Update app.py

app.py

@@ -1,180 +1,6 @@
 import gradio as gr
-import os
-import torch
-import gc
-from typing import Optional
 
-# Check if we're running on Hugging Face Spaces
-IS_SPACES = os.environ.get("SPACE_ID") is not None
-
-def check_gpu_memory():
-    """Check available GPU memory"""
-    if torch.cuda.is_available():
-        return torch.cuda.get_device_properties(0).total_memory / 1024**3
-    return 0
-
-def load_model():
-    """Load the HunyuanVideo model with error handling"""
-    try:
-        # For Hugging Face Spaces, we need to be careful with memory
-        if IS_SPACES:
-            print("Running on Hugging Face Spaces")
-            gpu_memory = check_gpu_memory()
-            print(f"Available GPU memory: {gpu_memory:.1f} GB")
-        
-        # Try to load the model
-        from transformers import AutoModel, AutoTokenizer
-        
-        model_name = "tencent/HunyuanVideo"
-        
-        # Use CPU if no GPU or limited memory
-        device = "cuda" if torch.cuda.is_available() and check_gpu_memory() > 8 else "cpu"
-        print(f"Using device: {device}")
-        
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-        
-        # Load model with appropriate settings for Spaces
-        model = AutoModel.from_pretrained(
-            model_name,
-            torch_dtype=torch.float16 if device == "cuda" else torch.float32,
-            device_map="auto" if device == "cuda" else None,
-            low_cpu_mem_usage=True
-        )
-        
-        return model, tokenizer, device
-        
-    except Exception as e:
-        print(f"Error loading model: {e}")
-        return None, None, "cpu"
-
-# Initialize model
-MODEL, TOKENIZER, DEVICE = load_model()
-
-def generate_video(prompt: str, duration: int = 5, resolution: str = "512x512") -> str:
-    """Generate video from text prompt"""
-    
-    if MODEL is None:
-        return "❌ Model not loaded. This might be due to memory limitations on Hugging Face Spaces."
-    
-    try:
-        # Clear GPU cache if using CUDA
-        if DEVICE == "cuda":
-            torch.cuda.empty_cache()
-            gc.collect()
-        
-        # Parse resolution
-        width, height = map(int, resolution.split('x'))
-        
-        # Basic validation
-        if not prompt.strip():
-            return "❌ Please enter a valid prompt."
-        
-        if duration < 1 or duration > 10:
-            return "❌ Duration must be between 1-10 seconds."
-        
-        # This is where you would implement the actual video generation
-        # For now, return a placeholder message
-        return f"""
-        ✅ Video generation request processed:
-        
-        📝 Prompt: {prompt}
-        ⏱️ Duration: {duration} seconds
-        📐 Resolution: {resolution}
-        🖥️ Device: {DEVICE}
-        
-        Note: Actual video generation implementation needed.
-        The model is loaded and ready for inference.
-        """
-        
-    except Exception as e:
-        return f"❌ Error during generation: {str(e)}"
-
-def get_system_info():
-    """Get system information for debugging"""
-    info = f"""
-    🖥️ **System Information:**
-    - Python: {os.sys.version.split()[0]}
-    - PyTorch: {torch.__version__}
-    - CUDA Available: {torch.cuda.is_available()}
-    - GPU Memory: {check_gpu_memory():.1f} GB
-    - Running on Spaces: {IS_SPACES}
-    - Device: {DEVICE}
-    - Model Loaded: {'✅' if MODEL is not None else '❌'}
-    """
-    return info
-
-# Create Gradio interface
-with gr.Blocks(title="HunyuanVideo Generator", theme=gr.themes.Soft()) as demo:
-    
-    gr.Markdown("# 🎬 HunyuanVideo Text-to-Video Generator")
-    gr.Markdown("Generate videos from text descriptions using the HunyuanVideo model.")
-    
-    with gr.Tab("Generate Video"):
-        with gr.Row():
-            with gr.Column(scale=1):
-                prompt_input = gr.Textbox(
-                    label="📝 Video Description",
-                    placeholder="A cat playing with a ball of yarn in a sunny garden...",
-                    lines=3,
-                    max_lines=5
-                )
-                
-                with gr.Row():
-                    duration_slider = gr.Slider(
-                        minimum=1,
-                        maximum=10,
-                        value=5,
-                        step=1,
-                        label="⏱️ Duration (seconds)"
-                    )
-                    
-                    resolution_dropdown = gr.Dropdown(
-                        choices=["256x256", "512x512", "768x768", "1024x1024"],
-                        value="512x512",
-                        label="📐 Resolution"
-                    )
-                
-                generate_btn = gr.Button("🎬 Generate Video", variant="primary", size="lg")
-                
-            with gr.Column(scale=1):
-                output_text = gr.Textbox(
-                    label="📋 Output",
-                    lines=10,
-                    show_copy_button=True
-                )
-        
-        # Event handler
-        generate_btn.click(
-            fn=generate_video,
-            inputs=[prompt_input, duration_slider, resolution_dropdown],
-            outputs=output_text
-        )
-        
-        # Example prompts
-        gr.Examples(
-            examples=[
-                ["A beautiful sunset over a calm ocean with gentle waves", 5, "512x512"],
-                ["A cat gracefully jumping between rooftops in a medieval town", 7, "768x768"],
-                ["Cherry blossoms falling in a Japanese garden", 4, "512x512"],
-                ["A spacecraft flying through a colorful nebula", 8, "1024x1024"]
-            ],
-            inputs=[prompt_input, duration_slider, resolution_dropdown]
-        )
-    
-    with gr.Tab("System Info"):
-        info_button = gr.Button("🔍 Check System Info")
-        info_output = gr.Markdown()
-        
-        info_button.click(
-            fn=get_system_info,
-            outputs=info_output
-        )
-
-# Launch the app
-if __name__ == "__main__":
-    demo.launch(
-        share=False,  # Hugging Face Spaces handles sharing
-        server_name="0.0.0.0",  # Important for Spaces
-        server_port=7860,  # Default port for Spaces
-        show_error=True
-    )
+gr.load(
+   "models/Lightricks/LTX-Video",
+   provider="fal-ai",
+).launch()