Update app.py

app.py

@@ -17,30 +17,33 @@ def process_frame(frame):
     return prediction[0][1]  # Assuming category 1 is jumpscare
 
 def convert_video_to_dumpscare(video_path, sensitivity):
-    cap = cv2.VideoCapture(video_path)
-    fps = cap.get(cv2.CAP_PROP_FPS)
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    
-    output_path = 'output_dumpscare.mp4'
-    out = cv2.VideoWriter(output_path, fourcc, fps, (int(cap.get(3)), int(cap.get(4))))
-
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        # Check if the frame is a jumpscare
-        if process_frame(frame) > sensitivity:  # Use sensitivity slider
-            out.write(frame)  # Write frame if it's a jumpscare
-
-    cap.release()
-    out.release()
-    return output_path
+    try:
+        cap = cv2.VideoCapture(video_path)
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+
+        output_path = 'output_dumpscare.mp4'
+        out = cv2.VideoWriter(output_path, fourcc, fps, (int(cap.get(3)), int(cap.get(4))))
+
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            # Check if the frame is a jumpscare
+            if process_frame(frame) > sensitivity:  # Use sensitivity slider
+                out.write(frame)  # Write frame if it's a jumpscare
+
+        cap.release()
+        out.release()
+        return output_path
+
+    except Exception as e:
+        return f"Error processing video: {str(e)}"
 
 # Gradio interface function
 def gradio_interface(video, sensitivity):
-    output_video = convert_video_to_dumpscare(video.name, sensitivity)
-    return output_video
+    return convert_video_to_dumpscare(video.name, sensitivity)
 
 # Set up Gradio app
 iface = gr.Interface(