app.py

import torch
from diffusers import AutoencoderKLWan, WanImageToVideoPipeline, UniPCMultistepScheduler
from diffusers.utils import export_to_video
from transformers import CLIPVisionModel
import gradio as gr
import tempfile
import spaces
from huggingface_hub import hf_hub_download
import numpy as np
from PIL import Image
import random
import logging
import gc
import time
import hashlib
from dataclasses import dataclass
from typing import Optional, Tuple
from functools import wraps
import threading
import os

# GPU 메모리 관리 설정
os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:512'

# 로깅 설정
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# 설정 관리
@dataclass
class VideoGenerationConfig:
    model_id: str = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
    lora_repo_id: str = "Kijai/WanVideo_comfy"
    lora_filename: str = "Wan21_CausVid_14B_T2V_lora_rank32.safetensors"
    mod_value: int = 32
    default_height: int = 512
    default_width: int = 512  # Zero GPU 환경을 위해 기본값 수정
    max_area: float = 480.0 * 832.0
    slider_min_h: int = 128
    slider_max_h: int = 832  # Zero GPU 환경을 위해 수정
    slider_min_w: int = 128
    slider_max_w: int = 832  # Zero GPU 환경을 위해 수정
    fixed_fps: int = 24
    min_frames: int = 8
    max_frames: int = 81
    default_prompt: str = "make this image come alive, cinematic motion, smooth animation"
    default_negative_prompt: str = "static, blurred, low quality, watermark, text"
    # GPU 메모리 최적화 설정
    enable_model_cpu_offload: bool = True
    enable_vae_slicing: bool = True
    enable_vae_tiling: bool = True
    
    @property
    def max_duration(self):
        """최대 허용 duration (초)"""
        return self.max_frames / self.fixed_fps
    
    @property
    def min_duration(self):
        """최소 허용 duration (초)"""
        return self.min_frames / self.fixed_fps

config = VideoGenerationConfig()
MAX_SEED = np.iinfo(np.int32).max

# 글로벌 락 (동시 실행 방지)
generation_lock = threading.Lock()

# 성능 측정 데코레이터
def measure_time(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        logger.info(f"{func.__name__} took {time.time()-start:.2f}s")
        return result
    return wrapper

# GPU 메모리 정리 함수
def clear_gpu_memory():
    """강력한 GPU 메모리 정리"""
    # Zero GPU 환경에서는 메인 프로세스에서 CUDA 초기화 방지
    if hasattr(spaces, 'GPU'):
        # Zero GPU 환경에서는 @spaces.GPU 내에서만 GPU 작업 수행
        gc.collect()
        return
    
    if torch.cuda.is_available():
        try:
            torch.cuda.empty_cache()
            torch.cuda.ipc_collect()
            gc.collect()
            
            # GPU 메모리 상태 로깅
            allocated = torch.cuda.memory_allocated() / 1024**3
            reserved = torch.cuda.memory_reserved() / 1024**3
            logger.info(f"GPU Memory - Allocated: {allocated:.2f}GB, Reserved: {reserved:.2f}GB")
        except Exception as e:
            logger.warning(f"GPU memory clear failed: {e}")
            gc.collect()

# 모델 관리자 (싱글톤 패턴)
class ModelManager:
    _instance = None
    _lock = threading.Lock()
    
    def __new__(cls):
        if cls._instance is None:
            with cls._lock:
                if cls._instance is None:
                    cls._instance = super().__new__(cls)
        return cls._instance
    
    def __init__(self):
        if not hasattr(self, '_initialized'):
            self._pipe = None
            self._is_loaded = False
            self._initialized = True
    
    @property
    def pipe(self):
        if not self._is_loaded:
            self._load_model()
        return self._pipe
    
    @measure_time
    def _load_model(self):
        """메모리 효율적인 모델 로딩"""
        with self._lock:
            if self._is_loaded:
                return
                
            try:
                logger.info("Loading model with memory optimizations...")
                clear_gpu_memory()
                
                # 모델 컴포넌트 로드 (메모리 효율적) - autocast 수정
                if torch.cuda.is_available() and not hasattr(spaces, 'GPU'):
                    # 일반 GPU 환경
                    with torch.amp.autocast('cuda', enabled=False):  # 수정된 부분
                        image_encoder = CLIPVisionModel.from_pretrained(
                            config.model_id, 
                            subfolder="image_encoder", 
                            torch_dtype=torch.float16,
                            low_cpu_mem_usage=True
                        )
                        
                        vae = AutoencoderKLWan.from_pretrained(
                            config.model_id, 
                            subfolder="vae", 
                            torch_dtype=torch.float16,
                            low_cpu_mem_usage=True
                        )
                else:
                    # CPU 환경 또는 Zero GPU 환경
                    image_encoder = CLIPVisionModel.from_pretrained(
                        config.model_id, 
                        subfolder="image_encoder", 
                        torch_dtype=torch.float16 if hasattr(spaces, 'GPU') else torch.float32,
                        low_cpu_mem_usage=True
                    )
                    
                    vae = AutoencoderKLWan.from_pretrained(
                        config.model_id, 
                        subfolder="vae", 
                        torch_dtype=torch.float16 if hasattr(spaces, 'GPU') else torch.float32,
                        low_cpu_mem_usage=True
                    )
                    
                self._pipe = WanImageToVideoPipeline.from_pretrained(
                    config.model_id, 
                    vae=vae, 
                    image_encoder=image_encoder, 
                    torch_dtype=torch.bfloat16 if (torch.cuda.is_available() or hasattr(spaces, 'GPU')) else torch.float32,
                    low_cpu_mem_usage=True,
                    use_safetensors=True
                )
                
                # 스케줄러 설정
                self._pipe.scheduler = UniPCMultistepScheduler.from_config(
                    self._pipe.scheduler.config, flow_shift=8.0
                )
                
                # LoRA 로드
                try:
                    causvid_path = hf_hub_download(
                        repo_id=config.lora_repo_id, filename=config.lora_filename
                    )
                    self._pipe.load_lora_weights(causvid_path, adapter_name="causvid_lora")
                    self._pipe.set_adapters(["causvid_lora"], adapter_weights=[0.95])
                    self._pipe.fuse_lora()
                    logger.info("LoRA weights loaded successfully")
                except Exception as e:
                    logger.warning(f"Failed to load LoRA weights: {e}")
                
                # GPU 최적화 설정
                if hasattr(spaces, 'GPU'):  # Zero GPU 환경
                    # Zero GPU 환경에서는 자동으로 처리됨
                    logger.info("Model loaded for Zero GPU environment")
                elif config.enable_model_cpu_offload and torch.cuda.is_available():
                    self._pipe.enable_model_cpu_offload()
                    logger.info("CPU offload enabled")
                elif torch.cuda.is_available():
                    self._pipe.to("cuda")
                    logger.info("Model moved to CUDA")
                else:
                    logger.info("Running on CPU")
                
                if config.enable_vae_slicing:
                    self._pipe.enable_vae_slicing()
                    
                if config.enable_vae_tiling:
                    self._pipe.enable_vae_tiling()
                
                # xFormers 메모리 효율적인 attention 활성화 (가능한 경우)
                try:
                    self._pipe.enable_xformers_memory_efficient_attention()
                    logger.info("xFormers memory efficient attention enabled")
                except:
                    logger.info("xFormers not available, using default attention")
                
                self._is_loaded = True
                logger.info("Model loaded successfully with optimizations")
                clear_gpu_memory()
                
            except Exception as e:
                logger.error(f"Error loading model: {e}")
                self._is_loaded = False
                clear_gpu_memory()
                raise
    
    def unload_model(self):
        """모델 언로드 및 메모리 해제"""
        with self._lock:
            if self._pipe is not None:
                del self._pipe
                self._pipe = None
                self._is_loaded = False
                clear_gpu_memory()
                logger.info("Model unloaded and memory cleared")

# 싱글톤 인스턴스
model_manager = ModelManager()

# 비디오 생성기 클래스
class VideoGenerator:
    def __init__(self, config: VideoGenerationConfig, model_manager: ModelManager):
        self.config = config
        self.model_manager = model_manager
    
    def calculate_dimensions(self, image: Image.Image) -> Tuple[int, int]:
        orig_w, orig_h = image.size
        if orig_w <= 0 or orig_h <= 0:
            return self.config.default_height, self.config.default_width
        
        aspect_ratio = orig_h / orig_w
        
        # Zero GPU 환경에서는 더 작은 max_area 사용
        if hasattr(spaces, 'GPU'):
            max_area = 640.0 * 640.0  # 409,600 pixels
        else:
            max_area = self.config.max_area
        
        calc_h = round(np.sqrt(max_area * aspect_ratio))
        calc_w = round(np.sqrt(max_area / aspect_ratio))
        
        calc_h = max(self.config.mod_value, (calc_h // self.config.mod_value) * self.config.mod_value)
        calc_w = max(self.config.mod_value, (calc_w // self.config.mod_value) * self.config.mod_value)
        
        # Zero GPU 환경에서 추가 제한
        if hasattr(spaces, 'GPU'):
            max_dim = 832
            new_h = int(np.clip(calc_h, self.config.slider_min_h, min(max_dim, self.config.slider_max_h)))
            new_w = int(np.clip(calc_w, self.config.slider_min_w, min(max_dim, self.config.slider_max_w)))
        else:
            new_h = int(np.clip(calc_h, self.config.slider_min_h, 
                               (self.config.slider_max_h // self.config.mod_value) * self.config.mod_value))
            new_w = int(np.clip(calc_w, self.config.slider_min_w, 
                               (self.config.slider_max_w // self.config.mod_value) * self.config.mod_value))
        
        return new_h, new_w
    
    def validate_inputs(self, image: Image.Image, prompt: str, height: int, 
                       width: int, duration: float, steps: int) -> Tuple[bool, Optional[str]]:
        if image is None:
            return False, "🖼️ Please upload an input image"
        
        if not prompt or len(prompt.strip()) == 0:
            return False, "✍️ Please provide a prompt"
        
        if len(prompt) > 500:
            return False, "⚠️ Prompt is too long (max 500 characters)"
        
        # 정확한 duration 범위 체크
        min_duration = self.config.min_duration
        max_duration = self.config.max_duration
        
        if duration < min_duration:
            return False, f"⏱️ Duration too short (min {min_duration:.1f}s)"
        
        if duration > max_duration:
            return False, f"⏱️ Duration too long (max {max_duration:.1f}s)"
        
        # Zero GPU 환경에서는 더 보수적인 제한 적용
        if hasattr(spaces, 'GPU'):  # Spaces 환경 체크
            if duration > 2.5:  # Zero GPU에서는 2.5초로 제한
                return False, "⏱️ In Zero GPU environment, duration is limited to 2.5s for stability"
            # 픽셀 수 기반 제한 (640x640 = 409,600 픽셀)
            max_pixels = 640 * 640
            if height * width > max_pixels:
                return False, f"📐 In Zero GPU environment, total pixels limited to {max_pixels:,} (e.g., 640×640, 512×832)"
            if height > 832 or width > 832:  # 한 변의 최대 길이
                return False, "📐 In Zero GPU environment, maximum dimension is 832 pixels"
        
        # GPU 메모리 체크 (Zero GPU 환경이 아닐 때만)
        if torch.cuda.is_available() and not hasattr(spaces, 'GPU'):
            try:
                free_memory = torch.cuda.get_device_properties(0).total_memory - torch.cuda.memory_allocated()
                required_memory = (height * width * 3 * 8 * duration * self.config.fixed_fps) / (1024**3)
                if free_memory < required_memory * 2:
                    clear_gpu_memory()
                    # 재확인
                    free_memory = torch.cuda.get_device_properties(0).total_memory - torch.cuda.memory_allocated()
                    if free_memory < required_memory * 1.5:
                        return False, "⚠️ Not enough GPU memory. Try smaller dimensions or shorter duration."
            except Exception as e:
                logger.warning(f"GPU memory check failed: {e}")
        
        return True, None
    
    def generate_unique_filename(self, seed: int) -> str:
        timestamp = int(time.time())
        unique_str = f"{timestamp}_{seed}_{random.randint(1000, 9999)}"
        hash_obj = hashlib.md5(unique_str.encode())
        return f"video_{hash_obj.hexdigest()[:8]}.mp4"

video_generator = VideoGenerator(config, model_manager)

# Gradio 함수들
def handle_image_upload(image):
    if image is None:
        return gr.update(value=config.default_height), gr.update(value=config.default_width)
    
    try:
        if not isinstance(image, Image.Image):
            raise ValueError("Invalid image format")
        
        new_h, new_w = video_generator.calculate_dimensions(image)
        return gr.update(value=new_h), gr.update(value=new_w)
        
    except Exception as e:
        logger.error(f"Error processing image: {e}")
        gr.Warning("⚠️ Error processing image")
        return gr.update(value=config.default_height), gr.update(value=config.default_width)

def get_duration(input_image, prompt, height, width, negative_prompt, 
                duration_seconds, guidance_scale, steps, seed, randomize_seed, progress):
    # Zero GPU 환경에서는 더 보수적인 시간 할당
    base_duration = 60
    
    # 단계별 추가 시간
    if steps > 8:
        base_duration += 30
    elif steps > 4:
        base_duration += 15
    
    # Duration별 추가 시간
    if duration_seconds > 2:
        base_duration += 20
    elif duration_seconds > 1.5:
        base_duration += 10
    
    # 해상도별 추가 시간 (픽셀 수 기반)
    pixels = height * width
    if pixels > 400000:  # 640x640 근처
        base_duration += 20
    elif pixels > 250000:  # 512x512 근처
        base_duration += 10
    
    # Zero GPU 환경에서는 최대 90초로 제한
    return min(base_duration, 90)

@spaces.GPU(duration=get_duration)
@measure_time
def generate_video(input_image, prompt, height, width, 
                   negative_prompt=config.default_negative_prompt, 
                   duration_seconds=1.5, guidance_scale=1, steps=4,
                   seed=42, randomize_seed=False, 
                   progress=gr.Progress(track_tqdm=True)):
    
    # 동시 실행 방지
    if not generation_lock.acquire(blocking=False):
        raise gr.Error("⏳ Another video is being generated. Please wait...")
    
    try:
        # Zero GPU 환경에서는 이제 GPU 사용 가능
        if hasattr(spaces, 'GPU') and torch.cuda.is_available():
            logger.info("GPU initialized in Zero GPU environment")
        
        progress(0.1, desc="🔍 Validating inputs...")
        
        # Zero GPU 환경에서 추가 검증
        if hasattr(spaces, 'GPU'):
            logger.info(f"Zero GPU environment detected. Duration: {duration_seconds}s, Resolution: {height}x{width}, Pixels: {height*width:,}")
        
        # 입력 검증
        is_valid, error_msg = video_generator.validate_inputs(
            input_image, prompt, height, width, duration_seconds, steps
        )
        if not is_valid:
            raise gr.Error(error_msg)
        
        # 메모리 정리
        clear_gpu_memory()
        
        progress(0.2, desc="🎯 Preparing image...")
        target_h = max(config.mod_value, (int(height) // config.mod_value) * config.mod_value)
        target_w = max(config.mod_value, (int(width) // config.mod_value) * config.mod_value)
        
        # 프레임 수 계산 (Zero GPU 환경에서 추가 제한)
        max_allowed_frames = int(2.5 * config.fixed_fps) if hasattr(spaces, 'GPU') else config.max_frames
        num_frames = min(
            int(round(duration_seconds * config.fixed_fps)),
            max_allowed_frames
        )
        num_frames = np.clip(num_frames, config.min_frames, max_allowed_frames)
        
        current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
        
        # 이미지 리사이즈 (메모리 효율적)
        resized_image = input_image.resize((target_w, target_h), Image.Resampling.LANCZOS)
        
        progress(0.3, desc="🎨 Loading model...")
        pipe = model_manager.pipe
        
        progress(0.4, desc="🎬 Generating video frames...")
        
        # 메모리 효율적인 생성
        device = "cuda" if torch.cuda.is_available() else "cpu"
        
        if device == "cuda":
            with torch.inference_mode(), torch.amp.autocast('cuda', enabled=True):  # 수정된 부분
                try:
                    output_frames_list = pipe(
                        image=resized_image,
                        prompt=prompt,
                        negative_prompt=negative_prompt,
                        height=target_h,
                        width=target_w,
                        num_frames=num_frames,
                        guidance_scale=float(guidance_scale),
                        num_inference_steps=int(steps),
                        generator=torch.Generator(device="cuda").manual_seed(current_seed),
                        return_dict=True
                    ).frames[0]
                except torch.cuda.OutOfMemoryError:
                    clear_gpu_memory()
                    raise gr.Error("💾 GPU out of memory. Try smaller dimensions or shorter duration.")
                except Exception as e:
                    logger.error(f"Generation error: {e}")
                    raise gr.Error(f"❌ Generation failed: {str(e)}")
        else:
            # CPU 환경
            with torch.inference_mode():
                try:
                    output_frames_list = pipe(
                        image=resized_image,
                        prompt=prompt,
                        negative_prompt=negative_prompt,
                        height=target_h,
                        width=target_w,
                        num_frames=num_frames,
                        guidance_scale=float(guidance_scale),
                        num_inference_steps=int(steps),
                        generator=torch.Generator().manual_seed(current_seed),
                        return_dict=True
                    ).frames[0]
                except Exception as e:
                    logger.error(f"Generation error: {e}")
                    raise gr.Error(f"❌ Generation failed: {str(e)}")
        
        progress(0.9, desc="💾 Saving video...")
        filename = video_generator.generate_unique_filename(current_seed)
        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
            video_path = tmpfile.name
        
        export_to_video(output_frames_list, video_path, fps=config.fixed_fps)
        
        progress(1.0, desc="✨ Complete!")
        logger.info(f"Video generated successfully: {num_frames} frames, {target_h}x{target_w}")
        
        # 성공 정보 반환
        info_text = f"✅ Generated {num_frames} frames at {target_h}x{target_w} with seed {current_seed}"
        gr.Info(info_text)
        
        return video_path, current_seed
        
    except gr.Error:
        # Gradio 에러는 그대로 전달
        raise
    except Exception as e:
        logger.error(f"Unexpected error: {e}")
        raise gr.Error(f"❌ Unexpected error: {str(e)}")
        
    finally:
        # 항상 메모리 정리 및 락 해제
        generation_lock.release()
        
        # 메모리 정리
        if 'output_frames_list' in locals():
            del output_frames_list
        if 'resized_image' in locals():
            del resized_image
        
        clear_gpu_memory()

# 개선된 CSS 스타일
css = """
.container {
    max-width: 1200px;
    margin: auto;
    padding: 20px;
}

.header {
    text-align: center;
    margin-bottom: 30px;
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    padding: 40px;
    border-radius: 20px;
    color: white;
    box-shadow: 0 10px 30px rgba(0,0,0,0.2);
    position: relative;
    overflow: hidden;
}

.header::before {
    content: '';
    position: absolute;
    top: -50%;
    left: -50%;
    width: 200%;
    height: 200%;
    background: radial-gradient(circle, rgba(255,255,255,0.1) 0%, transparent 70%);
    animation: pulse 4s ease-in-out infinite;
}

@keyframes pulse {
    0%, 100% { transform: scale(1); opacity: 0.5; }
    50% { transform: scale(1.1); opacity: 0.8; }
}

.header h1 {
    font-size: 3em;
    margin-bottom: 10px;
    text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
    position: relative;
    z-index: 1;
}

.header p {
    font-size: 1.2em;
    opacity: 0.95;
    position: relative;
    z-index: 1;
}

.gpu-status {
    position: absolute;
    top: 10px;
    right: 10px;
    background: rgba(0,0,0,0.3);
    padding: 5px 15px;
    border-radius: 20px;
    font-size: 0.8em;
}

.main-content {
    background: rgba(255, 255, 255, 0.95);
    border-radius: 20px;
    padding: 30px;
    box-shadow: 0 5px 20px rgba(0,0,0,0.1);
    backdrop-filter: blur(10px);
}

.input-section {
    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
    padding: 25px;
    border-radius: 15px;
    margin-bottom: 20px;
}

.generate-btn {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    color: white;
    font-size: 1.3em;
    padding: 15px 40px;
    border-radius: 30px;
    border: none;
    cursor: pointer;
    transition: all 0.3s ease;
    box-shadow: 0 5px 15px rgba(102, 126, 234, 0.4);
    width: 100%;
    margin-top: 20px;
}

.generate-btn:hover {
    transform: translateY(-2px);
    box-shadow: 0 7px 20px rgba(102, 126, 234, 0.6);
}

.generate-btn:active {
    transform: translateY(0);
}

.video-output {
    background: #f8f9fa;
    padding: 20px;
    border-radius: 15px;
    text-align: center;
    min-height: 400px;
    display: flex;
    align-items: center;
    justify-content: center;
}

.accordion {
    background: rgba(255, 255, 255, 0.7);
    border-radius: 10px;
    margin-top: 15px;
    padding: 15px;
}

.slider-container {
    background: rgba(255, 255, 255, 0.5);
    padding: 15px;
    border-radius: 10px;
    margin: 10px 0;
}

body {
    background: linear-gradient(-45deg, #ee7752, #e73c7e, #23a6d5, #23d5ab);
    background-size: 400% 400%;
    animation: gradient 15s ease infinite;
}

@keyframes gradient {
    0% { background-position: 0% 50%; }
    50% { background-position: 100% 50%; }
    100% { background-position: 0% 50%; }
}

.warning-box {
    background: rgba(255, 193, 7, 0.1);
    border: 1px solid rgba(255, 193, 7, 0.3);
    border-radius: 10px;
    padding: 15px;
    margin: 10px 0;
    color: #856404;
    font-size: 0.9em;
}

.info-box {
    background: rgba(52, 152, 219, 0.1);
    border: 1px solid rgba(52, 152, 219, 0.3);
    border-radius: 10px;
    padding: 15px;
    margin: 10px 0;
    color: #2c5282;
    font-size: 0.9em;
}

.footer {
    text-align: center;
    margin-top: 30px;
    color: #666;
    font-size: 0.9em;
}

/* 로딩 애니메이션 개선 */
.progress-bar {
    background: linear-gradient(90deg, #667eea 0%, #764ba2 50%, #667eea 100%);
    background-size: 200% 100%;
    animation: loading 1.5s ease-in-out infinite;
}

@keyframes loading {
    0% { background-position: 0% 0%; }
    100% { background-position: 200% 0%; }
}
"""

# Gradio UI
with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
    with gr.Column(elem_classes="container"):
        # Header with GPU status
        gr.HTML("""
        <div class="header">
            <h1>🎬 AI Video Magic Studio</h1>
            <p>Transform your images into captivating videos with Wan 2.1 + CausVid LoRA</p>
            <div class="gpu-status">🖥️ Zero GPU Optimized</div>
        </div>
        """)
        
        # GPU 메모리 경고
        gr.HTML("""
        <div class="warning-box">
            <strong>💡 Zero GPU Performance Tips:</strong>
            <ul style="margin: 5px 0; padding-left: 20px;">
                <li>Maximum duration: 2.5 seconds (limited by Zero GPU)</li>
                <li>Maximum total pixels: 409,600 (e.g., 640×640, 512×832, 448×896)</li>
                <li>Maximum single dimension: 832 pixels</li>
                <li>Use 4-6 steps for optimal speed/quality balance</li>
                <li>Wait between generations to avoid queue errors</li>
            </ul>
        </div>
        """)
        
        # 새로운 정보 박스 추가
        gr.HTML("""
        <div class="info-box">
            <strong>🎯 Quick Start Guide:</strong>
            <ol style="margin: 5px 0; padding-left: 20px;">
                <li>Upload your image - AI will calculate optimal dimensions</li>
                <li>Enter a creative prompt or use the default</li>
                <li>Adjust duration (1.5s recommended for best results)</li>
                <li>Click Generate and wait ~60 seconds</li>
            </ol>
        </div>
        """)
        
        with gr.Row(elem_classes="main-content"):
            with gr.Column(scale=1):
                gr.Markdown("### 📸 Input Settings")
                
                with gr.Column(elem_classes="input-section"):
                    input_image = gr.Image(
                        type="pil", 
                        label="🖼️ Upload Your Image",
                        elem_classes="image-upload"
                    )
                    
                    prompt_input = gr.Textbox(
                        label="✨ Animation Prompt",
                        value=config.default_prompt,
                        placeholder="Describe how you want your image to move...",
                        lines=2
                    )
                    
                    duration_input = gr.Slider(
                        minimum=round(config.min_duration, 1),
                        maximum=2.5 if hasattr(spaces, 'GPU') else round(config.max_duration, 1),  # Zero GPU 환경 제한
                        step=0.1,
                        value=1.5,  # 안전한 기본값
                        label="⏱️ Video Duration (seconds) - Limited to 2.5s in Zero GPU",
                        elem_classes="slider-container"
                    )
                
                with gr.Accordion("🎛️ Advanced Settings", open=False, elem_classes="accordion"):
                    negative_prompt = gr.Textbox(
                        label="🚫 Negative Prompt",
                        value=config.default_negative_prompt,
                        lines=2
                    )
                    
                    with gr.Row():
                        seed = gr.Slider(
                            minimum=0,
                            maximum=MAX_SEED,
                            step=1,
                            value=42,
                            label="🎲 Seed"
                        )
                        randomize_seed = gr.Checkbox(
                            label="🔀 Randomize",
                            value=True
                        )
                    
                    with gr.Row():
                        height_slider = gr.Slider(
                            minimum=config.slider_min_h,
                            maximum=config.slider_max_h,
                            step=config.mod_value,
                            value=config.default_height,
                            label="📏 Height (max 832px in Zero GPU)"
                        )
                        width_slider = gr.Slider(
                            minimum=config.slider_min_w,
                            maximum=config.slider_max_w,
                            step=config.mod_value,
                            value=config.default_width,
                            label="📐 Width (max 832px in Zero GPU)"
                        )
                    
                    steps_slider = gr.Slider(
                        minimum=1,
                        maximum=30,
                        step=1,
                        value=4,
                        label="🔧 Quality Steps (4-8 recommended)"
                    )
                    
                    guidance_scale = gr.Slider(
                        minimum=0.0,
                        maximum=20.0,
                        step=0.5,
                        value=1.0,
                        label="🎯 Guidance Scale",
                        visible=False
                    )
                
                generate_btn = gr.Button(
                    "🎬 Generate Video",
                    variant="primary",
                    elem_classes="generate-btn"
                )
            
            with gr.Column(scale=1):
                gr.Markdown("### 🎥 Generated Video")
                video_output = gr.Video(
                    label="",
                    autoplay=True,
                    elem_classes="video-output"
                )
                
                gr.HTML("""
                <div class="footer">
                    <p>💡 Tip: For best results, use clear images with good lighting and distinct subjects</p>
                </div>
                """)
        
        # Examples - 파일명 확인 필요
        try:
            gr.Examples(
                examples=[
                    ["peng.png", "a penguin playfully dancing in the snow, Antarctica", 512, 512],
                    ["forg.jpg", "the frog jumps around", 576, 320],  # 16:9 aspect ratio within limits
                ],
                inputs=[input_image, prompt_input, height_slider, width_slider],
                outputs=[video_output, seed],
                fn=generate_video,
                cache_examples=False  # 캐시 비활성화로 메모리 절약
            )
        except Exception as e:
            logger.warning(f"Failed to load examples: {e}")

        # 개선사항 요약 (작게)
        gr.HTML("""
        <div style="background: rgba(255,255,255,0.9); border-radius: 10px; padding: 15px; margin-top: 20px; font-size: 0.8em; text-align: center;">
            <p style="margin: 0; color: #666;">
                <strong style="color: #667eea;">Enhanced with:</strong> 
                🛡️ GPU Crash Protection • ⚡ Memory Optimization • 🎨 Modern UI • 🔧 Clean Architecture
            </p>
        </div>
        """)
    
    # Event handlers
    input_image.upload(
        fn=handle_image_upload,
        inputs=[input_image],
        outputs=[height_slider, width_slider]
    )
    
    input_image.clear(
        fn=handle_image_upload,
        inputs=[input_image],
        outputs=[height_slider, width_slider]
    )
    
    generate_btn.click(
        fn=generate_video,
        inputs=[
            input_image, prompt_input, height_slider, width_slider,
            negative_prompt, duration_input, guidance_scale, 
            steps_slider, seed, randomize_seed
        ],
        outputs=[video_output, seed]
    )

if __name__ == "__main__":
    # Zero GPU 환경 체크 로깅
    if hasattr(spaces, 'GPU'):
        logger.info("Running in Zero GPU environment")
    else:
        logger.info("Running in standard environment")
    
    # 앱 실행
    demo.queue(concurrency_count=1)  # 동시 실행 제한
    demo.launch()