Delete app-backup.py

app-backup.py

@@ -1,294 +0,0 @@
-# ===== CRITICAL: Import spaces FIRST before any CUDA operations =====
-try:
-    import spaces
-    HF_SPACES = True
-except ImportError:
-    # If running locally, create a dummy decorator
-    def spaces_gpu_decorator(duration=60):
-        def decorator(func):
-            return func
-        return decorator
-    spaces = type('spaces', (), {'GPU': spaces_gpu_decorator})()
-    HF_SPACES = False
-    print("Warning: Running without Hugging Face Spaces GPU allocation")
-
-# ===== Now import other libraries =====
-import random
-import os
-import uuid
-import re
-import time
-from datetime import datetime
-
-import gradio as gr
-import numpy as np
-import requests
-import torch
-from diffusers import DiffusionPipeline
-from PIL import Image
-
-# ===== OpenAI 설정 =====
-from openai import OpenAI
-
-# Add error handling for API key
-try:
-    client = OpenAI(api_key=os.getenv("LLM_API"))
-except Exception as e:
-    print(f"Warning: OpenAI client initialization failed: {e}")
-    client = None
-
-# ===== 프롬프트 증강용 스타일 프리셋 =====
-STYLE_PRESETS = {
-    "None": "",
-    "Realistic Photo": "photorealistic, 8k, ultra-detailed, cinematic lighting, realistic skin texture",
-    "Oil Painting": "oil painting, rich brush strokes, canvas texture, baroque lighting",
-    "Comic Book": "comic book style, bold ink outlines, cel shading, vibrant colors",
-    "Watercolor": "watercolor illustration, soft gradients, splatter effect, pastel palette",
-}
-
-# ===== 저장 폴더 =====
-SAVE_DIR = "saved_images"
-if not os.path.exists(SAVE_DIR):
-    os.makedirs(SAVE_DIR, exist_ok=True)
-
-# ===== 디바이스 & 모델 로드 =====
-device = "cuda" if torch.cuda.is_available() else "cpu"
-print(f"Using device: {device}")
-
-repo_id = "black-forest-labs/FLUX.1-dev"
-adapter_id = "seawolf2357/kim-korea"
-
-# Add error handling for model loading
-try:
-    pipeline = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16)
-    pipeline.load_lora_weights(adapter_id)
-    pipeline = pipeline.to(device)
-    print("Model loaded successfully")
-except Exception as e:
-    print(f"Error loading model: {e}")
-    pipeline = None
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-# ===== 한글 여부 판별 =====
-HANGUL_RE = re.compile(r"[\u3131-\u318E\uAC00-\uD7A3]+")
-
-def is_korean(text: str) -> bool:
-    return bool(HANGUL_RE.search(text))
-
-# ===== 번역 & 증강 함수 =====
-
-def openai_translate(text: str, retries: int = 3) -> str:
-    """한글을 영어로 번역 (OpenAI GPT-4o-mini 사용). 영어 입력이면 그대로 반환."""
-    if not is_korean(text):
-        return text
-    
-    if client is None:
-        print("Warning: OpenAI client not available, returning original text")
-        return text
-
-    for attempt in range(retries):
-        try:
-            res = client.chat.completions.create(
-                model="gpt-4o-mini",
-                messages=[
-                    {
-                        "role": "system",
-                        "content": "Translate the following Korean prompt into concise, descriptive English suitable for an image generation model. Keep the meaning, do not add new concepts."
-                    },
-                    {"role": "user", "content": text}
-                ],
-                temperature=0.3,
-                max_tokens=256,
-            )
-            return res.choices[0].message.content.strip()
-        except Exception as e:
-            print(f"[translate] attempt {attempt + 1} failed: {e}")
-            time.sleep(2)
-    return text  # 번역 실패 시 원문 그대로
-
-def prepare_prompt(user_prompt: str, style_key: str) -> str:
-    """한글이면 번역하고, 선택한 스타일 프리셋을 붙여서 최종 프롬프트를 만든다."""
-    prompt_en = openai_translate(user_prompt)
-    style_suffix = STYLE_PRESETS.get(style_key, "")
-    if style_suffix:
-        final_prompt = f"{prompt_en}, {style_suffix}"
-    else:
-        final_prompt = prompt_en
-    return final_prompt
-
-# ===== 이미지 저장 =====
-
-def save_generated_image(image: Image.Image, prompt: str) -> str:
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    unique_id = str(uuid.uuid4())[:8]
-    filename = f"{timestamp}_{unique_id}.png"
-    filepath = os.path.join(SAVE_DIR, filename)
-    image.save(filepath)
-
-    # 메타데이터 저장
-    metadata_file = os.path.join(SAVE_DIR, "metadata.txt")
-    with open(metadata_file, "a", encoding="utf-8") as f:
-        f.write(f"{filename}|{prompt}|{timestamp}\n")
-    return filepath
-
-# ===== Diffusion 호출 =====
-
-def run_pipeline(prompt: str, seed: int, width: int, height: int, guidance_scale: float, num_steps: int, lora_scale: float):
-    if pipeline is None:
-        raise ValueError("Model pipeline not loaded")
-    
-    generator = torch.Generator(device=device).manual_seed(int(seed))
-    result = pipeline(
-        prompt=prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_steps,
-        width=width,
-        height=height,
-        generator=generator,
-        joint_attention_kwargs={"scale": lora_scale},
-    ).images[0]
-    return result
-
-# ===== Gradio inference 래퍼 =====
-
-@spaces.GPU(duration=60)
-def generate_image(
-    user_prompt: str,
-    style_key: str,
-    seed: int = 42,
-    randomize_seed: bool = True,
-    width: int = 1024,
-    height: int = 768,
-    guidance_scale: float = 3.5,
-    num_inference_steps: int = 30,
-    lora_scale: float = 1.0,
-    progress=None,
-):
-    try:
-        if randomize_seed:
-            seed = random.randint(0, MAX_SEED)
-
-        # 1) 번역 + 증강
-        final_prompt = prepare_prompt(user_prompt, style_key)
-        print(f"Final prompt: {final_prompt}")
-
-        # 2) 파이프라인 호출
-        image = run_pipeline(final_prompt, seed, width, height, guidance_scale, num_inference_steps, lora_scale)
-
-        # 3) 저장
-        save_generated_image(image, final_prompt)
-
-        return image, seed
-    
-    except Exception as e:
-        print(f"Error generating image: {e}")
-        # Return a placeholder or error message
-        error_image = Image.new('RGB', (width, height), color='red')
-        return error_image, seed
-
-# ===== 예시 프롬프트 (한국어/영어 혼용 허용) =====
-
-examples = [
-    "Mr. KIM이 두 손으로 'Fighting!' 현수막을 들고 있는 모습, 애국심과 국가 발전에 대한 의지를 보여주고 있다.",   
-    "Mr. KIM이 양팔을 들어 올리며 승리의 표정으로 환호하는 모습, 승리와 미래에 대한 희망을 보여주고 있다.",
-    "Mr. KIM이 운동복을 입고 공원에서 조깅하는 모습, 건강한 생활습관과 활기찬 리더십을 보여주고 있다.",  
-    "Mr. KIM이 붐비는 거리에서 여성 시민들과 따뜻하게 악수하는 모습, 여성 유권자들에 대한 진정한 관심과 소통을 보여주고 있다.",
-    "Mr. KIM이 선거 유세장에서 지평선을 향해 손가락으로 가리키며 영감을 주는 제스처를 취하고 있고, 여성들과 아이들이 박수를 치고 있다.",
-    "Mr. KIM이 지역 행사에 참여하여 열정적으로 응원하는 여성 지지자들에게 둘러싸여 있는 모습.",
-    "Mr. KIM visiting a local market, engaging in friendly conversation with female vendors and shopkeepers.",
-    "Mr. KIM walking through a university campus, discussing education policies with female students and professors.",    
-    "Mr. KIM delivering a powerful speech in front of a large crowd with confident gestures and determined expression.",
-    "Mr. KIM in a dynamic interview setting, passionately outlining his visions for the future.",
-    "Mr. KIM preparing for an important debate, surrounded by paperwork, looking focused and resolute.",
-]
-
-# ===== 커스텀 CSS (붉은 톤 유지) =====
-custom_css = """
-:root {
-    --color-primary: #8F1A3A;
-    --color-secondary: #FF4B4B;
-    --background-fill-primary: linear-gradient(to right, #FFF5F5, #FED7D7, #FEB2B2);
-}
-footer {visibility: hidden;}
-.gradio-container {background: var(--background-fill-primary);} 
-.title {color: var(--color-primary)!important; font-size:3rem!important; font-weight:700!important; text-align:center; margin:1rem 0; font-family:'Playfair Display',serif;}
-.subtitle {color:#4A5568!important; font-size:1.2rem!important; text-align:center; margin-bottom:1.5rem; font-style:italic;}
-.collection-link {text-align:center; margin-bottom:2rem; font-size:1.1rem;}
-.collection-link a {color:var(--color-primary); text-decoration:underline; transition:color .3s ease;}
-.collection-link a:hover {color:var(--color-secondary);} 
-.model-description{background:rgba(255,255,255,.8); border-radius:12px; padding:24px; margin:20px 0; box-shadow:0 4px 12px rgba(0,0,0,.05); border-left:5px solid var(--color-primary);} 
-button.primary{background:var(--color-primary)!important; color:#fff!important; transition:all .3s ease;} 
-button:hover{transform:translateY(-2px); box-shadow:0 5px 15px rgba(0,0,0,.1);} 
-.input-container{border-radius:10px; box-shadow:0 2px 8px rgba(0,0,0,.05); background:rgba(255,255,255,.6); padding:20px; margin-bottom:1rem;} 
-.advanced-settings{margin-top:1rem; padding:1rem; border-radius:10px; background:rgba(255,255,255,.6);} 
-.example-region{background:rgba(255,255,255,.5); border-radius:10px; padding:1rem; margin-top:1rem;} 
-"""
-
-# ===== Gradio UI =====
-def create_interface():
-    with gr.Blocks(css=custom_css, analytics_enabled=False) as demo:
-        gr.HTML('<div class="title">Mr. KIM in KOREA</div>')
-        gr.HTML('<div class="collection-link"><a href="https://huggingface.co/collections/openfree/painting-art-ai-681453484ec15ef5978bbeb1" target="_blank">Visit the LoRA Model Collection</a></div>')
-
-        with gr.Group(elem_classes="model-description"):
-            gr.HTML("""
-            <p>
-            본 모델은 연구 목적으로 특정인의 얼굴과 외모를 학습한 LoRA 모델입니다.<br>
-            목적외의 용도로 무단 사용 않도록 유의해 주세요.<br>
-            (예시 prompt 사용 시 반드시 'kim'을 포함하여야 최적의 결과를 얻을 수 있습니다.)
-            </p>
-            """)
-
-        # ===== 메인 입력 =====
-        with gr.Column():
-            with gr.Row(elem_classes="input-container"):
-                user_prompt = gr.Text(label="Prompt", max_lines=1, value=examples[0])
-                style_select = gr.Radio(label="Style Preset", choices=list(STYLE_PRESETS.keys()), value="None", interactive=True)
-                run_button = gr.Button("Generate", variant="primary")
-
-            result_image = gr.Image(label="Generated Image")
-            seed_output = gr.Number(label="Seed")
-
-            # ===== 고급 설정 =====
-            with gr.Accordion("Advanced Settings", open=False, elem_classes="advanced-settings"):
-                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42)
-                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                with gr.Row():
-                    width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
-                    height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=768)
-                with gr.Row():
-                    guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=3.5)
-                    num_inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=50, step=1, value=30)
-                    lora_scale = gr.Slider(label="LoRA scale", minimum=0.0, maximum=1.0, step=0.1, value=1.0)
-
-            # ===== 예시 영역 =====
-            with gr.Group(elem_classes="example-region"):
-                gr.Markdown("### Examples")
-                gr.Examples(examples=examples, inputs=user_prompt, cache_examples=False)
-
-        # ===== 이벤트 =====
-        run_button.click(
-            fn=generate_image,
-            inputs=[
-                user_prompt,
-                style_select,
-                seed,
-                randomize_seed,
-                width,
-                height,
-                guidance_scale,
-                num_inference_steps,
-                lora_scale,
-            ],
-            outputs=[result_image, seed_output],
-        )
-    
-    return demo
-
-# ===== 애플리케이션 실행 =====
-if __name__ == "__main__":
-    demo = create_interface()
-    demo.queue()
-    demo.launch()