Update app.py

app.py

@@ -40,79 +40,57 @@ np.random.seed(42)
 colors = np.random.randint(0, 255, size=(len(model.names), 3), dtype=np.uint8)
 
 def process_video(video_path):
-    # Ensure we have a valid path
-    if video_path is None:
-        return None
+    cap = cv2.VideoCapture(video_path)
     
-    try:
-        # For newer Gradio versions, video might be returned as a tuple
-        if isinstance(video_path, tuple) and len(video_path) >= 1:
-            video_path = video_path[0]
-        # Or a dict with a 'name' key
-        elif isinstance(video_path, dict) and 'name' in video_path:
-            video_path = video_path['name']
-        # Make sure it's a string
-        video_path = str(video_path)
-        
-        cap = cv2.VideoCapture(video_path)
-        
-        if not cap.isOpened():
-            print(f"Error: Could not open video file at {video_path}")
-            return None
+    if not cap.isOpened():
+        return "Error: Could not open video file."
 
-        frame_width = int(cap.get(3))
-        frame_height = int(cap.get(4))
-        fps = cap.get(cv2.CAP_PROP_FPS)
-        
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-        output_path = "output_video.mp4"
-        out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
+    frame_width = int(cap.get(3))
+    frame_height = int(cap.get(4))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    output_path = "output_video.mp4"
+    out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
 
-        total_frames = 0
-        total_time = 0
+    total_frames = 0
+    total_time = 0
 
-        while cap.isOpened():
-            ret, frame = cap.read()
-            if not ret:
-                break  
-            
-            start_time = time.time()
-            
-            # Convert frame for YOLOv5
-            img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            results = model(img, size=640)
-            
-            inference_time = time.time() - start_time
-            total_time += inference_time
-            total_frames += 1
-            
-            detections = results.pred[0].cpu().numpy()
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break  
+        
+        start_time = time.time()
+        
+        # Convert frame for YOLOv5
+        img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        results = model(img, size=640)
+        
+        inference_time = time.time() - start_time
+        total_time += inference_time
+        total_frames += 1
+        
+        detections = results.pred[0].cpu().numpy()
 
-            for *xyxy, conf, cls in detections:
-                x1, y1, x2, y2 = map(int, xyxy)
-                class_id = int(cls)
-                color = colors[class_id].tolist()
-                cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3, lineType=cv2.LINE_AA)
-                label = f"{model.names[class_id]} {conf:.2f}"
-                cv2.putText(frame, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 255), 2)
+        for *xyxy, conf, cls in detections:
+            x1, y1, x2, y2 = map(int, xyxy)
+            class_id = int(cls)
+            color = colors[class_id].tolist()
+            cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3, lineType=cv2.LINE_AA)
+            label = f"{model.names[class_id]} {conf:.2f}"
+            cv2.putText(frame, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 255), 2)
 
-            # Calculate FPS
-            avg_fps = total_frames / total_time if total_time > 0 else 0
-            cv2.putText(frame, f"FPS: {avg_fps:.2f}", (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        # Calculate FPS
+        avg_fps = total_frames / total_time if total_time > 0 else 0
+        cv2.putText(frame, f"FPS: {avg_fps:.2f}", (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
 
-            out.write(frame)
+        out.write(frame)
 
-        cap.release()
-        out.release()
-        
-        print(f"Video processed successfully, output at: {output_path}")
-        return output_path
+    cap.release()
+    out.release()
     
-    except Exception as e:
-        print(f"Error processing video: {str(e)}")
-        import traceback
-        traceback.print_exc()
-        return None
+    return output_path
 
 def process_image(image):
     img = np.array(image)
@@ -182,11 +160,9 @@ with gr.Blocks(css=css, title="Video & Image Object Detection by YOLOv5") as dem
     with gr.Tabs():
         with gr.TabItem("Video Detection", elem_classes="tab-item"):
             with gr.Row():
-                # Keep using gr.Video but with source="upload" parameter
                 video_input = gr.Video(
                     label="Upload Video", 
-                    interactive=True,
-                    source="upload",  # Explicitly set upload as source
+                    interactive=True, 
                     elem_id="video-input"
                 )