naan",
-            "rogan_josh", "palak_paneer", "samosa", "chole", "tandoori_chicken",
-            "aloo_gobi", "vindaloo", "dosa", "idli", "vada",
-            "rajma", "pav_bhaji", "korma", "malai_kofta", "jalebi",
-            "paratha", "bhindi_masala", "chicken_tikka_masala",
-            "pho", "banh_mi", "laksa", "nasi_goreng", "rendang", "satay",
-            "adobo", "sinigang", "hainan_chicken_rice", "char_kway_teow",
-            "lechon", "soto_ayam", "bubur_ayam", "nasi_lemak", "mee_goreng"
-        ]
-        container_labels = ["bowl", "plate", "dish"]
-
-        portion_size = request.portion_size
-        if portion_size is None or portion_size <= 0:
-            portion_size = estimate_portion_size(
-                yolo_results,
-                img_width=img.width,
-                img_height=img.height,
-                reference_object_size=request.reference_object_size,
-                food_labels=food_labels,
-                container_labels=container_labels,
-                top_food=top_food
-            )
-            logger.info(f"Estimated portion size: {portion_size} grams")
 
-        # Whitelist food labels
+        # Fix: whitelist food labels
         food_label_whitelist = [
             "pizza", "salad", "chicken", "chicken_wings", "shrimp_and_grits",
-            "lasagna", "risotto", "burger", "sandwich", "pasta",
-            "akpu", "fufu", "egusi_soup", "jollof_rice", "pounded_yam",
-            "banga_soup", "bitterleaf_soup", "edikaikong_soup", "okra_soup",
-            "ogbono_soup", "nkwobi", "moin_moin", "puff_puff", "suya",
-            "abacha", "amala", "ewa_agoyin", "ofada_stew", "pepper_soup",
-            "kimchi", "bibimbap", "bulgogi", "japchae", "tteokbokki",
-            "samgyeopsal", "kimchi_jjigae", "doenjang_jjigae", "sundubu_jjigae",
-            "galbi", "kimbap", "jajangmyeon", "naengmyeon", "dakgalbi",
-            "haemul_pajeon", "samgyetang", "bossam", "seolleongtang",
-            "mandu", "yangnyeom_chicken", "gamjatang", "jokbal", "budae_jjigae",
-            "haemul_tang", "dongtae_jjigae", "kongguksu", "mul_naengmyeon",
-            "tteokguk", "miyeokguk",
-            "sushi", "ramen", "udon", "tempura", "sashimi", "onigiri",
-            "yakitori", "miso_soup", "okonomiyaki", "takoyaki",
-            "donburi", "gyudon", "katsu_curry", "soba", "tonkatsu",
-            "shabu_shabu", "natto", "unagi", "chawanmushi", "tamagoyaki",
-            "yakisoba", "omurice", "kare_raisu", "oyakodon", "gyoza",
-            "fried_rice", "dumplings", "mapo_tofu", "kung_pao_chicken",
-            "sweet_sour_pork", "chow_mein", "spring_rolls", "peking_duck",
-            "dim_sum", "hot_pot", "xiaolongbao", "char_siu", "wonton_soup",
-            "egg_foo_young", "beef_broccoli", "szechuan_chicken", "lo_mein",
-            "hunan_pork", "crispy_duck", "ma_la_tang", "dan_dan_noodles",
-            "zha_jiang_mian", "lion_head_meatballs",
-            "pad_thai", "tom_yum", "green_curry", "red_curry", "som_tam",
-            "massaman_curry", "khao_soi", "pad_kra_pao", "tom_kha_gai",
-            "larb", "panang_curry", "pad_see_ew", "khao_man_gai",
-            "nam_tok", "gaeng_som", "khao_pad", "mango_sticky_rice",
-            "satay_chicken", "thai_fried_rice", "tod_man_pla", "kuay_teow",
-            "butter_chicken", "biryani", "paneer_tikka", "dal", "naan",
-            "rogan_josh", "palak_paneer", "samosa", "chole", "tandoori_chicken",
-            "aloo_gobi", "vindaloo", "dosa", "idli", "vada",
-            "rajma", "pav_bhaji", "korma", "malai_kofta", "jalebi",
-            "paratha", "bhindi_masala", "chicken_tikka_masala",
-            "pho", "banh_mi", "laksa", "nasi_goreng", "rendang", "satay",
-            "adobo", "sinigang", "hainan_chicken_rice", "char_kway_teow",
-            "lechon", "soto_ayam", "bubur_ayam", "nasi_lemak", "mee_goreng"
+            "lasagna", "risotto", "burger", "sandwich", "pasta"
         ]
-
         non_food_items = [
             r.names[int(cls)]
             for r in yolo_results
@@ -382,171 +137,16 @@ async def analyze_food(request: ImageRequest, credentials: HTTPAuthorizationCred
         ]
 
         is_non_food = len(non_food_items) > len(food_items) and max(food_items.values(), default=0) < 0.5
-        nutrients, micronutrients, calories = calculate_nutrients(food_items, portion_size)
+
+        nutrients, micronutrients, calories = calculate_nutrients(food_items, request.portion_size)
 
         ingredient_map = {
             "pizza": ["dough", "tomato sauce", "cheese"],
             "salad": ["lettuce", "tomato", "cucumber"],
             "chicken_curry": ["chicken", "curry sauce", "spices"],
             "lasagna": ["pasta", "tomato sauce", "cheese", "meat"],
-            "risotto": ["rice", "broth", "parmesan"],
-            "akpu": ["cassava", "water"],
-            "fufu": ["cassava", "water"],
-            "egusi_soup": ["egusi seeds", "vegetables", "palm oil", "meat"],
-            "jollof_rice": ["rice", "tomato", "pepper", "onion"],
-            "pounded_yam": ["yam", "water"],
-            "banga_soup": ["palm fruit", "fish", "spices"],
-            "bitterleaf_soup": ["bitterleaf", "meat", "palm oil"],
-            "edikaikong_soup": ["waterleaf", "ugwu", "meat", "periwinkle"],
-            "okra_soup": ["okra", "meat", "palm oil"],
-            "ogbono_soup": ["ogbono seeds", "meat", "palm oil"],
-            "nkwobi": ["cow foot", "palm oil", "spices"],
-            "moin_moin": ["beans", "pepper", "oil"],
-            "puff_puff": ["flour", "sugar", "yeast"],
-            "suya": ["beef", "peanut spice", "onion"],
-            "abacha": ["cassava", "palm oil", "fish"],
-            "amala": ["yam flour", "water"],
-            "ewa_agoyin": ["beans", "palm oil", "pepper"],
-            "ofada_stew": ["pepper", "locust beans", "meat"],
-            "pepper_soup": ["meat", "pepper", "spices"],
-            "kimchi": ["napa cabbage", "chili powder", "garlic", "ginger"],
-            "bibimbap": ["rice", "mixed vegetables", "gochujang", "egg"],
-            "bulgogi": ["beef", "soy sauce", "garlic", "sesame oil"],
-            "japchae": ["sweet potato noodles", "vegetables", "soy sauce", "beef"],
-            "tteokbokki": ["rice cakes", "red chili paste", "fish cakes"],
-            "samgyeopsal": ["pork belly", "garlic", "sesame oil"],
-            "kimchi_jjigae": ["kimchi", "pork", "tofu", "green onions"],
-            "doenjang_jjigae": ["soybean paste", "tofu", "vegetables", "mushrooms"],
-            "sundubu_jjigae": ["soft tofu", "seafood", "chili paste", "egg"],
-            "galbi": ["short ribs", "soy sauce", "garlic", "sugar"],
-            "kimbap": ["rice", "seaweed", "vegetables", "meat"],
-            "jajangmyeon": ["noodles", "black bean sauce", "pork", "vegetables"],
-            "naengmyeon": ["buckwheat noodles", "beef broth", "cucumber", "egg"],
-            "dakgalbi": ["chicken", "gochujang", "cabbage", "sweet potato"],
-            "haemul_pajeon": ["seafood", "green onions", "flour", "egg"],
-            "samgyetang": ["chicken", "ginseng", "jujube", "rice"],
-            "bossam": ["pork belly", "cabbage", "garlic", "ssamjang"],
-            "seolleongtang": ["beef", "bone broth", "noodles", "green onions"],
-            "mandu": ["dumpling wrapper", "pork", "cabbage", "garlic"],
-            "yangnyeom_chicken": ["chicken", "gochujang", "soy sauce", "honey"],
-            "gamjatang": ["pork spine", "potato", "perilla leaves", "chili"],
-            "jokbal": ["pig's feet", "soy sauce", "ginger", "garlic"],
-            "budae_jjigae": ["sausage", "spam", "kimchi", "noodles"],
-            "haemul_tang": ["seafood", "radish", "chili", "broth"],
-            "dongtae_jjigae": ["pollack", "tofu", "radish", "chili"],
-            "kongguksu": ["soybean noodles", "soy milk", "cucumber", "sesame"],
-            "mul_naengmyeon": ["buckwheat noodles", "cold broth", "beef", "egg"],
-            "tteokguk": ["rice cake", "beef broth", "egg", "seaweed"],
-            "miyeokguk": ["seaweed", "beef", "soy sauce", "garlic"],
-            "sushi": ["rice", "raw fish", "seaweed", "vinegar"],
-            "ramen": ["noodles", "broth", "pork", "seaweed"],
-            "udon": ["thick noodles", "broth", "green onions", "fish cake"],
-            "tempura": ["shrimp", "vegetables", "batter", "soy dipping sauce"],
-            "sashimi": ["raw fish", "soy sauce", "wasabi"],
-            "onigiri": ["rice", "seaweed", "fish", "pickled plum"],
-            "yakitori": ["chicken", "skewers", "soy sauce", "mirin"],
-            "miso_soup": ["miso paste", "tofu", "seaweed", "green onions"],
-            "okonomiyaki": ["cabbage", "batter", "sauce", "bonito flakes"],
-            "takoyaki": ["octopus", "batter", "sauce", "bonito flakes"],
-            "donburi": ["rice", "meat", "egg", "onion"],
-            "gyudon": ["beef", "rice", "onion", "soy sauce"],
-            "katsu_curry": ["breaded cutlet", "curry sauce", "rice"],
-            "soba": ["buckwheat noodles", "soy dipping sauce", "green onions"],
-            "tonkatsu": ["pork cutlet", "bread crumbs", "cabbage", "sauce"],
-            "shabu_shabu": ["beef", "vegetables", "broth", "dipping sauce"],
-            "natto": ["fermented soybeans", "soy sauce", "mustard"],
-            "unagi": ["eel", "soy sauce", "mirin", "rice"],
-            "chawanmushi": ["egg custard", "shrimp", "mushrooms", "gingko"],
-            "tamagoyaki": ["egg", "soy sauce", "mirin", "sugar"],
-            "yakisoba": ["noodles", "pork", "cabbage", "sauce"],
-            "omurice": ["rice", "egg", "ketchup", "chicken"],
-            "kare_raisu": ["curry", "rice", "carrot", "potato"],
-            "oyakodon": ["chicken", "egg", "onion", "rice"],
-            "gyoza": ["dumpling wrapper", "pork", "cabbage", "garlic"],
-            "fried_rice": ["rice", "egg", "vegetables", "soy sauce"],
-            "dumplings": ["pork", "cabbage", "wrapper", "ginger"],
-            "mapo_tofu": ["tofu", "ground pork", "sichuan pepper", "chili oil"],
-            "kung_pao_chicken": ["chicken", "peanuts", "chili peppers", "soy sauce"],
-            "sweet_sour_pork": ["pork", "pineapple", "bell peppers", "sweet sour sauce"],
-            "chow_mein": ["noodles", "vegetables", "meat", "soy sauce"],
-            "spring_rolls": ["wrapper", "cabbage", "carrot", "pork"],
-            "peking_duck": ["duck", "pancakes", "hoisin sauce", "cucumber"],
-            "dim_sum": ["various fillings", "wrapper", "bamboo steamer"],
-            "hot_pot": ["broth", "beef", "vegetables", "tofu"],
-            "xiaolongbao": ["pork", "soup", "wrapper", "ginger"],
-            "char_siu": ["pork", "honey", "soy sauce", "hoisin"],
-            "wonton_soup": ["wontons", "broth", "shrimp", "pork"],
-            "egg_foo_young": ["egg", "vegetables", "meat", "gravy"],
-            "beef_broccoli": ["beef", "broccoli", "soy sauce", "garlic"],
-            "szechuan_chicken": ["chicken", "sichuan pepper", "chili", "peanuts"],
-            "lo_mein": ["noodles", "vegetables", "meat", "soy sauce"],
-            "hunan_pork": ["pork", "chili", "garlic", "soy sauce"],
-            "crispy_duck": ["duck", "soy sauce", "spices", "hoisin"],
-            "ma_la_tang": ["broth", "noodles", "vegetables", "spices"],
-            "dan_dan_noodles": ["noodles", "pork", "sichuan pepper", "peanut sauce"],
-            "zha_jiang_mian": ["noodles", "pork", "bean sauce", "cucumber"],
-            "lion_head_meatballs": ["pork", "water chestnuts", "egg", "broth"],
-            "pad_thai": ["rice noodles", "shrimp", "tamarind paste", "peanuts"],
-            "tom_yum": ["shrimp", "lemongrass", "chili", "galangal"],
-            "green_curry": ["coconut milk", "green chili", "chicken", "bamboo shoots"],
-            "red_curry": ["coconut milk", "red chili", "chicken", "basil"],
-            "som_tam": ["green papaya", "chili", "lime", "fish sauce"],
-            "massaman_curry": ["coconut milk", "beef", "potatoes", "peanuts"],
-            "khao_soi": ["egg noodles", "coconut curry", "chicken", "chili"],
-            "pad_kra_pao": ["basil", "chicken", "chili", "fish sauce"],
-            "tom_kha_gai": ["coconut milk", "chicken", "galangal", "lemongrass"],
-            "larb": ["minced pork", "lime", "fish sauce", "chili"],
-            "panang_curry": ["coconut milk", "peanut", "chicken", "chili"],
-            "pad_see_ew": ["wide noodles", "soy sauce", "chicken", "broccoli"],
-            "khao_man_gai": ["chicken", "rice", "cucumber", "chili sauce"],
-            "nam_tok": ["beef", "lime", "fish sauce", "chili"],
-            "gaeng_som": ["fish", "tamarind", "chili", "vegetables"],
-            "khao_pad": ["rice", "shrimp", "egg", "soy sauce"],
-            "mango_sticky_rice": ["sticky rice", "mango", "coconut milk"],
-            "satay_chicken": ["chicken", "peanut sauce", "skewers"],
-            "thai_fried_rice": ["rice", "shrimp", "egg", "fish sauce"],
-            "tod_man_pla": ["fish cakes", "chili paste", "lime leaves"],
-            "kuay_teow": ["rice noodles", "broth", "beef", "herbs"],
-            "butter_chicken": ["chicken", "tomato", "butter", "cream"],
-            "biryani": ["rice", "chicken", "spices", "yogurt"],
-            "paneer_tikka": ["paneer", "spices", "yogurt", "bell peppers"],
-            "dal": ["lentils", "spices", "tomato", "ghee"],
-            "naan": ["flour", "yeast", "butter", "yogurt"],
-            "rogan_josh": ["lamb", "yogurt", "spices", "tomato"],
-            "palak_paneer": ["spinach", "paneer", "spices", "cream"],
-            "samosa": ["pastry", "potato", "peas", "spices"],
-            "chole": ["chickpeas", "tomato", "spices", "onion"],
-            "tandoori_chicken": ["chicken", "yogurt", "spices", "lemon"],
-            "aloo_gobi": ["potato", "cauliflower", "spices", "tomato"],
-            "vindaloo": ["pork", "vinegar", "chili", "spices"],
-            "dosa": ["rice batter", "lentils", "potato filling"],
-            "idli": ["rice", "lentils", "steamed"],
-            "vada": ["lentils", "spices", "fried"],
-            "rajma": ["kidney beans", "tomato", "spices", "onion"],
-            "pav_bhaji": ["mixed vegetables", "spices", "butter", "bun"],
-            "korma": ["chicken", "yogurt", "cream", "spices"],
-            "malai_kofta": ["paneer balls", "cream", "tomato", "spices"],
-            "jalebi": ["flour", "sugar syrup", "saffron"],
-            "paratha": ["flour", "ghee", "stuffed vegetables"],
-            "bhindi_masala": ["okra", "spices", "tomato", "onion"],
-            "chicken_tikka_masala": ["chicken", "tomato", "cream", "spices"],
-            "pho": ["rice noodles", "beef", "broth", "herbs"],
-            "banh_mi": ["baguette", "pork", "pickled vegetables", "cilantro"],
-            "laksa": ["coconut milk", "noodles", "chicken", "chili"],
-            "nasi_goreng": ["rice", "chicken", "shrimp paste", "egg"],
-            "rendang": ["beef", "coconut milk", "lemongrass", "spices"],
-            "satay": ["chicken", "peanut sauce", "skewers", "soy sauce"],
-            "adobo": ["chicken", "soy sauce", "vinegar", "garlic"],
-            "sinigang": ["pork", "tamarind", "vegetables", "broth"],
-            "hainan_chicken_rice": ["chicken", "rice", "cucumber", "chili sauce"],
-            "char_kway_teow": ["flat noodles", "shrimp", "soy sauce", "egg"],
-            "lechon": ["roast pig", "garlic", "lemongrass"],
-            "soto_ayam": ["chicken", "noodles", "turmeric", "broth"],
-            "bubur_ayam": ["rice porridge", "chicken", "ginger", "green onions"],
-            "nasi_lemak": ["coconut rice", "sambal", "egg", "anchovies"],
-            "mee_goreng": ["noodles", "chicken", "soy sauce", "chili"]
+            "risotto": ["rice", "broth", "parmesan"]
         }
-
         ingredients = [
             {"name": food, "probability": prob, "subclasses": ingredient_map.get(food.lower(), [])}
             for food, prob in food_items.items()
@@ -566,45 +166,40 @@ async def analyze_food(request: ImageRequest, credentials: HTTPAuthorizationCred
             "micronutrients": micronutrients,
             "calories": calories,
             "source": "huggingface",
-            "was_cropped": was_cropped,
-            "estimated_portion_size": portion_size,
-            "reference_object_size": request.reference_object_size
+            "was_cropped": was_cropped
         }
     except Exception as e:
         logger.error(f"Analysis failed: {str(e)}")
         raise HTTPException(status_code=500, detail=str(e))
 
+# ==============================
 # Gradio interface
-def gradio_analyze(image, portion_size=None, reference_object_size=None):
+# ==============================
+def gradio_analyze(image):
     try:
-        if image is None:
-            return {"error": "Please upload a valid image."}
-        
         buffered = BytesIO()
         image.save(buffered, format="JPEG")
         base64_image = base64.b64encode(buffered.getvalue()).decode()
-        request = ImageRequest(image=base64_image, portion_size=portion_size, reference_object_size=reference_object_size)
+        request = ImageRequest(image=base64_image, portion_size=100.0)
 
+        # Always create a new event loop
         loop = asyncio.new_event_loop()
         asyncio.set_event_loop(loop)
-        result = loop.run_until_complete(analyze_food(request, HTTPAuthorizationCredentials(scheme="Bearer", credentials=os.getenv("HUGGINGFACE_API_TOKEN"))))
+        result = loop.run_until_complete(analyze_food(request))
         loop.close()
         return result
+
     except Exception as e:
         return {"error": str(e)}
 
 iface = gr.Interface(
     fn=gradio_analyze,
-    inputs=[
-        gr.Image(type="pil", label="Upload Food Image"),
-        gr.Number(label="Portion Size (grams, optional, leave blank to estimate)", value=None),
-        gr.Number(label="Reference Object Size (cm, e.g., plate diameter, optional)", value=None)
-    ],
+    inputs=gr.Image(type="pil"),
     outputs="json",
     title="Food Analysis API",
-    description="Upload an image to analyze food items and nutritional content, including Nigerian foods like akpu, egusi, and jollof rice. Portion size is optional; if not provided, it will be estimated based on the food's area in the image. Provide a reference object size (e.g., plate diameter in cm) for better accuracy."
+    description="Upload an image to analyze food items, non-food items, and nutritional content."
 )
 
 if __name__ == "__main__":
     threading.Thread(target=lambda: uvicorn.run(app, host="0.0.0.0", port=8000)).start()
-    iface.launch(server_name="0.0.0.0", server_port=7860, share=False)
+    iface.launch(server_name="0.0.0.0", server_port=7860, share=True)

batch_analyze.py

@@ -10,9 +10,9 @@ from oauth2client.service_account import ServiceAccountCredentials
 # =========================
 # CONFIG
 # =========================
-ANALYZE_URL = "https://versatile153-nutri.hf.space/analyze_food"  # HF Space API URL
-IMAGE_DIR = "images"  # Folder containing images
-SHEET_ID = "1N4I1HzxycvDvD7Tm4f7m-4ppp7yy46SQzJ1ALEOOZl0"
+ANALYZE_URL = "http://localhost:8000/analyze_food"  # Or HuggingFace Space URL
+IMAGE_DIR = "images_batch"  # Folder for test images
+SHEET_ID = "1N4I1HzxycvDvD7Tm4f7m-4ppp7yy46SQzJ1ALEOOZl0"  # <-- Your sheet ID
 
 # Google Sheets credentials
 scope = ["https://spreadsheets.google.com/feeds",
@@ -30,12 +30,14 @@ except Exception as e:
 # HELPERS
 # =========================
 def encode_image(image_path):
+    """Convert image to base64 for sending to API."""
     img = Image.open(image_path).convert("RGB")
     buffered = BytesIO()
     img.save(buffered, format="JPEG")
     return base64.b64encode(buffered.getvalue()).decode("utf-8")
 
 def analyze_image(image_path):
+    """Send image to analysis API."""
     try:
         base64_img = encode_image(image_path)
         payload = {"image": base64_img, "portion_size": 100.0}
@@ -46,11 +48,12 @@ def analyze_image(image_path):
         return {"error": str(e)}
 
 # =========================
-# MAIN PROCESS
+# MAIN LOOP
 # =========================
 def process_images():
     files = [f for f in os.listdir(IMAGE_DIR) if f.lower().endswith((".jpg", ".png", ".jpeg"))][:500]
 
+    # Add headers only if sheet is empty
     if len(sheet.get_all_values()) == 0:
         sheet.insert_row([
             "Filename", "Food Items", "Non-Food Items",
@@ -62,9 +65,10 @@ def process_images():
         path = os.path.join(IMAGE_DIR, file)
         result = analyze_image(path)
 
+        print(f"Result for {file}: {result}")  # Debug print
+
         if "error" in result:
             row = [file, "ERROR", result["error"], "", "", "", "", "", ""]
-            print(f"[{i}/{len(files)}] ❌ {file}: {result['error']}")
         else:
             food_items = json.dumps(result.get("food_items", {}))
             non_food = json.dumps(result.get("non_food_items", []))
@@ -80,15 +84,12 @@ def process_images():
                 nutrients.get("sodium", 0),
                 result.get("calories", 0)
             ]
-            print(f"[{i}/{len(files)}] ✅ Added {file}")
 
         try:
             sheet.append_row(row)
+            print(f"[{i}/{len(files)}] ✅ Added {file} to Google Sheet")
         except Exception as e:
             print(f"[{i}/{len(files)}] ❌ Failed to add {file}: {e}")
 
-# =========================
-# RUN AUTOMATICALLY
-# =========================
 if __name__ == "__main__":
     process_images()