Update app.py

app.py

@@ -5,10 +5,23 @@ from PIL import Image
 import numpy as np
 import tempfile
 import os
+import atexit
+import shutil
 
 # Load YOLO model
 model = YOLO("yolov8n.pt")
 
+# Create a temporary directory for this session
+TEMP_DIR = tempfile.mkdtemp()
+
+def cleanup_temp_files():
+    """Clean up temporary files on exit."""
+    if os.path.exists(TEMP_DIR):
+        shutil.rmtree(TEMP_DIR, ignore_errors=True)
+
+# Register cleanup function
+atexit.register(cleanup_temp_files)
+
 def detect_objects_image(image):
     """Process image with YOLO detection."""
     if image is None:
@@ -38,32 +51,49 @@ def detect_objects_video(video):
     width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
     height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
     
-    # Create output video file
-    output_path = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4').name
+    # Create output video file in our temp directory
+    output_path = os.path.join(TEMP_DIR, f"output_{os.urandom(8).hex()}.mp4")
     fourcc = cv2.VideoWriter_fourcc(*'mp4v')
     out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
     
-    # Process each frame
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-        
-        # Perform detection
-        results = model(frame)
-        annotated_frame = results[0].plot()
-        
-        # Convert RGB to BGR for cv2.VideoWriter
-        annotated_frame_bgr = cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR)
-        
-        # Write annotated frame
-        out.write(annotated_frame_bgr)
-    
-    cap.release()
-    out.release()
+    try:
+        # Process each frame
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+            
+            # Perform detection
+            results = model(frame)
+            annotated_frame = results[0].plot()
+            
+            # Convert RGB to BGR for cv2.VideoWriter
+            annotated_frame_bgr = cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR)
+            
+            # Write annotated frame
+            out.write(annotated_frame_bgr)
+    finally:
+        cap.release()
+        out.release()
     
     return output_path
 
+# Periodic cleanup function to remove old processed videos
+def periodic_cleanup():
+    """Remove files older than 1 hour from temp directory."""
+    import time
+    current_time = time.time()
+    for filename in os.listdir(TEMP_DIR):
+        filepath = os.path.join(TEMP_DIR, filename)
+        if os.path.isfile(filepath):
+            file_age = current_time - os.path.getmtime(filepath)
+            # Delete files older than 1 hour (3600 seconds)
+            if file_age > 3600:
+                try:
+                    os.remove(filepath)
+                except Exception:
+                    pass
+
 # Create Gradio interface
 with gr.Blocks(title="YOLO Object Detection", theme=gr.themes.Soft()) as demo:
     gr.Markdown("# 🔍 YOLO Object Detection App")
@@ -86,13 +116,6 @@ with gr.Blocks(title="YOLO Object Detection", theme=gr.themes.Soft()) as demo:
                         label="Detection Results",
                         height=400
                     )
-            
-            # Image examples
-            gr.Examples(
-                examples=[],  # You can add example image paths here
-                inputs=image_input,
-                label="Example Images"
-            )
         
         # Video Detection Tab
         with gr.TabItem("🎥 Video Detection"):
@@ -140,6 +163,9 @@ with gr.Blocks(title="YOLO Object Detection", theme=gr.themes.Soft()) as demo:
         inputs=video_input,
         outputs=video_output
     )
+    
+    # Run periodic cleanup every time the interface loads
+    demo.load(periodic_cleanup)
 
 # Launch the app
 if __name__ == "__main__":