app_working.py

import os
import tempfile
import gradio as gr
import torch
import torchaudio
from loguru import logger
from typing import Optional, Tuple
import random
import numpy as np
import requests
import json

# Simplified working version without loading large models

def create_demo_audio(video_file, text_prompt: str, duration: float = 5.0) -> str:
    """Create a simple demo audio file"""
    sample_rate = 48000
    duration_samples = int(duration * sample_rate)
    
    # Generate a simple tone as demo
    t = torch.linspace(0, duration, duration_samples)
    frequency = 440  # A note
    audio = 0.3 * torch.sin(2 * 3.14159 * frequency * t)
    
    # Add some variation based on text prompt length
    if text_prompt:
        freq_mod = len(text_prompt) * 10
        audio += 0.1 * torch.sin(2 * 3.14159 * freq_mod * t)
    
    # Save to temporary file
    temp_dir = tempfile.mkdtemp()
    audio_path = os.path.join(temp_dir, "demo_audio.wav")
    torchaudio.save(audio_path, audio.unsqueeze(0), sample_rate)
    
    return audio_path

def process_video_demo(video_file, text_prompt: str, guidance_scale: float, inference_steps: int, sample_nums: int) -> Tuple[list, str]:
    """Working demo version that generates simple audio"""
    
    if video_file is None:
        return [], "❌ Please upload a video file!"
    
    if text_prompt is None:
        text_prompt = ""
    
    try:
        logger.info(f"Processing video in demo mode: {video_file}")
        logger.info(f"Text prompt: {text_prompt}")
        
        # Generate simple demo audio
        video_outputs = []
        for i in range(min(sample_nums, 3)):  # Limit to 3 samples
            demo_audio = create_demo_audio(video_file, f"{text_prompt}_sample_{i+1}")
            
            # For demo, just return the audio file path
            # In a real implementation, this would be merged with video
            video_outputs.append(demo_audio)
        
        success_msg = f"""✅ Demo Generation Complete!

📹 **Processed**: {os.path.basename(video_file) if hasattr(video_file, 'name') else 'Video file'}
📝 **Prompt**: "{text_prompt}"
⚙️ **Settings**: CFG={guidance_scale}, Steps={inference_steps}, Samples={sample_nums}

🎵 **Generated**: {len(video_outputs)} demo audio sample(s)

⚠️ **Note**: This is a working demo with synthetic audio. 
For real AI-generated Foley audio, run locally with the full model:
https://github.com/Tencent-Hunyuan/HunyuanVideo-Foley"""
        
        return video_outputs, success_msg
        
    except Exception as e:
        logger.error(f"Demo processing failed: {str(e)}")
        return [], f"❌ Demo processing failed: {str(e)}"

def create_working_interface():
    """Create a working Gradio interface"""
    
    css = """
    .gradio-container {
        font-family: 'Inter', sans-serif;
        background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
    }
    
    .main-header {
        text-align: center;
        padding: 2rem;
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        border-radius: 20px;
        margin-bottom: 2rem;
        color: white;
    }
    
    .demo-notice {
        background: #e8f4fd;
        border: 2px solid #1890ff;
        border-radius: 10px;
        padding: 1rem;
        margin: 1rem 0;
        color: #0050b3;
    }
    """
    
    with gr.Blocks(css=css, title="HunyuanVideo-Foley Demo") as app:
        
        # Header
        with gr.Column(elem_classes=["main-header"]):
            gr.HTML("""
            <h1>🎵 HunyuanVideo-Foley</h1>
            <p>Working Demo Version</p>
            """)
        
        # Demo Notice
        gr.HTML("""
        <div class="demo-notice">
            <strong>🎯 Working Demo:</strong> This version generates synthetic audio to demonstrate the interface. 
            Upload a video and try the controls to see how it works!<br>
            <strong>For real AI audio:</strong> Visit the <a href="https://github.com/Tencent-Hunyuan/HunyuanVideo-Foley" target="_blank">original repository</a>
        </div>
        """)
        
        with gr.Row():
            # Input Section
            with gr.Column(scale=1):
                gr.Markdown("### 📹 Video Input")
                
                video_input = gr.Video(
                    label="Upload Video",
                    info="Upload any video file to test the interface"
                )
                
                text_input = gr.Textbox(
                    label="🎯 Audio Description",
                    placeholder="Describe the audio you want (affects demo tone)",
                    lines=3
                )
                
                with gr.Row():
                    guidance_scale = gr.Slider(
                        minimum=1.0,
                        maximum=10.0,
                        value=4.0,
                        step=0.1,
                        label="🎚️ CFG Scale"
                    )
                    
                    inference_steps = gr.Slider(
                        minimum=10,
                        maximum=100,
                        value=50,
                        step=5,
                        label="⚡ Steps"
                    )
                    
                    sample_nums = gr.Slider(
                        minimum=1,
                        maximum=3,
                        value=1,
                        step=1,
                        label="🎲 Samples"
                    )
                
                generate_btn = gr.Button("🎵 Generate Demo Audio", variant="primary")
            
            # Output Section
            with gr.Column(scale=1):
                gr.Markdown("### 🎵 Generated Audio")
                
                audio_output_1 = gr.Audio(label="Sample 1", visible=True)
                audio_output_2 = gr.Audio(label="Sample 2", visible=False)
                audio_output_3 = gr.Audio(label="Sample 3", visible=False)
                
                status_output = gr.Textbox(
                    label="Status",
                    interactive=False,
                    lines=6
                )
        
        # Event handlers
        def update_visibility(sample_nums):
            return [
                gr.update(visible=True),  # Sample 1 always visible
                gr.update(visible=sample_nums >= 2),
                gr.update(visible=sample_nums >= 3)
            ]
        
        def process_demo(video_file, text_prompt, guidance_scale, inference_steps, sample_nums):
            audio_files, status_msg = process_video_demo(
                video_file, text_prompt, guidance_scale, inference_steps, int(sample_nums)
            )
            
            # Prepare outputs
            outputs = [None, None, None]
            for i, audio_file in enumerate(audio_files[:3]):
                outputs[i] = audio_file
            
            return outputs[0], outputs[1], outputs[2], status_msg
        
        # Connect events
        sample_nums.change(
            fn=update_visibility,
            inputs=[sample_nums],
            outputs=[audio_output_1, audio_output_2, audio_output_3]
        )
        
        generate_btn.click(
            fn=process_demo,
            inputs=[video_input, text_input, guidance_scale, inference_steps, sample_nums],
            outputs=[audio_output_1, audio_output_2, audio_output_3, status_output]
        )
        
        # Footer
        gr.HTML("""
        <div style="text-align: center; padding: 2rem; color: #666;">
            <p>🎭 <strong>Demo Version:</strong> Generates synthetic audio for interface demonstration</p>
            <p>🚀 <strong>Full Version:</strong> <a href="https://github.com/Tencent-Hunyuan/HunyuanVideo-Foley" target="_blank">GitHub Repository</a></p>
        </div>
        """)
    
    return app

if __name__ == "__main__":
    # Setup logging
    logger.remove()
    logger.add(lambda msg: print(msg, end=''), level="INFO")
    
    logger.info("Starting HunyuanVideo-Foley Working Demo...")
    
    # Create and launch app
    app = create_working_interface()
    
    logger.info("Demo app ready - will generate synthetic audio for testing")
    
    app.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        debug=False,
        show_error=True
    )