Update app.py

app.py

@@ -1,64 +1,3 @@
-# import torch
-# import requests
-# import os
-# import pandas as pd
-# from PIL import Image, ImageDraw, ImageFont
-# import gradio as gr
-
-# # روابط وتحميل الموديل
-# 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 = torch.hub.load('ultralytics/yolov5', 'custom', path=model_path, source='local')
-
-# # تحميل معلومات الطعام
-# food_info = pd.read_csv("food_cleaned.csv")
-
-# # دالة لإحضار البيانات الغذائية
-# def get_nutrition(label):
-#     row = food_info[food_info["Food_Name"].str.lower() == label.lower()]
-#     if row.empty:
-#         return "No info", "?", "?", "?"
-#     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"{cals} kcal", f"{fat}g fat", f"{protein}g protein", f"{carbs}g carbs"
-
-# # دالة الكشف
-# def detect(image):
-#     results = model(image)
-#     labels = results.names
-#     df = results.pandas().xyxy[0]
-
-#     img = Image.fromarray(results.render()[0])
-#     draw = ImageDraw.Draw(img)
-
-#     for _, row in df.iterrows():
-#         label = row["name"]
-#         cal, fat, pro, carb = get_nutrition(label)
-#         text = f"{label}: {cal}, {fat}, {pro}, {carb}"
-#         draw.text((row["xmin"], row["ymin"] - 10), text, fill=(255, 0, 0))
-
-#     return img
-
-# # Gradio app
-# demo = gr.Interface(
-#     fn=detect,
-#     inputs=gr.Image(type="pil"),
-#     outputs=gr.Image(type="pil"),
-#     title="YOLOv5 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
@@ -111,66 +50,3 @@ 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()
-