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| import gradio as gr | |
| import os | |
| from huggingface_hub import InferenceClient | |
| import tempfile | |
| import shutil | |
| from pathlib import Path | |
| from typing import Optional, Union | |
| import time | |
| # ------------------------- | |
| # Utilities | |
| # ------------------------- | |
| def cleanup_temp_files(): | |
| try: | |
| temp_dir = tempfile.gettempdir() | |
| for file_path in Path(temp_dir).glob("*.mp4"): | |
| try: | |
| if file_path.stat().st_mtime < (time.time() - 300): | |
| file_path.unlink(missing_ok=True) | |
| except Exception: | |
| pass | |
| except Exception as e: | |
| print(f"Cleanup error: {e}") | |
| def _client_from_token(token: Optional[str]) -> InferenceClient: | |
| if not token: | |
| raise gr.Error("Please sign in first. This app requires your Hugging Face login.") | |
| # IMPORTANT: do not set bill_to when using user OAuth tokens | |
| return InferenceClient( | |
| provider="fal-ai", | |
| api_key=token, | |
| ) | |
| def _save_bytes_as_temp_mp4(data: bytes) -> str: | |
| temp_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) | |
| try: | |
| temp_file.write(data) | |
| temp_file.flush() | |
| return temp_file.name | |
| finally: | |
| temp_file.close() | |
| def text_to_video(prompt, token: gr.OAuthToken | None, duration=5, aspect_ratio="16:9", resolution="720p", *_): | |
| """Generate video from text prompt""" | |
| try: | |
| if token is None or not getattr(token, "token", None): | |
| return None, "❌ Sign in with Hugging Face to continue. This app uses your inference provider credits." | |
| if not prompt or prompt.strip() == "": | |
| return None, "Please enter a text prompt" | |
| cleanup_temp_files() | |
| # Create client with user's token | |
| client = _client_from_token(token.token) | |
| # Generate video from text | |
| try: | |
| video = client.text_to_video( | |
| prompt, | |
| model="akhaliq/veo3.1-fast", | |
| ) | |
| except Exception as e: | |
| import requests | |
| if isinstance(e, requests.HTTPError) and getattr(e.response, "status_code", None) == 403: | |
| return None, "❌ Access denied by provider (403). Make sure your HF account has credits/permission for provider 'fal-ai' and model 'akhaliq/veo3.1-fast'." | |
| raise | |
| # Save the video to a temporary file | |
| video_path = _save_bytes_as_temp_mp4(video) | |
| return video_path, f"✅ Video generated successfully from prompt: '{prompt[:50]}...'" | |
| except gr.Error as e: | |
| return None, f"❌ {str(e)}" | |
| except Exception as e: | |
| return None, f"❌ Generation failed. If this keeps happening, check your provider quota or try again later." | |
| def image_to_video(image, prompt, token: gr.OAuthToken | None, duration=5, aspect_ratio="16:9", resolution="720p", *_): | |
| """Generate video from image and prompt""" | |
| try: | |
| if token is None or not getattr(token, "token", None): | |
| return None, "❌ Sign in with Hugging Face to continue. This app uses your inference provider credits." | |
| if image is None: | |
| return None, "Please upload an image" | |
| if not prompt or prompt.strip() == "": | |
| return None, "Please enter a prompt describing the motion" | |
| cleanup_temp_files() | |
| # Read the image file | |
| if isinstance(image, str): | |
| # If image is a file path | |
| with open(image, "rb") as image_file: | |
| input_image = image_file.read() | |
| else: | |
| # If image is already bytes or similar | |
| import io | |
| from PIL import Image as PILImage | |
| # Convert to bytes if necessary | |
| if isinstance(image, PILImage.Image): | |
| buffer = io.BytesIO() | |
| image.save(buffer, format='PNG') | |
| input_image = buffer.getvalue() | |
| else: | |
| # Assume it's a numpy array or similar | |
| pil_image = PILImage.fromarray(image) | |
| buffer = io.BytesIO() | |
| pil_image.save(buffer, format='PNG') | |
| input_image = buffer.getvalue() | |
| # Create client with user's token | |
| client = _client_from_token(token.token) | |
| # Generate video from image | |
| try: | |
| video = client.image_to_video( | |
| input_image, | |
| prompt=prompt, | |
| model="akhaliq/veo3.1-fast-image-to-video", | |
| ) | |
| except Exception as e: | |
| import requests | |
| if isinstance(e, requests.HTTPError) and getattr(e.response, "status_code", None) == 403: | |
| return None, "❌ Access denied by provider (403). Make sure your HF account has credits/permission for provider 'fal-ai' and model 'akhaliq/veo3.1-fast-image-to-video'." | |
| raise | |
| # Save the video to a temporary file | |
| video_path = _save_bytes_as_temp_mp4(video) | |
| return video_path, f"✅ Video generated successfully with motion: '{prompt[:50]}...'" | |
| except gr.Error as e: | |
| return None, f"❌ {str(e)}" | |
| except Exception as e: | |
| return None, f"❌ Generation failed. If this keeps happening, check your provider quota or try again later." | |
| def clear_text_tab(): | |
| """Clear text-to-video tab""" | |
| return "", None, "" | |
| def clear_image_tab(): | |
| """Clear image-to-video tab""" | |
| return None, "", None, "" | |
| # Custom CSS for better styling | |
| custom_css = """ | |
| .container { | |
| max-width: 1200px; | |
| margin: auto; | |
| } | |
| .header-link { | |
| text-decoration: none; | |
| color: #2196F3; | |
| font-weight: bold; | |
| } | |
| .header-link:hover { | |
| text-decoration: underline; | |
| } | |
| .status-box { | |
| padding: 10px; | |
| border-radius: 5px; | |
| margin-top: 10px; | |
| } | |
| .notice { | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); | |
| color: white; | |
| padding: 14px 16px; | |
| border-radius: 12px; | |
| margin: 18px auto 6px; | |
| max-width: 860px; | |
| text-align: center; | |
| font-size: 0.98rem; | |
| } | |
| .mobile-link-container { | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); | |
| padding: 1.5em; | |
| border-radius: 10px; | |
| text-align: center; | |
| margin: 1em 0; | |
| box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); | |
| } | |
| .mobile-link { | |
| color: white !important; | |
| font-size: 1.2em; | |
| font-weight: bold; | |
| text-decoration: none; | |
| display: inline-block; | |
| padding: 0.5em 1.5em; | |
| background: rgba(255, 255, 255, 0.2); | |
| border-radius: 25px; | |
| transition: all 0.3s ease; | |
| } | |
| .mobile-link:hover { | |
| background: rgba(255, 255, 255, 0.3); | |
| transform: translateY(-2px); | |
| box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2); | |
| } | |
| .mobile-text { | |
| color: white; | |
| margin-bottom: 0.5em; | |
| font-size: 1.1em; | |
| } | |
| """ | |
| # Create the Gradio interface | |
| with gr.Blocks(css=custom_css, theme=gr.themes.Soft(), title="AI Video Generator (Paid)") as demo: | |
| gr.HTML( | |
| """ | |
| <div style="text-align:center; max-width:900px; margin:0 auto;"> | |
| <h1 style="font-size:2.2em; margin-bottom:6px;">Veo 3.1 Fast</h1> | |
| <p style="color:#777; margin:0 0 8px;">Generate videos via the Hugging Face Inference Providers</p> | |
| <div class="notice"> | |
| <b>Heads up:</b> This is a paid app that uses <b>your</b> inference provider credits when you run generations. | |
| Free users get <b>$0.10 in included credits</b>. <b>PRO users</b> get <b>$2 in included credits</b> | |
| and can continue using beyond that (with billing). | |
| <a href='http://huggingface.co/subscribe/pro?source=veo3' target='_blank' style='color:#fff; text-decoration:underline; font-weight:bold;'>Subscribe to PRO</a> | |
| for more credits. Please sign in with your Hugging Face account to continue. | |
| <br><a href='https://huggingface.co/settings/inference-providers/overview' target='_blank' style='color:#fff; text-decoration:underline; font-weight:bold;'>Check your billing usage here</a> | |
| </div> | |
| </div> | |
| """ | |
| ) | |
| # Add mobile link section | |
| gr.HTML( | |
| """ | |
| <div class="mobile-link-container"> | |
| <div class="mobile-text">📱 On mobile? Use the optimized version:</div> | |
| <a href="https://akhaliq-veo3-1-fast.hf.space" target="_blank" class="mobile-link"> | |
| 🚀 Open Mobile Version | |
| </a> | |
| </div> | |
| """ | |
| ) | |
| gr.HTML( | |
| """ | |
| <p style="text-align: center; font-size: 0.9em; color: #999; margin-top: 10px;"> | |
| Built with <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="color:#667eea; text-decoration:underline;">anycoder</a> | |
| </p> | |
| """ | |
| ) | |
| # Add login button - required for OAuth | |
| login_btn = gr.LoginButton("Sign in with Hugging Face") | |
| with gr.Tabs() as tabs: | |
| # Text-to-Video Tab | |
| with gr.Tab("📝 Text to Video", id=0): | |
| gr.Markdown("### Transform your text descriptions into dynamic videos") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| text_prompt = gr.Textbox( | |
| label="Text Prompt", | |
| placeholder="Describe the video you want to create... (e.g., 'A young man walking on the street during sunset')", | |
| lines=4, | |
| max_lines=6 | |
| ) | |
| with gr.Row(): | |
| text_generate_btn = gr.Button("🎬 Generate Video", variant="primary", scale=2) | |
| text_clear_btn = gr.ClearButton(value="🗑️ Clear", scale=1) | |
| text_status = gr.Textbox( | |
| label="Status", | |
| interactive=False, | |
| visible=True, | |
| elem_classes=["status-box"] | |
| ) | |
| with gr.Column(scale=1): | |
| text_video_output = gr.Video( | |
| label="Generated Video", | |
| autoplay=True, | |
| show_download_button=True, | |
| height=400 | |
| ) | |
| # Examples for text-to-video | |
| gr.Examples( | |
| examples=[ | |
| ["A serene beach at sunset with gentle waves"], | |
| ["A bustling city street with neon lights at night"], | |
| ["A majestic eagle soaring through mountain peaks"], | |
| ["An astronaut floating in space near the International Space Station"], | |
| ["Cherry blossoms falling in slow motion in a Japanese garden"], | |
| ], | |
| inputs=text_prompt, | |
| label="Example Prompts" | |
| ) | |
| # Image-to-Video Tab | |
| with gr.Tab("🖼️ Image to Video", id=1): | |
| gr.Markdown("### Bring your static images to life with motion") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| image_input = gr.Image( | |
| label="Upload Image", | |
| type="pil", | |
| height=300 | |
| ) | |
| image_prompt = gr.Textbox( | |
| label="Motion Prompt", | |
| placeholder="Describe how the image should move... (e.g., 'The cat starts to dance')", | |
| lines=3, | |
| max_lines=5 | |
| ) | |
| with gr.Row(): | |
| image_generate_btn = gr.Button("🎬 Animate Image", variant="primary", scale=2) | |
| image_clear_btn = gr.ClearButton(value="🗑️ Clear", scale=1) | |
| image_status = gr.Textbox( | |
| label="Status", | |
| interactive=False, | |
| visible=True, | |
| elem_classes=["status-box"] | |
| ) | |
| with gr.Column(scale=1): | |
| image_video_output = gr.Video( | |
| label="Generated Video", | |
| autoplay=True, | |
| show_download_button=True, | |
| height=400 | |
| ) | |
| # Examples for image-to-video | |
| gr.Examples( | |
| examples=[ | |
| [None, "The person starts walking forward"], | |
| [None, "The animal begins to run"], | |
| [None, "Camera slowly zooms in while the subject smiles"], | |
| [None, "The flowers sway gently in the breeze"], | |
| [None, "The clouds move across the sky in time-lapse"], | |
| ], | |
| inputs=[image_input, image_prompt], | |
| label="Example Motion Prompts" | |
| ) | |
| # How to Use section | |
| with gr.Accordion("📖 How to Use", open=False): | |
| gr.Markdown( | |
| """ | |
| ### Text to Video: | |
| 1. Enter a detailed description of the video you want to create | |
| 2. Optionally adjust advanced settings (duration, aspect ratio, resolution) | |
| 3. Click "Generate Video" and wait for the AI to create your video | |
| 4. Download or preview your generated video | |
| ### Image to Video: | |
| 1. Upload an image you want to animate | |
| 2. Describe the motion or action you want to add to the image | |
| 3. Optionally adjust advanced settings | |
| 4. Click "Animate Image" to bring your image to life | |
| 5. Download or preview your animated video | |
| ### Tips for Better Results: | |
| - Be specific and descriptive in your prompts | |
| - For image-to-video, describe natural motions that fit the image | |
| - Use high-quality input images for better results | |
| - Experiment with different prompts to get the desired effect | |
| ### Mobile Users: | |
| - For the best mobile experience, use the optimized version at: https://akhaliq-veo3-1-fast.hf.space | |
| """ | |
| ) | |
| # Event handlers | |
| text_generate_btn.click( | |
| fn=text_to_video, | |
| inputs=[text_prompt, login_btn], | |
| outputs=[text_video_output, text_status], | |
| show_progress="full", | |
| queue=False, | |
| api_name=False, | |
| show_api=False | |
| ) | |
| text_clear_btn.click( | |
| fn=clear_text_tab, | |
| inputs=[], | |
| outputs=[text_prompt, text_video_output, text_status], | |
| queue=False | |
| ) | |
| image_generate_btn.click( | |
| fn=image_to_video, | |
| inputs=[image_input, image_prompt, login_btn], | |
| outputs=[image_video_output, image_status], | |
| show_progress="full", | |
| queue=False, | |
| api_name=False, | |
| show_api=False | |
| ) | |
| image_clear_btn.click( | |
| fn=clear_image_tab, | |
| inputs=[], | |
| outputs=[image_input, image_prompt, image_video_output, image_status], | |
| queue=False | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| try: | |
| cleanup_temp_files() | |
| 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}") | |
| demo.queue(status_update_rate="auto", api_open=False, default_concurrency_limit=None) | |
| demo.launch( | |
| show_api=False, | |
| share=False, | |
| show_error=True, | |
| enable_monitoring=False, | |
| quiet=True, | |
| ssr_mode=True | |
| ) |