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( provider="fal-ai", api_key=os.environ.get("HF_TOKEN"), bill_to="huggingface", ) def verify_pro_status(token: Optional[Union[gr.OAuthToken, str]]) -> bool: """Verifies if the user is a Hugging Face PRO user or part of an enterprise org.""" if not token: return False if isinstance(token, gr.OAuthToken): token_str = token.token elif isinstance(token, str): token_str = token else: return False try: user_info = whoami(token=token_str) return ( user_info.get("isPro", False) or any(org.get("isEnterprise", False) for org in user_info.get("orgs", [])) ) except Exception as e: 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, size: str = "1280x720", api_key: Optional[str] = None ) -> Tuple[Optional[str], str]: """ 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: temp_client = InferenceClient( provider="fal-ai", api_key=api_key, bill_to="huggingface", ) else: temp_client = client if not os.environ.get("HF_TOKEN") and not api_key: return None, "❌ Please set HF_TOKEN environment variable." # Call Sora-2 through Hugging Face Inference API video_bytes = temp_client.text_to_video( prompt, model="akhaliq/sora-2", ) # 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 except Exception as e: error_msg = f"❌ Error generating video: {str(e)}" return None, error_msg def generate_with_pro_auth( prompt: str, oauth_token: Optional[gr.OAuthToken] = None ) -> Tuple[Optional[str], str]: """ Wrapper function that checks if user is PRO before generating video. """ # Check if user is PRO if not verify_pro_status(oauth_token): raise gr.Error("Access Denied. This app is exclusively for Hugging Face PRO users. Please subscribe to PRO to use this app.") if not prompt or prompt.strip() == "": return None, "❌ Please enter a prompt" # Use the environment token for API calls (with bill_to="huggingface") # Don't use the user's OAuth token for the API call video_path, status = generate_video( prompt, duration=8, size="1280x720", api_key=None # This will use the environment HF_TOKEN ) return video_path, status def simple_generate(prompt: str) -> Optional[str]: """Simplified wrapper for examples that only returns video.""" if not prompt or prompt.strip() == "": return None video_path, _ = generate_video(prompt, duration=8, size="1280x720", api_key=None) return video_path def create_ui(): """Create the Gradio interface with PRO verification.""" css = ''' .logo-dark{display: none} .dark .logo-dark{display: block !important} .dark .logo-light{display: none} #sub_title{margin-top: -20px !important} .pro-badge{ background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 4px 12px; border-radius: 20px; font-size: 0.9em; font-weight: bold; display: inline-block; margin-left: 8px; } ''' with gr.Blocks(title="Sora-2 Text-to-Video Generator", theme=gr.themes.Soft(), css=css) as demo: gr.HTML("""

🎬 Sora-2 Text-to-Video Generator PRO

Generate stunning videos using OpenAI's Sora-2 model

Exclusive access for Hugging Face PRO users. Subscribe to PRO →

Built with anycoder

""") # Login button for OAuth gr.LoginButton() # PRO message for non-PRO users pro_message = gr.Markdown(visible=False) # Main interface (hidden by default) main_interface = gr.Column(visible=False) with main_interface: gr.HTML("""

✨ Welcome PRO User! You have full access to Sora-2.

""") with gr.Row(): with gr.Column(scale=1): prompt_input = gr.Textbox( label="Enter your prompt", placeholder="Describe the video you want to create...", lines=4 ) with gr.Accordion("Advanced Settings", open=False): gr.Markdown("*Coming soon: Duration and resolution controls*") generate_btn = gr.Button("🎥 Generate Video", variant="primary", size="lg") with gr.Column(scale=1): video_output = gr.Video( label="Generated Video", height=400, interactive=False, show_download_button=True ) status_output = gr.Textbox( label="Status", interactive=False, visible=True ) # Hidden manual token input removed - not needed anymore # Examples section with queue disabled gr.Examples( examples=[ "A serene beach at sunset with waves gently rolling onto the shore", "A butterfly emerging from its chrysalis in slow motion", "Northern lights dancing across a starry night sky", "A bustling city street transitioning from day to night in timelapse", "A close-up of coffee being poured into a cup with steam rising", "Cherry blossoms falling in slow motion in a Japanese garden" ], inputs=prompt_input, outputs=video_output, fn=simple_generate, # Examples use simplified function cache_examples=False, api_name=False, show_api=False, ) # Event handler for generation with queue disabled generate_btn.click( fn=generate_with_pro_auth, inputs=[prompt_input], outputs=[video_output, status_output], queue=False, api_name=False, show_api=False, ) # Footer gr.HTML("""

Thank you for being a PRO user! 🤗

""") def control_access(profile: Optional[gr.OAuthProfile] = None, oauth_token: Optional[gr.OAuthToken] = None): """Control interface visibility based on PRO status.""" if not profile: # User not logged in return gr.update(visible=False), gr.update(visible=False) if verify_pro_status(oauth_token): # User is PRO - show main interface return gr.update(visible=True), gr.update(visible=False) else: # User is not PRO - show upgrade message message = """ ## ✨ Exclusive Access for PRO Users Thank you for your interest in the Sora-2 Text-to-Video Generator! This advanced AI video generation tool is available exclusively for Hugging Face **PRO** members. ### What you get with PRO: - ✅ Unlimited access to Sora-2 video generation - ✅ High-quality video outputs up to 1280x720 - ✅ Fast generation times with priority queue - ✅ Access to other exclusive PRO Spaces - ✅ Support the development of cutting-edge AI tools ### Ready to create amazing videos?
🚀 Become a PRO Today!

Join thousands of creators who are already using PRO tools to bring their ideas to life.

""" return gr.update(visible=False), gr.update(visible=True, value=message) # Check access on load demo.load( control_access, inputs=None, outputs=[main_interface, pro_message] ) return demo # 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) app.launch( show_api=False, enable_monitoring=False, quiet=True, max_threads=10, # Limit threads to prevent resource exhaustion )