Update app.py

app.py

@@ -1,26 +1,38 @@
 import cv2
 import torch
-import gradio
+import gradio as gr  # Ensure correct import with alias
 import numpy as np
 from ultralytics import YOLO
 
+# Debug: Check environment
+print(f"Torch version: {torch.__version__}")
+print(f"Gradio version: {gr.__version__}")
+print(f"Ultralytics version: {YOLO.__version__}")
+print(f"CUDA available: {torch.cuda.is_available()}")
+
 # Load YOLOv8 model
 device = "cuda" if torch.cuda.is_available() else "cpu"
-print(f"Using device: {device}")  # Debug
+print(f"Using device: {device}")
 model = YOLO('./data/best.pt').to(device)
 
 def process_video(video):
+    if video is None:
+        return "Error: No video uploaded"
+    
     cap = cv2.VideoCapture(video)
+    if not cap.isOpened():
+        return "Error: Could not open video file"
+    
     frame_width, frame_height = 320, 240  # Smaller resolution
     fps = cap.get(cv2.CAP_PROP_FPS)
     
     output_path = "processed_output.mp4"
     fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    out = cv2.VideoWriter(output_path, fourcc, fourcc, fps, (frame_width, frame_height))
+    out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
     
     frame_count = 0
     frame_skip = 5  # Process every 5th frame
-    max_frames = 100  # Limit to 100 frames for testing
+    max_frames = 100  # Limit for testing
     
     while True:
         ret, frame = cap.read()
@@ -32,13 +44,10 @@ def process_video(video):
             continue
             
         frame = cv2.resize(frame, (frame_width, frame_height))
-        print(f"Processing frame {frame_count}")  # Debug
+        print(f"Processing frame {frame_count}")
         
-        # Inference
         results = model(frame)
         annotated_frame = results[0].plot()
-        
-        # Write frame
         out.write(annotated_frame)
     
     cap.release()
@@ -51,7 +60,8 @@ iface = gr.Interface(
     inputs=gr.Video(label="Upload Video"),
     outputs=gr.Video(label="Processed Video"),
     title="YOLOv8 Object Detection",
-    description="Upload a short video for testing"
+    description="Upload a short video for object detection"
 )
 
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()