Update app.py

app.py

@@ -15,42 +15,28 @@ model.conf = 0.5
 if device.type == 'cuda':
     model.half()
 
-def process_frame(video):
-    """Reads a frame from the webcam video stream and applies YOLOv5 detection."""
-    cap = cv2.VideoCapture(video)  # Open the webcam stream
-    
-    if not cap.isOpened():
-        print("Error: Could not open video stream.")
-        return None
-    
-    ret, frame = cap.read()
-    cap.release()
-    
-    if not ret:
-        print("Error: Could not read frame.")
+def process_frame(image):
+    """Processes a frame from the webcam and applies YOLOv5 object detection."""
+    if image is None:
         return None
     
     try:
-        print("Processing frame...")
-        image_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-        
+        image_pil = Image.fromarray(image)
         with torch.no_grad():
             results = model(image_pil)
         
         rendered_images = results.render()
-        processed_image = np.array(rendered_images[0]) if rendered_images else frame
-        print("Frame processed successfully!")
-        return processed_image
+        return np.array(rendered_images[0]) if rendered_images else image
     
     except Exception as e:
-        print(f"Processing error: {e}")
-        return frame
+        print(f"Error processing frame: {e}")
+        return image
 
 def process_uploaded_image(image):
-    """Processes the uploaded image and applies YOLOv5 object detection."""
+    """Processes an uploaded image and applies YOLOv5 object detection."""
     if image is None:
         return None
-
+    
     try:
         image_pil = Image.fromarray(image)
         with torch.no_grad():
@@ -58,7 +44,7 @@ def process_uploaded_image(image):
 
         rendered_images = results.render()
         return np.array(rendered_images[0]) if rendered_images else image
-
+    
     except Exception as e:
         print(f"Error processing image: {e}")
         return image
@@ -71,17 +57,19 @@ with gr.Blocks(title="Real-Time Object Detection") as app:
         # 📷 Live Webcam Tab
         with gr.TabItem("📷 Live Camera"):
             with gr.Row():
-                webcam_input = gr.Video(label="Live Feed")  
+                webcam_input = gr.Image(source="webcam", label="Live Feed")  
                 live_output = gr.Image(label="Processed Feed")
-            webcam_input.stream(process_frame, inputs=webcam_input, outputs=live_output) 
+            
+            # Process frame when new frame is available
+            webcam_input.change(process_frame, inputs=webcam_input, outputs=live_output)
 
         # 🖼️ Image Upload Tab (With Submit Button)
         with gr.TabItem("🖼️ Image Upload"):
             with gr.Row():
                 upload_input = gr.Image(type="numpy", label="Upload Image")
-                submit_button = gr.Button("Submit")  
+                submit_button = gr.Button("Submit") 
                 upload_output = gr.Image(label="Detection Result")
 
-            submit_button.click(process_uploaded_image, inputs=upload_input, outputs=upload_output) 
+            submit_button.click(process_uploaded_image, inputs=upload_input, outputs=upload_output)  
 
 app.queue().launch(server_name="0.0.0.0", server_port=7860, share=False)