Update app.py

app.py

@@ -1,8 +1,10 @@
 import gradio as gr
 import os
 import tempfile
+import shutil
 from typing import Optional, Tuple, Union
 from huggingface_hub import InferenceClient, whoami
+from pathlib import Path
 
 # Initialize Hugging Face Inference Client with fal-ai provider
 client = InferenceClient(
@@ -33,6 +35,21 @@ def verify_pro_status(token: Optional[Union[gr.OAuthToken, str]]) -> bool:
         print(f"Could not verify user's PRO/Enterprise status: {e}")
         return False
 
+def cleanup_temp_files():
+    """Clean up old temporary video files to prevent storage overflow."""
+    try:
+        temp_dir = tempfile.gettempdir()
+        # Clean up old .mp4 files in temp directory
+        for file_path in Path(temp_dir).glob("*.mp4"):
+            try:
+                # Remove files older than 5 minutes
+                if file_path.stat().st_mtime < (os.time.time() - 300):
+                    file_path.unlink(missing_ok=True)
+            except Exception:
+                pass  # Ignore errors for individual files
+    except Exception as e:
+        print(f"Cleanup error: {e}")
+
 def generate_video(
     prompt: str,
     duration: int = 8,
@@ -43,6 +60,9 @@ def generate_video(
     Generate video using Sora-2 through Hugging Face Inference API with fal-ai provider.
     Returns tuple of (video_path, status_message).
     """
+    # Clean up old files before generating new ones
+    cleanup_temp_files()
+    
     try:
         # Use provided API key or environment variable
         if api_key:
@@ -62,10 +82,15 @@ def generate_video(
             model="akhaliq/sora-2",
         )
         
-        # Save to temporary file
-        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp_file:
-            tmp_file.write(video_bytes)
-            video_path = tmp_file.name
+        # Save to temporary file with proper cleanup
+        # Use NamedTemporaryFile with delete=True but keep reference
+        temp_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
+        try:
+            temp_file.write(video_bytes)
+            temp_file.flush()
+            video_path = temp_file.name
+        finally:
+            temp_file.close()
         
         status_message = f"✅ Video generated successfully!"
         return video_path, status_message
@@ -285,6 +310,15 @@ def create_ui():
 
 # Launch the application
 if __name__ == "__main__":
+    # Clean up any leftover files on startup
+    try:
+        cleanup_temp_files()
+        # Also try to clear Gradio's cache
+        if os.path.exists("gradio_cached_examples"):
+            shutil.rmtree("gradio_cached_examples", ignore_errors=True)
+    except Exception as e:
+        print(f"Initial cleanup error: {e}")
+    
     app = create_ui()
     # Launch without special auth parameters and no queue
     # OAuth is enabled via Space metadata (hf_oauth: true in README.md)
@@ -292,4 +326,5 @@ if __name__ == "__main__":
         show_api=False,
         enable_monitoring=False,
         quiet=True,
+        max_threads=10,  # Limit threads to prevent resource exhaustion
     )