Update app.py

app.py

@@ -1,31 +1,36 @@
 import torch
-import gradio as gr
-from PIL import Image
 import requests
+import os
+from PIL import Image
+import gradio as gr
 
-# Download the model from Hugging Face model repo
+# اسم الملف المحلي للموديل
 model_path = "best.pt"
+
+# رابط التحميل من الريبو بتاعك على Hugging Face
+model_url = "https://huggingface.co/AlaaElsayed/yolospace/resolve/main/best.pt"
+
+# حمل الموديل لو مش موجود
 if not os.path.exists(model_path):
-    url = "https://huggingface.co/AlaaElsayed/YoloModel/resolve/main/best.pt"
-    r = requests.get(url)
-    with open(model_path, 'wb') as f:
+    r = requests.get(model_url)
+    with open(model_path, "wb") as f:
         f.write(r.content)
 
-# Load the YOLOv5 model
-model = torch.hub.load('ultralytics/yolov5', 'custom', path=model_path, source='local')
+# تحميل موديل YOLO
+model = torch.hub.load('ultralytics/yolov8', 'custom', path=model_path, source='local')
 
-# Define the detection function
+# دالة الكشف
 def detect(image):
     results = model(image)
-    return Image.fromarray(results.render()[0])  # Convert to PIL for Gradio
+    return Image.fromarray(results.render()[0])
 
-# Create Gradio interface
+# واجهة Gradio
 demo = gr.Interface(
     fn=detect,
     inputs=gr.Image(type="pil"),
     outputs=gr.Image(type="pil"),
-    title="YOLOv5 Object Detector",
-    description="Upload an image and let YOLOv5 detect objects using your custom model!"
+    title="YOLO Food Detector",
+    description="Upload a food image and yolo will detect the items!"
 )
 
-demo.launch()
+demo.launch()