Update app.py

app.py

@@ -1,51 +1,104 @@
+# from ultralytics import YOLO
+# import pandas as pd
+# from PIL import Image, ImageDraw
+# import gradio as gr
+
+# # تحميل الموديل
+# model = YOLO("best.pt")
+
+# # تحميل بيانات التغذية
+# food_df = pd.read_csv("food_cleaned.csv")
+
+# # جلب القيم الغذائية
+# def get_nutrition(label):
+#     row = food_df[food_df["Food_Name"].str.lower() == label.lower()]
+#     if row.empty:
+#         return "No data"
+#     cals = row["Calories_per_100g"].values[0]
+#     fat = row["Fat_g"].values[0]
+#     protein = row["Protein_g"].values[0]
+#     carbs = row["Carbs_g"].values[0]
+#     return f"{label}: {cals} kcal, {fat}g fat, {protein}g protein, {carbs}g carbs"
+
+# # دالة الكشف والرسم
+# def detect(image):
+#     results = model.predict(image)
+#     result = results[0]
+#     boxes = result.boxes
+#     names = model.names
+
+#     img = Image.fromarray(result.plot())  # الصورة عليها البوكسات
+#     draw = ImageDraw.Draw(img)
+
+#     for box in boxes:
+#         cls_id = int(box.cls[0])
+#         label = names[cls_id]
+#         nutrition = get_nutrition(label)
+#         xy = box.xyxy[0].tolist()
+#         draw.text((xy[0], xy[1] - 10), nutrition, fill=(255, 0, 0))
+
+#     return img
+
+# # Gradio app
+# demo = gr.Interface(
+#     fn=detect,
+#     inputs=gr.Image(type="pil"),
+#     outputs=gr.Image(type="pil"),
+#     title="YOLOv8 Food Detector + Nutrition Info",
+#     description="Upload an image of food and see calories and nutrients!"
+# )
+
+# demo.launch()
+
+
 from ultralytics import YOLO
 import pandas as pd
-from PIL import Image, ImageDraw
+from PIL import Image
 import gradio as gr
 
 # تحميل الموديل
 model = YOLO("best.pt")
 
-# تحميل بيانات التغذية
+# تحميل بيانات التغذية   
 food_df = pd.read_csv("food_cleaned.csv")
 
 # جلب القيم الغذائية
 def get_nutrition(label):
     row = food_df[food_df["Food_Name"].str.lower() == label.lower()]
     if row.empty:
-        return "No data"
-    cals = row["Calories_per_100g"].values[0]
-    fat = row["Fat_g"].values[0]
-    protein = row["Protein_g"].values[0]
-    carbs = row["Carbs_g"].values[0]
-    return f"{label}: {cals} kcal, {fat}g fat, {protein}g protein, {carbs}g carbs"
-
-# دالة الكشف والرسم
+        return {"label": label, "info": "No data"}
+    return {
+        "label": label,
+        "calories": float(row["Calories_per_100g"].values[0]),
+        "fat": float(row["Fat_g"].values[0]),
+        "protein": float(row["Protein_g"].values[0]),
+        "carbs": float(row["Carbs_g"].values[0])
+    }
+
+# دالة الكشف
 def detect(image):
     results = model.predict(image)
     result = results[0]
     boxes = result.boxes
     names = model.names
 
-    img = Image.fromarray(result.plot())  # الصورة عليها البوكسات
-    draw = ImageDraw.Draw(img)
+    detected_info = []
 
     for box in boxes:
         cls_id = int(box.cls[0])
         label = names[cls_id]
         nutrition = get_nutrition(label)
-        xy = box.xyxy[0].tolist()
-        draw.text((xy[0], xy[1] - 10), nutrition, fill=(255, 0, 0))
+        detected_info.append(nutrition)
 
-    return img
+    return Image.fromarray(result.plot()), detected_info
 
 # Gradio app
 demo = gr.Interface(
     fn=detect,
     inputs=gr.Image(type="pil"),
-    outputs=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 see calories and nutrients!"
+    description="Upload an image of food and get calories and nutrients without overlaying on the image."
 )
 
 demo.launch()