Update app.py

app.py

@@ -1,10 +1,11 @@
 import gradio as gr
 import torch
 import cv2
+import os
 from ultralytics import YOLO
-import spaces 
-@spaces.GPU  
+import spaces  
 
+@spaces.GPU  # Ensures GPU is allocated for this function
 
 class CrowdDetection:
     def __init__(self, yolo_model_path="yolov8n.pt", crowd_threshold=10):
@@ -30,9 +31,16 @@ class CrowdDetection:
             raise ValueError("YOLO model failed to load")
         
         cap = cv2.VideoCapture(video_path)
+        
+        # Ensure video is opened successfully
+        if not cap.isOpened():
+            raise ValueError(f"Failed to open video: {video_path}")
+
         output_path = "output_crowd.mp4"
+        output_full_path = os.path.abspath(output_path)  # Convert to absolute path
+        
         fourcc = cv2.VideoWriter_fourcc(*"mp4v")
-        out = cv2.VideoWriter(output_path, fourcc, int(cap.get(cv2.CAP_PROP_FPS)),
+        out = cv2.VideoWriter(output_full_path, fourcc, int(cap.get(cv2.CAP_PROP_FPS)),
                               (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))))
         
         while cap.isOpened():
@@ -64,16 +72,29 @@ class CrowdDetection:
         
         cap.release()
         out.release()
-        return output_path
+
+        # Ensure output file exists before returning
+        if not os.path.exists(output_full_path):
+            raise FileNotFoundError(f"Output video not found: {output_full_path}")
+
+        print(f"Processed video saved at: {output_full_path}")
+        return output_full_path
 
 def process_video(video):
     try:
+        print(f"Received video: {video}")
+        
         detector = CrowdDetection()
         output_video = detector.detect_crowd(video)
+
+        if not os.path.exists(output_video):  # Ensure output file exists
+            raise FileNotFoundError(f"Output video does not exist: {output_video}")
+
+        print(f"Returning processed video: {output_video}")
         return output_video
     except Exception as e:
         print(f"Video processing error: {e}")
-        return None
+        return None  # Prevent crashing the app
 
 # Gradio Interface for Hugging Face Spaces
 interface = gr.Interface(
@@ -83,6 +104,5 @@ interface = gr.Interface(
     title="Crowd Detection using YOLOv8"
 )
 
-# Remove share=True for Hugging Face Spaces
 if __name__ == "__main__":
-    interface.launch()
+    interface.launch()