app.py

import spaces
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
from huggingface_hub import snapshot_download
import shutil

from hunyuanvideo_foley.utils.model_utils import load_model
from hunyuanvideo_foley.utils.feature_utils import feature_process
from hunyuanvideo_foley.utils.model_utils import denoise_process
from hunyuanvideo_foley.utils.media_utils import merge_audio_video

# Global variables for model storage
model_dict = None
cfg = None
device = None

# need to modify the model path
MODEL_PATH = os.environ.get("HIFI_FOLEY_MODEL_PATH", "./pretrained_models/")
CONFIG_PATH = "configs/hunyuanvideo-foley-xxl.yaml"

def download_model_from_hf(repo_id: str = "tencent/HunyuanVideo-Foley", local_dir: str = "./pretrained_models") -> str:
    """HuggingFace에서 모델을 로컬 디렉토리로 자동 다운로드"""
    try:
        logger.info(f"HuggingFace에서 모델 다운로드 시작: {repo_id}")
        logger.info(f"다운로드 대상 디렉토리: {local_dir}")
        
        # 确保本地目录存在
        os.makedirs(local_dir, exist_ok=True)
        
        # 下载整个仓库
        snapshot_download(
            repo_id=repo_id,
            local_dir=local_dir,
            resume_download=True,  # 支持断点续传
            local_files_only=False,  # 允许从网络下载
        )
        
        logger.info(f"✅ 모델 다운로드 성공! 저장 위치: {local_dir}")
        return f"✅ {repo_id}에서 모델 다운로드 성공!"
        
    except Exception as e:
        error_msg = f"❌ 모델 다운로드 실패: {str(e)}"
        logger.error(error_msg)
        return error_msg

def setup_device(device_str: str = "auto", gpu_id: int = 0) -> torch.device:
    """Setup computing device"""
    if device_str == "auto":
        if torch.cuda.is_available():
            device = torch.device(f"cuda:{gpu_id}")
            logger.info(f"Using CUDA device: {device}")
        elif torch.backends.mps.is_available():
            device = torch.device("mps")
            logger.info("Using MPS device")
        else:
            device = torch.device("cpu")
            logger.info("Using CPU device")
    else:
        if device_str == "cuda":
            device = torch.device(f"cuda:{gpu_id}")
        else:
            device = torch.device(device_str)
        logger.info(f"Using specified device: {device}")
    
    return device

def auto_load_models() -> str:
    """사전 설정된 모델을 자동으로 로드"""
    global model_dict, cfg, device
    
    try:
        # 모델 경로가 존재하지 않으면 HuggingFace에서 다운로드 시도
        if not os.path.exists(MODEL_PATH):
            logger.info(f"모델 경로 {MODEL_PATH}가 존재하지 않아 HuggingFace에서 다운로드 시작...")
            download_result = download_model_from_hf(local_dir=MODEL_PATH.rstrip('/'))
            if "실패" in download_result:
                return download_result
                
        # 구성 파일이 존재하지 않으면 HuggingFace에서 다운로드 시도
        if not os.path.exists(CONFIG_PATH):
            logger.info(f"구성 파일 {CONFIG_PATH}가 존재하지 않아 HuggingFace에서 다운로드 시도...")
            # pretrained_models/ 구성 경로인 경우 다운로드 시도
            if CONFIG_PATH.startswith("configs/"):
                config_dir = os.path.dirname(CONFIG_PATH)
                if not os.path.exists(config_dir):
                    download_result = download_model_from_hf(local_dir="./")
                    if "실패" in download_result:
                        return download_result
            
            # 구성 파일 존재 여부 최종 확인
            if not os.path.exists(CONFIG_PATH):
                return f"❌ 구성 파일을 찾을 수 없음: {CONFIG_PATH}"
        
        # Use GPU by default
        device = setup_device("auto", 0)
        
        # 모델 로드
        logger.info("모델 로딩 중...")
        logger.info(f"모델 경로: {MODEL_PATH}")
        logger.info(f"구성 경로: {CONFIG_PATH}")
        
        model_dict, cfg = load_model(MODEL_PATH, CONFIG_PATH, device)
        
        logger.info("✅ 모델 로딩 성공!")
        return "✅ 모델 로딩 성공!"
        
    except Exception as e:
        logger.error(f"모델 로딩 실패: {str(e)}")
        return f"❌ 모델 로딩 실패: {str(e)}"

@spaces.GPU(duration=120)
@torch.inference_mode()
def infer_single_video(
    video_file, 
    text_prompt: str, 
    guidance_scale: float = 4.5, 
    num_inference_steps: int = 50,
    sample_nums: int = 1
) -> Tuple[list, str]:
    """Single video inference"""
    global model_dict, cfg, device
    
    if model_dict is None or cfg is None:
        return [], "❌ 먼저 모델을 로드해주세요!"
    
    if video_file is None:
        return [], "❌ 비디오 파일을 업로드해주세요!"
    
    # Allow empty text prompt, use empty string if no prompt provided
    if text_prompt is None:
        text_prompt = ""
    text_prompt = text_prompt.strip()
    
    try:
        logger.info(f"Processing video: {video_file}")
        logger.info(f"Text prompt: {text_prompt}")
        
        # Feature processing
        visual_feats, text_feats, audio_len_in_s = feature_process(
            video_file,
            text_prompt,
            model_dict,
            cfg
        )
        
        # Denoising process to generate multiple audio samples
        # Note: The model now generates sample_nums audio samples per inference
        # The denoise_process function returns audio with shape [batch_size, channels, samples]
        logger.info(f"{sample_nums}개 오디오 샘플 생성 중...")
        audio, sample_rate = denoise_process(
            visual_feats,
            text_feats,
            audio_len_in_s,
            model_dict,
            cfg,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            batch_size=sample_nums
        )
        
        # Create temporary files to save results
        temp_dir = tempfile.mkdtemp()
        video_outputs = []
        
        # Process each generated audio sample
        for i in range(sample_nums):
            # Save audio file
            audio_output = os.path.join(temp_dir, f"generated_audio_{i+1}.wav")
            torchaudio.save(audio_output, audio[i], sample_rate)
            
            # Merge video and audio
            video_output = os.path.join(temp_dir, f"video_with_audio_{i+1}.mp4")
            merge_audio_video(audio_output, video_file, video_output)
            video_outputs.append(video_output)
        
        logger.info(f"추론 완료! {sample_nums}개 샘플 생성됨.")
        return video_outputs, f"✅ {sample_nums}개 오디오 샘플이 성공적으로 생성되었습니다!"
        
    except Exception as e:
        logger.error(f"추론 실패: {str(e)}")
        return [], f"❌ 추론 실패: {str(e)}"

def update_video_outputs(video_list, status_msg):
    """Update video outputs based on the number of generated samples"""
    # Initialize all outputs as None
    outputs = [None] * 6
    
    # Set values based on generated videos
    for i, video_path in enumerate(video_list[:6]):  # Max 6 samples
        outputs[i] = video_path
    
    # Return all outputs plus status message
    return tuple(outputs + [status_msg])

def create_gradio_interface():
    """Create Gradio interface"""
    
    # Custom CSS for beautiful interface with better contrast
    css = """
    .gradio-container {
        font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
        background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
        min-height: 100vh;
    }
    
    .main-header {
        text-align: center;
        padding: 2rem 0;
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        border-radius: 20px;
        margin-bottom: 2rem;
        box-shadow: 0 8px 32px rgba(0,0,0,0.15);
    }
    
    .main-header h1 {
        color: white;
        font-size: 3rem;
        font-weight: 700;
        margin-bottom: 0.5rem;
        text-shadow: 0 2px 10px rgba(0,0,0,0.3);
    }
    
    .main-header p {
        color: rgba(255, 255, 255, 0.95);
        font-size: 1.2rem;
        font-weight: 300;
    }
    
    .status-card {
        background: white;
        border-radius: 15px;
        padding: 1rem;
        margin-bottom: 1.5rem;
        border: 1px solid #e1e5e9;
        box-shadow: 0 4px 20px rgba(0,0,0,0.08);
    }
    
    .status-card label {
        color: #2d3748 !important;
        font-weight: 600 !important;
    }
    
    .usage-guide h3 {
        color: #2d3748 !important;
        font-weight: 600 !important;
        margin-bottom: 0.5rem !important;
    }
    
    .usage-guide p {
        color: #4a5568 !important;
        font-size: 1rem !important;
        line-height: 1.6 !important;
        margin: 0.5rem 0 !important;
    }
    
    .usage-guide strong {
        color: #1a202c !important;
        font-weight: 700 !important;
    }
    
    .usage-guide em {
        color: #1a202c !important;
        font-weight: 700 !important;
        font-style: normal !important;
    }
    
    .main-interface {
        margin-bottom: 2rem;
    }
    
    .input-section {
        background: white;
        border-radius: 20px;
        padding: 2rem;
        margin-right: 1rem;
        box-shadow: 0 8px 32px rgba(0,0,0,0.1);
        border: 1px solid #e1e5e9;
    }
    
    .input-section h3 {
        color: #2d3748 !important;
        font-weight: 600 !important;
        margin-bottom: 1rem !important;
    }
    
    .input-section label {
        color: #4a5568 !important;
        font-weight: 500 !important;
    }
    
    .output-section {
        background: white;
        border-radius: 20px;
        padding: 2rem;
        margin-left: 1rem;
        box-shadow: 0 8px 32px rgba(0,0,0,0.1);
        border: 1px solid #e1e5e9;
    }
    
    .output-section h3 {
        color: #2d3748 !important;
        font-weight: 600 !important;
        margin-bottom: 1rem !important;
    }
    
    .output-section label {
        color: #4a5568 !important;
        font-weight: 500 !important;
    }
    
    .examples-section h3 {
        color: #2d3748 !important;
        font-weight: 600 !important;
        margin-bottom: 1.5rem !important;
    }
    
    .generate-btn {
        background: linear-gradient(45deg, #667eea, #764ba2) !important;
        border: none !important;
        color: white !important;
        font-weight: 600 !important;
        font-size: 1.1rem !important;
        padding: 12px 30px !important;
        border-radius: 25px !important;
        box-shadow: 0 4px 15px rgba(102, 126, 234, 0.4) !important;
        transition: all 0.3s ease !important;
    }
    
    .generate-btn:hover {
        transform: translateY(-2px) !important;
        box-shadow: 0 8px 25px rgba(102, 126, 234, 0.6) !important;
    }
    

    
    .examples-section {
        background: white;
        border-radius: 20px;
        padding: 2rem;
        margin-top: 2rem;
        box-shadow: 0 8px 32px rgba(0,0,0,0.1);
        border: 1px solid #e1e5e9;
    }
    
    .examples-section p {
        color: #4a5568 !important;
        margin-bottom: 1rem !important;
    }
    
    .example-row {
        background: #f8fafc;
        border: 1px solid #e2e8f0;
        border-radius: 15px;
        padding: 1.5rem;
        margin: 1rem 0;
        transition: all 0.3s ease;
        align-items: center;
    }
    
    .example-row:hover {
        border-color: #667eea;
        transform: translateY(-2px);
        box-shadow: 0 4px 20px rgba(102, 126, 234, 0.15);
    }
    
    .example-row .markdown {
        color: #2d3748 !important;
    }
    
    .example-row .markdown p {
        color: #2d3748 !important;
        margin: 0.5rem 0 !important;
        line-height: 1.5 !important;
    }
    
    .example-row .markdown strong {
        color: #1a202c !important;
        font-weight: 600 !important;
    }
    
    /* Example grid layout styles */
    .example-grid-row {
        margin: 1rem 0;
        gap: 1rem;
    }
    
    .example-item {
        background: #f8fafc;
        border: 1px solid #e2e8f0;
        border-radius: 15px;
        padding: 1rem;
        transition: all 0.3s ease;
        margin: 0.25rem;
        max-width: 250px;
        margin-left: auto;
        margin-right: auto;
    }
    
    .example-item:hover {
        border-color: #667eea;
        transform: translateY(-2px);
        box-shadow: 0 4px 20px rgba(102, 126, 234, 0.15);
    }
    
    .example-caption {
        margin: 0.5rem 0 !important;
        min-height: 2.8rem !important;
        display: flex !important;
        align-items: flex-start !important;
    }
    
    .example-caption p {
        color: #2d3748 !important;
        font-size: 0.9rem !important;
        line-height: 1.4 !important;
        margin: 0.5rem 0 !important;
    }
    
    /* Multi-video gallery styles */
    .additional-samples {
        margin-top: 1rem;
        gap: 0.5rem;
    }
    
    .additional-samples .gradio-video {
        border-radius: 10px;
        overflow: hidden;
    }
    
    /* Video gallery responsive layout */
    .video-gallery {
        display: grid;
        gap: 1rem;
        margin-top: 1rem;
    }
    
    .video-gallery.single {
        grid-template-columns: 1fr;
    }
    
    .video-gallery.dual {
        grid-template-columns: 1fr 1fr;
    }
    
    .video-gallery.multi {
        grid-template-columns: repeat(2, 1fr);
        grid-template-rows: auto auto auto;
    }
    
    .footer-text {
        color: #718096 !important;
        text-align: center;
        padding: 2rem;
        font-size: 0.9rem;
    }
    
    /* Video component styling for consistent size */
    .input-section video,
    .output-section video,
    .example-row video {
        width: 100% !important;
        height: 300px !important;
        object-fit: contain !important;
        border-radius: 10px !important;
        background-color: #000 !important;
    }
    
    .example-row video {
        height: 150px !important;
    }
    
    /* Fix for additional samples video display */
    .additional-samples video {
        height: 150px !important;
        object-fit: contain !important;
        border-radius: 10px !important;
        background-color: #000 !important;
    }
    
    .additional-samples .gradio-video {
        border-radius: 10px !important;
        overflow: hidden !important;
        background-color: #000 !important;
    }
    
    .additional-samples .gradio-video > div {
        background-color: #000 !important;
        border-radius: 10px !important;
    }
    
    /* Video container styling */
    .input-section .video-container,
    .output-section .video-container,
    .example-row .video-container {
        background-color: #000 !important;
        border-radius: 10px !important;
        display: flex !important;
        align-items: center !important;
        justify-content: center !important;
        overflow: hidden !important;
    }
    
    /* Ensure proper alignment */
    .example-row {
        display: flex !important;
        align-items: stretch !important;
    }
    
    .example-row > div {
        display: flex !important;
        flex-direction: column !important;
        justify-content: center !important;
    }
    
    /* Video wrapper for better control */
    .video-wrapper {
        position: relative !important;
        width: 100% !important;
        background: #000 !important;
        border-radius: 10px !important;
        overflow: hidden !important;
        display: flex !important;
        align-items: center !important;
        justify-content: center !important;
    }
    """
    
    with gr.Blocks(css=css, title="HunyuanVideo-Foley") as app:
        
        # Main header
        with gr.Column(elem_classes=["main-header"]):
            gr.HTML("""
            <h1>🎵 혼원 비디오 폴리 사운드 생성 모델</h1>
            <p>텍스트-비디오-오디오 합성: 비디오와 텍스트 설명에서 사실적인 오디오 생성</p>
            """)
        
        # Usage Guide
        with gr.Column(elem_classes=["status-card"]):
            gr.Markdown("""
            ### 📋 빠른 시작 가이드
            **1.** 비디오 파일 업로드\t**2.** 선택적 텍스트 설명 추가\t**3.** 샘플 수 조정 (1-6)\t**4.** 오디오 생성 클릭
            
            💡 빠른 시작을 위해 버튼을 클릭하여 준비된 예제를 로드할 수 있습니다.
            """, elem_classes=["usage-guide"])
        
        # Main inference interface - Input and Results side by side
        with gr.Row(elem_classes=["main-interface"]):
            # Input section
            with gr.Column(scale=1, elem_classes=["input-section"]):
                gr.Markdown("### 📹 비디오 입력")
                
                video_input = gr.Video(
                    label="비디오 업로드",
                    height=300
                )
                
                text_input = gr.Textbox(
                    label="🎯 오디오 설명 (영어)",
                    placeholder="A person walks on frozen ice",
                    lines=3,
                )
                
                with gr.Row():
                    guidance_scale = gr.Slider(
                        minimum=1.0,
                        maximum=10.0,
                        value=4.5,
                        step=0.1,
                        label="🎚️ CFG 스케일",
                    )
                    
                    inference_steps = gr.Slider(
                        minimum=10,
                        maximum=100,
                        value=50,
                        step=5,
                        label="⚡ 단계",
                    )
                    
                    sample_nums = gr.Slider(
                        minimum=1,
                        maximum=6,
                        value=1,
                        step=1,
                        label="🎲 샘플 수",
                    )
                
                generate_btn = gr.Button(
                    "🎵 오디오 생성", 
                    variant="primary",
                    elem_classes=["generate-btn"]
                )
            
            # Results section
            with gr.Column(scale=1, elem_classes=["output-section"]):
                gr.Markdown("### 🎥 생성 결과")
                
                # Multi-video gallery for displaying multiple generated samples
                with gr.Column():
                    # Primary video (Sample 1)
                    video_output_1 = gr.Video(
                        label="샘플 1",
                        height=250,
                        visible=True
                    )
                    
                    # Additional videos (Samples 2-6) - initially hidden
                    with gr.Row(elem_classes=["additional-samples"]):
                        with gr.Column(scale=1):
                            video_output_2 = gr.Video(
                                label="샘플 2",
                                height=150,
                                visible=False
                            )
                            video_output_3 = gr.Video(
                                label="샘플 3", 
                                height=150,
                                visible=False
                            )
                        with gr.Column(scale=1):
                            video_output_4 = gr.Video(
                                label="샘플 4",
                                height=150,
                                visible=False
                            )
                            video_output_5 = gr.Video(
                                label="샘플 5",
                                height=150,
                                visible=False
                            )
                    
                    # Sample 6 - full width
                    video_output_6 = gr.Video(
                        label="샘플 6",
                        height=150,
                        visible=False
                    )
                
                result_text = gr.Textbox(
                    label="상태",
                    interactive=False,
                    lines=2
                )
        
        # Examples section at the bottom
        with gr.Column(elem_classes=["examples-section"]):
            gr.Markdown("### 🌟 예제")
            gr.Markdown("위 인터페이스로 로드하려면 예제를 클릭하세요")
            
            # Define your custom examples here - 8 examples total
            examples_data = [
                # Example 1
                {
                    "caption": "A person walks on frozen ice",
                    "video_path": "examples/1_video.mp4",
                    "result_path": "examples/1_result.mp4"
                },
                # Example 2
                {
                    "caption": "With a faint sound as their hands parted, the two embraced, a soft 'mm' escaping between them.",
                    "video_path": "examples/2_video.mp4",
                    "result_path": "examples/2_result.mp4"
                },
                # Example 3
                {
                    "caption": "The sound of the number 3's bouncing footsteps is as light and clear as glass marbles hitting the ground. Each step carries a magical sound.", 
                    "video_path": "examples/3_video.mp4",
                    "result_path": "examples/3_result.mp4"
                },
                # Example 4
                {
                    "caption": "gentle gurgling of the stream's current, and music plays in the background which is a beautiful and serene piano solo with a hint of classical charm, evoking a sense of peace and serenity in people's hearts.",
                    "video_path": "examples/4_video.mp4",
                    "result_path": "examples/4_result.mp4"
                },
                # Example 5 - Add your new examples here
                {
                    "caption": "snow crunching under the snowboard's edge.",
                    "video_path": "examples/5_video.mp4",
                    "result_path": "examples/5_result.mp4"
                },
                # Example 6
                {
                    "caption": "The crackling of the fire, the whooshing of the flames, and the occasional crisp popping of charred leaves filled the forest.",
                    "video_path": "examples/6_video.mp4",
                    "result_path": "examples/6_result.mp4"
                },
                # Example 7
                {
                    "caption": "humming of the scooter engine accelerates slowly.",
                    "video_path": "examples/7_video.mp4",
                    "result_path": "examples/7_result.mp4"
                },
                # Example 8
                {
                    "caption": "splash of water and loud thud as person hits the surface.",
                    "video_path": "examples/8_video.mp4",
                    "result_path": "examples/8_result.mp4"
                }
            ]
            
            # Create example grid - 4 examples per row, 2 rows total
            example_buttons = []
            for row in range(2):  # 2 rows
                with gr.Row(elem_classes=["example-grid-row"]):
                    for col in range(4):  # 4 columns
                        idx = row * 4 + col
                        if idx < len(examples_data):
                            example = examples_data[idx]
                            
                            with gr.Column(scale=1, elem_classes=["example-item"]):
                                # Video thumbnail
                                if os.path.exists(example['video_path']):
                                    example_video = gr.Video(
                                        value=example['video_path'],
                                        label=f"Example {idx+1}",
                                        interactive=False,
                                        show_label=True,
                                        height=180
                                    )
                                else:
                                    example_video = gr.HTML(f"""
                                    <div style="background: #f0f0f0; padding: 15px; text-align: center; border-radius: 8px; height: 180px; display: flex; align-items: center; justify-content: center;">
                                        <div>
                                            <p style="color: #666; margin: 0; font-size: 12px;">📹 Video not found</p>
                                            <small style="color: #999; font-size: 10px;">{example['video_path']}</small>
                                        </div>
                                    </div>
                                    """)
                                
                                # Caption (truncated for grid layout)
                                caption_preview = example['caption'][:60] + "..." if len(example['caption']) > 60 else example['caption']
                                gr.Markdown(f"{caption_preview}", elem_classes=["example-caption"])
                                
                                # Load button
                                example_btn = gr.Button(
                                    f"예제 {idx+1} 로드",
                                    variant="secondary",
                                    size="sm"
                                )
                                example_buttons.append((example_btn, example))
        
        # Event handlers
        def process_inference(video_file, text_prompt, guidance_scale, inference_steps, sample_nums):
            # Generate videos
            video_list, status_msg = infer_single_video(
                video_file, text_prompt, guidance_scale, inference_steps, int(sample_nums)
            )
            # Update outputs with proper visibility
            return update_video_outputs(video_list, status_msg)
        
        # Add dynamic visibility control based on sample_nums
        def update_visibility(sample_nums):
            sample_nums = int(sample_nums)
            return [
                gr.update(visible=True),  # Sample 1 always visible
                gr.update(visible=sample_nums >= 2),  # Sample 2
                gr.update(visible=sample_nums >= 3),  # Sample 3
                gr.update(visible=sample_nums >= 4),  # Sample 4
                gr.update(visible=sample_nums >= 5),  # Sample 5
                gr.update(visible=sample_nums >= 6),  # Sample 6
            ]
        
        # Update visibility when sample_nums changes
        sample_nums.change(
            fn=update_visibility,
            inputs=[sample_nums],
            outputs=[video_output_1, video_output_2, video_output_3, video_output_4, video_output_5, video_output_6]
        )
        
        generate_btn.click(
            fn=process_inference,
            inputs=[video_input, text_input, guidance_scale, inference_steps, sample_nums],
            outputs=[
                video_output_1,  # Sample 1 value
                video_output_2,  # Sample 2 value  
                video_output_3,  # Sample 3 value
                video_output_4,  # Sample 4 value
                video_output_5,  # Sample 5 value
                video_output_6,  # Sample 6 value
                result_text
            ]
        )
        
        # Add click handlers for example buttons
        for btn, example in example_buttons:
            def create_example_handler(ex):
                def handler():
                    # Check if files exist, if not, return placeholder message
                    if os.path.exists(ex['video_path']):
                        video_file = ex['video_path']
                    else:
                        video_file = None
                        
                    if os.path.exists(ex['result_path']):
                        result_video = ex['result_path']
                    else:
                        result_video = None
                    
                    status_msg = f"✅ Loaded example with caption: {ex['caption'][:50]}..."
                    if not video_file:
                        status_msg += f"\n⚠️ Video file not found: {ex['video_path']}"
                    if not result_video:
                        status_msg += f"\n⚠️ Result video not found: {ex['result_path']}"
                        
                    return video_file, ex['caption'], result_video, status_msg
                return handler
            
            btn.click(
                fn=create_example_handler(example),
                outputs=[video_input, text_input, video_output_1, result_text]
            )
        
        # Footer
        gr.HTML("""
        <div class="footer-text">
            <p>🚀 HunyuanVideo-Foley로 구동 | 비디오와 텍스트 설명에서 고품질 오디오 생성</p>
        </div>
        """)
    
    return app

def set_manual_seed(global_seed):
    random.seed(global_seed)
    np.random.seed(global_seed)
    torch.manual_seed(global_seed)

if __name__ == "__main__":
    set_manual_seed(1)
    # Setup logging
    logger.remove()
    logger.add(lambda msg: print(msg, end=''), level="INFO")
    
    # Auto-load model
    logger.info("애플리케이션 시작 및 모델 로딩...")
    model_load_result = auto_load_models()
    logger.info(model_load_result)
    
    # Create and launch Gradio app
    app = create_gradio_interface()
    
    # Log completion status
    if "successfully" in model_load_result:
        logger.info("애플리케이션 준비 완료, 모델 로드 완료")
    
    # Test
    app.launch(
        server_name="0.0.0.0",
        #  server_port=8080,
        share=False,
        debug=False,
        show_error=True
    )