Update app.py

app.py

@@ -50,22 +50,24 @@
 
 # demo.launch()
 
+
 from ultralytics import YOLO
 import pandas as pd
 from PIL import Image
 import gradio as gr
+from collections import Counter
 
-# تحميل الموديل
+# Load YOLO model
 model = YOLO("best.pt")
 
-# تحميل بيانات التغذية   
+# Load food nutrition data
 food_df = pd.read_csv("food_cleaned.csv")
 
-# جلب القيم الغذائية
+# Retrieve nutrition info for a given label
 def get_nutrition(label):
     row = food_df[food_df["Food_Name"].str.lower() == label.lower()]
     if row.empty:
-        return {"label": label, "info": "No data"}
+        return {"label": label, "info": "No data found"}
     return {
         "label": label,
         "calories": float(row["Calories_per_100g"].values[0]),
@@ -74,32 +76,66 @@ def get_nutrition(label):
         "carbs": float(row["Carbs_g"].values[0])
     }
 
-# دالة الكشف
+# Detection and nutrition calculation
 def detect(image):
     results = model.predict(image)
     result = results[0]
     boxes = result.boxes
     names = model.names
 
-    detected_info = []
-
+    detected_labels = []
     for box in boxes:
         cls_id = int(box.cls[0])
         label = names[cls_id]
+        detected_labels.append(label)
+
+    label_counts = Counter(detected_labels)
+
+    total_calories = 0
+    total_fat = 0
+    total_protein = 0
+    total_carbs = 0
+    detailed_items = []
+
+    for label, count in label_counts.items():
         nutrition = get_nutrition(label)
-        detected_info.append(nutrition)
+        if "info" in nutrition:
+            continue
+        nutrition["count"] = count
+        nutrition["total_calories"] = round(nutrition["calories"] * count, 2)
+        nutrition["total_fat"] = round(nutrition["fat"] * count, 2)
+        nutrition["total_protein"] = round(nutrition["protein"] * count, 2)
+        nutrition["total_carbs"] = round(nutrition["carbs"] * count, 2)
+
+        total_calories += nutrition["total_calories"]
+        total_fat += nutrition["total_fat"]
+        total_protein += nutrition["total_protein"]
+        total_carbs += nutrition["total_carbs"]
+
+        detailed_items.append(nutrition)
+
+    overall_summary = {
+        "Total Calories": round(total_calories, 2),
+        "Total Fat": round(total_fat, 2),
+        "Total Protein": round(total_protein, 2),
+        "Total Carbs": round(total_carbs, 2)
+    }
 
-    return Image.fromarray(result.plot()), detected_info
+    return Image.fromarray(result.plot()), {"summary": overall_summary, "details": detailed_items}
 
-# Gradio app
+# Gradio web app
 demo = gr.Interface(
     fn=detect,
     inputs=gr.Image(type="pil"),
-    outputs=[gr.Image(type="pil", label="Detected Image"), gr.JSON(label="Nutrition Info")],
-    title="YOLOv8 Food Detector + Nutrition Info",
-    description="Upload an image of food and get calories and nutrients without overlaying on the image."
+    outputs=[
+        gr.Image(type="pil", label="Detected Image"),
+        gr.JSON(label="Nutrition Info")
+    ],
+    title="Smart Food Detector - Nutrition Calculator",
+    description="Upload a food image to get total calories, fat, protein, and carbs."
 )
 
 demo.launch()
 
 
+