Delete app.py

app.py

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-import os
-import gradio as gr
-import random
-import time
-import logging
-import google.generativeai as genai
-
-import torch
-import numpy as np
-from diffusers import DiffusionPipeline
-from transformers import pipeline as hf_pipeline
-import re
-
-##############################################################################
-# 1) ZeroGPU Environment Setup + Device and Dtype Configuration
-##############################################################################
-try:
-    import zerogpu
-    zerogpu.init()
-    print("ZeroGPU initialized successfully")
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-except ImportError:
-    print("ZeroGPU package not installed, continuing without it")
-    if os.getenv("ZERO_GPU"):
-        print("ZeroGPU environment variable is set but zerogpu package is not installed.")
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-except Exception as e:
-    print(f"Error initializing ZeroGPU: {e}")
-    print("Continuing without ZeroGPU")
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-
-dtype = torch.bfloat16 if device == "cuda" else torch.float32
-
-print(f"Using device: {device}, dtype: {dtype}")
-
-##############################################################################
-# 2) Load Models: Translation Model, Diffusion Pipeline
-##############################################################################
-try:
-    translator = hf_pipeline(
-        "translation",
-        model="Helsinki-NLP/opus-mt-ko-en",
-        device=0 if device == "cuda" else -1
-    )
-
-    pipe = DiffusionPipeline.from_pretrained(
-        "black-forest-labs/FLUX.1-schnell",
-        torch_dtype=dtype
-    ).to(device)
-    
-    print("Models loaded successfully")
-except Exception as e:
-    print(f"Error loading models: {e}")
-    def dummy_translator(text):
-        return [{'translation_text': text}]
-    
-    class DummyPipe:
-        def __call__(self, **kwargs):
-            from PIL import Image
-            import numpy as np
-            dummy_img = Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8))
-            class DummyResult:
-                def __init__(self, img):
-                    self.images = [img]
-            return DummyResult(dummy_img)
-    
-    translator = dummy_translator
-    pipe = DummyPipe()
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 2048
-
-##############################################################################
-# Korean detection and input text cleaning functions
-##############################################################################
-def contains_korean(text):
-    for char in text:
-        if ord('가') <= ord(char) <= ord('힣'):
-            return True
-    return False
-
-def clean_input_text(text):
-    """
-    Allows only Korean, English, numbers, whitespace and common punctuation marks.
-    Adjust allowed characters as needed.
-    """
-    allowed = re.compile(r'[^ㄱ-ㅎ가-힣a-zA-Z0-9\s\.\,\!\?\-\:\;\'\"]')
-    cleaned_text = allowed.sub('', text)
-    return cleaned_text
-
-def log_unexpected_characters(text):
-    allowed = re.compile(r'[ㄱ-ㅎ가-힣a-zA-Z0-9\s\.\,\!\?\-\:\;\'\"]')
-    unexpected_chars = [char for char in text if not allowed.match(char)]
-    if unexpected_chars:
-        print("Unexpected characters found:", set(unexpected_chars))
-    else:
-        print("No unexpected characters in the input text.")
-
-##############################################################################
-# Image Generation Function
-##############################################################################
-def generate_design_image(prompt, seed=42, randomize_seed=True, width=1024, height=1024, num_inference_steps=4):
-    original_prompt = prompt
-    translated = False
-    
-    # Clean the input text
-    prompt = clean_input_text(prompt)
-    
-    # Pre-process: if input is too long, trim to 1000 characters
-    if len(prompt) > 1000:
-        prompt = prompt[:1000]
-    
-    if contains_korean(prompt):
-        # When calling translation, add max_length and truncation options to avoid length issues
-        translation = translator(prompt, max_length=400, truncation=True)
-        prompt = translation[0]['translation_text']
-        translated = True
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    
-    generator = torch.Generator(device=device).manual_seed(seed)
-    
-    image = pipe(
-        prompt=prompt,
-        width=width,
-        height=height,
-        num_inference_steps=num_inference_steps,
-        generator=generator,
-        guidance_scale=0.0
-    ).images[0]
-    
-    return image
-
-##############################################################################
-# Logging Setup
-##############################################################################
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    handlers=[
-        logging.FileHandler("api_debug.log"),
-        logging.StreamHandler()
-    ]
-)
-logger = logging.getLogger("idea_generator")
-
-##############################################################################
-# Gemini API Key
-##############################################################################
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
-genai.configure(api_key=GEMINI_API_KEY)
-
-##############################################################################
-# Optional Transformation Choice Function
-##############################################################################
-def choose_alternative(transformation):
-    if "/" not in transformation:
-        return transformation
-    parts = transformation.split("/")
-    if len(parts) != 2:
-        return random.choice([part.strip() for part in parts])
-    left = parts[0].strip()
-    right = parts[1].strip()
-    if " " in left:
-        tokens = left.split(" ", 1)
-        prefix = tokens[0]
-        if not right.startswith(prefix):
-            option1 = left
-            option2 = prefix + " " + right
-        else:
-            option1 = left
-            option2 = right
-        return random.choice([option1, option2])
-    else:
-        return random.choice([left, right])
-
-##############################################################################
-# Transformation Categories Dictionaries
-##############################################################################
-# Korean version
-physical_transformation_categories = {
-    "센서 기능": [
-        "시각 센서/감지", "청각 센서/감지", "촉각 센서/감지", "미각 센서/감지", "후각 센서/감지",
-        "온도 센서/감지", "습도 센서/감지", "압력 센서/감지", "가속도 센서/감지", "회전 센서/감지",
-        "근접 센서/감지", "위치 센서/감지", "운동 센서/감지", "가스 센서/감지", "적외선 센서/감지",
-        "자외선 센서/감지", "방사선 센서/감지", "자기장 센서/감지", "전기장 센서/감지", "화학물질 센서/감지",
-        "생체신호 센서/감지", "진동 센서/감지", "소음 센서/감지", "빛 세기 센서/감지", "빛 파장 센서/감지",
-        "기울기 센서/감지", "pH 센서/감지", "전류 센서/감지", "전압 센서/감지", "이미지 센서/감지",
-        "거리 센서/감지", "깊이 센서/감지", "중력 센서/감지", "속도 센서/감지", "흐름 센서/감지",
-        "수위 센서/감지", "탁도 센서/감지", "염도 센서/감지", "금속 감지", "압전 센서/감지",
-        "광전 센서/감지", "열전대 센서/감지", "홀 효과 센서/감지", "초음파 센서/감지", "레이더 센서/감지",
-        "라이다 센서/감지", "터치 센서/감지", "제스처 센서/감지", "심박 센서/감지", "혈압 센서/감지"
-    ],
-    "크기와 형태 변화": [
-        "부피 늘어남/줄어듦", "길이 늘어남/줄어듦", "너비 늘어남/줄어듦", "높이 늘어남/줄어듦",
-        "밀도 변화", "무게 증가/감소", "모양 변형", "상태 변화", "불균등 변형",
-        "복잡한 형태 변형", "비틀림/꼬임", "불균일한 확장/축소", "모서리 둥글게/날카롭게",
-        "깨짐/갈라짐", "여러 조각 나눠짐", "물 저항", "먼지 저항", "찌그러짐/복원",
-        "접힘/펼쳐짐", "압착/팽창", "늘어남/수축", "구겨짐/평평해짐", "뭉개짐/단단해짐",
-        "말림/펴짐", "꺾임/구부러짐"
-    ],
-    "표면 및 외관 변화": [
-        "색상 변화", "질감 변화", "투명/불투명 변화", "반짝임/무광 변화",
-        "빛 반사 정도 변화", "무늬 변화", "각도에 따른 색상 변화", "빛에 따른 색상 변화",
-        "온도에 따른 색상 변화", "홀로그램 효과", "표면 각도별 빛 반사", "표면 모양 변형",
-        "초미세 표면 구조 변화", "자가 세정 효과", "얼룩/패턴 생성", "흐림/선명함 변화",
-        "광택/윤기 변화", "색조/채도 변화", "발광/형광", "빛 산란 효과",
-        "빛 흡수 변화", "반투명 효과", "그림자 효과 변화", "자외선 반응 변화",
-        "야광 효과"
-    ],
-    "물질의 상태 변화": [
-        "고체/액체/기체 전환", "결정화/용해", "산화/부식", "딱딱해짐/부드러워짐",
-        "특수 상태 전환", "무정형/결정형 전환", "성분 분리", "미세 입자 형성/분해",
-        "젤 형성/풀어짐", "준안정 상태 변화", "분자 자가 정렬/분해", "상태변화 지연 현상",
-        "녹음", "굳음", "증발/응축", "승화/증착", "침전/부유", "분산/응집",
-        "건조/습윤", "팽윤/수축", "동결/해동", "풍화/침식", "충전/방전",
-        "결합/분리", "발효/부패"
-    ],
-    "움직임 특성 변화": [
-        "가속/감속", "일정 속도 유지", "진동/진동 감소", "부딪힘/튕김",
-        "회전 속도 증가/감소", "회전 방향 변화", "불규칙 움직임", "멈췄다 미끄러지는 현상",
-        "공진/반공진", "유체 속 저항/양�� 변화", "움직임 저항 변화", "복합 진동 움직임",
-        "특수 유체 속 움직임", "회전-이동 연계 움직임", "관성 정지", "충격 흡수",
-        "충격 전달", "운동량 보존", "마찰력 변화", "관성 탈출", "불안정 균형",
-        "동적 안정성", "흔들림 감쇠", "경로 예측성", "회피 움직임"
-    ],
-    "구조적 변화": [
-        "부품 추가/제거", "조립/분해", "접기/펴기", "변형/원상복구", "최적 구조 변화",
-        "자가 재배열", "자연 패턴 형성/소멸", "규칙적 패턴 변화", "모듈식 변형",
-        "복잡성 증가 구조", "원래 모양 기억 효과", "시간에 따른 형태 변화", "부분 제거",
-        "부분 교체", "결합", "분리", "분할/통합", "중첩/겹침", "내부 구조 변화",
-        "외부 구조 변화", "중심축 이동", "균형점 변화", "계층 구조 변화", "지지 구조 변화",
-        "응력 분산 구조", "충격 흡수 구조", "그리드/매트릭스 구조 변화", "상호 연결성 변화"
-    ],
-    "공간 이동": [
-        "앞/뒤 이동", "좌/우 이동", "위/아래 이동", "세로축 회전(고개 끄덕임)",
-        "가로축 회전(고개 젓기)", "길이축 회전(옆으로 기울임)", "원 운동", "나선형 이동",
-        "관성에 의한 미끄러짐", "회전축 변화", "불규칙 회전", "흔들림 운동", "포물선 이동",
-        "무중력 부유", "수면 위 부유", "점프/도약", "슬라이딩", "롤링", "자유 낙하",
-        "왕복 운동", "탄성 튕김", "관통", "회피 움직임", "지그재그 이동", "스윙 운동"
-    ],
-    "시간 관련 변화": [
-        "노화/풍화", "마모/부식", "색 바램/변색", "손상/회복", "수명 주기 변화",
-        "사용자 상호작용에 따른 적응", "학습 기반 형태 최적화", "시간에 따른 물성 변화",
-        "집단 기억 효과", "문화적 의미 변화", "지연 반응", "이전 상태 의존 변화",
-        "점진적 시간 변화", "진화적 변화", "주기적 재생", "계절 변화 적응",
-        "생체리듬 변화", "생애 주기 단계", "성장/퇴화", "자기 복구/재생",
-        "자연 순환 적응", "지속성/일시성", "기억 효과", "지연된 작용", "누적 효과"
-    ],
-    "빛과 시각 효과": [
-        "발광/소등", "빛 투과/차단", "빛 산란/집중", "색상 스펙트럼 변화", "빛 회절",
-        "빛 간섭", "홀로그램 생성", "레이저 효과", "빛 편광", "형광/인광",
-        "자외선/적외선 발광", "광학적 착시", "빛 굴절", "그림자 생성/제거",
-        "색수차 효과", "무지개 효과", "글로우 효과", "플래시 효과", "조명 패턴",
-        "빔 효과", "광 필터 효과", "빛의 방향성 변화", "투영 효과", "빛 감지/반응",
-        "광도 변화"
-    ],
-    "소리와 진동 효과": [
-        "소리 발생/소멸", "소리 높낮이 변화", "소리 크기 변화", "음색 변화",
-        "공명/반공명", "음향 진동", "초음파/저음파 발생", "음향 집중/분산",
-        "음향 반사/흡수", "음향 도플러 효과", "음파 간섭", "음향 공진",
-        "진동 패턴 변화", "타악 효과", "음향 피드백", "음향 차폐/증폭",
-        "소리 지향성", "음향 왜곡", "비트 생성", "하모닉스 생성", "주파수 변조",
-        "음향 충격파", "음향 필터링"
-    ]
-}
-
-##############################################################################
-# Gemini API Call Function (Language Independent)
-##############################################################################
-def query_gemini_api(prompt):
-    try:
-        model = genai.GenerativeModel('gemini-2.0-flash-thinking-exp-01-21')
-        response = model.generate_content(prompt)
-        try:
-            if hasattr(response, 'text'):
-                return response.text
-            if hasattr(response, 'candidates') and response.candidates:
-                candidate = response.candidates[0]
-                if hasattr(candidate, 'content'):
-                    content = candidate.content
-                    if hasattr(content, 'parts') and content.parts:
-                        if len(content.parts) > 0:
-                            return content.parts[0].text
-            if hasattr(response, 'parts') and response.parts:
-                if len(response.parts) > 0:
-                    return response.parts[0].text
-            return "Unable to generate a response. API response structure is different than expected."
-        except Exception as inner_e:
-            logger.error(f"Error processing response: {inner_e}")
-            return f"An error occurred while processing the response: {str(inner_e)}"
-    except Exception as e:
-        logger.error(f"Error calling Gemini API: {e}")
-        if "API key not valid" in str(e):
-            return "API key is not valid. Please check your GEMINI_API_KEY environment variable."
-        return f"An error occurred while calling the API: {str(e)}"
-
-##############################################################################
-# Description Expansion Functions (LLM) - Korean and English Versions
-##############################################################################
-def enhance_with_llm(base_description, obj_name, category):
-    prompt = f"""
-다음은 '{obj_name}'의 '{category}' 관련 간단한 설명입니다:
-"{base_description}"
-위 내용을 보다 구체화하여, 
-1) 창의적인 모델/컨셉/형상의 변화에 대한 이해, 
-2) 혁신 포인트와 기능성 등을 중심으로 
-3~4문장의 아이디어로 확장해 주세요.
-"""
-    return query_gemini_api(prompt)
-
-def enhance_with_llm_en(base_description, obj_name, category):
-    prompt = f"""
-Below is a brief description related to '{category}' for '{obj_name}':
-"{base_description}"
-Please expand the above content into a more detailed explanation, focusing on:
-1) Creative transformation of the model/concept/shape,
-2) Innovative aspects and functionality,
-in 3-4 sentences.
-"""
-    return query_gemini_api(prompt)
-
-##############################################################################
-# Transformation Idea Generation Functions for Both Languages
-##############################################################################
-def generate_single_object_transformation_for_category_lang(obj, selected_category, categories_dict, lang="ko"):
-    transformations = categories_dict.get(selected_category)
-    if not transformations:
-        return {}
-    transformation = choose_alternative(random.choice(transformations))
-    if lang == "ko":
-        base_description = f"{obj}이(가) {transformation} 현상을 보인다"
-    else:
-        base_description = f"{obj} exhibits {transformation}"
-    return {selected_category: {"base": base_description, "enhanced": None}}
-
-def generate_two_objects_interaction_for_category_lang(obj1, obj2, selected_category, categories_dict, lang="ko"):
-    transformations = categories_dict.get(selected_category)
-    if not transformations:
-        return {}
-    transformation = choose_alternative(random.choice(transformations))
-    if lang == "ko":
-        template = random.choice([
-            "{obj1}이(가) {obj2}에 결합하여 {change}가 발생했다",
-            "{obj1}과(와) {obj2}이(가) 충돌하면서 {change}가 일어났다"
-        ])
-    else:
-        template = random.choice([
-            "{obj1} combined with {obj2} resulted in {change}",
-            "A collision between {obj1} and {obj2} led to {change}"
-        ])
-    base_description = template.format(obj1=obj1, obj2=obj2, change=transformation)
-    return {selected_category: {"base": base_description, "enhanced": None}}
-
-def generate_three_objects_interaction_for_category_lang(obj1, obj2, obj3, selected_category, categories_dict, lang="ko"):
-    transformations = categories_dict.get(selected_category)
-    if not transformations:
-        return {}
-    transformation = choose_alternative(random.choice(transformations))
-    if lang == "ko":
-        template = random.choice([
-            "{obj1}, {obj2}, {obj3}이(가) 삼각형 구조로 결합하여 {change}가 발생했다",
-            "{obj1}이(가) {obj2}와(과) {obj3} 사이에서 매개체 역할을 하며 {change}를 촉진했다"
-        ])
-    else:
-        template = random.choice([
-            "{obj1}, {obj2}, and {obj3} formed a triangular structure resulting in {change}",
-            "{obj1} acted as an intermediary between {obj2} and {obj3}, facilitating {change}"
-        ])
-    base_description = template.format(obj1=obj1, obj2=obj2, obj3=obj3, change=transformation)
-    return {selected_category: {"base": base_description, "enhanced": None}}
-
-def enhance_descriptions_lang(results, objects, lang="ko"):
-    obj_name = " 및 ".join([obj for obj in objects if obj]) if lang=="ko" else " and ".join([obj for obj in objects if obj])
-    for category, result in results.items():
-        if lang == "ko":
-            result["enhanced"] = enhance_with_llm(result["base"], obj_name, category)
-        else:
-            result["enhanced"] = enhance_with_llm_en(result["base"], obj_name, category)
-    return results
-
-def generate_transformations_lang(text1, text2, text3, selected_category, categories_dict, lang="ko"):
-    if text2 and text3:
-        results = generate_three_objects_interaction_for_category_lang(text1, text2, text3, selected_category, categories_dict, lang)
-        objects = [text1, text2, text3]
-    elif text2:
-        results = generate_two_objects_interaction_for_category_lang(text1, text2, selected_category, categories_dict, lang)
-        objects = [text1, text2]
-    else:
-        results = generate_single_object_transformation_for_category_lang(text1, selected_category, categories_dict, lang)
-        objects = [text1]
-    return enhance_descriptions_lang(results, objects, lang)
-
-def format_results_lang(results, lang="ko"):
-    formatted = ""
-    if lang == "ko":
-        for category, result in results.items():
-            formatted += f"## {category}\n**기본 아이디어**: {result['base']}\n\n**확장된 아이디어**: {result['enhanced']}\n\n---\n\n"
-    else:
-        for category, result in results.items():
-            formatted += f"## {category}\n**Base Idea**: {result['base']}\n\n**Expanded Idea**: {result['enhanced']}\n\n---\n\n"
-    return formatted
-
-def process_inputs_lang(text1, text2, text3, selected_category, categories_dict, lang="ko", progress=gr.Progress()):
-    text1 = text1.strip() if text1 else None
-    text2 = text2.strip() if text2 else None
-    text3 = text3.strip() if text3 else None
-    
-    if not text1:
-        return "오류: 최소 하나의 키워드를 입력해주세요." if lang=="ko" else "Error: Please enter at least one keyword."
-    
-    if lang == "ko":
-        progress(0.05, desc="아이디어 생성 준비 중...")
-        time.sleep(0.3)
-        progress(0.1, desc="창의적인 아이디어 생성 시작...")
-    else:
-        progress(0.05, desc="Preparing idea generation...")
-        time.sleep(0.3)
-        progress(0.1, desc="Generating creative idea...")
-    
-    results = generate_transformations_lang(text1, text2, text3, selected_category, categories_dict, lang)
-    
-    if lang == "ko":
-        progress(0.8, desc="결과 포맷팅 중...")
-        formatted = format_results_lang(results, lang)
-        progress(1.0, desc="완료!")
-    else:
-        progress(0.8, desc="Formatting results...")
-        formatted = format_results_lang(results, lang)
-        progress(1.0, desc="Done!")
-    return formatted
-
-def process_all_lang(text1, text2, text3, selected_category, categories_dict, lang="ko", progress=gr.Progress()):
-    idea_result = process_inputs_lang(text1, text2, text3, selected_category, categories_dict, lang, progress)
-    image_result = generate_design_image(
-        idea_result, 
-        seed=42, 
-        randomize_seed=True, 
-        width=1024, 
-        height=1024, 
-        num_inference_steps=4
-    )
-    return idea_result, image_result
-
-##############################################################################
-# Warning Message Function for API Key (Language Specific)
-##############################################################################
-def get_warning_message_lang(lang="ko"):
-    if not GEMINI_API_KEY:
-        return "⚠️ 환경 변수 GEMINI_API_KEY가 설정되지 않았습니다. Gemini API 키를 설정하세요." if lang=="ko" else "⚠️ The GEMINI_API_KEY environment variable is not set. Please set your Gemini API key."
-    return ""
-
-##############################################################################
-# Helper function for caching examples in the English tab
-##############################################################################
-def process_all_lang_example(text1, text2, text3, selected_category):
-    # 고정된 state값(physical_transformation_categories_en, "en")으로 호출
-    return process_all_lang(text1, text2, text3, selected_category, physical_transformation_categories_en, "en")
-
-##############################################################################
-# Gradio UI with Two Tabs: English (Main Home) and Korean
-##############################################################################
-with gr.Blocks(
-    title="Idea Transformer",
-    theme=gr.themes.Soft(primary_hue="teal", secondary_hue="slate", neutral_hue="neutral")
-) as demo:
-    
-    gr.HTML("""
-    <style>
-    body { 
-        background: linear-gradient(135deg, #e0eafc, #cfdef3); 
-        font-family: 'Arial', sans-serif; 
-    }
-    .gradio-container { 
-        padding: 20px; 
-    }
-    h1, h2 { 
-        text-align: center; 
-    }
-    h1 { 
-        color: #333; 
-    }
-    h2 { 
-        color: #555; 
-    }
-    .output { 
-        background-color: #ffffff; 
-        padding: 15px; 
-        border-radius: 8px; 
-    }
-    .gr-button { 
-        background-color: #4CAF50; 
-        color: white; 
-        border: none; 
-        border-radius: 4px; 
-        padding: 8px 16px; 
-    }
-    .progress-message { 
-        color: #2196F3; 
-        font-weight: bold; 
-        margin-top: 10px; 
-    }
-    </style>
-    """)
-    
-    with gr.Tabs():
-        with gr.Tab(label="English"):
-            gr.Markdown("# 🚀 Idea Transformer")
-            gr.Markdown("Based on up to **three keywords** and a **selected category**, this tool generates a creative transformation idea and a design image using the expanded idea as a prompt.")
-            
-            warning_en = gr.Markdown(get_warning_message_lang("en"))
-            
-            with gr.Row():
-                with gr.Column(scale=1):
-                    text_input1_en = gr.Textbox(label="Keyword 1 (required)", placeholder="e.g., Smartphone")
-                    text_input2_en = gr.Textbox(label="Keyword 2 (optional)", placeholder="e.g., Artificial Intelligence")
-                    text_input3_en = gr.Textbox(label="Keyword 3 (optional)", placeholder="e.g., Healthcare")
-                    category_radio_en = gr.Radio(
-                        label="Select Category",
-                        choices=list(physical_transformation_categories_en.keys()),
-                        value=list(physical_transformation_categories_en.keys())[0],
-                        info="Select a category."
-                    )
-                    status_msg_en = gr.Markdown("💡 Click the 'Generate Idea' button to create an idea and design image based on the selected category.")
-                    
-                    processing_indicator_en = gr.HTML("""
-                    <div style="display: flex; justify-content: center; align-items: center; margin: 10px 0;">
-                        <div style="border: 5px solid #f3f3f3; border-top: 5px solid #3498db; border-radius: 50%; width: 30px; height: 30px; animation: spin 2s linear infinite;"></div>
-                        <p style="margin-left: 10px; font-weight: bold; color: #3498db;">Processing...</p>
-                    </div>
-                    <style>
-                    @keyframes spin {
-                        0% { transform: rotate(0deg); }
-                        100% { transform: rotate(360deg); }
-                    }
-                    </style>
-                    """, visible=False)
-                    
-                    submit_button_en = gr.Button("Generate Idea", variant="primary")
-                
-                with gr.Column(scale=2):
-                    idea_output_en = gr.Markdown(label="Idea Output")
-                    generated_image_en = gr.Image(label="Generated Design Image", type="pil")
-            
-            gr.Examples(
-                examples=[
-                    ["Smartphone", "", "", "Sensor Functions"],
-                    ["Car", "", "", "Size and Shape Change"],
-                    ["Car", "Artificial Intelligence", "", "Surface and Appearance Change"],
-                    ["Drone", "Artificial Intelligence", "", "Material State Change"],
-                    ["Sneakers", "Wearable", "Health", "Structural Change"],
-                ],
-                inputs=[text_input1_en, text_input2_en, text_input3_en, category_radio_en],
-                fn=process_all_lang_example,
-                outputs=[idea_output_en, generated_image_en],
-                cache_examples=True
-            )
-            
-            def show_processing_indicator_en():
-                return gr.update(visible=True)
-            
-            def hide_processing_indicator_en():
-                return gr.update(visible=False)
-            
-            submit_button_en.click(
-                fn=show_processing_indicator_en,
-                inputs=None,
-                outputs=processing_indicator_en
-            ).then(
-                fn=process_all_lang,
-                inputs=[text_input1_en, text_input2_en, text_input3_en, category_radio_en, gr.State(physical_transformation_categories_en), gr.State("en")],
-                outputs=[idea_output_en, generated_image_en]
-            ).then(
-                fn=hide_processing_indicator_en,
-                inputs=None,
-                outputs=processing_indicator_en
-            )
-        
-        with gr.Tab(label="한국어"):
-            gr.Markdown("# 🚀 아이디어 트랜스포머")
-            gr.Markdown("입력한 **키워드**(최대 3개)와 **카테고리**를 바탕으로, 창의적인 모델/컨셉/형상 변화 아이디어를 생성하고, 해당 확장 아이디어를 프롬프트로 하여 디자인 이미지를 생성합니다.")
-            
-            warning_ko = gr.Markdown(get_warning_message_lang("ko"))
-            
-            with gr.Row():
-                with gr.Column(scale=1):
-                    text_input1_ko = gr.Textbox(label="키워드 1 (필수)", placeholder="예: 스마트폰")
-                    text_input2_ko = gr.Textbox(label="키워드 2 (선택)", placeholder="예: 인공지능")
-                    text_input3_ko = gr.Textbox(label="키워드 3 (선택)", placeholder="예: 헬스케어")
-                    category_radio_ko = gr.Radio(
-                        label="카테고리 선택",
-                        choices=list(physical_transformation_categories.keys()),
-                        value=list(physical_transformation_categories.keys())[0],
-                        info="출력할 카테고리를 선택하세요."
-                    )
-                    status_msg_ko = gr.Markdown("💡 '아이디어 생성하기' 버튼을 클릭하면 선택한 카테고리에 해당하는 아이디어와 디자인 이미지가 생성됩니다.")
-                    
-                    processing_indicator_ko = gr.HTML("""
-                    <div style="display: flex; justify-content: center; align-items: center; margin: 10px 0;">
-                        <div style="border: 5px solid #f3f3f3; border-top: 5px solid #3498db; border-radius: 50%; width: 30px; height: 30px; animation: spin 2s linear infinite;"></div>
-                        <p style="margin-left: 10px; font-weight: bold; color: #3498db;">처리 중입니다...</p>
-                    </div>
-                    <style>
-                    @keyframes spin {
-                        0% { transform: rotate(0deg); }
-                        100% { transform: rotate(360deg); }
-                    }
-                    </style>
-                    """, visible=False)
-                    
-                    submit_button_ko = gr.Button("아이디어 생성하기", variant="primary")
-                
-                with gr.Column(scale=2):
-                    idea_output_ko = gr.Markdown(label="아이디어 결과")
-                    generated_image_ko = gr.Image(label="생성된 디자인 이미지", type="pil")
-            
-            gr.Examples(
-                examples=[
-                    ["스마트폰", "", "", "센서 기능"],
-                    ["자동차", "", "", "크기와 형태 변화"],
-                    ["자동차", "인공지능", "", "표면 및 외관 변화"],
-                    ["드론", "인공지능", "", "물질의 상태 변화"],
-                    ["운동화", "웨어러블", "건강", "구조적 변화"],
-                ],
-                inputs=[text_input1_ko, text_input2_ko, text_input3_ko, category_radio_ko]
-            )
-            
-            def show_processing_indicator_ko():
-                return gr.update(visible=True)
-            
-            def hide_processing_indicator_ko():
-                return gr.update(visible=False)
-            
-            submit_button_ko.click(
-                fn=show_processing_indicator_ko,
-                inputs=None,
-                outputs=processing_indicator_ko
-            ).then(
-                fn=process_all_lang,
-                inputs=[text_input1_ko, text_input2_ko, text_input3_ko, category_radio_ko, gr.State(physical_transformation_categories), gr.State("ko")],
-                outputs=[idea_output_ko, generated_image_ko]
-            ).then(
-                fn=hide_processing_indicator_ko,
-                inputs=None,
-                outputs=processing_indicator_ko
-            )
-
-if __name__ == "__main__":
-    demo.launch(debug=True)