"""
AETHER Proto-AGI v2.2 - Core Infrastructure Module
AI/데이터 인프라, 유틸리티, 외부 서비스 통합
"""

import json
import sqlite3
import hashlib
import numpy as np
import requests
from datetime import datetime, timedelta
from dataclasses import dataclass, field, asdict
from typing import Optional, List, Dict, Any, Generator, Tuple
from enum import Enum
from pathlib import Path
from itertools import product
import os
import re
import random
import shutil
from urllib.parse import urlparse, urljoin
import time as time_module
import tempfile

try:
    from bs4 import BeautifulSoup
    HAS_BS4 = True
except ImportError:
    HAS_BS4 = False
    print("⚠️ beautifulsoup4 미설치. pip install beautifulsoup4")

try:
    from groq import Groq
    HAS_GROQ = True
except ImportError:
    HAS_GROQ = False
    print("⚠️ groq 미설치. pip install groq")

try:
    import PyPDF2
    HAS_PYPDF2 = True
except ImportError:
    HAS_PYPDF2 = False
    print("⚠️ PyPDF2 미설치. pip install PyPDF2")

try:
    from docx import Document as DocxDocument
    HAS_DOCX = True
except ImportError:
    HAS_DOCX = False
    print("⚠️ python-docx 미설치. pip install python-docx")

# ==================== 상수 및 설정 ====================

PERSISTENT_DIR = "/data"
LOCAL_FALLBACK_DIR = "./data"
BACKUP_INTERVAL_MINUTES = 30

VECTOR_DIM = 384
MAX_CONCURRENT_USERS = 10
MAX_QUEUE_SIZE = 30
STATUS_UPDATE_RATE = 10

MEMORY_CONFIG = {
    "short_term": {"max_items": 50, "ttl_hours": 24},
    "mid_term": {"max_items": 200, "ttl_days": 30},
    "long_term": {"max_items": 1000, "ttl_days": 365}
}

BRAVE_SEARCH_PURPOSES = {
    "土": {"purpose": "목표 관련 최신 동향 및 전체 맥락", "query_prefix": "latest trends"},
    "金": {"purpose": "팩트체크 및 반론 근거 검증", "query_prefix": "fact check verify"},
    "水": {"purpose": "심층 자료 조사 및 근거 수집", "query_prefix": "research data evidence"},
    "木": {"purpose": "유사 사례 및 영감 소스 탐색", "query_prefix": "innovative examples case study"},
    "火": {"purpose": "구현 방법 및 기술 문서 검색", "query_prefix": "how to implement tutorial"}
}

COMIC_CSS = """
@import url('https://fonts.googleapis.com/css2?family=Bangers&family=Comic+Neue:wght@400;700&family=Noto+Sans+KR:wght@400;700&display=swap');
.gradio-container { background-color: #FEF9C3 !important; background-image: radial-gradient(#1F2937 1px, transparent 1px) !important; background-size: 20px 20px !important; min-height: 100vh !important; font-family: 'Noto Sans KR', 'Comic Neue', cursive, sans-serif !important; }
footer, .footer, .gradio-container footer, .built-with, [class*="footer"], .gradio-footer, a[href*="gradio.app"] { display: none !important; visibility: hidden !important; height: 0 !important; }
.header-container { text-align: center; padding: 25px 20px; background: #3B82F6; border: 4px solid #1F2937; border-radius: 12px; margin-bottom: 20px; box-shadow: 8px 8px 0 #1F2937; }
.header-title { font-family: 'Bangers', cursive !important; color: #FFF !important; font-size: 3rem !important; text-shadow: 3px 3px 0 #1F2937 !important; letter-spacing: 3px !important; margin: 0 !important; }
.header-subtitle { font-family: 'Noto Sans KR', sans-serif !important; font-size: 1.1rem !important; color: #FEF9C3 !important; margin-top: 8px !important; font-weight: 700 !important; }
.element-badge { display: inline-block; padding: 8px 16px; border-radius: 20px; font-size: 0.95rem; margin: 4px; font-weight: 700; border: 3px solid #1F2937; box-shadow: 3px 3px 0 #1F2937; font-family: 'Noto Sans KR', sans-serif !important; }
.badge-earth { background: linear-gradient(135deg, #D2691E, #8B4513); color: #FFF; }
.badge-metal { background: linear-gradient(135deg, #E8E8E8, #C0C0C0); color: #1F2937; }
.badge-water { background: linear-gradient(135deg, #00BFFF, #1E90FF); color: #FFF; }
.badge-wood { background: linear-gradient(135deg, #32CD32, #228B22); color: #FFF; }
.badge-fire { background: linear-gradient(135deg, #FF6347, #FF4500); color: #FFF; }
.gr-panel, .gr-box, .gr-form, .block, .gr-group { background: #FFF !important; border: 3px solid #1F2937 !important; border-radius: 8px !important; box-shadow: 5px 5px 0 #1F2937 !important; }
.gr-button-primary, button.primary, .gr-button.primary { background: #EF4444 !important; border: 3px solid #1F2937 !important; border-radius: 8px !important; color: #FFF !important; font-family: 'Bangers', cursive !important; font-size: 1.2rem !important; letter-spacing: 2px !important; padding: 12px 24px !important; box-shadow: 4px 4px 0 #1F2937 !important; text-shadow: 1px 1px 0 #1F2937 !important; transition: all 0.2s !important; }
.gr-button-primary:hover, button.primary:hover { background: #DC2626 !important; transform: translate(-2px, -2px) !important; box-shadow: 6px 6px 0 #1F2937 !important; }
.gr-button-secondary, button.secondary { background: #10B981 !important; border: 3px solid #1F2937 !important; border-radius: 8px !important; color: #FFF !important; font-weight: 700 !important; }
textarea, input[type="text"], input[type="number"] { background: #FFF !important; border: 3px solid #1F2937 !important; border-radius: 8px !important; color: #1F2937 !important; font-family: 'Noto Sans KR', sans-serif !important; font-weight: 700 !important; }
textarea:focus, input[type="text"]:focus { border-color: #3B82F6 !important; box-shadow: 3px 3px 0 #3B82F6 !important; }
.info-box { background: #FACC15 !important; border: 3px solid #1F2937 !important; border-radius: 8px !important; padding: 12px 15px !important; margin: 10px 0 !important; box-shadow: 4px 4px 0 #1F2937 !important; font-family: 'Noto Sans KR', sans-serif !important; font-weight: 700 !important; color: #1F2937 !important; }
.final-report-box { background: #ECFDF5 !important; border: 4px solid #10B981 !important; border-radius: 12px !important; box-shadow: 6px 6px 0 #059669 !important; padding: 5px !important; }
.orchestration-log textarea { background: #1F2937 !important; color: #10B981 !important; font-family: 'Courier New', monospace !important; border: 3px solid #374151 !important; border-radius: 8px !important; font-size: 0.85rem !important; line-height: 1.4 !important; }
label, .gr-input-label, .gr-block-label { color: #1F2937 !important; font-family: 'Noto Sans KR', sans-serif !important; font-weight: 700 !important; }
.gr-accordion { background: #E0F2FE !important; border: 3px solid #1F2937 !important; border-radius: 8px !important; box-shadow: 4px 4px 0 #1F2937 !important; }
.tab-nav button { font-family: 'Noto Sans KR', sans-serif !important; font-weight: 700 !important; border: 2px solid #1F2937 !important; margin: 2px !important; background: #FFF !important; transition: all 0.2s !important; }
.tab-nav button.selected { background: #3B82F6 !important; color: #FFF !important; box-shadow: 3px 3px 0 #1F2937 !important; }
.footer-comic { text-align: center; padding: 20px; background: #3B82F6; border: 4px solid #1F2937; border-radius: 12px; margin-top: 20px; box-shadow: 6px 6px 0 #1F2937; }
.footer-comic p { font-family: 'Noto Sans KR', sans-serif !important; color: #FFF !important; margin: 5px 0 !important; font-weight: 700 !important; }
::-webkit-scrollbar { width: 12px; height: 12px; }
::-webkit-scrollbar-track { background: #FEF9C3; border: 2px solid #1F2937; }
::-webkit-scrollbar-thumb { background: #3B82F6; border: 2px solid #1F2937; border-radius: 6px; }
::-webkit-scrollbar-thumb:hover { background: #EF4444; }
::selection { background: #FACC15; color: #1F2937; }
.gr-slider input[type="range"] { accent-color: #3B82F6 !important; }
.gr-json { background: #FFF !important; border: 3px solid #1F2937 !important; border-radius: 8px !important; }
.gr-plot { background: #FFF !important; border: 3px solid #1F2937 !important; border-radius: 8px !important; box-shadow: 4px 4px 0 #1F2937 !important; }
.huggingface-space-link, a[href*="huggingface.co/spaces"], button[class*="share"], .share-button, [class*="hf-logo"], .gr-share-btn, #hf-logo, .hf-icon, svg[class*="hf"], div[class*="huggingface"], a[class*="huggingface"], .svelte-1rjryqp, header a[href*="huggingface"], .space-header { display: none !important; visibility: hidden !important; opacity: 0 !important; pointer-events: none !important; }
.gr-radio label { padding: 12px 16px !important; border: 2px solid #1F2937 !important; border-radius: 8px !important; margin: 4px 0 !important; background: #FFF !important; transition: all 0.2s ease !important; cursor: pointer !important; }
.gr-radio label:hover { background: #FEF3C7 !important; transform: translateX(3px) !important; }
.gr-radio input:checked + label { background: linear-gradient(135deg, #3B82F6, #1E40AF) !important; color: #FFF !important; box-shadow: 3px 3px 0 #1F2937 !important; }
.gr-radio input:disabled + label { opacity: 0.5 !important; cursor: not-allowed !important; background: #E5E7EB !important; color: #9CA3AF !important; }
.model-info-box { background: #FEF3C7 !important; border: 2px dashed #F59E0B !important; border-radius: 8px !important; padding: 8px 12px !important; font-size: 0.9rem !important; }
.upload-box { background: #F0FDF4 !important; border: 3px dashed #10B981 !important; border-radius: 12px !important; padding: 20px !important; }
.download-btn { background: #8B5CF6 !important; color: white !important; }
"""

# ==================== 데이터 클래스 ====================

class UncertaintyLevel(Enum):
    HIGH = "high"
    MEDIUM = "medium"
    LOW = "low"

@dataclass
class QualityAssessment:
    factual_confidence: float
    logical_coherence: float
    completeness: float
    specificity: float
    overall_score: float
    uncertainty_flags: List[str] = field(default_factory=list)
    needs_verification: List[str] = field(default_factory=list)
    recommendations: List[str] = field(default_factory=list)

@dataclass
class GoalClarity:
    clarity_score: float
    ambiguous_terms: List[str]
    missing_context: List[str]
    suggested_clarifications: List[str]
    is_actionable: bool
    goal_type: str = "general"

@dataclass
class Memory:
    id: str
    content: str
    memory_type: str
    element: str
    goal_context: str
    importance: float
    access_count: int
    created_at: str
    last_accessed: str
    embedding: Optional[List[float]] = None
    metadata: Dict[str, Any] = field(default_factory=dict)

@dataclass
class SearchResult:
    query: str
    element: str
    results: List[Dict]
    timestamp: str

@dataclass
class Knowledge:
    id: str
    goal: str
    query: str
    element: str
    search_results: str
    agent_output: str
    final_result: str
    embedding: List[float]
    created_at: str
    access_count: int = 0

# ==================== 스토리지 관리 ====================

_STORAGE_BASE_PATH = None

def _try_enable_persistent_storage_via_api() -> bool:
    hf_token = os.getenv("HF_TOKEN")
    space_id = os.getenv("SPACE_ID")
    if not hf_token:
        print("⚠️ HF_TOKEN 환경변수 없음 - API 활성화 불가")
        return False
    if not space_id:
        print("⚠️ SPACE_ID 환경변수 없음 - HF Spaces 환경이 아닌 것 같습니다")
        return False
    print(f"🔄 Persistent Storage API 활성화 시도...")
    print(f"   Space ID: {space_id}")
    try:
        from huggingface_hub import HfApi, SpaceStorage
        api = HfApi(token=hf_token)
        try:
            runtime = api.get_space_runtime(repo_id=space_id)
            current_storage = getattr(runtime, 'storage', None)
            if current_storage:
                print(f"✅ Persistent Storage 이미 활성화됨: {current_storage}")
                return True
            else:
                print("📦 Persistent Storage 비활성화 상태 - 활성화 시도...")
        except Exception as e:
            print(f"⚠️ Space 상태 확인 실패: {e}")
        api.request_space_storage(repo_id=space_id, storage=SpaceStorage.SMALL)
        print("✅ Persistent Storage SMALL 활성화 요청 완료!")
        print("⏳ Space가 재시작됩니다. 잠시 후 /data 디렉토리가 생성됩니다.")
        return True
    except ImportError:
        print("⚠️ huggingface_hub 라이브러리 없음")
        return False
    except Exception as e:
        error_msg = str(e)
        if "already" in error_msg.lower() or "exists" in error_msg.lower():
            print(f"✅ Persistent Storage 이미 활성화됨")
            return True
        elif "payment" in error_msg.lower() or "billing" in error_msg.lower():
            print(f"⚠️ 결제 정보 필요: {error_msg}")
        else:
            print(f"⚠️ API 활성화 실패: {error_msg}")
        return False

def _determine_storage_path() -> str:
    global _STORAGE_BASE_PATH
    if _STORAGE_BASE_PATH is not None:
        return _STORAGE_BASE_PATH
    print("\n" + "=" * 60)
    print("🔍 스토리지 초기화 중...")
    print("=" * 60)
    if os.path.exists(PERSISTENT_DIR):
        try:
            test_file = os.path.join(PERSISTENT_DIR, ".write_test")
            with open(test_file, "w") as f:
                f.write("test")
            os.remove(test_file)
            _STORAGE_BASE_PATH = PERSISTENT_DIR
            print(f"✅ HF Spaces 영구 스토리지 활성화: {PERSISTENT_DIR}")
            existing_files = [f for f in os.listdir(PERSISTENT_DIR) if f.endswith('.db') or f.endswith('.json')]
            if existing_files:
                print(f"📁 기존 파일 발견: {existing_files}")
            else:
                print("📁 기존 파일 없음 (새로운 스토리지)")
            print("=" * 60 + "\n")
            return _STORAGE_BASE_PATH
        except Exception as e:
            print(f"⚠️ /data 쓰기 테스트 실패: {e}")
    else:
        print(f"⚠️ {PERSISTENT_DIR} 디렉토리 없음")
        print("\n🚀 API로 Persistent Storage 활성화 시도...")
        if _try_enable_persistent_storage_via_api():
            print("💡 API 요청 완료. Space 재시작 후 /data 사용 가능")
    os.makedirs(LOCAL_FALLBACK_DIR, exist_ok=True)
    _STORAGE_BASE_PATH = LOCAL_FALLBACK_DIR
    print(f"\n🟡 현재 로컬 스토리지 사용: {LOCAL_FALLBACK_DIR}")
    print("   (Persistent Storage 활성화 후 Space 재시작 필요)")
    print("=" * 60 + "\n")
    return _STORAGE_BASE_PATH

def get_persistent_path(filename: str) -> str:
    base_path = _determine_storage_path()
    return os.path.join(base_path, filename)

def get_storage_info() -> dict:
    base_path = _determine_storage_path()
    db_path = os.path.join(base_path, "soma_ohaeng.db")
    info = {
        "base_path": base_path,
        "is_persistent": base_path == PERSISTENT_DIR,
        "db_path": db_path,
        "db_exists": os.path.exists(db_path),
        "db_size": 0,
        "files": []
    }
    if os.path.exists(db_path):
        info["db_size"] = os.path.getsize(db_path)
    if os.path.exists(base_path):
        info["files"] = [f for f in os.listdir(base_path) if not f.startswith('.')]
    return info

def ensure_persistent_storage():
    base_path = _determine_storage_path()
    os.makedirs(base_path, exist_ok=True)
    return base_path == PERSISTENT_DIR

def migrate_to_persistent_storage():
    base_path = _determine_storage_path()
    local_files = ["soma_ohaeng.db", "creat.json"]
    for filename in local_files:
        target_path = os.path.join(base_path, filename)
        if os.path.exists(target_path):
            size = os.path.getsize(target_path)
            print(f"📁 {filename} 이미 존재 ({size:,} bytes)")
            continue
        source_paths = [
            filename,
            os.path.join("./data", filename),
            os.path.join("/tmp", filename),
        ]
        for source_path in source_paths:
            if os.path.exists(source_path) and source_path != target_path:
                try:
                    shutil.copy2(source_path, target_path)
                    print(f"✅ 마이그레이션: {source_path} → {target_path}")
                    break
                except Exception as e:
                    print(f"⚠️ 마이그레이션 실패 {source_path}: {e}")

def backup_database():
    db_path = get_persistent_path("soma_ohaeng.db")
    if os.path.exists(db_path):
        backup_name = f"soma_ohaeng_backup_{datetime.now().strftime('%Y%m%d_%H%M')}.db"
        backup_path = get_persistent_path(backup_name)
        try:
            shutil.copy2(db_path, backup_path)
            print(f"✅ DB 백업 완료: {backup_path}")
            cleanup_old_backups()
            return True
        except Exception as e:
            print(f"⚠️ 백업 실패: {e}")
    return False

def cleanup_old_backups():
    base_path = _determine_storage_path()
    try:
        backup_files = sorted([
            f for f in os.listdir(base_path) 
            if f.startswith("soma_ohaeng_backup_") and f.endswith(".db")
        ], reverse=True)
        for old_backup in backup_files[5:]:
            try:
                os.remove(os.path.join(base_path, old_backup))
                print(f"🗑️ 오래된 백업 삭제: {old_backup}")
            except:
                pass
    except:
        pass

def verify_db_persistence():
    db_path = get_persistent_path("soma_ohaeng.db")
    print(f"\n🔍 DB 영속성 검증:")
    print(f"   경로: {db_path}")
    print(f"   존재: {os.path.exists(db_path)}")
    if os.path.exists(db_path):
        print(f"   크기: {os.path.getsize(db_path):,} bytes")
        try:
            conn = sqlite3.connect(db_path)
            c = conn.cursor()
            c.execute("SELECT name FROM sqlite_master WHERE type='table'")
            tables = [row[0] for row in c.fetchall()]
            print(f"   테이블: {tables}")
            for table in tables:
                c.execute(f"SELECT COUNT(*) FROM {table}")
                count = c.fetchone()[0]
                print(f"   - {table}: {count}개 레코드")
            conn.close()
        except Exception as e:
            print(f"   DB 읽기 오류: {e}")
    print()

# 초기화
ensure_persistent_storage()
migrate_to_persistent_storage()
verify_db_persistence()

DB_PATH = get_persistent_path("soma_ohaeng.db")
CREAT_JSON_PATH = get_persistent_path("creat.json")

# ==================== 헬퍼 함수 ====================

def ensure_string(value: Any) -> str:
    if value is None:
        return ""
    if isinstance(value, str):
        return value
    if isinstance(value, (list, dict)):
        return json.dumps(value, ensure_ascii=False)
    return str(value)

def safe_float(value: Any, default: float = 0.0) -> float:
    if value is None:
        return default
    try:
        return float(value)
    except (ValueError, TypeError):
        return default

# ==================== TimeAwareness ====================

class TimeAwareness:
    @staticmethod
    def now() -> datetime:
        return datetime.now()
    
    @staticmethod
    def get_formatted_time() -> str:
        now = datetime.now()
        weekdays = ["월요일", "화요일", "수요일", "목요일", "금요일", "토요일", "일요일"]
        weekday = weekdays[now.weekday()]
        return now.strftime(f"%Y년 %m월 %d일 ({weekday}) %H:%M:%S")
    
    @staticmethod
    def get_context_time() -> Dict:
        now = datetime.now()
        hour = now.hour
        if 5 <= hour < 12: time_of_day, greeting = "오전", "좋은 아침입니다"
        elif 12 <= hour < 14: time_of_day, greeting = "점심", "점심 시간입니다"
        elif 14 <= hour < 18: time_of_day, greeting = "오후", "좋은 오후입니다"
        elif 18 <= hour < 22: time_of_day, greeting = "저녁", "좋은 저녁입니다"
        else: time_of_day, greeting = "밤", "밤늦게까지 수고하십니다"
        month = now.month
        if month <= 3: quarter, half = "1분기", "상반기"
        elif month <= 6: quarter, half = "2분기", "상반기"
        elif month <= 9: quarter, half = "3분기", "하반기"
        else: quarter, half = "4분기", "하반기"
        return {
            "datetime": now, "formatted": TimeAwareness.get_formatted_time(),
            "year": now.year, "month": now.month, "day": now.day,
            "hour": now.hour, "minute": now.minute,
            "weekday": now.strftime("%A"),
            "weekday_kr": ["월요일", "화요일", "수요일", "목요일", "금요일", "토요일", "일요일"][now.weekday()],
            "time_of_day": time_of_day, "greeting": greeting,
            "quarter": quarter, "half": half, "timestamp": now.timestamp()
        }
    
    @staticmethod
    def get_time_prompt() -> str:
        ctx = TimeAwareness.get_context_time()
        return f"""[현재 시간 정보]
- 일시: {ctx['formatted']}
- 시간대: {ctx['time_of_day']}
- 분기: {ctx['year']}년 {ctx['quarter']} ({ctx['half']})
"""

# ==================== FileProcessor ====================

class FileProcessor:
    @staticmethod
    def extract_text_from_pdf(file_path: str) -> str:
        if not HAS_PYPDF2:
            return "[ERROR] PyPDF2가 설치되지 않았습니다. pip install PyPDF2"
        try:
            text_content = []
            with open(file_path, 'rb') as file:
                pdf_reader = PyPDF2.PdfReader(file)
                for page_num, page in enumerate(pdf_reader.pages):
                    page_text = page.extract_text()
                    if page_text:
                        text_content.append(f"[페이지 {page_num + 1}]\n{page_text}")
            return "\n\n".join(text_content) if text_content else "[PDF에서 텍스트를 추출할 수 없습니다]"
        except Exception as e:
            return f"[PDF 읽기 오류: {str(e)}]"
    
    @staticmethod
    def extract_text_from_docx(file_path: str) -> str:
        if not HAS_DOCX:
            return "[ERROR] python-docx가 설치되지 않았습니다. pip install python-docx"
        try:
            doc = DocxDocument(file_path)
            paragraphs = [para.text for para in doc.paragraphs if para.text.strip()]
            return "\n\n".join(paragraphs) if paragraphs else "[문서에서 텍스트를 추출할 수 없습니다]"
        except Exception as e:
            return f"[DOCX 읽기 오류: {str(e)}]"
    
    @staticmethod
    def extract_text_from_txt(file_path: str) -> str:
        try:
            with open(file_path, 'r', encoding='utf-8') as f:
                return f.read()
        except UnicodeDecodeError:
            try:
                with open(file_path, 'r', encoding='cp949') as f:
                    return f.read()
            except:
                return "[텍스트 파일 인코딩 오류]"
        except Exception as e:
            return f"[파일 읽기 오류: {str(e)}]"
    
    @staticmethod
    def process_uploaded_file(file) -> Tuple[str, str]:
        if file is None:
            return "", ""
        file_path = file.name if hasattr(file, 'name') else str(file)
        file_name = os.path.basename(file_path)
        file_ext = os.path.splitext(file_name)[1].lower()
        if file_ext == '.pdf':
            text = FileProcessor.extract_text_from_pdf(file_path)
            file_info = f"📄 PDF 파일: {file_name}"
        elif file_ext in ['.docx', '.doc']:
            text = FileProcessor.extract_text_from_docx(file_path)
            file_info = f"📝 Word 문서: {file_name}"
        elif file_ext in ['.txt', '.md', '.csv']:
            text = FileProcessor.extract_text_from_txt(file_path)
            file_info = f"📃 텍스트 파일: {file_name}"
        elif file_ext == '.json':
            text = FileProcessor.extract_text_from_txt(file_path)
            file_info = f"📋 JSON 파일: {file_name}"
        else:
            text = f"[지원하지 않는 파일 형식: {file_ext}]"
            file_info = f"❌ 지원 불가: {file_name}"
        if len(text) > 50000:
            text = text[:50000] + f"\n\n[... 총 {len(text):,}자 중 50,000자만 표시 ...]"
        return text, file_info

# ==================== ExportManager ====================

class ExportManager:
    @staticmethod
    def export_to_markdown(report: str, goal: str) -> str:
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"aether_report_{timestamp}.md"
        filepath = os.path.join(tempfile.gettempdir(), filename)
        content = f"""# AETHER Proto-AGI 분석 보고서
**생성 일시**: {datetime.now().strftime("%Y년 %m월 %d일 %H:%M:%S")}
**분석 목표**: {goal}
---
{report}
---
*이 보고서는 AETHER Proto-AGI (SOMA 오행 순환 · SLAI 자기학습 · MAIA 창발)에 의해 자동 생성되었습니다.*
"""
        with open(filepath, 'w', encoding='utf-8') as f:
            f.write(content)
        return filepath
    
    @staticmethod
    def export_to_json(report: str, goal: str, log: str, element_outputs: Dict) -> str:
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"aether_report_{timestamp}.json"
        filepath = os.path.join(tempfile.gettempdir(), filename)
        data = {
            "meta": {
                "generator": "AETHER Proto-AGI",
                "version": "2.2",
                "timestamp": datetime.now().isoformat(),
                "goal": goal
            },
            "report": report,
            "orchestration_log": log,
            "element_outputs": element_outputs,
            "analysis_summary": {
                "total_elements": len(element_outputs),
                "elements_processed": list(element_outputs.keys())
            }
        }
        with open(filepath, 'w', encoding='utf-8') as f:
            json.dump(data, f, ensure_ascii=False, indent=2)
        return filepath
    
    @staticmethod
    def export_log_to_txt(log: str) -> str:
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"aether_log_{timestamp}.txt"
        filepath = os.path.join(tempfile.gettempdir(), filename)
        with open(filepath, 'w', encoding='utf-8') as f:
            f.write(f"AETHER Proto-AGI 오케스트레이션 로그\n")
            f.write(f"생성 시간: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
            f.write("=" * 60 + "\n\n")
            f.write(log)
        return filepath

# ==================== OutputFormatter ====================

class OutputFormatter:
    ELEMENT_STYLES = {
        "土": {"emoji": "🟤", "name": "감독", "icon": "🏛️"},
        "金": {"emoji": "⚪", "name": "비평", "icon": "⚔️"},
        "水": {"emoji": "🔵", "name": "리서치", "icon": "🔬"},
        "木": {"emoji": "🟢", "name": "창발", "icon": "💡"},
        "火": {"emoji": "🔴", "name": "실행", "icon": "🚀"}
    }
    
    @staticmethod
    def header(title: str, emoji: str = "🌀") -> str:
        return f"\n{'━' * 50}\n  {emoji}  {title}\n{'━' * 50}\n"
    
    @staticmethod
    def element_header(element_name: str) -> str:
        style = OutputFormatter.ELEMENT_STYLES.get(element_name, {})
        emoji = style.get("emoji", "●")
        name = style.get("name", element_name)
        icon = style.get("icon", "")
        return f"\n┏━━ {emoji} {element_name}({name}) {icon} ━━┓\n"
    
    @staticmethod
    def search_result(count: int, cached: bool = False) -> str:
        status = "📦캐시" if cached else "🔍검색"
        bar = "█" * min(count, 10) + "░" * (10 - min(count, 10))
        return f"  {status} │{bar}│ {count}건\n"
    
    @staticmethod
    def satisfaction_bar(score: float) -> str:
        filled = int(score * 20)
        bar = "█" * filled + "░" * (20 - filled)
        pct = score * 100
        if score >= 0.85: emoji, status = "🎯", "달성"
        elif score >= 0.7: emoji, status = "📈", "양호"
        elif score >= 0.5: emoji, status = "🔄", "진행"
        else: emoji, status = "⚠️", "개선"
        return f"  {emoji} 만족도: {bar} {pct:.0f}% ({status})\n"
    
    @staticmethod
    def cycle_start(iteration: int, time_str: str) -> str:
        return f"\n╔══ 🔄 순환 #{iteration} ══╗\n║ ⏰ {time_str}\n╚{'═' * 30}╝\n"
    
    @staticmethod
    def compact_json(parsed: Dict, element_name: str) -> str:
        lines = []
        style = OutputFormatter.ELEMENT_STYLES.get(element_name, {})
        emoji = style.get("emoji", "●")
        if element_name == "土":
            if "assessment" in parsed:
                assessment = str(parsed['assessment'])[:200]
                lines.append(f"  {emoji} 평가: {assessment}...")
            if "direction" in parsed:
                lines.append(f"  → 방향: {str(parsed['direction'])[:150]}")
            if "fact_check" in parsed:
                lines.append(f"  ✓ 팩트: {str(parsed['fact_check'])[:100]}")
        elif element_name == "金":
            if "critiques" in parsed and parsed["critiques"]:
                lines.append(f"  {emoji} 비판점 {len(parsed['critiques'])}개:")
                for c in parsed["critiques"][:2]:
                    lines.append(f"    • {str(c)[:80]}")
            if "risks" in parsed and parsed["risks"]:
                lines.append(f"  ⚠️ 리스크 {len(parsed['risks'])}개:")
                for r in parsed["risks"][:2]:
                    lines.append(f"    • {str(r)[:80]}")
            if "verified_facts" in parsed and parsed["verified_facts"]:
                lines.append(f"  ✅ 검증 {len(parsed['verified_facts'])}개")
        elif element_name == "水":
            if "findings" in parsed and parsed["findings"]:
                lines.append(f"  {emoji} 발견 {len(parsed['findings'])}건:")
                for f in parsed["findings"][:3]:
                    lines.append(f"    • {str(f)[:80]}")
            if "evidence" in parsed and parsed["evidence"]:
                lines.append(f"  📚 근거 {len(parsed['evidence'])}건")
            if "gaps" in parsed and parsed["gaps"]:
                lines.append(f"  ❓ 추가조사 {len(parsed['gaps'])}건")
        elif element_name == "木":
            if "ideas" in parsed and parsed["ideas"]:
                lines.append(f"  {emoji} 아이디어 {len(parsed['ideas'])}개 생성")
            if "selected_top3" in parsed and parsed["selected_top3"]:
                lines.append(f"  🏆 TOP 3:")
                for i, idea in enumerate(parsed["selected_top3"][:3], 1):
                    lines.append(f"    {i}. {str(idea)[:70]}...")
            if "novel_connections" in parsed and parsed["novel_connections"]:
                lines.append(f"  🔗 새로운 연결 {len(parsed['novel_connections'])}개")
        elif element_name == "火":
            if "result" in parsed:
                result = str(parsed['result'])[:300]
                lines.append(f"  {emoji} 결과:")
                for i, chunk in enumerate(result.split('. ')[:4]):
                    if chunk.strip():
                        lines.append(f"    {chunk.strip()}.")
            if "deliverables" in parsed and parsed["deliverables"]:
                lines.append(f"  📦 산출물 {len(parsed['deliverables'])}개:")
                for d in parsed["deliverables"][:3]:
                    lines.append(f"    • {str(d)[:60]}")
            if "implementation" in parsed:
                lines.append(f"  🔧 구현: {str(parsed['implementation'])[:100]}...")
        return "\n".join(lines) + "\n" if lines else ""
    
    @staticmethod
    def final_marker() -> str:
        return "\n" + "═" * 50 + "\n  🎉 목표 달성 완료!\n" + "═" * 50 + "\n"

# ==================== ReportGenerator ====================

class ReportGenerator:
    @staticmethod
    def generate(state, stats: Dict) -> str:
        goal = state.goal
        score = state.satisfaction_score
        iterations = state.iteration
        element_outputs = state.element_outputs
        earth_data = element_outputs.get("土", {})
        metal_data = element_outputs.get("金", {})
        water_data = element_outputs.get("水", {})
        wood_data = element_outputs.get("木", {})
        fire_data = element_outputs.get("火", {})
        main_result = fire_data.get("result", "")
        key_insight = wood_data.get("key_insight", wood_data.get("reframe", ""))
        report = f"""# 🎯 분석 결과
---
## 📋 질문
> **{goal}**
---
## 💡 핵심 답변
"""
        if main_result:
            report += f"{main_result}\n\n"
        else:
            assessment = earth_data.get("assessment", "")
            if assessment:
                report += f"{assessment}\n\n"
            else:
                report += "*분석이 완료되지 않았습니다. 순환 횟수를 늘려 다시 시도해주세요.*\n\n"
        if key_insight:
            report += f"""---
## 🔑 핵심 인사이트
{key_insight}
"""
        top3 = wood_data.get("selected_top3", [])
        if top3:
            report += """---
## 💡 주요 아이디어
"""
            for i, idea in enumerate(top3[:3], 1):
                report += f"**{i}.** {idea}\n\n"
        verified = metal_data.get("verified_facts", [])
        if verified:
            report += """---
## ✅ 검증된 사실
"""
            for fact in verified[:5]:
                report += f"- {fact}\n"
            report += "\n"
        risks = metal_data.get("risks", [])
        if risks:
            report += """---
## ⚠️ 주요 리스크
"""
            for risk in risks[:3]:
                report += f"- {risk}\n"
            report += "\n"
        findings = water_data.get("findings", [])
        if findings:
            report += """---
## 📊 주요 발견
"""
            for finding in findings[:5]:
                report += f"- {finding}\n"
            report += "\n"
        if hasattr(state, 'metacog_assessments') and state.metacog_assessments:
            avg_quality = sum(a.overall_score for a in state.metacog_assessments) / len(state.metacog_assessments)
            report += f"""---
## 🧠 메타인지 품질 평가
| 지표 | 평균 점수 |
|------|-----------|
| 종합 품질 | {'█' * int(avg_quality * 10)}{'░' * (10 - int(avg_quality * 10))} {avg_quality:.0%} |
| 분석 단계 | {len(state.metacog_assessments)}회 평가 |
"""
        report += f"""---
## 📊 분석 정보
| 항목 | 값 |
|------|-----|
| 분석 신뢰도 | {'█' * int(score * 10)}{'░' * (10 - int(score * 10))} {score:.0%} |
| 분석 순환 | {iterations}회 |
| 세션 ID | `{state.session_id}` |
| 생성 시간 | {TimeAwareness.get_formatted_time()} |
"""
        return report
    
    @staticmethod
    def generate_progress(state) -> str:
        progress_bar = '█' * int(state.satisfaction_score * 10) + '░' * (10 - int(state.satisfaction_score * 10))
        current_element = ""
        if state.history:
            last = state.history[-1]
            elem_name = last.get("element", "")
            elem_map = {
                "土": "🟤 土 감독", "金": "⚪ 金 비평", "水": "🔵 水 리서치",
                "木": "🟢 木 창발", "火": "🔴 火 실행"
            }
            current_element = elem_map.get(elem_name, elem_name)
        return f"""# ⏳ 분석 진행 중...
## 📋 질문
> **{state.goal}**
## 📊 현재 상태
| 항목 | 상태 |
|------|------|
| 진행 순환 | {state.iteration}회 |
| 현재 단계 | {current_element} |
| 진행도 | {progress_bar} {state.satisfaction_score:.0%} |
---
💡 **오케스트레이션 로그**를 펼쳐서 실시간 분석 과정을 확인하세요.
*土(감독) → 金(비평) → 水(리서치) → 木(창발) → 火(실행) 순환 중...*
"""

# ==================== MetaCognition (계속) ====================

class MetaCognition:
    UNCERTAINTY_MARKERS = [
        "아마", "maybe", "perhaps", "possibly", "might", "could be",
        "불확실", "uncertain", "unclear", "추측", "guess", "assume",
        "~일 수 있", "~할 수도", "정확하지 않", "확인 필요",
        "것 같", "듯하", "보임", "추정"
    ]
    HEDGE_WORDS = [
        "일반적으로", "보통", "대체로", "주로", "often", "usually",
        "typically", "generally", "sometimes", "occasionally",
        "대부분", "많은 경우"
    ]
    FACTUAL_INDICATORS = [
        r'\d{4}년', r'\d+%', r'\d+억', r'\d+조', r'\$\d+',
        r'[A-Z]{2,}', r'「.+」', r'".+"', r'\[출처\]',
        r'\d+월', r'\d+일', r'약 \d+', r'총 \d+'
    ]
    LOGIC_CONNECTORS = [
        "따라서", "그러므로", "때문에", "결과적으로", "왜냐하면",
        "therefore", "because", "consequently", "thus", "hence",
        "이로 인해", "그 결과", "이에 따라"
    ]
    ABSTRACT_WARNINGS = [
        "플랫폼", "시스템", "프레임워크", "프로그램", "메커니즘",
        "글로벌 거버넌스", "국제 협력", "전략적 파트너십",
        "~를 개발", "~를 구축", "~를 설계"
    ]
    
    def __init__(self):
        self.confidence_history: Dict[str, List[float]] = {}
        self.failure_patterns: List[Dict] = []
        self.domain_confidence: Dict[str, float] = {}
        self.uncertainty_threshold = 0.35
        self.min_specificity_threshold = 0.4
    
    def assess_response_quality(self, response: str, goal: str, context: Dict = None) -> QualityAssessment:
        if not response or not response.strip():
            return QualityAssessment(
                factual_confidence=0.0, logical_coherence=0.0,
                completeness=0.0, specificity=0.0, overall_score=0.0,
                uncertainty_flags=["빈 응답"],
                needs_verification=["전체 재생성 필요"],
                recommendations=["응답 재생성 필요"]
            )
        factual = self._assess_factual_grounding(response)
        logic = self._assess_logical_coherence(response)
        complete = self._assess_completeness(response, goal)
        specific = self._assess_specificity(response)
        abstract_penalty = self._check_abstract_expressions(response)
        uncertainties = self._identify_uncertainties(response)
        verifications = self._identify_verification_needs(response, goal)
        weights = {"factual": 0.3, "logic": 0.2, "complete": 0.3, "specific": 0.2}
        overall = (factual * weights["factual"] + 
                   logic * weights["logic"] +
                   complete * weights["complete"] + 
                   specific * weights["specific"])
        if len(uncertainties) > 3:
            overall *= 0.85
        overall *= (1 - abstract_penalty * 0.15)
        recommendations = self._generate_recommendations(
            factual, logic, complete, specific, uncertainties, abstract_penalty
        )
        return QualityAssessment(
            factual_confidence=round(factual, 3),
            logical_coherence=round(logic, 3),
            completeness=round(complete, 3),
            specificity=round(specific, 3),
            overall_score=round(max(0, min(1, overall)), 3),
            uncertainty_flags=uncertainties,
            needs_verification=verifications,
            recommendations=recommendations
        )
    
    def _assess_factual_grounding(self, response: str) -> float:
        score = 0.3
        for pattern in self.FACTUAL_INDICATORS:
            matches = re.findall(pattern, response)
            score += min(len(matches) * 0.05, 0.25)
        source_patterns = [r'\[출처[:\s]', r'에 따르면', r'보도에 의하면', r'발표한 바', r'연구에 따르면']
        for pattern in source_patterns:
            if re.search(pattern, response):
                score += 0.1
        uncertainty_count = sum(1 for marker in self.UNCERTAINTY_MARKERS if marker in response.lower())
        score -= uncertainty_count * 0.05
        return max(0.0, min(1.0, score))
    
    def _assess_logical_coherence(self, response: str) -> float:
        score = 0.5
        connector_count = sum(1 for conn in self.LOGIC_CONNECTORS if conn in response)
        score += min(connector_count * 0.08, 0.25)
        sentences = re.split(r'[.!?。]', response)
        if len(sentences) > 3:
            score += 0.1
        structure_patterns = [r'첫째|둘째|셋째', r'1\.|2\.|3\.', r'###\s', r'\*\*[^*]+\*\*', r'\|.*\|']
        for pattern in structure_patterns:
            if re.search(pattern, response):
                score += 0.05
        contradictions = self._detect_contradictions(response)
        score -= len(contradictions) * 0.15
        return max(0.0, min(1.0, score))
    
    def _detect_contradictions(self, response: str) -> List[str]:
        contradictions = []
        contradiction_pairs = [
            (r'증가.{0,30}감소', "증가/감소 모순"),
            (r'성공.{0,30}실패', "성공/실패 모순"),
            (r'긍정.{0,30}부정', "긍정/부정 모순"),
            (r'불가능.{0,20}가능', "가능/불가능 모순"),
            (r'강화.{0,20}약화', "강화/약화 모순"),
        ]
        for pattern, desc in contradiction_pairs:
            if re.search(pattern, response):
                contradictions.append(desc)
        return contradictions
    
    def _assess_completeness(self, response: str, goal: str) -> float:
        goal_keywords = self._extract_keywords(goal)
        if not goal_keywords:
            return 0.5
        response_lower = response.lower()
        covered = sum(1 for kw in goal_keywords if kw.lower() in response_lower)
        coverage_ratio = covered / len(goal_keywords) if goal_keywords else 0
        length_score = min(len(response) / 1500, 1.0) * 0.3
        return min(1.0, coverage_ratio * 0.7 + length_score)
    
    def _extract_keywords(self, text: str) -> List[str]:
        stopwords = {
            '의', '가', '이', '은', '들', '는', '좀', '잘', '를', '을', '로', '으로',
            '에', '와', '과', '도', '만', '라', '하다', '있다', '되다', '이다', '그',
            '저', '것', '수', '등', '더', '때', 'the', 'a', 'an', 'is', 'are', 'was',
            'were', 'be', 'been', 'being', 'have', 'has', 'had', 'do', 'does', 'did',
            'will', 'would', 'could', 'should', 'may', 'might', 'must', 'shall', 'can',
            'need', 'to', 'of', 'in', 'for', 'on', 'with', 'at', 'by', 'from', 'as',
            'what', 'which', 'who', 'how', 'why', 'when', 'where'
        }
        words = re.findall(r'[가-힣a-zA-Z0-9]+', text)
        keywords = [w for w in words if len(w) > 1 and w.lower() not in stopwords]
        return keywords[:15]
    
    def _assess_specificity(self, response: str) -> float:
        score = 0.2
        number_matches = re.findall(r'\d+(?:\.\d+)?(?:%|억|만|천|조|달러|원)?', response)
        score += min(len(number_matches) * 0.04, 0.25)
        proper_nouns = re.findall(r'[A-Z][a-z]+(?:\s[A-Z][a-z]+)*', response)
        korean_proper = re.findall(
            r'(?:미국|중국|일본|한국|유럽|러시아|인도|독일|영국|프랑스|'
            r'삼성|애플|구글|마이크로소프트|테슬라|아마존|화웨이|TSMC|엔비디아|'
            r'트럼프|바이든|시진핑|푸틴|OpenAI|Anthropic)', response
        )
        score += min((len(proper_nouns) + len(korean_proper)) * 0.03, 0.25)
        date_patterns = re.findall(r'\d{4}년|\d{1,2}월|\d{1,2}일|20\d{2}', response)
        score += min(len(date_patterns) * 0.05, 0.15)
        vague_terms = ['것', '등', '여러', '다양한', '많은', '일부', '어떤', 'something', 'various', 'many', 'some']
        vague_count = sum(1 for term in vague_terms if term in response)
        score -= vague_count * 0.03
        return max(0.0, min(1.0, score))
    
    def _check_abstract_expressions(self, response: str) -> float:
        count = 0
        for expr in self.ABSTRACT_WARNINGS:
            if expr in response:
                count += 1
        return min(count / 5, 1.0)
    
    def _identify_uncertainties(self, response: str) -> List[str]:
        uncertainties = []
        response_lower = response.lower()
        for marker in self.UNCERTAINTY_MARKERS:
            if marker in response_lower:
                idx = response_lower.find(marker)
                context = response[max(0, idx-20):min(len(response), idx+50)]
                uncertainties.append(f"불확실: '{marker}' - ...{context}...")
                if len(uncertainties) >= 5:
                    break
        return uncertainties
    
    def _identify_verification_needs(self, response: str, goal: str) -> List[str]:
        needs = []
        numbers = re.findall(r'\d+(?:\.\d+)?(?:%|억|조)', response)
        if numbers:
            needs.append(f"숫자 데이터 검증 필요: {', '.join(numbers[:3])}")
        quotes = re.findall(r'"([^"]+)"', response)
        if quotes:
            needs.append(f"인용문 검증 필요: {len(quotes)}개")
        return needs[:5]
    
    def _generate_recommendations(self, factual: float, logic: float, 
                                   complete: float, specific: float,
                                   uncertainties: List[str],
                                   abstract_penalty: float) -> List[str]:
        recommendations = []
        if factual < 0.5:
            recommendations.append("검색 결과에서 구체적 사실/수치 인용 강화")
        if logic < 0.5:
            recommendations.append("논리 연결어 사용하여 인과관계 명확화")
        if complete < 0.5:
            recommendations.append("목표의 모든 측면에 대한 답변 보완")
        if specific < 0.4:
            recommendations.append("국가명, 기업명, 수치 등 구체적 정보 추가")
        if len(uncertainties) > 2:
            recommendations.append("불확실한 표현 대신 검증된 사실로 대체")
        if abstract_penalty > 0.3:
            recommendations.append("'플랫폼', '시스템' 등 추상적 용어 대신 구체적 방안 제시")
        return recommendations[:5]
    
    def analyze_goal_clarity(self, goal: str) -> GoalClarity:
        score = 0.5
        ambiguous = []
        missing = []
        suggestions = []
        ambiguous_patterns = [
            ('좋은', "좋은의 기준 모호"),
            ('최적', "최적의 기준 모호"),
            ('효과적', "효과적의 기준 모호"),
            ('적절한', "적절함의 기준 모호"),
            ('더 나은', "비교 대상 불명확"),
        ]
        for term, reason in ambiguous_patterns:
            if term in goal:
                ambiguous.append(reason)
                score -= 0.1
        if not re.search(r'\d{4}|20\d{2}', goal):
            missing.append("시간 범위")
            suggestions.append("시간 범위를 명시해주세요 (예: 2025년, 향후 5년)")
        if len(goal) < 20:
            missing.append("상세 맥락")
            suggestions.append("구체적인 맥락이나 조건을 추가해주세요")
        goal_type = self._detect_goal_type(goal)
        if goal_type in ["prediction", "strategy", "comparison", "analysis"]:
            score += 0.1
        is_actionable = score >= 0.5 and len(missing) <= 2
        return GoalClarity(
            clarity_score=round(max(0, min(1, score)), 3),
            ambiguous_terms=ambiguous,
            missing_context=missing,
            suggested_clarifications=suggestions[:5],
            is_actionable=is_actionable,
            goal_type=goal_type
        )
    
    def _detect_goal_type(self, goal: str) -> str:
        goal_lower = goal.lower()
        if any(kw in goal_lower for kw in ['예측', '전망', '될까', '될 것', '미래', '2025', '2026', '2027', '2028']):
            return "prediction"
        elif any(kw in goal_lower for kw in ['전략', '방법', '어떻게', '방안', '계획', '수립']):
            return "strategy"
        elif any(kw in goal_lower for kw in ['비교', '차이', 'vs', 'VS', '대', '어느 것']):
            return "comparison"
        elif any(kw in goal_lower for kw in ['분석', '왜', '원인', '영향', '현황']):
            return "analysis"
        elif any(kw in goal_lower for kw in ['특허', '발명', '아이디어', '혁신']):
            return "invention"
        elif any(kw in goal_lower for kw in ['소설', '스토리', '시나리오', '웹툰']):
            return "story"
        elif any(kw in goal_lower for kw in ['요리', '레시피', '음식']):
            return "recipe"
        else:
            return "general"
    
    def should_ask_clarification(self, goal: str) -> Tuple[bool, Optional[str]]:
        clarity = self.analyze_goal_clarity(goal)
        if clarity.clarity_score < self.uncertainty_threshold:
            return True, self._build_clarification_message(clarity)
        if not clarity.is_actionable:
            return True, self._build_clarification_message(clarity)
        return False, None
    
    def _build_clarification_message(self, clarity: GoalClarity) -> str:
        msg_parts = ["🤔 목표를 더 명확히 해주시면 더 좋은 분석이 가능합니다:\n"]
        if clarity.ambiguous_terms:
            msg_parts.append(f"• 모호한 표현: {', '.join(clarity.ambiguous_terms[:3])}")
        if clarity.missing_context:
            msg_parts.append(f"• 누락된 맥락: {', '.join(clarity.missing_context[:3])}")
        if clarity.suggested_clarifications:
            msg_parts.append("\n💡 제안:")
            for i, sugg in enumerate(clarity.suggested_clarifications[:3], 1):
                msg_parts.append(f"  {i}. {sugg}")
        return "\n".join(msg_parts)
    
    def format_assessment_for_log(self, assessment: QualityAssessment) -> str:
        lines = [
            "┌─ 🧠 메타인지 평가 ─┐",
            f"│ 사실근거: {'█' * int(assessment.factual_confidence * 10)}{'░' * (10 - int(assessment.factual_confidence * 10))} {assessment.factual_confidence:.0%}",
            f"│ 논리성:   {'█' * int(assessment.logical_coherence * 10)}{'░' * (10 - int(assessment.logical_coherence * 10))} {assessment.logical_coherence:.0%}",
            f"│ 완성도:   {'█' * int(assessment.completeness * 10)}{'░' * (10 - int(assessment.completeness * 10))} {assessment.completeness:.0%}",
            f"│ 구체성:   {'█' * int(assessment.specificity * 10)}{'░' * (10 - int(assessment.specificity * 10))} {assessment.specificity:.0%}",
            f"│ ────────────────",
            f"│ 종합:     {'█' * int(assessment.overall_score * 10)}{'░' * (10 - int(assessment.overall_score * 10))} {assessment.overall_score:.0%}",
        ]
        if assessment.uncertainty_flags:
            lines.append(f"│ ⚠️ 불확실: {len(assessment.uncertainty_flags)}건")
        if assessment.recommendations:
            lines.append(f"│ 💡 권장: {assessment.recommendations[0][:25]}...")
        lines.append("└────────────────────┘")
        return "\n".join(lines)

# ==================== BraveSearchClient ====================

class BraveSearchClient:
    def __init__(self):
        self.api_key = os.getenv("BRAVE_API_KEY")
        self.base_url = "https://api.search.brave.com/res/v1/web/search"
    
    def search(self, query: str, element: str, count: int = 5) -> Dict:
        if not self.api_key:
            return {"success": False, "error": "BRAVE_API_KEY 환경변수가 설정되지 않았습니다.", "results": []}
        purpose_info = BRAVE_SEARCH_PURPOSES.get(element, {})
        query_prefix = purpose_info.get("query_prefix", "")
        optimized_query = f"{query_prefix} {query}".strip()
        headers = {
            "Accept": "application/json",
            "Accept-Encoding": "gzip",
            "X-Subscription-Token": self.api_key
        }
        params = {"q": optimized_query, "count": count, "text_decorations": False, "search_lang": "ko"}
        try:
            response = requests.get(self.base_url, headers=headers, params=params, timeout=10)
            response.raise_for_status()
            data = response.json()
            results = []
            for item in data.get("web", {}).get("results", []):
                results.append({
                    "title": item.get("title", ""),
                    "url": item.get("url", ""),
                    "description": item.get("description", ""),
                    "age": item.get("age", "")
                })
            return {
                "success": True, "query": optimized_query, "element": element,
                "purpose": purpose_info.get("purpose", ""), "results": results, "count": len(results)
            }
        except requests.exceptions.RequestException as e:
            return {"success": False, "error": str(e), "results": []}
    
    def format_for_prompt(self, search_result: Dict) -> str:
        if not search_result.get("success") or not search_result.get("results"):
            return "[검색 결과 없음]"
        lines = [f"[Brave Search - {search_result.get('purpose', '')}]", f"쿼리: {search_result.get('query', '')}", ""]
        for i, result in enumerate(search_result.get("results", [])[:5], 1):
            lines.append(f"{i}. **{result.get('title', '')}**")
            lines.append(f"   {result.get('description', '')[:200]}...")
            lines.append(f"   출처: {result.get('url', '')}")
            lines.append("")
        return "\n".join(lines)

# ==================== URLCrawler ====================

class URLCrawler:
    def __init__(self):
        self.session = requests.Session()
        self.session.headers.update({
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
            "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8",
            "Accept-Language": "ko-KR,ko;q=0.9,en-US;q=0.8,en;q=0.7"
        })
    
    @staticmethod
    def extract_urls(text: str) -> List[str]:
        full_url_pattern = r'(?:https?://)?(?:www\.)?(?:[a-zA-Z0-9][-a-zA-Z0-9]*\.)+[a-zA-Z]{2,}(?:/[^\s]*)?'
        urls = re.findall(full_url_pattern, text)
        cleaned_urls = []
        for url in urls:
            url = url.strip('.,;:!?()')
            if not url.startswith(('http://', 'https://')):
                url = 'https://' + url
            cleaned_urls.append(url)
        return list(set(cleaned_urls))
    
    def crawl(self, url: str, max_length: int = 5000) -> Dict:
        if not HAS_BS4:
            return {"success": False, "error": "beautifulsoup4가 설치되지 않았습니다.", "url": url}
        try:
            if not url.startswith(('http://', 'https://')):
                url = 'https://' + url
            response = self.session.get(url, timeout=15, allow_redirects=True)
            response.raise_for_status()
            if response.encoding is None or response.encoding == 'ISO-8859-1':
                response.encoding = response.apparent_encoding or 'utf-8'
            soup = BeautifulSoup(response.text, 'html.parser')
            for element in soup(['script', 'style', 'nav', 'footer', 'header', 'aside', 'form', 'button', 'iframe', 'noscript']):
                element.decompose()
            title = soup.title.string if soup.title else ""
            meta_desc = ""
            meta_tag = soup.find('meta', attrs={'name': 'description'})
            if meta_tag:
                meta_desc = meta_tag.get('content', '')
            main_content = soup.find('main') or soup.find('article') or soup.find('body')
            if main_content:
                text = main_content.get_text(separator='\n', strip=True)
            else:
                text = soup.get_text(separator='\n', strip=True)
            lines = [line.strip() for line in text.split('\n') if line.strip()]
            text = '\n'.join(lines)
            if len(text) > max_length:
                text = text[:max_length] + "...[truncated]"
            links = []
            for a_tag in soup.find_all('a', href=True)[:10]:
                href = a_tag['href']
                link_text = a_tag.get_text(strip=True)
                if href.startswith('/'):
                    href = urljoin(url, href)
                if href.startswith('http'):
                    links.append({"text": link_text[:50], "url": href})
            return {
                "success": True, "url": url, "final_url": response.url, "title": title,
                "meta_description": meta_desc, "content": text, "content_length": len(text),
                "links": links, "status_code": response.status_code,
                "crawled_at": TimeAwareness.get_formatted_time()
            }
        except requests.exceptions.Timeout:
            return {"success": False, "error": "요청 시간 초과 (15초)", "url": url}
        except requests.exceptions.SSLError:
            return {"success": False, "error": "SSL 인증서 오류", "url": url}
        except requests.exceptions.RequestException as e:
            return {"success": False, "error": str(e), "url": url}
        except Exception as e:
            return {"success": False, "error": f"크롤링 오류: {str(e)}", "url": url}

# ==================== SimpleVectorDB ====================

class SimpleVectorDB:
    def __init__(self, dimension: int = VECTOR_DIM):
        self.dimension = dimension
        self.vectors: Dict[str, np.ndarray] = {}
        self.metadata: Dict[str, Dict] = {}
    
    def add(self, id: str, vector: List[float], metadata: Dict = None):
        self.vectors[id] = np.array(vector, dtype=np.float32)
        self.metadata[id] = metadata or {}
    
    def search(self, query_vector: List[float], top_k: int = 5) -> List[Tuple[str, float, Dict]]:
        if not self.vectors:
            return []
        query = np.array(query_vector, dtype=np.float32)
        results = []
        for id, vec in self.vectors.items():
            similarity = np.dot(query, vec) / (np.linalg.norm(query) * np.linalg.norm(vec) + 1e-8)
            results.append((id, float(similarity), self.metadata.get(id, {})))
        results.sort(key=lambda x: x[1], reverse=True)
        return results[:top_k]
    
    def delete(self, id: str):
        self.vectors.pop(id, None)
        self.metadata.pop(id, None)
    
    def simple_embed(self, text: str) -> List[float]:
        hash_bytes = hashlib.sha384(text.encode()).digest()
        embedding = [float(b) / 255.0 - 0.5 for b in hash_bytes]
        while len(embedding) < self.dimension:
            embedding.extend(embedding[:self.dimension - len(embedding)])
        return embedding[:self.dimension]

# ==================== SLAI Memory System (일부 생략, 너무 길어서) ====================

class SLAIMemorySystem:
    def __init__(self, db_path: str = DB_PATH):
        self.db_path = db_path
        self.vector_db = SimpleVectorDB()
        self.knowledge_vector_db = SimpleVectorDB()
        self._init_db()
        self._load_vectors()
    
    def _init_db(self):
        conn = sqlite3.connect(self.db_path)
        c = conn.cursor()
        c.execute("""CREATE TABLE IF NOT EXISTS memories (
            id TEXT PRIMARY KEY, content TEXT NOT NULL, memory_type TEXT NOT NULL,
            element TEXT, goal_context TEXT, importance REAL DEFAULT 0.5,
            access_count INTEGER DEFAULT 0, created_at TEXT, last_accessed TEXT,
            embedding BLOB, metadata TEXT)""")
        c.execute("""CREATE TABLE IF NOT EXISTS learning_patterns (
            id TEXT PRIMARY KEY, pattern_type TEXT, input_pattern TEXT,
            output_pattern TEXT, success_count INTEGER DEFAULT 0,
            fail_count INTEGER DEFAULT 0, confidence REAL DEFAULT 0.5,
            created_at TEXT, updated_at TEXT)""")
        c.execute("""CREATE TABLE IF NOT EXISTS sessions (
            id TEXT PRIMARY KEY, goal TEXT, iterations INTEGER, satisfied INTEGER,
            satisfaction_score REAL, final_output TEXT, created_at TEXT, history TEXT)""")
        c.execute("""CREATE TABLE IF NOT EXISTS knowledge (
            id TEXT PRIMARY KEY, goal TEXT NOT NULL, query TEXT, element TEXT,
            search_results TEXT, agent_output TEXT, final_result TEXT,
            embedding BLOB, created_at TEXT, access_count INTEGER DEFAULT 0,
            tags TEXT, quality_score REAL DEFAULT 0.5)""")
        c.execute("""CREATE TABLE IF NOT EXISTS search_cache (
            id TEXT PRIMARY KEY, query TEXT NOT NULL, element TEXT,
            results TEXT, created_at TEXT, expiry_at TEXT)""")
        conn.commit()
        conn.close()
        print(f"✅ DB 초기화 완료: {self.db_path}")
    
    def _load_vectors(self):
        conn = sqlite3.connect(self.db_path)
        c = conn.cursor()
        c.execute("SELECT id, content, embedding, metadata FROM memories WHERE embedding IS NOT NULL")
        for row in c.fetchall():
            id, content, embedding_blob, metadata_str = row
            if embedding_blob:
                embedding = json.loads(embedding_blob)
                metadata = json.loads(metadata_str) if metadata_str else {}
                metadata["content"] = content
                self.vector_db.add(id, embedding, metadata)
        c.execute("SELECT id, goal, final_result, embedding FROM knowledge WHERE embedding IS NOT NULL")
        for row in c.fetchall():
            id, goal, final_result, embedding_blob = row
            if embedding_blob:
                embedding = json.loads(embedding_blob)
                self.knowledge_vector_db.add(id, embedding, {
                    "goal": goal,
                    "final_result": final_result[:500] if final_result else ""
                })
        conn.close()
    
    def store_memory(self, content: str, memory_type: str, element: str, goal_context: str, importance: float = 0.5) -> str:
        content = ensure_string(content)
        memory_type = ensure_string(memory_type)
        element = ensure_string(element)
        goal_context = ensure_string(goal_context)
        importance = safe_float(importance, 0.5)
        memory_id = hashlib.md5(f"{content}{datetime.now().isoformat()}".encode()).hexdigest()[:16]
        now = datetime.now().isoformat()
        embedding = self.vector_db.simple_embed(content)
        conn = sqlite3.connect(self.db_path)
        c = conn.cursor()
        c.execute("""INSERT OR REPLACE INTO memories 
            (id, content, memory_type, element, goal_context, importance, access_count, 
             created_at, last_accessed, embedding, metadata) 
            VALUES (?, ?, ?, ?, ?, ?, 0, ?, ?, ?, ?)""",
            (memory_id, content, memory_type, element, goal_context, importance,
             now, now, json.dumps(embedding), json.dumps({})))
        conn.commit()
        conn.close()
        self.vector_db.add(memory_id, embedding, {
            "content": content, "memory_type": memory_type,
            "element": element, "goal_context": goal_context
        })
        self._cleanup_memories(memory_type)
        return memory_id
    
    def retrieve_memories(self, query: str, memory_type: str = None, top_k: int = 5) -> List[Dict]:
        query_embedding = self.vector_db.simple_embed(query)
        results = self.vector_db.search(query_embedding, top_k * 2)
        filtered = []
        for id, similarity, metadata in results:
            if memory_type and metadata.get("memory_type") != memory_type:
                continue
            filtered.append({
                "id": id, "content": metadata.get("content", ""),
                "similarity": similarity, "memory_type": metadata.get("memory_type"),
                "element": metadata.get("element")
            })
            if len(filtered) >= top_k:
                break
        self._update_access_count([r["id"] for r in filtered])
        return filtered
    
    def promote_memory(self, memory_id: str):
        conn = sqlite3.connect(self.db_path)
        c = conn.cursor()
        c.execute("SELECT memory_type, access_count, importance FROM memories WHERE id = ?", (memory_id,))
        row = c.fetchone()
        if row:
            current_type, access_count, importance = row
            if current_type == "short_term" and (access_count >= 3 or importance >= 0.7):
                new_type = "mid_term"
            elif current_type == "mid_term" and (access_count >= 10 or importance >= 0.85):
                new_type = "long_term"
            else:
                new_type = current_type
            if new_type != current_type:
                c.execute("UPDATE memories SET memory_type = ? WHERE id = ?", (new_type, memory_id))
                conn.commit()
        conn.close()
    
    def store_knowledge(self, goal: str, query: str, element: str, search_results: Dict,
                       agent_output: str, final_result: Any, quality_score: float = 0.5,
                       tags: List[str] = None) -> str:
        knowledge_id = hashlib.md5(f"{goal}{element}{datetime.now().isoformat()}".encode()).hexdigest()[:16]
        now = datetime.now().isoformat()
        goal = ensure_string(goal)
        query = ensure_string(query)
        element = ensure_string(element)
        agent_output = ensure_string(agent_output)
        final_result = ensure_string(final_result)
        quality_score = safe_float(quality_score, 0.5)
        if isinstance(search_results, dict):
            search_results_str = json.dumps(search_results, ensure_ascii=False)
        else:
            search_results_str = ensure_string(search_results)
        if tags is None:
            tags = []
        tags_str = json.dumps(tags, ensure_ascii=False)
        embedding_text = f"{goal} {final_result[:500]}"
        embedding = self.knowledge_vector_db.simple_embed(embedding_text)
        conn = sqlite3.connect(self.db_path)
        c = conn.cursor()
        c.execute("""INSERT OR REPLACE INTO knowledge 
            (id, goal, query, element, search_results, agent_output, final_result,
             embedding, created_at, access_count, tags, quality_score) 
            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, 0, ?, ?)""",
            (knowledge_id, goal, query, element, search_results_str, agent_output,
             final_result, json.dumps(embedding), now, tags_str, quality_score))
        conn.commit()
        conn.close()
        self.knowledge_vector_db.add(knowledge_id, embedding, {
            "goal": goal, "final_result": final_result[:500]
        })
        return knowledge_id
    
    def retrieve_knowledge(self, query: str, top_k: int = 5, min_similarity: float = 0.3) -> List[Dict]:
        query_embedding = self.knowledge_vector_db.simple_embed(query)
        results = self.knowledge_vector_db.search(query_embedding, top_k * 2)
        filtered = []
        conn = sqlite3.connect(self.db_path)
        c = conn.cursor()
        for id, similarity, metadata in results:
            if similarity < min_similarity:
                continue
            c.execute("""SELECT goal, query, element, search_results, agent_output, 
                        final_result, quality_score, created_at FROM knowledge WHERE id = ?""", (id,))
            row = c.fetchone()
            if row:
                try:
                    search_results_parsed = json.loads(row[3]) if row[3] else {}
                except:
                    search_results_parsed = {}
                filtered.append({
                    "id": id, "similarity": similarity, "goal": row[0], "query": row[1],
                    "element": row[2], "search_results": search_results_parsed,
                    "agent_output": row[4], "final_result": row[5],
                    "quality_score": row[6], "created_at": row[7]
                })
                c.execute("UPDATE knowledge SET access_count = access_count + 1 WHERE id = ?", (id,))
            if len(filtered) >= top_k:
                break
        conn.commit()
        conn.close()
        return filtered
    
    def get_related_knowledge_for_element(self, goal: str, element: str, top_k: int = 3) -> List[Dict]:
        conn = sqlite3.connect(self.db_path)
        c = conn.cursor()
        query_embedding = self.knowledge_vector_db.simple_embed(goal)
        results = self.knowledge_vector_db.search(query_embedding, top_k * 3)
        filtered = []
        for id, similarity, metadata in results:
            c.execute("SELECT element, agent_output, final_result FROM knowledge WHERE id = ?", (id,))
            row = c.fetchone()
            if row and row[0] == element:
                filtered.append({
                    "id": id, "similarity": similarity,
                    "agent_output": row[1][:300] if row[1] else "",
                    "final_result": row[2][:300] if row[2] else ""
                })
            if len(filtered) >= top_k:
                break
        conn.close()
        return filtered
    
    def cache_search(self, query: str, element: str, results: Dict, ttl_hours: int = 24) -> str:
        cache_id = hashlib.md5(f"{query}{element}".encode()).hexdigest()[:16]
        now = datetime.now()
        expiry = now + timedelta(hours=ttl_hours)
        conn = sqlite3.connect(self.db_path)
        c = conn.cursor()
        c.execute("""INSERT OR REPLACE INTO search_cache 
            (id, query, element, results, created_at, expiry_at) VALUES (?, ?, ?, ?, ?, ?)""",
            (cache_id, query, element, json.dumps(results, ensure_ascii=False),
             now.isoformat(), expiry.isoformat()))
        conn.commit()
        conn.close()
        return cache_id
    
    def get_cached_search(self, query: str, element: str) -> Optional[Dict]:
        cache_id = hashlib.md5(f"{query}{element}".encode()).hexdigest()[:16]
        conn = sqlite3.connect(self.db_path)
        c = conn.cursor()
        c.execute("""SELECT results, expiry_at FROM search_cache 
            WHERE id = ? AND expiry_at > ?""", (cache_id, datetime.now().isoformat()))
        row = c.fetchone()
        conn.close()
        if row:
            return json.loads(row[0])
        return None
    
    def learn_pattern(self, input_pattern: str, output_pattern: str, pattern_type: str, success: bool):
        input_pattern = ensure_string(input_pattern)
        output_pattern = ensure_string(output_pattern)
        pattern_type = ensure_string(pattern_type)
        pattern_id = hashlib.md5(f"{pattern_type}{input_pattern}".encode()).hexdigest()[:16]
        now = datetime.now().isoformat()
        conn = sqlite3.connect(self.db_path)
        c = conn.cursor()
        c.execute("SELECT success_count, fail_count FROM learning_patterns WHERE id = ?", (pattern_id,))
        row = c.fetchone()
        if row:
            success_count, fail_count = row
            if success:
                success_count += 1
            else:
                fail_count += 1
            confidence = success_count / (success_count + fail_count + 1)
            c.execute("""UPDATE learning_patterns 
                SET success_count = ?, fail_count = ?, confidence = ?, updated_at = ? WHERE id = ?""",
                (success_count, fail_count, confidence, now, pattern_id))
        else:
            success_count = 1 if success else 0
            fail_count = 0 if success else 1
            confidence = success_count / (success_count + fail_count + 1)
            c.execute("""INSERT INTO learning_patterns 
                (id, pattern_type, input_pattern, output_pattern, success_count, fail_count, confidence, created_at, updated_at)
                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)""",
                (pattern_id, pattern_type, input_pattern, output_pattern,
                 success_count, fail_count, confidence, now, now))
        conn.commit()
        conn.close()
    
    def get_learned_patterns(self, pattern_type: str, min_confidence: float = 0.5) -> List[Dict]:
        conn = sqlite3.connect(self.db_path)
        c = conn.cursor()
        c.execute("""SELECT input_pattern, output_pattern, confidence FROM learning_patterns 
            WHERE pattern_type = ? AND confidence >= ? ORDER BY confidence DESC""",
            (pattern_type, min_confidence))
        results = [{"input": r[0], "output": r[1], "confidence": r[2]} for r in c.fetchall()]
        conn.close()
        return results
    
    def save_session(self, state):
        conn = sqlite3.connect(self.db_path)
        c = conn.cursor()
        c.execute("""INSERT OR REPLACE INTO sessions 
            (id, goal, iterations, satisfied, satisfaction_score, final_output, created_at, history)
            VALUES (?, ?, ?, ?, ?, ?, ?, ?)""",
            (state.session_id, state.goal, state.iteration, 1 if state.satisfied else 0,
             state.satisfaction_score, ensure_string(state.final_output),
             datetime.now().isoformat(), json.dumps(state.history, ensure_ascii=False)))
        conn.commit()
        conn.close()
    
    def _update_access_count(self, memory_ids: List[str]):
        conn = sqlite3.connect(self.db_path)
        c = conn.cursor()
        now = datetime.now().isoformat()
        for mid in memory_ids:
            c.execute("""UPDATE memories SET access_count = access_count + 1, last_accessed = ? WHERE id = ?""",
                (now, mid))
        conn.commit()
        conn.close()
    
    def _cleanup_memories(self, memory_type: str):
        config = MEMORY_CONFIG.get(memory_type, {})
        max_items = config.get("max_items", 100)
        conn = sqlite3.connect(self.db_path)
        c = conn.cursor()
        c.execute("""DELETE FROM memories WHERE id IN 
            (SELECT id FROM memories WHERE memory_type = ? ORDER BY last_accessed DESC LIMIT -1 OFFSET ?)""",
            (memory_type, max_items))
        conn.commit()
        conn.close()
    
    def get_memory_stats(self) -> Dict:
        conn = sqlite3.connect(self.db_path)
        c = conn.cursor()
        stats = {}
        for mem_type in ["short_term", "mid_term", "long_term"]:
            c.execute("SELECT COUNT(*) FROM memories WHERE memory_type = ?", (mem_type,))
            stats[mem_type] = c.fetchone()[0]
        c.execute("SELECT COUNT(*) FROM learning_patterns")
        stats["patterns"] = c.fetchone()[0]
        c.execute("SELECT COUNT(*) FROM sessions")
        stats["sessions"] = c.fetchone()[0]
        c.execute("SELECT COUNT(*) FROM knowledge")
        stats["knowledge"] = c.fetchone()[0]
        c.execute("SELECT COUNT(*) FROM search_cache")
        stats["search_cache"] = c.fetchone()[0]
        conn.close()
        return stats
    
    def get_dashboard_data(self) -> Dict:
        conn = sqlite3.connect(self.db_path)
        c = conn.cursor()
        data = {
            "memory": {}, "knowledge": {}, "learning": {},
            "sessions": {}, "elements": {}, "timeline": {}
        }
        for mem_type in ["short_term", "mid_term", "long_term"]:
            c.execute("SELECT COUNT(*) FROM memories WHERE memory_type = ?", (mem_type,))
            data["memory"][mem_type] = c.fetchone()[0]
        c.execute("SELECT COUNT(*) FROM knowledge")
        data["knowledge"]["total"] = c.fetchone()[0]
        c.execute("SELECT AVG(quality_score) FROM knowledge")
        avg_quality = c.fetchone()[0]
        data["knowledge"]["avg_quality"] = round(avg_quality, 3) if avg_quality else 0
        c.execute("SELECT SUM(access_count) FROM knowledge")
        total_access = c.fetchone()[0]
        data["knowledge"]["total_access"] = total_access or 0
        c.execute("SELECT COUNT(*) FROM learning_patterns")
        data["learning"]["total_patterns"] = c.fetchone()[0]
        c.execute("SELECT AVG(confidence) FROM learning_patterns")
        avg_conf = c.fetchone()[0]
        data["learning"]["avg_confidence"] = round(avg_conf, 3) if avg_conf else 0
        c.execute("SELECT SUM(success_count), SUM(fail_count) FROM learning_patterns")
        row = c.fetchone()
        success = row[0] or 0
        fail = row[1] or 0
        data["learning"]["success_rate"] = round(success / (success + fail + 1) * 100, 1)
        data["learning"]["total_attempts"] = success + fail
        c.execute("SELECT COUNT(*) FROM sessions")
        data["sessions"]["total"] = c.fetchone()[0]
        c.execute("SELECT COUNT(*) FROM sessions WHERE satisfied = 1")
        data["sessions"]["completed"] = c.fetchone()[0]
        c.execute("SELECT AVG(satisfaction_score) FROM sessions")
        avg_sat = c.fetchone()[0]
        data["sessions"]["avg_satisfaction"] = round(avg_sat, 3) if avg_sat else 0
        c.execute("SELECT AVG(iterations) FROM sessions")
        avg_iter = c.fetchone()[0]
        data["sessions"]["avg_iterations"] = round(avg_iter, 1) if avg_iter else 0
        for element in ["土", "金", "水", "木", "火"]:
            c.execute("SELECT COUNT(*) FROM knowledge WHERE element = ?", (element,))
            data["elements"][element] = c.fetchone()[0]
        data["timeline"]["dates"] = []
        data["timeline"]["knowledge_count"] = []
        data["timeline"]["memory_count"] = []
        data["timeline"]["session_count"] = []
        for i in range(6, -1, -1):
            date = (datetime.now() - timedelta(days=i)).strftime("%Y-%m-%d")
            data["timeline"]["dates"].append(date[-5:])
            c.execute("SELECT COUNT(*) FROM knowledge WHERE DATE(created_at) = ?", (date,))
            data["timeline"]["knowledge_count"].append(c.fetchone()[0])
            c.execute("SELECT COUNT(*) FROM memories WHERE DATE(created_at) = ?", (date,))
            data["timeline"]["memory_count"].append(c.fetchone()[0])
            c.execute("SELECT COUNT(*) FROM sessions WHERE DATE(created_at) = ?", (date,))
            data["timeline"]["session_count"].append(c.fetchone()[0])
        
        # Quality distribution
        c.execute("SELECT quality_score FROM knowledge WHERE quality_score IS NOT NULL")
        quality_scores = [row[0] for row in c.fetchall()]
        quality_dist = {
            "very_low": sum(1 for q in quality_scores if 0 <= q < 0.2),
            "low": sum(1 for q in quality_scores if 0.2 <= q < 0.4),
            "medium": sum(1 for q in quality_scores if 0.4 <= q < 0.6),
            "high": sum(1 for q in quality_scores if 0.6 <= q < 0.8),
            "very_high": sum(1 for q in quality_scores if 0.8 <= q <= 1.0)
        }
        data["quality_distribution"] = quality_dist
        
        # Element performance
        element_performance = {}
        for element in ["土", "金", "水", "木", "火"]:
            c.execute("SELECT AVG(quality_score), COUNT(*) FROM knowledge WHERE element = ?", (element,))
            row = c.fetchone()
            avg_quality = (row[0] * 100) if row[0] else 0
            count = row[1]
            element_performance[element] = {
                "avg_quality": avg_quality,
                "avg_speed": min(100, count * 3),
                "count": count
            }
        data["element_performance"] = element_performance
        
        intelligence_score = self._calculate_intelligence_score(data)
        data["intelligence"] = intelligence_score
        conn.close()
        return data
    
    def _calculate_intelligence_score(self, data: Dict) -> Dict:
        scores = {}
        total_memory = sum(data["memory"].values())
        long_term_ratio = data["memory"].get("long_term", 0) / max(total_memory, 1)
        scores["memory_score"] = min(100, total_memory * 2 + long_term_ratio * 50)
        knowledge_count = data["knowledge"].get("total", 0)
        avg_quality = data["knowledge"].get("avg_quality", 0)
        scores["knowledge_score"] = min(100, knowledge_count * 1.5 + avg_quality * 30)
        patterns = data["learning"].get("total_patterns", 0)
        success_rate = data["learning"].get("success_rate", 0)
        scores["learning_score"] = min(100, patterns * 3 + success_rate * 0.5)
        sessions = data["sessions"].get("total", 0)
        completed = data["sessions"].get("completed", 0)
        completion_rate = completed / max(sessions, 1) * 100
        scores["experience_score"] = min(100, sessions * 2 + completion_rate * 0.3)
        scores["total"] = round(
            scores["memory_score"] * 0.2 +
            scores["knowledge_score"] * 0.3 +
            scores["learning_score"] * 0.25 +
            scores["experience_score"] * 0.25, 1)
        total = scores["total"]
        if total < 20:
            scores["level"], scores["level_name"] = "🌱 초보", "Novice"
        elif total < 40:
            scores["level"], scores["level_name"] = "🌿 성장", "Growing"
        elif total < 60:
            scores["level"], scores["level_name"] = "🌳 숙련", "Skilled"
        elif total < 80:
            scores["level"], scores["level_name"] = "🎋 전문", "Expert"
        else:
            scores["level"], scores["level_name"] = "🏆 마스터", "Master"
        return scores

# ==================== EmergenceEngine ====================

class EmergenceEngine:
    def __init__(self, creat_json_path: str = CREAT_JSON_PATH):
        self.creat_data = self._load_creat_json(creat_json_path)
    
    def _load_creat_json(self, path: str) -> Dict:
        if Path(path).exists():
            with open(path, "r", encoding="utf-8") as f:
                return json.load(f)
        return {"_meta": {"description": "기본 창발 매트릭스"}}
    
    def _detect_question_type(self, context: str) -> str:
        if any(kw in context for kw in ["예측", "전망", "될까", "될 것", "미래", "2025", "2026", "2027", "2028"]):
            return "예측"
        elif any(kw in context for kw in ["전략", "방법", "어떻게", "방안", "계획", "수립"]):
            return "전략"
        elif any(kw in context for kw in ["비교", "차이", "vs", "VS", "대", "어느 것"]):
            return "비교"
        elif any(kw in context for kw in ["분석", "왜", "원인", "영향", "현황"]):
            return "분석"
        elif any(kw in context for kw in ["특허", "발명", "아이디어", "혁신", "신기술"]):
            return "발명"
        elif any(kw in context for kw in ["소설", "스토리", "시나리오", "웹툰", "드라마", "영화", "캐릭터", "플롯"]):
            return "스토리"
        elif any(kw in context for kw in ["요리", "레시피", "음식", "만들기", "조리", "맛", "재료"]):
            return "레시피"
        else:
            return "일반"
    
    def _detect_domains(self, context: str) -> List[str]:
        domains = []
        if any(kw in context for kw in ["패권", "전쟁", "국제", "외교", "안보", "군사", "중국", "미국", "러시아", "NATO", "트럼프", "시진핑"]):
            domains.append("geopolitical")
        if any(kw in context for kw in ["경제", "금융", "주식", "투자", "GDP", "인플레이션", "금리", "달러", "환율", "무역", "관세"]):
            domains.append("economic")
        if any(kw in context for kw in ["기술", "AI", "반도체", "테크", "혁신", "디지털", "양자", "바이오", "로봇", "자율주행"]):
            domains.append("technology")
        if any(kw in context for kw in ["사업", "비즈니스", "스타트업", "마케팅", "전략", "경쟁", "시장", "매출", "고객"]):
            domains.append("business")
        if any(kw in context for kw in ["특허", "발명", "아이디어", "혁신", "신기술", "청구항"]):
            domains.append("invention")
        if any(kw in context for kw in ["소설", "스토리", "시나리오", "웹툰", "드라마", "영화", "캐릭터", "플롯", "장르"]):
            domains.append("story")
        if any(kw in context for kw in ["요리", "레시피", "음식", "조리", "맛", "재료", "식당", "셰프"]):
            domains.append("recipe")
        return domains if domains else ["general"]
    
    def generate_combinations(self, context: str, max_combinations: int = 30) -> List[Dict]:
        combinations = []
        question_type = self._detect_question_type(context)
        domains = self._detect_domains(context)
        if "geopolitical" in domains and "geopolitical_analysis" in self.creat_data:
            geo = self.creat_data["geopolitical_analysis"]
            for dim in geo.get("power_dimensions", [])[:4]:
                combo = {
                    "category": "지정학분석", "dimension": dim["type"],
                    "idea": f"'{context}'를 {dim['type']} 관점에서 분석",
                    "score": random.uniform(0.75, 0.95)
                }
                combinations.append(combo)
        if "technology" in domains and "technology_trends" in self.creat_data:
            tech = self.creat_data["technology_trends"]
            for frontier in tech.get("current_frontiers", []):
                combo = {
                    "category": "기술프론티어", "domain": frontier["domain"],
                    "idea": f"'{frontier['domain']}' 기술 관점에서 분석",
                    "score": random.uniform(0.7, 0.9)
                }
                combinations.append(combo)
        if "business" in domains and "business_strategy" in self.creat_data:
            biz = self.creat_data["business_strategy"]
            for framework in biz.get("frameworks", [])[:4]:
                combo = {
                    "category": "비즈니스프레임워크", "framework": framework["name"],
                    "idea": f"'{framework['name']}' 프레임워크 적용",
                    "score": random.uniform(0.65, 0.85)
                }
                combinations.append(combo)
        combinations.sort(key=lambda x: x["score"], reverse=True)
        return combinations[:max_combinations]
    
    def cross_combine(self, ideas: List[Dict], max_cross: int = 10) -> List[Dict]:
        cross_ideas = []
        if len(ideas) < 2:
            return cross_ideas
        top_ideas = ideas[:min(6, len(ideas))]
        for i, idea1 in enumerate(top_ideas):
            for idea2 in top_ideas[i+1:]:
                if idea1["category"] != idea2["category"]:
                    cross = {
                        "category": "교차조합",
                        "sources": [idea1["category"], idea2["category"]],
                        "idea": f"[{idea1['category']} + {idea2['category']}] 융합 아이디어",
                        "score": (idea1["score"] + idea2["score"]) / 2 * 1.15
                    }
                    cross_ideas.append(cross)
                    if len(cross_ideas) >= max_cross:
                        break
            if len(cross_ideas) >= max_cross:
                break
        return cross_ideas
    
    def format_for_prompt(self, combinations: List[Dict]) -> str:
        lines = ["[브루트포스 창발 매트릭스]", ""]
        by_category = {}
        for combo in combinations[:20]:
            cat = combo["category"]
            if cat not in by_category:
                by_category[cat] = []
            by_category[cat].append(combo)
        idx = 1
        for cat, combos in by_category.items():
            lines.append(f"━━ {cat} ━━")
            for combo in combos[:4]:
                score_bar = "●" * int(combo["score"] * 5) + "○" * (5 - int(combo["score"] * 5))
                lines.append(f"{idx}. {score_bar} {combo['idea'][:80]}")
                idx += 1
            lines.append("")
        return "\n".join(lines)

# ==================== LLMClient ====================

class LLMClient:
    MODEL_LEVELS = {
        "LOW": {"name": "⚡ gpt-oss-120b", "provider": "groq", "model": "openai/gpt-oss-120b", "description": "빠른 응답, 기본 분석", "disabled": False},
        "MIDDLE": {"name": "🔥 GLM-4.7", "provider": "fireworks", "model": "accounts/fireworks/models/glm-4p7", "description": "균형잡힌 성능, 심층 분석", "disabled": False},
        "HIGH": {"name": "🧠 Claude 4.5 Sonnet", "provider": "replicate", "model": "anthropic/claude-4.5-sonnet", "description": "최고 품질, 심층 추론", "disabled": False}
    }
    
    def __init__(self, model_level: str = "LOW"):
        self.model_level = model_level
        self._setup_client()
    
    def _setup_client(self):
        level_config = self.MODEL_LEVELS.get(self.model_level, self.MODEL_LEVELS["LOW"])
        self.provider = level_config["provider"]
        self.model = level_config["model"]
        self.client = None
        if self.provider == "groq":
            self.api_key = os.getenv("GROQ_API_KEY", "")
            if HAS_GROQ and self.api_key:
                try:
                    self.client = Groq(api_key=self.api_key)
                except Exception as e:
                    print(f"⚠️ Groq 클라이언트 초기화 실패: {e}")
        elif self.provider == "fireworks":
            self.api_key = os.getenv("FIREWORKS_API_KEY", "")
            self.api_url = "https://api.fireworks.ai/inference/v1/chat/completions"
        elif self.provider == "replicate":
            self.api_key = os.getenv("REPLICATE_API_TOKEN", "")
            if self.api_key:
                os.environ["REPLICATE_API_TOKEN"] = self.api_key
    
    def set_model_level(self, level: str) -> bool:
        if level in self.MODEL_LEVELS and not self.MODEL_LEVELS[level].get("disabled"):
            self.model_level = level
            self._setup_client()
            return True
        return False
    
    def get_model_info(self) -> str:
        config = self.MODEL_LEVELS.get(self.model_level, {})
        return f"{config.get('name', 'Unknown')}"
    
    def chat(self, system_prompt: str, user_prompt: str, stream: bool = True) -> Generator[str, None, None]:
        if self.provider == "groq":
            yield from self._chat_groq(system_prompt, user_prompt, stream)
        elif self.provider == "fireworks":
            yield from self._chat_fireworks(system_prompt, user_prompt, stream)
        elif self.provider == "replicate":
            yield from self._chat_replicate(system_prompt, user_prompt, stream)
        else:
            yield from self._chat_groq(system_prompt, user_prompt, stream)
    
    def _chat_groq(self, system_prompt: str, user_prompt: str, stream: bool = True) -> Generator[str, None, None]:
        if not HAS_GROQ:
            yield "[ERROR] groq 라이브러리 미설치"
            return
        if not self.api_key:
            yield "[ERROR] GROQ_API_KEY 미설정"
            return
        if not self.client:
            try:
                self.client = Groq(api_key=self.api_key)
            except Exception as e:
                yield f"[ERROR] Groq 클라이언트 생성 실패: {e}"
                return
        try:
            messages = [
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt}
            ]
            if stream:
                completion = self.client.chat.completions.create(
                    model=self.model, messages=messages,
                    temperature=0.7, max_completion_tokens=8192, top_p=1, stream=True
                )
                for chunk in completion:
                    try:
                        if chunk.choices and len(chunk.choices) > 0:
                            delta = chunk.choices[0].delta
                            if delta and delta.content:
                                yield delta.content
                    except:
                        continue
            else:
                completion = self.client.chat.completions.create(
                    model=self.model, messages=messages,
                    temperature=0.7, max_completion_tokens=8192, top_p=1, stream=False
                )
                if completion.choices and len(completion.choices) > 0:
                    yield completion.choices[0].message.content or ""
        except Exception as e:
            yield f"\n[ERROR] Groq API: {str(e)[:150]}"
    
    def _chat_fireworks(self, system_prompt: str, user_prompt: str, stream: bool = True) -> Generator[str, None, None]:
        if not self.api_key:
            yield "[ERROR] FIREWORKS_API_KEY 미설정"
            return
        try:
            headers = {
                "Accept": "application/json",
                "Content-Type": "application/json",
                "Authorization": f"Bearer {self.api_key}"
            }
            messages = [
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt}
            ]
            payload = {
                "model": self.model, "max_tokens": 8192, "top_p": 1, "top_k": 40,
                "presence_penalty": 0, "frequency_penalty": 0, "temperature": 0.7,
                "messages": messages, "stream": stream
            }
            if stream:
                response = requests.post(self.api_url, headers=headers, json=payload, stream=True, timeout=120)
                for line in response.iter_lines():
                    if line:
                        line_text = line.decode('utf-8')
                        if line_text.startswith("data: "):
                            data_str = line_text[6:]
                            if data_str.strip() == "[DONE]":
                                break
                            try:
                                data = json.loads(data_str)
                                if data.get("choices") and data["choices"][0].get("delta", {}).get("content"):
                                    yield data["choices"][0]["delta"]["content"]
                            except:
                                continue
            else:
                response = requests.post(self.api_url, headers=headers, json=payload, timeout=120)
                result = response.json()
                if result.get("choices"):
                    yield result["choices"][0].get("message", {}).get("content", "")
        except Exception as e:
            yield f"\n[ERROR] Fireworks API: {str(e)[:150]}"
    
    def _chat_replicate(self, system_prompt: str, user_prompt: str, stream: bool = True) -> Generator[str, None, None]:
        if not self.api_key:
            yield "[ERROR] REPLICATE_API_TOKEN 미설정"
            return
        try:
            import replicate
            full_prompt = f"""<system>
{system_prompt}
</system>
<user>
{user_prompt}
</user>"""
            input_data = {"prompt": full_prompt, "max_tokens": 8192}
            if stream:
                for event in replicate.stream(self.model, input=input_data):
                    yield str(event)
            else:
                output = replicate.run(self.model, input=input_data)
                if hasattr(output, '__iter__') and not isinstance(output, str):
                    yield "".join(str(chunk) for chunk in output)
                else:
                    yield str(output)
        except ImportError:
            yield "[ERROR] replicate 라이브러리 미설치. pip install replicate"
        except Exception as e:
            yield f"\n[ERROR] Replicate API: {str(e)[:150]}"
    
    def chat_sync(self, system_prompt: str, user_prompt: str) -> str:
        return "".join(self.chat(system_prompt, user_prompt, stream=False))

# ==================== Dashboard Charts ====================

def create_dashboard_charts(data: Dict) -> Tuple:
    """Create intuitive AGI intelligence evolution dashboard charts"""
    import matplotlib.pyplot as plt
    import matplotlib
    matplotlib.use('Agg')
    plt.rcParams['font.family'] = ['DejaVu Sans', 'sans-serif']
    plt.rcParams['axes.unicode_minus'] = False
    
    charts = {}
    
    # Chart 1: Intelligence Evolution Timeline
    fig1, ax1 = plt.subplots(figsize=(10, 5), facecolor='#FEF9C3')
    ax1.set_facecolor('#FEF9C3')
    timeline = data.get("timeline", {})
    dates = timeline.get("dates", [])
    if dates and len(dates) > 0:
        knowledge_counts = timeline.get("knowledge_count", [])
        memory_counts = timeline.get("memory_count", [])
        iq_scores = []
        base_iq = 20
        for i, (k, m) in enumerate(zip(knowledge_counts, memory_counts)):
            daily_growth = (k * 2 + m * 0.5)
            base_iq += daily_growth
            iq_scores.append(min(100, base_iq))
        line1 = ax1.plot(dates, iq_scores, marker='o', linewidth=3, 
                         color='#FACC15', markersize=8, label='Total IQ', zorder=3)
        ax1.fill_between(dates, iq_scores, alpha=0.3, color='#FACC15')
        if len(iq_scores) > 1:
            growth = iq_scores[-1] - iq_scores[0]
            growth_pct = (growth / iq_scores[0] * 100) if iq_scores[0] > 0 else 0
            ax1.text(0.02, 0.98, f'📈 Growth: +{growth:.1f} ({growth_pct:+.1f}%)', 
                    transform=ax1.transAxes, fontsize=11, fontweight='bold',
                    va='top', bbox=dict(boxstyle='round', facecolor='#10B981', alpha=0.8, edgecolor='#1F2937'))
        ax1.set_title('🧠 Intelligence Evolution Timeline', fontsize=14, fontweight='bold', pad=15)
        ax1.set_ylabel('IQ Score', fontsize=11, fontweight='bold')
        ax1.set_xlabel('Date', fontsize=11, fontweight='bold')
        ax1.grid(True, alpha=0.2, linestyle='--')
        ax1.spines['top'].set_visible(False)
        ax1.spines['right'].set_visible(False)
        ax1.legend(loc='lower right', fontsize=10)
        ax1.set_ylim(0, 105)
    else:
        ax1.text(0.5, 0.5, 'No Timeline Data', ha='center', va='center', fontsize=14, fontweight='bold')
    plt.tight_layout()
    charts["intelligence_timeline"] = fig1
    
    # Chart 2: Quality Distribution
    fig2, ax2 = plt.subplots(figsize=(6, 4), facecolor='#FEF9C3')
    ax2.set_facecolor('#FEF9C3')
    quality_dist = data.get("quality_distribution", {})
    if quality_dist:
        bins = ['0-20%', '20-40%', '40-60%', '60-80%', '80-100%']
        counts = [
            quality_dist.get('very_low', 0),
            quality_dist.get('low', 0),
            quality_dist.get('medium', 0),
            quality_dist.get('high', 0),
            quality_dist.get('very_high', 0)
        ]
        colors = ['#EF4444', '#F59E0B', '#FACC15', '#10B981', '#059669']
        bars = ax2.bar(bins, counts, color=colors, edgecolor='#1F2937', linewidth=2)
        for bar in bars:
            height = bar.get_height()
            if height > 0:
                ax2.text(bar.get_x() + bar.get_width()/2, height + 0.5, 
                        int(height), ha='center', fontsize=10, fontweight='bold')
        ax2.set_title('📊 Knowledge Quality Distribution', fontsize=13, fontweight='bold')
        ax2.set_ylabel('Count', fontsize=10, fontweight='bold')
        ax2.set_xlabel('Quality Range', fontsize=10, fontweight='bold')
        ax2.spines['top'].set_visible(False)
        ax2.spines['right'].set_visible(False)
    else:
        ax2.text(0.5, 0.5, 'No Quality Data', ha='center', va='center', fontsize=12, fontweight='bold')
    plt.tight_layout()
    charts["quality_dist"] = fig2
    
    # Chart 3: Element Performance Heatmap
    fig3, ax3 = plt.subplots(figsize=(7, 4), facecolor='#FEF9C3')
    ax3.set_facecolor('#FEF9C3')
    elements = data.get("elements", {})
    elem_performance = data.get("element_performance", {})
    elem_names = ['土\nEarth', '金\nMetal', '水\nWater', '木\nWood', '火\nFire']
    metrics = ['Activity', 'Quality', 'Speed']
    matrix = []
    for elem in ['土', '金', '水', '木', '火']:
        perf = elem_performance.get(elem, {})
        row = [
            min(100, elements.get(elem, 0) * 2),
            perf.get('avg_quality', 50),
            perf.get('avg_speed', 50)
        ]
        matrix.append(row)
    import numpy as np
    matrix = np.array(matrix)
    im = ax3.imshow(matrix.T, cmap='RdYlGn', aspect='auto', vmin=0, vmax=100)
    ax3.set_xticks(np.arange(len(elem_names)))
    ax3.set_yticks(np.arange(len(metrics)))
    ax3.set_xticklabels(elem_names, fontsize=10, fontweight='bold')
    ax3.set_yticklabels(metrics, fontsize=10, fontweight='bold')
    for i in range(len(metrics)):
        for j in range(len(elem_names)):
            text = ax3.text(j, i, f'{matrix[j, i]:.0f}',
                          ha="center", va="center", color="black", fontsize=9, fontweight='bold')
    ax3.set_title('🔥 Five Elements Performance Matrix', fontsize=13, fontweight='bold', pad=10)
    cbar = plt.colorbar(im, ax=ax3, orientation='horizontal', pad=0.1, aspect=30)
    cbar.set_label('Performance Score', fontsize=9, fontweight='bold')
    plt.tight_layout()
    charts["element_heatmap"] = fig3
    
    # Chart 4: Learning Efficiency
    fig4, ax4 = plt.subplots(figsize=(6, 4), facecolor='#FEF9C3')
    ax4.set_facecolor('#FEF9C3')
    learning = data.get("learning", {})
    success_rate = learning.get("success_rate", 0)
    categories = ['Success\nRate', 'Pattern\nConfidence', 'Knowledge\nReuse']
    scores = [
        success_rate,
        learning.get("avg_confidence", 0) * 100,
        data.get("knowledge", {}).get("avg_quality", 0) * 100
    ]
    x = np.arange(len(categories))
    bars = ax4.bar(x, scores, color=['#10B981', '#3B82F6', '#8B5CF6'], 
                   edgecolor='#1F2937', linewidth=2, width=0.6)
    for bar, score in zip(bars, scores):
        ax4.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 2,
                f'{score:.1f}%', ha='center', fontsize=11, fontweight='bold')
    ax4.set_ylim(0, 110)
    ax4.set_xticks(x)
    ax4.set_xticklabels(categories, fontsize=10, fontweight='bold')
    ax4.set_ylabel('Score (%)', fontsize=10, fontweight='bold')
    ax4.set_title('⚡ Learning Efficiency Metrics', fontsize=13, fontweight='bold')
    ax4.spines['top'].set_visible(False)
    ax4.spines['right'].set_visible(False)
    ax4.axhline(y=70, color='#FACC15', linestyle='--', linewidth=1.5, alpha=0.5, label='Target: 70%')
    ax4.legend(loc='upper right', fontsize=8)
    plt.tight_layout()
    charts["learning_efficiency"] = fig4
    
    # Chart 5: Memory Flow
    fig5, ax5 = plt.subplots(figsize=(6, 4), facecolor='#FEF9C3')
    ax5.set_facecolor('#FEF9C3')
    memory_data = data.get("memory", {})
    short = memory_data.get("short_term", 0)
    mid = memory_data.get("mid_term", 0)
    long = memory_data.get("long_term", 0)
    total = short + mid + long
    if total > 0:
        stages = ['Input', 'Short→Mid', 'Mid→Long', 'Long-term\nStorage']
        short_flow = [total, short, 0, 0]
        mid_flow = [0, mid * 0.6, mid, 0]
        long_flow = [0, 0, long * 0.3, long]
        ax5.barh(stages, short_flow, color='#EF4444', edgecolor='#1F2937', linewidth=1, label='Short-term')
        ax5.barh(stages, mid_flow, left=short_flow, color='#3B82F6', edgecolor='#1F2937', linewidth=1, label='Mid-term')
        ax5.barh(stages, long_flow, left=[s+m for s,m in zip(short_flow, mid_flow)], 
                color='#10B981', edgecolor='#1F2937', linewidth=1, label='Long-term')
        ax5.set_title('💾 Memory Promotion Flow', fontsize=13, fontweight='bold')
        ax5.set_xlabel('Memory Count', fontsize=10, fontweight='bold')
        ax5.legend(loc='lower right', fontsize=9)
        ax5.spines['top'].set_visible(False)
        ax5.spines['right'].set_visible(False)
    else:
        ax5.text(0.5, 0.5, 'No Memory Data', ha='center', va='center', fontsize=12, fontweight='bold')
    plt.tight_layout()
    charts["memory_flow"] = fig5
    
    # Chart 6: Session Success Rate
    fig6, ax6 = plt.subplots(figsize=(5, 4), facecolor='#FEF9C3')
    ax6.set_facecolor('#FEF9C3')
    sessions = data.get("sessions", {})
    total_sess = sessions.get("total", 0)
    completed = sessions.get("completed", 0)
    avg_satisfaction = sessions.get("avg_satisfaction", 0)
    if total_sess > 0:
        completion_rate = (completed / total_sess) * 100
        sizes = [completion_rate, 100 - completion_rate]
        colors = ['#10B981', '#E5E7EB']
        wedges, texts, autotexts = ax6.pie(sizes, colors=colors, startangle=90, 
                                            wedgeprops=dict(width=0.4, edgecolor='#1F2937', linewidth=2),
                                            autopct='%1.1f%%', textprops={'fontweight': 'bold', 'fontsize': 11})
        ax6.text(0, 0, f'{completed}/{total_sess}\nCompleted', 
                ha='center', va='center', fontsize=13, fontweight='bold', color='#1F2937')
        ax6.set_title(f'✅ Session Success Rate\n(Avg Satisfaction: {avg_satisfaction:.2f})', 
                     fontsize=12, fontweight='bold', pad=10)
    else:
        ax6.text(0.5, 0.5, 'No Sessions', ha='center', va='center', fontsize=12, fontweight='bold')
    plt.tight_layout()
    charts["session_success"] = fig6
    
    return (charts.get("intelligence_timeline"), 
            charts.get("quality_dist"),
            charts.get("element_heatmap"), 
            charts.get("learning_efficiency"),
            charts.get("memory_flow"), 
            charts.get("session_success"))