Update app.py

app.py

@@ -59,28 +59,16 @@
 # demo.launch()
 
 
-
-import os
-import requests
 from ultralytics import YOLO
 import pandas as pd
 from PIL import Image, ImageDraw
 import gradio as gr
 
-# تحميل الموديل من Hugging Face لو مش موجود
-model_path = "best.pt"
-model_url = "https://huggingface.co/AlaaElsayed/yolospace/resolve/main/best.pt"
-
-if not os.path.exists(model_path):
-    r = requests.get(model_url)
-    with open(model_path, "wb") as f:
-        f.write(r.content)
-
 # تحميل الموديل
-model = YOLO(model_path)
+model = YOLO("best.pt")
 
 # تحميل بيانات التغذية
-food_df = pd.read_csv("food_cleaned.csv")
+food_df = pd.read_csv("Food.csv")
 
 # جلب القيم الغذائية
 def get_nutrition(label):
@@ -100,7 +88,7 @@ def detect(image):
     boxes = result.boxes
     names = model.names
 
-    img = Image.fromarray(result.plot())  # الصورة مع البوكسات
+    img = Image.fromarray(result.plot())  # الصورة عليها البوكسات
     draw = ImageDraw.Draw(img)
 
     for box in boxes:
@@ -123,3 +111,66 @@ demo = gr.Interface(
 
 demo.launch()
 
+# import os
+# import requests
+# from ultralytics import YOLO
+# import pandas as pd
+# from PIL import Image, ImageDraw
+# import gradio as gr
+
+# # تحميل الموديل من Hugging Face لو مش موجود
+# model_path = "best.pt"
+# model_url = "https://huggingface.co/AlaaElsayed/yolospace/resolve/main/best.pt"
+
+# if not os.path.exists(model_path):
+#     r = requests.get(model_url)
+#     with open(model_path, "wb") as f:
+#         f.write(r.content)
+
+# # تحميل الموديل
+# model = YOLO(model_path)
+
+# # 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 مع 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()
+