Update app.py

app.py

@@ -1,49 +1,103 @@
-import torch
-import requests
-import os
+# 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, ImageFont
+from PIL import Image, ImageDraw
 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')
+model = YOLO("best.pt")
 
-# تحميل معلومات الطعام
-food_info = pd.read_csv("food_cleaned.csv")
+# تحميل بيانات التغذية
+food_df = pd.read_csv("food_cleaned.csv")
 
-# دالة لإحضار البيانات الغذائية
+# جلب القيم الغذائية
 def get_nutrition(label):
-    row = food_info[food_info["Food_Name"].str.lower() == label.lower()]
+    row = food_df[food_df["Food_Name"].str.lower() == label.lower()]
     if row.empty:
-        return "No info", "?", "?", "?"
+        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"{cals} kcal", f"{fat}g fat", f"{protein}g protein", f"{carbs}g carbs"
+    return f"{label}: {cals} kcal, {fat}g fat, {protein}g protein, {carbs}g carbs"
 
-# دالة الكشف
+# دالة الكشف والرسم
 def detect(image):
-    results = model(image)
-    labels = results.names
-    df = results.pandas().xyxy[0]
+    results = model.predict(image)
+    result = results[0]
+    boxes = result.boxes
+    names = model.names
 
-    img = Image.fromarray(results.render()[0])
+    img = Image.fromarray(result.plot())  # الصورة عليها البوكسات
     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))
+    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
 
@@ -52,7 +106,7 @@ demo = gr.Interface(
     fn=detect,
     inputs=gr.Image(type="pil"),
     outputs=gr.Image(type="pil"),
-    title="YOLOv5 Food Detector + Nutrition Info",
+    title="YOLOv8 Food Detector + Nutrition Info",
     description="Upload an image of food and see calories and nutrients!"
 )