Update app.py

app.py

@@ -1,4 +1,6 @@
 import zipfile
+import time
+import threading
 def unzip_content():
     try:
         # First try using Python's zipfile
@@ -52,14 +54,18 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
 
-def create_video(images_list, video_name='morphing_video.mp4'):
+def create_video(images_list):
     """Create video from a list of image tensors"""
     if not images_list:
         print("No images provided.")
         return None
 
-    with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as temp_video:
-        video_writer = imageio.get_writer(temp_video.name, fps=10)
+    # Create a unique filename in the current directory
+    video_path = os.path.join(os.getcwd(), f"output_{int(time.time())}.mp4")
+    
+    try:
+        video_writer = imageio.get_writer(video_path, fps=10, codec='libx264', quality=7,
+                                        output_params=['-movflags', 'faststart'])
         
         for img_tensor in images_list:
             # Convert tensor to numpy array
@@ -68,7 +74,13 @@ def create_video(images_list, video_name='morphing_video.mp4'):
             video_writer.append_data(img)
 
         video_writer.close()
-        return temp_video.name
+        return video_path
+            
+    except Exception as e:
+        if os.path.exists(video_path):
+            os.remove(video_path)
+        raise e
+
 
 def save_from_tensors(tensor):
     """Process tensor and return the processed version"""
@@ -226,11 +238,33 @@ def generate_art(include_text, exclude_text, extras_text, num_iterations):
     except Exception as e:
         raise e
 
+
 def gradio_interface(include_text, exclude_text, extras_text, num_iterations):
+    video_path = None
     try:
         video_path = generate_art(include_text, exclude_text, extras_text, int(num_iterations))
-        return video_path
+        
+        if not os.path.exists(video_path):
+            return "Video generation failed"
+            
+        # Create a copy of the video path before scheduling deletion
+        response_path = video_path
+        
+        # Schedule the video file for deletion after a delay
+        def cleanup():
+            try:
+                if os.path.exists(video_path):
+                    os.remove(video_path)
+            except:
+                pass
+                
+        threading.Timer(10.0, cleanup).start()
+        
+        return response_path
+        
     except Exception as e:
+        if video_path and os.path.exists(video_path):
+            os.remove(video_path)
         return f"An error occurred: {str(e)}"
 
 # Try to unzip content at startup