app.py

from fastapi import FastAPI, BackgroundTasks, Request
from fastapi.responses import HTMLResponse, JSONResponse, Response
from fastapi.staticfiles import StaticFiles
import pathlib, os, uvicorn, base64, json, uuid, time
from typing import Dict, List, Any, Optional
import asyncio
import logging
import threading
import concurrent.futures
import requests
import fitz

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

BASE = pathlib.Path(__file__).parent
app = FastAPI()
app.mount("/static", StaticFiles(directory=BASE), name="static")

CACHE_DIR = BASE / "cache"
if not CACHE_DIR.exists():
    CACHE_DIR.mkdir(parents=True)

EMBEDDING_DIR = BASE / "embeddings"
if not EMBEDDING_DIR.exists():
    EMBEDDING_DIR.mkdir(parents=True)

# Fireworks AI API 설정 (VLM 모델)
FIREWORKS_API_KEY = os.getenv("FIREWORKS_API", "").strip()  # 줄바꿈/공백 제거
FIREWORKS_API_URL = "https://api.fireworks.ai/inference/v1/chat/completions"
FIREWORKS_VLM_MODEL = "accounts/fireworks/models/qwen3-vl-235b-a22b-instruct"
HAS_VALID_API_KEY = bool(FIREWORKS_API_KEY)

if HAS_VALID_API_KEY:
    logger.info("Fireworks AI VLM API 키 설정 완료")
else:
    logger.warning("유효한 Fireworks AI API 키가 없습니다. AI 기능이 제한됩니다.")

# 고정 PDF 파일 경로
PROMPT_PDF_PATH = BASE / "prompt.pdf"
PROMPT_PDF_ID = "prompt_pdf_main"

# PDF 설정 (prompt.pdf만 유지)
PDF_FILES = {
    "prompt": {
        "path": BASE / "prompt.pdf",
        "id": "prompt_pdf_main",
        "name": "샘플-프롬프트북"
    }
}

# 현재 선택된 PDF (기본값: prompt)
current_pdf_key = "prompt"

pdf_cache: Dict[str, Dict[str, Any]] = {}
cache_locks = {}
pdf_embeddings: Dict[str, Dict[str, Any]] = {}

# VLM 분석 상태 추적 (메모리)
analysis_status: Dict[str, Dict[str, Any]] = {}


def get_cache_path(pdf_name: str):
    return CACHE_DIR / f"{pdf_name}_cache.json"


def get_embedding_path(pdf_id: str):
    return EMBEDDING_DIR / f"{pdf_id}_embedding.json"


def get_analysis_cache_path(pdf_id: str):
    """VLM 분석 결과 캐시 경로"""
    return EMBEDDING_DIR / f"{pdf_id}_vlm_analysis.json"


def load_analysis_cache(pdf_id: str) -> Optional[Dict[str, Any]]:
    """VLM 분석 캐시 로드"""
    cache_path = get_analysis_cache_path(pdf_id)
    if cache_path.exists():
        try:
            with open(cache_path, "r", encoding="utf-8") as f:
                data = json.load(f)
                logger.info(f"VLM 분석 캐시 로드 완료: {pdf_id}")
                return data
        except Exception as e:
            logger.error(f"분석 캐시 로드 오류: {e}")
    return None


def save_analysis_cache(pdf_id: str, analysis_data: Dict[str, Any]):
    """VLM 분석 결과 캐시 저장"""
    cache_path = get_analysis_cache_path(pdf_id)
    try:
        with open(cache_path, "w", encoding="utf-8") as f:
            json.dump(analysis_data, f, ensure_ascii=False, indent=2)
        logger.info(f"VLM 분석 캐시 저장 완료: {pdf_id}")
    except Exception as e:
        logger.error(f"분석 캐시 저장 오류: {e}")


def get_pdf_page_as_base64(pdf_path: str, page_num: int, scale: float = 1.0) -> str:
    """PDF 페이지를 base64 이미지로 변환"""
    try:
        doc = fitz.open(pdf_path)
        if page_num >= doc.page_count:
            doc.close()
            return None
        
        page = doc[page_num]
        pix = page.get_pixmap(matrix=fitz.Matrix(scale, scale))
        img_data = pix.tobytes("jpeg", 85)
        b64_img = base64.b64encode(img_data).decode('utf-8')
        doc.close()
        return b64_img
    except Exception as e:
        logger.error(f"PDF 페이지 이미지 변환 오류: {e}")
        return None


def get_pdf_pages_as_base64(pdf_path: str, start_page: int = 0, max_pages: int = 10, scale: float = 0.7) -> List[Dict[str, Any]]:
    """PDF 여러 페이지를 base64 이미지 리스트로 변환 (배치 처리용)"""
    try:
        doc = fitz.open(pdf_path)
        total_pages = doc.page_count
        end_page = min(start_page + max_pages, total_pages)
        
        images = []
        for page_num in range(start_page, end_page):
            page = doc[page_num]
            pix = page.get_pixmap(matrix=fitz.Matrix(scale, scale))
            img_data = pix.tobytes("jpeg", 75)
            b64_img = base64.b64encode(img_data).decode('utf-8')
            images.append({
                "page": page_num + 1,
                "image_base64": b64_img
            })
        
        doc.close()
        logger.info(f"PDF {start_page+1}~{end_page}/{total_pages}페이지 이미지 변환 완료")
        return images, total_pages
    except Exception as e:
        logger.error(f"PDF 페이지들 이미지 변환 오류: {e}")
        return [], 0


def call_fireworks_vlm_api(messages: List[Dict], max_tokens: int = 4096, temperature: float = 0.6) -> str:
    """Fireworks AI VLM API 호출 (이미지 분석 지원)"""
    if not HAS_VALID_API_KEY:
        raise Exception("API 키가 설정되지 않았습니다.")
    
    payload = {
        "model": FIREWORKS_VLM_MODEL,
        "max_tokens": max_tokens,
        "top_p": 1,
        "top_k": 40,
        "presence_penalty": 0,
        "frequency_penalty": 0,
        "temperature": temperature,
        "messages": messages
    }
    
    headers = {
        "Accept": "application/json",
        "Content-Type": "application/json",
        "Authorization": f"Bearer {FIREWORKS_API_KEY}"
    }
    
    response = requests.post(FIREWORKS_API_URL, headers=headers, data=json.dumps(payload), timeout=180)
    
    if response.status_code != 200:
        raise Exception(f"API 오류: {response.status_code} - {response.text}")
    
    result = response.json()
    return result["choices"][0]["message"]["content"]


def analyze_batch_pages_sync(pdf_path: str, start_page: int, batch_size: int = 5) -> str:
    """배치 페이지 분석 (동기)"""
    page_images, total_pages = get_pdf_pages_as_base64(pdf_path, start_page, batch_size, scale=0.6)
    
    if not page_images:
        return ""
    
    content_parts = []
    for img_data in page_images:
        content_parts.append({
            "type": "image_url",
            "image_url": {
                "url": f"data:image/jpeg;base64,{img_data['image_base64']}"
            }
        })
    
    page_range = f"{start_page + 1}~{start_page + len(page_images)}"
    content_parts.append({
        "type": "text",
        "text": f"""위 이미지들은 PDF 문서의 {page_range}페이지입니다.

각 페이지의 내용을 상세하게 분석하여 텍스트로 추출해주세요.
- 모든 텍스트 내용을 빠짐없이 추출
- 표, 차트, 그래프가 있으면 내용 설명
- 이미지가 있으면 설명
- 페이지별로 구분하여 작성

한국어로 작성해주세요."""
    })

    messages = [{"role": "user", "content": content_parts}]
    
    return call_fireworks_vlm_api(messages, max_tokens=4096, temperature=0.3)


async def analyze_pdf_with_vlm_batched(pdf_id: str, force_refresh: bool = False) -> Dict[str, Any]:
    """VLM으로 PDF 배치 분석 후 캐시에 저장"""
    global analysis_status
    
    # 이미 분석 중인지 확인
    if pdf_id in analysis_status and analysis_status[pdf_id].get("status") == "analyzing":
        logger.info(f"PDF {pdf_id} 이미 분석 중...")
        return {"status": "analyzing", "progress": analysis_status[pdf_id].get("progress", 0)}
    
    # 캐시 확인
    if not force_refresh:
        cached = load_analysis_cache(pdf_id)
        if cached:
            analysis_status[pdf_id] = {"status": "completed", "progress": 100}
            return cached
    
    pdf_path = str(PROMPT_PDF_PATH)
    if not PROMPT_PDF_PATH.exists():
        analysis_status[pdf_id] = {"status": "error", "error": "PDF 파일 없음"}
        return {"error": "PDF 파일을 찾을 수 없습니다."}
    
    if not HAS_VALID_API_KEY:
        analysis_status[pdf_id] = {"status": "error", "error": "API 키 없음"}
        return {"error": "API 키가 설정되지 않았습니다."}
    
    # 분석 시작
    analysis_status[pdf_id] = {"status": "analyzing", "progress": 0, "started_at": time.time()}
    
    try:
        # PDF 총 페이지 수 확인
        doc = fitz.open(pdf_path)
        total_pages = doc.page_count
        doc.close()
        
        logger.info(f"PDF 분석 시작: 총 {total_pages}페이지")
        
        # 배치로 나눠서 분석 (5페이지씩)
        batch_size = 5
        all_analyses = []
        
        for start_page in range(0, min(total_pages, 25), batch_size):  # 최대 25페이지
            try:
                progress = int((start_page / min(total_pages, 25)) * 100)
                analysis_status[pdf_id]["progress"] = progress
                logger.info(f"배치 분석 중: {start_page + 1}페이지부터 (진행률: {progress}%)")
                
                # 동기 함수를 별도 스레드에서 실행
                loop = asyncio.get_event_loop()
                batch_result = await loop.run_in_executor(
                    None, 
                    analyze_batch_pages_sync, 
                    pdf_path, 
                    start_page, 
                    batch_size
                )
                
                if batch_result:
                    all_analyses.append(f"### 페이지 {start_page + 1}~{min(start_page + batch_size, total_pages)}\n{batch_result}")
                
                # API 레이트 리밋 방지
                await asyncio.sleep(2)
                
            except Exception as batch_error:
                logger.error(f"배치 {start_page} 분석 오류: {batch_error}")
                all_analyses.append(f"### 페이지 {start_page + 1}~{min(start_page + batch_size, total_pages)}\n[분석 실패: {str(batch_error)}]")
        
        # 전체 분석 결과 합치기
        combined_analysis = "\n\n".join(all_analyses)
        
        # 성공한 분석이 있는지 확인
        successful_analyses = [a for a in all_analyses if "[분석 실패:" not in a]
        if not successful_analyses:
            logger.error("모든 배치 분석 실패")
            analysis_status[pdf_id] = {"status": "error", "error": "모든 페이지 분석 실패"}
            return {"error": "PDF 분석에 실패했습니다. API 키를 확인해주세요."}
        
        # 요약 생성
        summary = ""
        if combined_analysis:
            try:
                summary_messages = [
                    {
                        "role": "system",
                        "content": "다음 PDF 분석 내용을 500자 이내로 요약해주세요. 핵심 내용과 주요 키워드를 포함해주세요."
                    },
                    {
                        "role": "user",
                        "content": combined_analysis[:8000]  # 토큰 제한
                    }
                ]
                summary = call_fireworks_vlm_api(summary_messages, max_tokens=1024, temperature=0.5)
            except Exception as sum_err:
                logger.error(f"요약 생성 오류: {sum_err}")
                summary = combined_analysis[:500] + "..."
        
        analysis_data = {
            "pdf_id": pdf_id,
            "total_pages": total_pages,
            "analyzed_pages": min(total_pages, 25),
            "analysis": combined_analysis,
            "summary": summary,
            "created_at": time.time()
        }
        
        # 캐시에 저장
        save_analysis_cache(pdf_id, analysis_data)
        
        analysis_status[pdf_id] = {"status": "completed", "progress": 100}
        logger.info(f"PDF 분석 완료: {pdf_id}")
        
        return analysis_data
        
    except Exception as e:
        logger.error(f"VLM PDF 분석 오류: {e}")
        analysis_status[pdf_id] = {"status": "error", "error": str(e)}
        return {"error": str(e)}


async def run_initial_analysis():
    """서버 시작 시 초기 분석 실행"""
    logger.info("초기 PDF 분석 시작...")
    try:
        result = await analyze_pdf_with_vlm_batched(PROMPT_PDF_ID)
        if "error" in result:
            logger.error(f"초기 분석 실패: {result['error']}")
        else:
            logger.info("초기 PDF 분석 완료!")
    except Exception as e:
        logger.error(f"초기 분석 예외: {e}")


def extract_pdf_text(pdf_path: str) -> List[Dict[str, Any]]:
    try:
        doc = fitz.open(pdf_path)
        chunks = []
        for page_num in range(len(doc)):
            page = doc[page_num]
            text = page.get_text("text")
            
            if not text.strip():
                text = page.get_text("blocks")
                if text:
                    text = "\n".join([block[4] for block in text if len(block) > 4 and isinstance(block[4], str)])
            
            if not text.strip():
                text = f"[페이지 {page_num + 1} - 이미지 기반 페이지]"
            
            chunks.append({
                "page": page_num + 1,
                "text": text.strip() if text.strip() else f"[페이지 {page_num + 1}]",
                "chunk_id": f"page_{page_num + 1}"
            })
        
        doc.close()
        return chunks
    except Exception as e:
        logger.error(f"PDF 텍스트 추출 오류: {e}")
        return []


async def query_pdf(pdf_id: str, query: str) -> Dict[str, Any]:
    """캐시된 VLM 분석 결과를 기반으로 질의응답"""
    try:
        if not HAS_VALID_API_KEY:
            return {
                "error": "Fireworks AI API 키가 설정되지 않았습니다.",
                "answer": "죄송합니다. 현재 AI 기능이 비활성화되어 있어 질문에 답변할 수 없습니다."
            }

        # 캐시된 분석 결과 확인
        analysis_data = load_analysis_cache(pdf_id)
        
        if not analysis_data:
            # 분석 상태 확인
            if pdf_id in analysis_status:
                status = analysis_status[pdf_id].get("status")
                if status == "analyzing":
                    progress = analysis_status[pdf_id].get("progress", 0)
                    return {"error": f"분석 진행 중 ({progress}%)", "answer": f"PDF 분석이 진행 중입니다 ({progress}%). 잠시만 기다려주세요."}
                elif status == "error":
                    return {"error": "분석 실패", "answer": f"PDF 분석에 실패했습니다: {analysis_status[pdf_id].get('error', '알 수 없는 오류')}"}
            return {"error": "분석 데이터 없음", "answer": "PDF가 아직 분석되지 않았습니다. 잠시 후 다시 시도해주세요."}
        
        analysis_text = analysis_data.get("analysis", "")
        total_pages = analysis_data.get("total_pages", 0)
        
        if not analysis_text:
            return {"error": "분석 데이터 없음", "answer": "PDF 분석 데이터를 찾을 수 없습니다."}

        # 캐시된 분석 내용을 기반으로 질문에 답변
        messages = [
            {
                "role": "system",
                "content": f"""당신은 PDF 문서 분석 전문가입니다. 
아래는 {total_pages}페이지 PDF 문서를 VLM으로 분석한 결과입니다.
이 분석 내용을 기반으로 사용자의 질문에 정확하고 친절하게 한국어로 답변해주세요.
답변할 때 관련 페이지 번호가 있으면 언급해주세요.
분석 내용에 없는 정보는 "해당 정보를 찾을 수 없습니다"라고 솔직히 답해주세요.

=== PDF 분석 결과 ===
{analysis_text[:12000]}
=================="""
            },
            {
                "role": "user",
                "content": query
            }
        ]

        try:
            answer = call_fireworks_vlm_api(messages, max_tokens=4096, temperature=0.6)
            return {
                "answer": answer,
                "pdf_id": pdf_id,
                "query": query
            }
        except Exception as api_error:
            logger.error(f"Fireworks API 호출 오류: {api_error}")
            error_message = str(api_error)
            return {"error": f"AI 오류: {error_message}", "answer": "처리 중 오류가 발생했습니다. 잠시 후 다시 시도해주세요."}
            
    except Exception as e:
        logger.error(f"질의응답 처리 오류: {e}")
        return {"error": str(e), "answer": "처리 중 오류가 발생했습니다."}


async def summarize_pdf(pdf_id: str) -> Dict[str, Any]:
    """캐시된 VLM 분석 결과에서 요약 추출"""
    try:
        if not HAS_VALID_API_KEY:
            return {
                "error": "Fireworks AI API 키가 설정되지 않았습니다.",
                "summary": "API 키가 없어 요약을 생성할 수 없습니다."
            }

        # 캐시된 분석 결과 확인
        analysis_data = load_analysis_cache(pdf_id)
        
        if not analysis_data:
            # 분석 상태 확인
            if pdf_id in analysis_status:
                status = analysis_status[pdf_id].get("status")
                if status == "analyzing":
                    progress = analysis_status[pdf_id].get("progress", 0)
                    return {"error": f"분석 진행 중", "summary": f"PDF 분석이 진행 중입니다 ({progress}%). 잠시만 기다려주세요."}
                elif status == "error":
                    return {"error": "분석 실패", "summary": f"PDF 분석에 실패했습니다."}
            return {"error": "분석 데이터 없음", "summary": "PDF가 아직 분석되지 않았습니다."}
        
        summary = analysis_data.get("summary", "")
        total_pages = analysis_data.get("total_pages", 0)
        analyzed_pages = analysis_data.get("analyzed_pages", total_pages)
        
        if summary:
            return {
                "summary": summary,
                "pdf_id": pdf_id,
                "total_pages": total_pages,
                "analyzed_pages": analyzed_pages
            }
        
        # 요약이 없으면 분석 내용에서 추출
        analysis_text = analysis_data.get("analysis", "")
        if analysis_text:
            return {
                "summary": analysis_text[:500] + "...",
                "pdf_id": pdf_id,
                "total_pages": total_pages
            }
        
        return {"error": "요약 없음", "summary": "요약을 생성할 수 없습니다."}
            
    except Exception as e:
        logger.error(f"PDF 요약 생성 오류: {e}")
        return {
            "error": str(e),
            "summary": "PDF 요약 중 오류가 발생했습니다."
        }


async def cache_pdf(pdf_path: str):
    try:
        pdf_file = pathlib.Path(pdf_path)
        pdf_name = pdf_file.stem

        if pdf_name not in cache_locks:
            cache_locks[pdf_name] = threading.Lock()

        if pdf_name in pdf_cache and pdf_cache[pdf_name].get("status") in ["processing", "completed"]:
            logger.info(f"PDF {pdf_name} 이미 캐싱 완료 또는 진행 중")
            return

        with cache_locks[pdf_name]:
            if pdf_name in pdf_cache and pdf_cache[pdf_name].get("status") in ["processing", "completed"]:
                return

            pdf_cache[pdf_name] = {"status": "processing", "progress": 0, "pages": []}

            cache_path = get_cache_path(pdf_name)
            if cache_path.exists():
                try:
                    with open(cache_path, "r") as cache_file:
                        cached_data = json.load(cache_file)
                        if cached_data.get("status") == "completed" and cached_data.get("pages"):
                            pdf_cache[pdf_name] = cached_data
                            pdf_cache[pdf_name]["status"] = "completed"
                            logger.info(f"캐시 파일에서 {pdf_name} 로드 완료")
                            return
                except Exception as e:
                    logger.error(f"캐시 파일 로드 실패: {e}")

            doc = fitz.open(pdf_path)
            total_pages = doc.page_count

            if total_pages > 0:
                page = doc[0]
                pix_thumb = page.get_pixmap(matrix=fitz.Matrix(0.2, 0.2))
                thumb_data = pix_thumb.tobytes("png")
                b64_thumb = base64.b64encode(thumb_data).decode('utf-8')
                thumb_src = f"data:image/png;base64,{b64_thumb}"
                pdf_cache[pdf_name]["pages"] = [{"thumb": thumb_src, "src": ""}]
                pdf_cache[pdf_name]["progress"] = 1
                pdf_cache[pdf_name]["total_pages"] = total_pages

            scale_factor = 1.0
            jpeg_quality = 80

            def process_page(page_num):
                try:
                    page = doc[page_num]
                    pix = page.get_pixmap(matrix=fitz.Matrix(scale_factor, scale_factor))
                    img_data = pix.tobytes("jpeg", jpeg_quality)
                    b64_img = base64.b64encode(img_data).decode('utf-8')
                    img_src = f"data:image/jpeg;base64,{b64_img}"
                    thumb_src = "" if page_num > 0 else pdf_cache[pdf_name]["pages"][0]["thumb"]
                    return {
                        "page_num": page_num,
                        "src": img_src,
                        "thumb": thumb_src
                    }
                except Exception as e:
                    logger.error(f"페이지 {page_num} 처리 오류: {e}")
                    return {
                        "page_num": page_num,
                        "src": "",
                        "thumb": "",
                        "error": str(e)
                    }

            pages = [None] * total_pages
            processed_count = 0
            batch_size = 5

            for batch_start in range(0, total_pages, batch_size):
                batch_end = min(batch_start + batch_size, total_pages)
                current_batch = list(range(batch_start, batch_end))

                with concurrent.futures.ThreadPoolExecutor(max_workers=min(5, batch_size)) as executor:
                    batch_results = list(executor.map(process_page, current_batch))

                for result in batch_results:
                    page_num = result["page_num"]
                    pages[page_num] = {
                        "src": result["src"],
                        "thumb": result["thumb"]
                    }
                    processed_count += 1
                    progress = round(processed_count / total_pages * 100)
                    pdf_cache[pdf_name]["progress"] = progress

                pdf_cache[pdf_name]["pages"] = pages

            pdf_cache[pdf_name] = {
                "status": "completed",
                "progress": 100,
                "pages": pages,
                "total_pages": total_pages
            }

            try:
                with open(cache_path, "w") as cache_file:
                    json.dump(pdf_cache[pdf_name], cache_file)
                logger.info(f"PDF {pdf_name} 캐싱 완료, {total_pages}페이지")
            except Exception as e:
                logger.error(f"최종 캐시 저장 실패: {e}")
    except Exception as e:
        import traceback
        logger.error(f"PDF 캐싱 오류: {str(e)}\n{traceback.format_exc()}")
        if 'pdf_name' in locals() and pdf_name in pdf_cache:
            pdf_cache[pdf_name]["status"] = "error"
            pdf_cache[pdf_name]["error"] = str(e)


@app.on_event("startup")
async def startup_event():
    if PROMPT_PDF_PATH.exists():
        logger.info(f"prompt.pdf 파일 발견: {PROMPT_PDF_PATH}")
        # 플립북 캐싱
        asyncio.create_task(cache_pdf(str(PROMPT_PDF_PATH)))
        # VLM 분석 - 에러 핸들링 포함
        asyncio.create_task(run_initial_analysis())
    else:
        logger.warning(f"prompt.pdf 파일을 찾을 수 없습니다: {PROMPT_PDF_PATH}")


@app.get("/api/pdf-list")
async def get_pdf_list():
    """사용 가능한 PDF 목록 반환"""
    global current_pdf_key
    pdf_list = []
    for key, info in PDF_FILES.items():
        pdf_list.append({
            "key": key,
            "name": info["name"],
            "exists": info["path"].exists(),
            "is_current": key == current_pdf_key
        })
    return {"pdfs": pdf_list, "current": current_pdf_key}


@app.get("/api/pdf-info")
async def get_pdf_info():
    global current_pdf_key
    pdf_info = PDF_FILES.get(current_pdf_key, PDF_FILES["prompt"])
    pdf_path = pdf_info["path"]
    pdf_id = pdf_info["id"]
    
    if not pdf_path.exists():
        return {"exists": False, "error": "PDF 파일을 찾을 수 없습니다"}
    
    pdf_name = pdf_path.stem
    is_cached = pdf_name in pdf_cache and pdf_cache[pdf_name].get("status") == "completed"
    
    # VLM 분석 캐시 확인
    analysis_cached = load_analysis_cache(pdf_id) is not None
    
    return {
        "path": str(pdf_path),
        "name": pdf_name,
        "display_name": pdf_info["name"],
        "id": pdf_id,
        "key": current_pdf_key,
        "exists": True,
        "cached": is_cached,
        "analysis_cached": analysis_cached
    }


@app.get("/api/analysis-status")
async def get_analysis_status():
    """VLM 분석 상태 확인"""
    global current_pdf_key
    pdf_info = PDF_FILES.get(current_pdf_key, PDF_FILES["prompt"])
    pdf_id = pdf_info["id"]
    
    # 먼저 캐시 파일 확인
    cached = load_analysis_cache(pdf_id)
    if cached:
        return {
            "status": "completed",
            "total_pages": cached.get("total_pages", 0),
            "analyzed_pages": cached.get("analyzed_pages", 0),
            "created_at": cached.get("created_at", 0)
        }
    
    # 메모리 상태 확인
    if pdf_id in analysis_status:
        status_info = analysis_status[pdf_id]
        return {
            "status": status_info.get("status", "unknown"),
            "progress": status_info.get("progress", 0),
            "error": status_info.get("error")
        }
    
    return {"status": "not_started"}


@app.post("/api/reanalyze-pdf")
async def reanalyze_pdf():
    """PDF 재분석 (캐시 무시)"""
    global current_pdf_key
    try:
        if not HAS_VALID_API_KEY:
            return JSONResponse(content={"error": "API 키가 설정되지 않았습니다."}, status_code=400)
        
        pdf_info = PDF_FILES.get(current_pdf_key, PDF_FILES["prompt"])
        pdf_id = pdf_info["id"]
        
        # 기존 캐시 삭제
        cache_path = get_analysis_cache_path(pdf_id)
        if cache_path.exists():
            cache_path.unlink()
            logger.info("기존 VLM 분석 캐시 삭제")
        
        # 상태 초기화
        if pdf_id in analysis_status:
            del analysis_status[pdf_id]
        
        # 백그라운드에서 재분석 시작
        asyncio.create_task(analyze_pdf_with_vlm_batched(pdf_id))
        
        return {"status": "started", "message": "PDF 재분석을 시작합니다."}
    except Exception as e:
        logger.error(f"재분석 시작 오류: {e}")
        return JSONResponse(content={"error": str(e)}, status_code=500)


@app.get("/api/pdf-thumbnail")
async def get_pdf_thumbnail():
    global current_pdf_key
    try:
        pdf_info = PDF_FILES.get(current_pdf_key, PDF_FILES["prompt"])
        pdf_path = pdf_info["path"]
        
        if not pdf_path.exists():
            return {"thumbnail": None, "error": "PDF 파일을 찾을 수 없습니다"}
        
        pdf_name = pdf_path.stem
        if pdf_name in pdf_cache and pdf_cache[pdf_name].get("pages"):
            if pdf_cache[pdf_name]["pages"][0].get("thumb"):
                return {"thumbnail": pdf_cache[pdf_name]["pages"][0]["thumb"]}

        doc = fitz.open(str(pdf_path))
        if doc.page_count > 0:
            page = doc[0]
            pix = page.get_pixmap(matrix=fitz.Matrix(0.2, 0.2))
            img_data = pix.tobytes("jpeg", 70)
            b64_img = base64.b64encode(img_data).decode('utf-8')
            asyncio.create_task(cache_pdf(str(pdf_path)))
            return {"thumbnail": f"data:image/jpeg;base64,{b64_img}"}
        return {"thumbnail": None}
    except Exception as e:
        logger.error(f"썸네일 생성 오류: {str(e)}")
        return {"error": str(e), "thumbnail": None}


@app.get("/api/cache-status")
async def get_cache_status():
    global current_pdf_key
    pdf_info = PDF_FILES.get(current_pdf_key, PDF_FILES["prompt"])
    pdf_name = pdf_info["path"].stem
    if pdf_name in pdf_cache:
        return pdf_cache[pdf_name]
    return {"status": "not_cached"}


@app.post("/api/ai/query-pdf")
async def api_query_pdf(query: Dict[str, str]):
    global current_pdf_key
    try:
        user_query = query.get("query", "")
        if not user_query:
            return JSONResponse(content={"error": "질문이 제공되지 않았습니다"}, status_code=400)

        pdf_info = PDF_FILES.get(current_pdf_key, PDF_FILES["prompt"])
        if not pdf_info["path"].exists():
            return JSONResponse(content={"error": "PDF 파일을 찾을 수 없습니다"}, status_code=404)

        result = await query_pdf(pdf_info["id"], user_query)
        if "answer" in result:
            return result
        if "error" in result:
            return JSONResponse(content=result, status_code=200)
        return result
    except Exception as e:
        logger.error(f"질의응답 API 오류: {e}")
        return JSONResponse(content={"error": str(e), "answer": "죄송합니다. 처리 중 오류가 발생했습니다."}, status_code=200)


@app.get("/api/ai/summarize-pdf")
async def api_summarize_pdf():
    global current_pdf_key
    try:
        pdf_info = PDF_FILES.get(current_pdf_key, PDF_FILES["prompt"])
        if not pdf_info["path"].exists():
            return JSONResponse(content={"error": "PDF 파일을 찾을 수 없습니다"}, status_code=404)

        result = await summarize_pdf(pdf_info["id"])
        if "summary" in result:
            return result
        if "error" in result:
            return JSONResponse(content=result, status_code=200)
        return result
    except Exception as e:
        logger.error(f"PDF 요약 API 오류: {e}")
        return JSONResponse(content={"error": str(e), "summary": "죄송합니다. 요약 생성 중 오류가 발생했습니다."}, status_code=200)


@app.get("/api/cached-pdf")
async def get_cached_pdf(background_tasks: BackgroundTasks):
    global current_pdf_key
    try:
        pdf_info = PDF_FILES.get(current_pdf_key, PDF_FILES["prompt"])
        pdf_path = pdf_info["path"]
        pdf_name = pdf_path.stem
        
        if pdf_name in pdf_cache:
            status = pdf_cache[pdf_name].get("status", "")
            if status == "completed":
                return pdf_cache[pdf_name]
            elif status == "processing":
                progress = pdf_cache[pdf_name].get("progress", 0)
                pages = pdf_cache[pdf_name].get("pages", [])
                total_pages = pdf_cache[pdf_name].get("total_pages", 0)
                return {
                    "status": "processing",
                    "progress": progress,
                    "pages": pages,
                    "total_pages": total_pages,
                    "available_pages": len([p for p in pages if p and p.get("src")])
                }

        background_tasks.add_task(cache_pdf, str(pdf_path))
        return {"status": "started", "progress": 0}
    except Exception as e:
        logger.error(f"캐시된 PDF 제공 오류: {str(e)}")
        return {"error": str(e), "status": "error"}


@app.get("/", response_class=HTMLResponse)
async def root():
    return get_html_content()


def get_html_content():
    return HTMLResponse(content=HTML)


HTML = """
<!doctype html>
<html lang="ko">
<head>
  <meta charset="utf-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>🎨 AI 플립북 - Comic Style</title>
  <link rel="stylesheet" href="/static/flipbook.css">
  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
  <script src="https://cdnjs.cloudflare.com/ajax/libs/marked/9.1.6/marked.min.js"></script>
  <link href="https://fonts.googleapis.com/css2?family=Bangers&family=Comic+Neue:wght@400;700&display=swap" rel="stylesheet">
  <script src="/static/three.js"></script>
  <script src="/static/iscroll.js"></script>
  <script src="/static/mark.js"></script>
  <script src="/static/mod3d.js"></script>
  <script src="/static/pdf.js"></script>
  <script src="/static/flipbook.js"></script>
  <script src="/static/flipbook.book3.js"></script>
  <script src="/static/flipbook.scroll.js"></script>
  <script src="/static/flipbook.swipe.js"></script>
  <script src="/static/flipbook.webgl.js"></script>
  <style>
  /* ============================================
     Comic Style CSS - Z-Image Style Applied
     ============================================ */
  :root {
    --primary-color: #3B82F6;
    --secondary-color: #8B5CF6;
    --accent-color: #FACC15;
    --ai-color: #10B981;
    --ai-hover: #059669;
    --bg-yellow: #FEF9C3;
    --text-dark: #1F2937;
    --card-bg: #ffffff;
    --shadow-comic: 5px 5px 0 #1F2937;
    --shadow-lg: 8px 8px 0 #1F2937;
    --border-comic: 3px solid #1F2937;
    --radius-sm: 8px;
    --radius-md: 12px;
    --transition: all 0.2s ease;
  }
  
  * {
    box-sizing: border-box;
  }
  
  body {
    margin: 0;
    font-family: 'Comic Neue', cursive, sans-serif;
    color: var(--text-dark);
    background-color: var(--bg-yellow);
    background-image: radial-gradient(#1F2937 1px, transparent 1px);
    background-size: 20px 20px;
    background-attachment: fixed;
    min-height: 100vh;
  }

  /* Header Info - Compact Comic Style */
  .header-info {
    position: fixed;
    top: 10px;
    left: 50%;
    transform: translateX(-50%);
    text-align: center;
    z-index: 100;
    background: linear-gradient(135deg, #3B82F6 0%, #8B5CF6 100%);
    border: 3px solid #1F2937;
    padding: 8px 25px;
    border-radius: 10px;
    box-shadow: 4px 4px 0 #1F2937;
  }
  
  .header-info .title {
    font-family: 'Bangers', cursive;
    font-size: 1.3rem;
    color: #FFF;
    text-shadow: 2px 2px 0 #1F2937;
    letter-spacing: 2px;
    margin: 0;
  }

  /* Floating AI Button - Comic Style */
  .floating-ai {
    position: fixed;
    top: 10px;
    right: 20px;
    width: 50px;
    height: 50px;
    border-radius: 50%;
    background: linear-gradient(135deg, #EF4444 0%, #F97316 100%);
    border: 3px solid #1F2937;
    box-shadow: 4px 4px 0 #1F2937;
    z-index: 9999;
    display: flex;
    justify-content: center;
    align-items: center;
    cursor: pointer;
    transition: var(--transition);
    overflow: hidden;
  }
  
  .floating-ai:hover {
    transform: translate(-2px, -2px);
    box-shadow: 6px 6px 0 #1F2937;
  }

  .floating-ai:active {
    transform: translate(2px, 2px);
    box-shadow: 2px 2px 0 #1F2937;
  }
  
  .floating-ai .icon {
    display: flex;
    justify-content: center;
    align-items: center;
    width: 100%;
    height: 100%;
    font-size: 20px;
    color: white;
    text-shadow: 1px 1px 0 #1F2937;
    transition: var(--transition);
  }
  
  .floating-ai .ai-title {
    position: absolute;
    right: 58px;
    background: #FFF;
    padding: 8px 14px;
    border-radius: 8px;
    border: 2px solid #1F2937;
    box-shadow: 3px 3px 0 #1F2937;
    font-family: 'Bangers', cursive;
    font-size: 0.95rem;
    letter-spacing: 1px;
    white-space: nowrap;
    pointer-events: none;
    opacity: 0;
    transform: translateX(10px);
    transition: all 0.3s ease;
    color: var(--text-dark);
  }
  
  .floating-ai:hover .ai-title {
    opacity: 1;
    transform: translateX(0);
  }

  /* Viewer Container - Comic Style */
  #viewer {
    width: 94%;
    height: 90vh;
    max-width: 94%;
    margin: 0;
    background: var(--card-bg);
    border: 4px solid #1F2937;
    border-radius: var(--radius-md);
    position: fixed;
    top: 50%;
    left: 50%;
    transform: translate(-50%, -50%);
    z-index: 1000;
    box-shadow: var(--shadow-lg);
    overflow: hidden;
  }

  .flipbook-container .fb3d-menu-bar {
    z-index: 2000 !important;
    opacity: 1 !important;
    bottom: 0 !important;
    background: linear-gradient(135deg, #FACC15 0%, #F59E0B 100%) !important;
    border-top: 3px solid #1F2937 !important;
    border-radius: 0 0 var(--radius-md) var(--radius-md) !important;
    padding: 12px 0 !important;
  }

  .flipbook-container .fb3d-menu-bar > ul > li > img,
  .flipbook-container .fb3d-menu-bar > ul > li > div {
    opacity: 1 !important;
    transform: scale(1.2) !important;
    filter: drop-shadow(2px 2px 0 #1F2937) !important;
  }

  .flipbook-container .fb3d-menu-bar > ul > li {
    margin: 0 12px !important;
  }

  .flipbook-container .fb3d-menu-bar > ul > li > span {
    background: #FFF !important;
    color: #1F2937 !important;
    border: 2px solid #1F2937 !important;
    border-radius: var(--radius-sm) !important;
    padding: 8px 12px !important;
    font-size: 13px !important;
    bottom: 55px !important;
    font-family: 'Comic Neue', cursive !important;
    font-weight: 700 !important;
    box-shadow: 3px 3px 0 #1F2937 !important;
  }

  @keyframes spin {
    0% { transform: rotate(0deg); }
    100% { transform: rotate(360deg); }
  }

  /* Loading Container - Comic Style */
  .loading-container {
    position: fixed;
    top: 50%;
    left: 50%;
    transform: translate(-50%, -50%);
    text-align: center;
    background: #FFF;
    border: 4px solid #1F2937;
    padding: 40px;
    border-radius: var(--radius-md);
    box-shadow: var(--shadow-lg);
    z-index: 9999;
  }

  .loading-spinner {
    border: 5px solid #FEF9C3;
    border-top: 5px solid #3B82F6;
    border-radius: 50%;
    width: 55px;
    height: 55px;
    margin: 0 auto;
    animation: spin 1s linear infinite;
  }

  .loading-text {
    margin-top: 20px;
    font-family: 'Bangers', cursive;
    font-size: 1.3rem;
    color: var(--text-dark);
    letter-spacing: 1px;
  }

  .progress-bar-container {
    width: 220px;
    height: 20px;
    background: #FEF9C3;
    border: 3px solid #1F2937;
    border-radius: 10px;
    margin-top: 15px;
    overflow: hidden;
  }

  .progress-bar {
    height: 100%;
    background: linear-gradient(to right, #3B82F6, #8B5CF6);
    border-radius: 7px;
    transition: width 0.3s ease;
  }

  .loading-pages {
    position: fixed;
    bottom: 20px;
    left: 50%;
    transform: translateX(-50%);
    background: #FFF;
    border: 3px solid #1F2937;
    padding: 12px 25px;
    border-radius: 25px;
    box-shadow: 4px 4px 0 #1F2937;
    font-family: 'Comic Neue', cursive;
    font-size: 14px;
    font-weight: 700;
    color: var(--text-dark);
    z-index: 9998;
  }

  /* AI Chat Container - Comic Style */
  #aiChatContainer {
    position: fixed;
    top: 0;
    right: 0;
    width: 420px;
    height: 100%;
    background: #FFF;
    border-left: 4px solid #1F2937;
    box-shadow: -8px 0 0 #1F2937;
    z-index: 10000;
    transform: translateX(100%);
    transition: transform 0.3s ease;
    display: flex;
    flex-direction: column;
  }

  #aiChatContainer.active {
    transform: translateX(0);
  }

  #aiChatHeader {
    display: flex;
    justify-content: space-between;
    align-items: center;
    padding: 20px;
    border-bottom: 4px solid #1F2937;
    background: linear-gradient(135deg, #EF4444 0%, #F97316 100%);
  }

  #aiChatHeader h3 {
    margin: 0;
    color: white;
    font-family: 'Bangers', cursive;
    font-size: 1.5rem;
    letter-spacing: 2px;
    text-shadow: 2px 2px 0 #1F2937;
    display: flex;
    align-items: center;
  }

  #aiChatHeader h3 i {
    margin-right: 10px;
  }

  #aiChatClose {
    background: #FFF;
    border: 3px solid #1F2937;
    cursor: pointer;
    font-size: 18px;
    color: #1F2937;
    width: 40px;
    height: 40px;
    border-radius: 8px;
    display: flex;
    align-items: center;
    justify-content: center;
    box-shadow: 3px 3px 0 #1F2937;
    transition: var(--transition);
  }

  #aiChatClose:hover {
    background: #FACC15;
    transform: translate(-2px, -2px);
    box-shadow: 5px 5px 0 #1F2937;
  }

  #aiChatMessages {
    flex: 1;
    overflow-y: auto;
    padding: 20px;
    background: #FEF9C3;
  }

  .chat-message {
    margin-bottom: 15px;
    display: flex;
    align-items: flex-start;
  }

  .chat-message.user {
    flex-direction: row-reverse;
  }

  .chat-avatar {
    width: 40px;
    height: 40px;
    border-radius: 8px;
    border: 3px solid #1F2937;
    display: flex;
    justify-content: center;
    align-items: center;
    flex-shrink: 0;
    box-shadow: 2px 2px 0 #1F2937;
  }

  .chat-message.user .chat-avatar {
    margin-left: 10px;
    background: linear-gradient(135deg, #8B5CF6 0%, #A855F7 100%);
    color: white;
  }

  .chat-message.ai .chat-avatar {
    margin-right: 10px;
    background: linear-gradient(135deg, #10B981 0%, #059669 100%);
    color: white;
  }

  .chat-bubble {
    max-width: 75%;
  }

  .chat-content {
    padding: 14px 18px;
    border-radius: 12px;
    border: 3px solid #1F2937;
    word-break: break-word;
    font-family: 'Comic Neue', cursive;
    font-size: 14px;
    font-weight: 700;
    line-height: 1.6;
    box-shadow: 3px 3px 0 #1F2937;
  }

  .chat-message.user .chat-content {
    background: linear-gradient(135deg, #8B5CF6 0%, #A855F7 100%);
    color: white;
    border-bottom-right-radius: 4px;
  }

  .chat-message.ai .chat-content {
    background: #FFF;
    color: #1F2937;
    border-bottom-left-radius: 4px;
  }

  /* 마크다운 렌더링 스타일 */
  .chat-content.markdown-body {
    font-size: 14px;
    line-height: 1.6;
  }

  .chat-content.markdown-body h1,
  .chat-content.markdown-body h2,
  .chat-content.markdown-body h3,
  .chat-content.markdown-body h4 {
    margin: 12px 0 8px 0;
    font-family: 'Bangers', cursive;
    color: #3B82F6;
    border-bottom: 2px solid #E5E7EB;
    padding-bottom: 4px;
  }

  .chat-content.markdown-body h1 { font-size: 1.4em; }
  .chat-content.markdown-body h2 { font-size: 1.25em; }
  .chat-content.markdown-body h3 { font-size: 1.1em; }
  .chat-content.markdown-body h4 { font-size: 1em; }

  .chat-content.markdown-body p {
    margin: 8px 0;
  }

  .chat-content.markdown-body strong {
    color: #8B5CF6;
    font-weight: 700;
  }

  .chat-content.markdown-body em {
    color: #059669;
    font-style: italic;
  }

  .chat-content.markdown-body ul,
  .chat-content.markdown-body ol {
    margin: 8px 0;
    padding-left: 20px;
  }

  .chat-content.markdown-body li {
    margin: 4px 0;
    position: relative;
  }

  .chat-content.markdown-body ul li::marker {
    color: #EF4444;
  }

  .chat-content.markdown-body ol li::marker {
    color: #3B82F6;
    font-weight: 700;
  }

  .chat-content.markdown-body blockquote {
    margin: 10px 0;
    padding: 8px 12px;
    border-left: 4px solid #FACC15;
    background: #FEF9C3;
    border-radius: 0 8px 8px 0;
    font-style: italic;
  }

  .chat-content.markdown-body code {
    background: #F3F4F6;
    padding: 2px 6px;
    border-radius: 4px;
    font-family: 'Consolas', monospace;
    font-size: 0.9em;
    color: #EF4444;
  }

  .chat-content.markdown-body pre {
    background: #1F2937;
    color: #F9FAFB;
    padding: 12px;
    border-radius: 8px;
    overflow-x: auto;
    margin: 10px 0;
  }

  .chat-content.markdown-body pre code {
    background: transparent;
    color: inherit;
    padding: 0;
  }

  .chat-content.markdown-body hr {
    border: none;
    border-top: 2px dashed #D1D5DB;
    margin: 12px 0;
  }

  .chat-content.markdown-body a {
    color: #3B82F6;
    text-decoration: underline;
  }

  .chat-content.markdown-body table {
    width: 100%;
    border-collapse: collapse;
    margin: 10px 0;
  }

  .chat-content.markdown-body th,
  .chat-content.markdown-body td {
    border: 2px solid #E5E7EB;
    padding: 8px;
    text-align: left;
  }

  .chat-content.markdown-body th {
    background: #F3F4F6;
    font-weight: 700;
  }

  .chat-time {
    font-size: 11px;
    color: #6B7280;
    margin-top: 6px;
    text-align: right;
    font-weight: 700;
  }

  .chat-message.ai .chat-time {
    text-align: left;
  }

  #aiChatForm {
    display: flex;
    padding: 15px 20px;
    border-top: 4px solid #1F2937;
    background: #FFF;
    gap: 10px;
  }

  #aiChatInput {
    flex: 1;
    padding: 14px 20px;
    border: 3px solid #1F2937;
    border-radius: 25px;
    font-family: 'Comic Neue', cursive;
    font-size: 14px;
    font-weight: 700;
    outline: none;
    transition: var(--transition);
    background: #FEF9C3;
  }

  #aiChatInput:focus {
    border-color: #3B82F6;
    box-shadow: 3px 3px 0 #3B82F6;
  }

  #aiChatSubmit {
    background: linear-gradient(135deg, #EF4444 0%, #F97316 100%);
    border: 3px solid #1F2937;
    color: white;
    width: 50px;
    height: 50px;
    border-radius: 50%;
    display: flex;
    justify-content: center;
    align-items: center;
    cursor: pointer;
    box-shadow: 3px 3px 0 #1F2937;
    transition: var(--transition);
  }

  #aiChatSubmit:hover {
    transform: translate(-2px, -2px);
    box-shadow: 5px 5px 0 #1F2937;
  }

  #aiChatSubmit:active {
    transform: translate(2px, 2px);
    box-shadow: 1px 1px 0 #1F2937;
  }

  #aiChatSubmit:disabled {
    background: #9CA3AF;
    cursor: not-allowed;
    transform: none;
    box-shadow: 3px 3px 0 #1F2937;
  }

  .typing-indicator {
    display: flex;
    align-items: center;
    padding: 10px;
  }

  .typing-indicator .chat-avatar {
    margin-right: 10px;
    background: linear-gradient(135deg, #10B981 0%, #059669 100%);
    color: white;
  }

  .typing-indicator span {
    height: 10px;
    width: 10px;
    background: #10B981;
    border-radius: 50%;
    display: inline-block;
    margin-right: 5px;
    border: 2px solid #1F2937;
    animation: typing 1s infinite;
  }

  .typing-indicator span:nth-child(2) { animation-delay: 0.2s; }
  .typing-indicator span:nth-child(3) { animation-delay: 0.4s; }

  @keyframes typing {
    0%, 100% { transform: translateY(0); }
    50% { transform: translateY(-8px); }
  }

  .error-container {
    position: fixed;
    top: 50%;
    left: 50%;
    transform: translate(-50%, -50%);
    text-align: center;
    background: #FFF;
    border: 4px solid #1F2937;
    padding: 40px;
    border-radius: var(--radius-md);
    box-shadow: var(--shadow-lg);
    z-index: 9999;
  }

  .error-container i {
    font-size: 50px;
    color: #EF4444;
    margin-bottom: 20px;
    text-shadow: 3px 3px 0 #1F2937;
  }

  .error-container p {
    font-family: 'Comic Neue', cursive;
    font-size: 16px;
    font-weight: 700;
    color: var(--text-dark);
    margin-bottom: 20px;
  }

  .error-container button {
    padding: 12px 30px;
    background: linear-gradient(135deg, #3B82F6 0%, #8B5CF6 100%);
    color: white;
    border: 3px solid #1F2937;
    border-radius: 8px;
    cursor: pointer;
    font-family: 'Bangers', cursive;
    font-size: 1.2rem;
    letter-spacing: 1px;
    box-shadow: 4px 4px 0 #1F2937;
    transition: var(--transition);
  }

  .error-container button:hover {
    transform: translate(-2px, -2px);
    box-shadow: 6px 6px 0 #1F2937;
  }

  /* Footer - Comic Style */
  .footer-comic {
    position: fixed;
    bottom: 10px;
    right: 10px;
    text-align: center;
    padding: 10px 20px;
    background: linear-gradient(135deg, #3B82F6 0%, #8B5CF6 100%);
    border: 3px solid #1F2937;
    border-radius: 10px;
    box-shadow: 4px 4px 0 #1F2937;
    z-index: 100;
  }

  .footer-comic p {
    font-family: 'Comic Neue', cursive;
    color: #FFF;
    margin: 3px 0;
    font-weight: 700;
    font-size: 0.85rem;
  }

  .footer-comic a {
    color: #FACC15;
    text-decoration: none;
    font-weight: 700;
  }

  .footer-comic a:hover {
    text-decoration: underline;
  }

  /* Scrollbar - Comic Style */
  ::-webkit-scrollbar {
    width: 14px;
    height: 14px;
  }

  ::-webkit-scrollbar-track {
    background: #FEF9C3;
    border: 2px solid #1F2937;
  }

  ::-webkit-scrollbar-thumb {
    background: linear-gradient(135deg, #3B82F6, #8B5CF6);
    border: 2px solid #1F2937;
    border-radius: 7px;
  }

  ::-webkit-scrollbar-thumb:hover {
    background: linear-gradient(135deg, #EF4444, #F97316);
  }

  ::selection {
    background: #FACC15;
    color: #1F2937;
  }

  @media (max-width: 768px) {
    .header-info {
      top: 8px;
      padding: 6px 15px;
      max-width: 60%;
    }
    
    .header-info .title {
      font-size: 1rem;
    }
    
    .floating-ai {
      width: 40px;
      height: 40px;
      top: 8px;
      right: 10px;
    }
    
    .floating-ai .icon {
      font-size: 16px;
    }
    
    #aiChatContainer {
      width: 100%;
    }
    
    #viewer {
      width: 98%;
      height: 92vh;
      top: 50%;
    }

    .footer-comic {
      display: none;
    }
  }
  </style>
</head>
<body>
  <!-- Header Info - Compact Comic Style -->
  <div class="header-info">
    <div class="title">📚 AI 플립북</div>
  </div>

  <!-- Floating AI Button - Comic Style -->
  <div id="aiButton" class="floating-ai">
    <div class="icon"><i class="fas fa-robot"></i></div>
    <div class="ai-title">🤖 AI 어시스턴트</div>
  </div>

  <!-- AI Chat Container -->
  <div id="aiChatContainer">
    <div id="aiChatHeader">
      <h3><i class="fas fa-robot"></i> AI 어시스턴트</h3>
      <button id="aiChatClose"><i class="fas fa-times"></i></button>
    </div>
    <div id="aiChatMessages"></div>
    <form id="aiChatForm">
      <input type="text" id="aiChatInput" placeholder="PDF에 대해 질문하세요..." autocomplete="off">
      <button type="submit" id="aiChatSubmit"><i class="fas fa-paper-plane"></i></button>
    </form>
  </div>

  <!-- PDF Viewer -->
  <div id="viewer"></div>
  <div id="loadingPages" class="loading-pages" style="display:none;">페이지 로딩 중... <span id="loadingPagesCount">0%</span></div>

  <!-- Footer - Comic Style -->
  <div class="footer-comic">
    <p style="font-family:'Bangers',cursive;font-size:1.1rem;letter-spacing:1px">📚 AI FLIPBOOK 📚</p>
    <p>Powered by VLM + 3D FlipBook</p>
    <p><a href="https://ginigen.ai" target="_blank">🏠 ginigen.ai</a></p>
  </div>

<script>
    let fb = null;
    const viewer = document.getElementById('viewer');
    pdfjsLib.GlobalWorkerOptions.workerSrc = '/static/pdf.worker.js';
    
    let pageLoadingInterval = null;
    let audioInitialized = false;
    let isAiChatActive = false;
    let isAiProcessing = false;
    let hasLoadedSummary = false;
    let analysisCheckInterval = null;

    function $id(id) { return document.getElementById(id); }

    function formatTime() {
      const now = new Date();
      return now.toLocaleTimeString('ko-KR', { hour: '2-digit', minute: '2-digit' });
    }

    function initializeAudio() {
      if (audioInitialized) return Promise.resolve();
      return new Promise((resolve) => {
        const audio = new Audio('/static/turnPage2.mp3');
        audio.volume = 0.01;
        audio.play().then(() => {
          audio.pause();
          audioInitialized = true;
          resolve();
        }).catch(() => {
          const initOnClick = () => {
            const tempAudio = new Audio('/static/turnPage2.mp3');
            tempAudio.volume = 0.01;
            tempAudio.play().then(() => {
              tempAudio.pause();
              audioInitialized = true;
              document.removeEventListener('click', initOnClick);
              resolve();
            }).catch(() => {});
          };
          document.addEventListener('click', initOnClick, { once: true });
          resolve();
        });
      });
    }

    function addChatMessage(content, isUser = false) {
      const messagesContainer = $id('aiChatMessages');
      const messageElement = document.createElement('div');
      messageElement.className = `chat-message ${isUser ? 'user' : 'ai'}`;
      
      // AI 메시지는 마크다운을 HTML로 변환
      let displayContent = content;
      if (!isUser && typeof marked !== 'undefined') {
        try {
          marked.setOptions({
            breaks: true,  // 줄바꿈을 <br>로 변환
            gfm: true      // GitHub Flavored Markdown
          });
          displayContent = marked.parse(content);
        } catch (e) {
          console.error('마크다운 파싱 오류:', e);
          displayContent = content;
        }
      }
      
      messageElement.innerHTML = `
        <div class="chat-avatar">
          <i class="fas ${isUser ? 'fa-user' : 'fa-robot'}"></i>
        </div>
        <div class="chat-bubble">
          <div class="chat-content markdown-body">${displayContent}</div>
          <div class="chat-time">${formatTime()}</div>
        </div>
      `;
      messagesContainer.appendChild(messageElement);
      messagesContainer.scrollTop = messagesContainer.scrollHeight;
      return messageElement;
    }

    function addTypingIndicator() {
      const messagesContainer = $id('aiChatMessages');
      const indicator = document.createElement('div');
      indicator.className = 'typing-indicator';
      indicator.id = 'typingIndicator';
      indicator.innerHTML = `
        <div class="chat-avatar"><i class="fas fa-robot"></i></div>
        <div><span></span><span></span><span></span></div>
      `;
      messagesContainer.appendChild(indicator);
      messagesContainer.scrollTop = messagesContainer.scrollHeight;
      return indicator;
    }

    function removeTypingIndicator() {
      const indicator = $id('typingIndicator');
      if (indicator) indicator.remove();
    }

    function toggleAiChat(show = true) {
      const container = $id('aiChatContainer');
      if (show) {
        container.classList.add('active');
        isAiChatActive = true;
        if (!hasLoadedSummary) {
          loadPdfSummary();
        }
        $id('aiChatInput').focus();
      } else {
        container.classList.remove('active');
        isAiChatActive = false;
      }
    }

    async function checkAnalysisStatus() {
      try {
        const response = await fetch('/api/analysis-status');
        const data = await response.json();
        return data;
      } catch (e) {
        console.error("분석 상태 확인 오류:", e);
        return { status: "error" };
      }
    }

    async function loadPdfSummary() {
      if (isAiProcessing || hasLoadedSummary) return;
      
      try {
        isAiProcessing = true;
        addTypingIndicator();
        
        // 분석 상태 확인
        const statusData = await checkAnalysisStatus();
        
        if (statusData.status === 'analyzing') {
          removeTypingIndicator();
          const progress = statusData.progress || 0;
          addChatMessage(`안녕하세요! 💥 현재 PDF를 AI가 분석하고 있습니다. 📊<br><br>진행률: <strong>${progress}%</strong><br><small style="color:#6B7280;">분석이 완료되면 자동으로 알려드리겠습니다.</small>`);
          hasLoadedSummary = true;
          isAiProcessing = false;
          
          // 분석 완료 폴링
          startAnalysisPolling();
          return;
        }
        
        if (statusData.status === 'error') {
          removeTypingIndicator();
          addChatMessage(`안녕하세요! 💥 PDF 분석 중 오류가 발생했습니다. ⚠️<br><br><small style="color:#EF4444;">${statusData.error || '알 수 없는 오류'}</small><br><br>페이지를 새로고침하거나 잠시 후 다시 시도해주세요.`);
          hasLoadedSummary = true;
          isAiProcessing = false;
          return;
        }
        
        if (statusData.status === 'not_started') {
          removeTypingIndicator();
          addChatMessage(`안녕하세요! 💥 PDF 분석이 아직 시작되지 않았습니다. 🔄<br><br><small style="color:#6B7280;">잠시만 기다려주세요...</small>`);
          hasLoadedSummary = true;
          isAiProcessing = false;
          startAnalysisPolling();
          return;
        }
        
        // 분석 완료됨 - 요약 가져오기
        const response = await fetch('/api/ai/summarize-pdf');
        const data = await response.json();
        
        removeTypingIndicator();
        
        if (data.summary) {
          const pageInfo = data.analyzed_pages ? ` (${data.analyzed_pages}/${data.total_pages}페이지 분석완료)` : '';
          addChatMessage(`안녕하세요! 💥 이 PDF에 대해 무엇이든 질문해주세요.${pageInfo}<br><br><strong>📄 PDF 요약:</strong><br>${data.summary}`);
        } else {
          addChatMessage("안녕하세요! 💥 PDF에 대해 질문해주세요. 최선을 다해 답변하겠습니다.");
        }
        hasLoadedSummary = true;
        
      } catch (error) {
        console.error("PDF 요약 로드 오류:", error);
        removeTypingIndicator();
        addChatMessage("안녕하세요! 💥 PDF에 대해 질문해주세요.");
        hasLoadedSummary = true;
      } finally {
        isAiProcessing = false;
      }
    }
    
    function startAnalysisPolling() {
      if (analysisCheckInterval) return;
      
      analysisCheckInterval = setInterval(async () => {
        try {
          const data = await checkAnalysisStatus();
          
          if (data.status === 'completed') {
            clearInterval(analysisCheckInterval);
            analysisCheckInterval = null;
            addChatMessage(`✅ PDF 분석이 완료되었습니다! (${data.analyzed_pages || data.total_pages}페이지)<br>이제 자유롭게 질문해주세요.`);
          } else if (data.status === 'analyzing') {
            // 진행률 업데이트 (선택적)
            console.log(`분석 진행 중: ${data.progress}%`);
          } else if (data.status === 'error') {
            clearInterval(analysisCheckInterval);
            analysisCheckInterval = null;
            addChatMessage(`⚠️ PDF 분석 실패: ${data.error || '알 수 없는 오류'}`);
          }
        } catch (e) {
          console.error("폴링 오류:", e);
        }
      }, 5000);  // 5초마다 확인
      
      // 5분 후 자동 중지
      setTimeout(() => {
        if (analysisCheckInterval) {
          clearInterval(analysisCheckInterval);
          analysisCheckInterval = null;
        }
      }, 300000);
    }

    async function submitQuestion(question) {
      if (isAiProcessing || !question.trim()) return;
      
      try {
        isAiProcessing = true;
        $id('aiChatSubmit').disabled = true;
        
        addChatMessage(question, true);
        
        // 분석 상태 확인
        const statusData = await checkAnalysisStatus();
        
        if (statusData.status !== 'completed') {
          if (statusData.status === 'analyzing') {
            addChatMessage(`PDF 분석이 진행 중입니다 (${statusData.progress || 0}%). 완료 후 질문해주세요. ⏳`);
          } else {
            addChatMessage("PDF 분석이 아직 완료되지 않았습니다. 잠시만 기다려주세요.");
          }
          isAiProcessing = false;
          $id('aiChatSubmit').disabled = false;
          $id('aiChatInput').value = question;
          return;
        }
        
        addTypingIndicator();
        
        const response = await fetch('/api/ai/query-pdf', {
          method: 'POST',
          headers: { 'Content-Type': 'application/json' },
          body: JSON.stringify({ query: question }),
          signal: AbortSignal.timeout(120000)
        });
        
        const data = await response.json();
        removeTypingIndicator();
        
        if (data.answer) {
          addChatMessage(data.answer);
        } else if (data.error) {
          addChatMessage(`죄송합니다. ${data.error}`);
        } else {
          addChatMessage("죄송합니다. 답변을 생성하지 못했습니다.");
        }
      } catch (error) {
        console.error("질문 제출 오류:", error);
        removeTypingIndicator();
        if (error.name === 'AbortError') {
          addChatMessage("응답 시간이 초과되었습니다. 잠시 후 다시 시도해주세요.");
        } else {
          addChatMessage("서버와 통신 중 오류가 발생했습니다. 잠시 후 다시 시도해주세요.");
        }
      } finally {
        isAiProcessing = false;
        $id('aiChatSubmit').disabled = false;
        $id('aiChatInput').value = '';
        $id('aiChatInput').focus();
      }
    }

    function showLoading(message) {
      hideLoading();
      const container = document.createElement('div');
      container.className = 'loading-container';
      container.id = 'loadingContainer';
      container.innerHTML = `
        <div class="loading-spinner"></div>
        <p class="loading-text">${message || '로딩 중...'}</p>
        <div class="progress-bar-container">
          <div id="progressBar" class="progress-bar" style="width: 0%;"></div>
        </div>
      `;
      document.body.appendChild(container);
    }

    function updateLoading(message, progress) {
      const text = document.querySelector('.loading-text');
      if (text) text.textContent = message;
      const bar = $id('progressBar');
      if (bar && progress !== undefined) bar.style.width = `${progress}%`;
    }

    function hideLoading() {
      const container = $id('loadingContainer');
      if (container) container.remove();
    }

    function showError(message) {
      hideLoading();
      const container = document.createElement('div');
      container.className = 'error-container';
      container.id = 'errorContainer';
      container.innerHTML = `
        <i class="fas fa-exclamation-circle"></i>
        <p>${message}</p>
        <button onclick="location.reload()">다시 시도</button>
      `;
      document.body.appendChild(container);
    }

    function createFlipBook(pages) {
      try {
        const windowWidth = window.innerWidth;
        const windowHeight = window.innerHeight;
        const aspectRatio = windowWidth / windowHeight;
        
        let width, height;
        if (aspectRatio > 1) {
          // 가로 모드: 높이 기준으로 90% 사용
          height = Math.min(windowHeight * 0.88, windowHeight - 60);
          width = height * aspectRatio * 0.75;
          if (width > windowWidth * 0.94) {
            width = windowWidth * 0.94;
            height = width / (aspectRatio * 0.75);
          }
        } else {
          // 세로 모드: 너비 기준으로 98% 사용
          width = Math.min(windowWidth * 0.98, windowWidth - 10);
          height = width / aspectRatio * 0.9;
          if (height > windowHeight * 0.9) {
            height = windowHeight * 0.9;
            width = height * aspectRatio * 0.9;
          }
        }

        viewer.style.width = Math.round(width) + 'px';
        viewer.style.height = Math.round(height) + 'px';

        const validPages = pages.map(page => {
          if (!page || !page.src) {
            return {
              src: 'data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTAwIiBoZWlnaHQ9IjEwMCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj48cmVjdCB3aWR0aD0iMTAwJSIgaGVpZ2h0PSIxMDAlIiBmaWxsPSIjZjVmNWY1Ii8+PC9zdmc+',
              thumb: ''
            };
          }
          return page;
        });

        fb = new FlipBook(viewer, {
          pages: validPages,
          viewMode: 'webgl',
          autoSize: true,
          flipDuration: 800,
          backgroundColor: '#fff',
          sound: true,
          assets: { flipMp3: '/static/turnPage2.mp3', hardFlipMp3: '/static/turnPage2.mp3' },
          controlsProps: {
            enableFullscreen: true,
            enableToc: true,
            enableDownload: false,
            enablePrint: false,
            enableZoom: true,
            enableShare: false,
            enableSearch: true,
            enableAutoPlay: true,
            enableSound: true,
            layout: 10,
            skin: 'light',
            autoNavigationTime: 3600,
            hideControls: false,
            paddingTop: 10,
            paddingLeft: 10,
            paddingRight: 10,
            paddingBottom: 10,
            pageTextureSize: 1024,
            thumbnails: true,
            autoHideControls: false,
            controlsTimeout: 8000
          }
        });

        window.addEventListener('resize', () => {
          if (fb) fb.resize();
        });

        console.log('FlipBook 생성 완료');
      } catch (error) {
        console.error('FlipBook 생성 오류:', error);
        showError("플립북을 생성하는 중 오류가 발생했습니다.");
      }
    }

    async function loadPDF() {
      try {
        showLoading("PDF 정보 확인 중...");
        
        const infoResponse = await fetch('/api/pdf-info');
        const pdfInfo = await infoResponse.json();
        
        if (!pdfInfo.exists) {
          hideLoading();
          showError("PDF 파일을 찾을 수 없습니다. prompt.pdf 파일을 확인해주세요.");
          return;
        }

        updateLoading("PDF 로딩 중...", 10);

        if (pdfInfo.cached) {
          const cacheResponse = await fetch('/api/cached-pdf');
          const cachedData = await cacheResponse.json();
          
          if (cachedData.status === "completed" && cachedData.pages) {
            hideLoading();
            createFlipBook(cachedData.pages);
            return;
          }
        }

        const cacheResponse = await fetch('/api/cached-pdf');
        let cachedData = await cacheResponse.json();

        if (cachedData.status === "completed" && cachedData.pages) {
          hideLoading();
          createFlipBook(cachedData.pages);
          return;
        }

        while (cachedData.status === "processing" || cachedData.status === "started") {
          await new Promise(resolve => setTimeout(resolve, 1000));
          
          const statusResponse = await fetch('/api/cache-status');
          cachedData = await statusResponse.json();
          
          if (cachedData.progress) {
            updateLoading(`PDF 처리 중... ${cachedData.progress}%`, cachedData.progress);
          }
          
          if (cachedData.status === "completed") {
            const finalResponse = await fetch('/api/cached-pdf');
            cachedData = await finalResponse.json();
            break;
          }
        }

        hideLoading();
        
        if (cachedData.pages && cachedData.pages.length > 0) {
          createFlipBook(cachedData.pages);
        } else {
          showError("PDF를 처리할 수 없습니다.");
        }

      } catch (error) {
        console.error("PDF 로드 오류:", error);
        hideLoading();
        showError("PDF를 로드하는 중 오류가 발생했습니다.");
      }
    }

    document.addEventListener('DOMContentLoaded', function() {
      initializeAudio();

      $id('aiButton').addEventListener('click', () => toggleAiChat(!isAiChatActive));
      $id('aiChatClose').addEventListener('click', () => toggleAiChat(false));

      $id('aiChatForm').addEventListener('submit', function(e) {
        e.preventDefault();
        const question = $id('aiChatInput').value.trim();
        if (question && !isAiProcessing) {
          submitQuestion(question);
        }
      });

      loadPDF();
    });
</script>
</body>
</html>
"""

if __name__ == "__main__":
    uvicorn.run("app:app", host="0.0.0.0", port=int(os.getenv("PORT", 7860)))