# app.py
import os
os.environ["PYDANTIC_V1_STYLE"] = "1"
os.environ["PYDANTIC_SKIP_VALIDATING_CORE_SCHEMAS"] = "1"
from flask import Flask, render_template, jsonify, request
from flask_socketio import SocketIO
import threading
import sqlite3
import gc
import time
import re
import traceback # <-- 에러 상세 출력을 위해 임포트
import requests # <-- Pydantic v1 환경을 위해 Together SDK 대신 requests 사용

# --- 외부 모듈 임포트 ---
import reg_embedding_system
import leximind_prompts

# --- Together AI SDK (제거됨) ---
# from together import Together

# --- eventlet monkey patch (Gunicorn + SocketIO 필수!) ---
import eventlet
eventlet.monkey_patch()

# --- Flask & SocketIO 설정 ---
app = Flask(__name__)
socketio = SocketIO(app, cors_allowed_origins="*", async_mode='eventlet')

# --- 전역 변수 ---
connected_clients = 0
search_document_number = 30
Filtered_search = False
filters = {"regulation_part": []}

# --- 경로 설정 ---
current_dir = os.path.dirname(os.path.abspath(__file__))
ResultFile_FolderAddress = os.path.join(current_dir, 'result.txt')

# --- RAG 데이터 경로 ---
# NOTE: Hugging Face Spaces에서 데이터가 /app/data에 있는지 확인해야 합니다.
region_paths = {
    "국내": "/app/data/KMVSS_RAG",
    "북미": "/app/data/FMVSS_RAG",
    "유럽": "/app/data/EUR_RAG"
}

# --- 프롬프트 ---
lexi_prompts = leximind_prompts.PromptLibrary()

# --- RAG 객체 ---
region_rag_objects = {}

# --- Together AI 설정 (SDK 대신 API 호출에 사용) ---
TOGETHER_API_KEY = os.getenv("TOGETHER_API_KEY")
if not TOGETHER_API_KEY:
    raise EnvironmentError("TOGETHER_API_KEY가 설정되지 않았습니다. Hugging Face Secrets에 추가하세요.")
# client = Together(api_key=TOGETHER_API_KEY) # <--- Together SDK 클라이언트 제거

# --- RAG 로딩 ---
def load_rag_objects():
    global region_rag_objects
    
    # 📢 [수정]: 로딩 스레드 시작 로그를 추가하여 Gunicorn 로그에서 확인 가능하게 함
    print(">>> [RAG_LOADER] RAG 로딩 스레드 시작 <<<")
    
    for region, path in region_paths.items():
        if not os.path.exists(path):
            msg = f"[{region}] 경로 없음: {path}"
            socketio.emit('message', {'message': msg})
            print(msg)
            continue

        try:
            socketio.emit('message', {'message': f"[{region}] RAG 로딩 중..."})
            
            # NOTE: reg_embedding_system 모듈이 현재 환경에 설치/존재하는지 확인해야 합니다.
            ensemble_retriever, vectorstore, sqlite_conn = reg_embedding_system.load_embedding_from_faiss(path)
            sqlite_conn.close()
            db_path = os.path.join(path, "metadata_mapping.db")
            new_conn = sqlite3.connect(db_path, check_same_thread=False)

            region_rag_objects[region] = {
                "ensemble_retriever": ensemble_retriever,
                "vectorstore": vectorstore,
                "sqlite_conn": new_conn
            }
            socketio.emit('message', {'message': f"[{region}] 로딩 완료"})
            print(f"[{region}] RAG 로딩 완료")

        except Exception as e:
            error_msg = f"[{region}] 로딩 실패: {str(e)}"
            print(error_msg)
            # 📢 [수정]: 상세한 에러 추적을 위해 traceback 추가
            traceback.print_exc() 
            socketio.emit('message', {'message': error_msg})

    socketio.emit('message', {'message': "Ready to Search"})
    print("Ready to Search")

# --- 웹 ---
@app.route('/')
def index():
    return render_template('chat.html')

# --- 메시지 ---
@app.route('/get_message', methods=['POST'])
def get_message():
    global Filtered_search, filters
    data = request.get_json()
    query = data.get('query', '').strip()
    regions = data.get('regions', [])
    selected_regulations = data.get('selectedRegulations', [])

    filters = {"regulation_part": []}
    Filtered_search = bool(selected_regulations)
    if selected_regulations:
        for reg in selected_regulations:
            title = reg.get('title', '')
            if title:
                filters["regulation_part"].append(title)

    Rag_Results = search_DB_from_multiple_regions(query, regions, region_rag_objects)
    AImessage = RegAI(query, Rag_Results, ResultFile_FolderAddress)

    return jsonify(message=AImessage)

# --- 법규 리스트 ---
@app.route('/get_reg_list', methods=['POST'])
def get_reg_list():
    data = request.get_json()
    selected_regions = data.get('regions', []) or ["국내", "북미", "유럽"]

    all_reg_list_part = []
    for region in selected_regions:
        rag = region_rag_objects.get(region)
        if not rag:
            continue
        try:
            conn = rag["sqlite_conn"]
            parts = reg_embedding_system.get_unique_metadata_values(conn, "regulation_part")
            all_reg_list_part.extend(parts)
        except Exception as e:
            print(f"[{region}] 법규 로드 실패: {e}")

    unique_parts = sorted(set(all_reg_list_part), key=reg_embedding_system.natural_sort_key)
    return jsonify(reg_list_part="\n".join(unique_parts))

# --- SocketIO ---
@socketio.on('connect')
def handle_connect():
    global connected_clients
    connected_clients += 1
    print(f"클라이언트 연결: {connected_clients}명")

@socketio.on('disconnect')
def handle_disconnect():
    global connected_clients
    connected_clients -= 1
    print(f"연결 해제: {connected_clients}명")
    if connected_clients <= 0:
        cleanup_connections()
        print("서버 종료")
        os._exit(0)

def cleanup_connections():
    for region, rag in region_rag_objects.items():
        try:
            rag["sqlite_conn"].close()
            print(f"[{region}] DB 연결 종료")
        except:
            pass

# --- Together AI 분석 (SDK -> requests 직접 호출로 변경) ---
def Gemma3_AI_analysis(query_txt, content_txt):
    content_txt = "\n".join(doc.page_content for doc in content_txt) if isinstance(content_txt, list) else str(content_txt)
    query_txt = str(query_txt)
    prompt = lexi_prompts.use_prompt(lexi_prompts.AI_system_prompt, query_txt=query_txt, content_txt=content_txt)

    headers = {
        "Authorization": f"Bearer {TOGETHER_API_KEY}",
        "Content-Type": "application/json"
    }
    payload = {
        "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo",
        "messages": [{"role": "user", "content": prompt}],
        "max_tokens": 1024,
        "temperature": 0.7
    }

    try:
        response = requests.post("https://api.together.xyz/v1/chat/completions", headers=headers, json=payload, timeout=120)
        response.raise_for_status() # HTTP 오류가 발생하면 예외 발생
        
        data = response.json()
        return data["choices"][0]["message"]["content"]
    except requests.exceptions.RequestException as e:
        print(f"Together AI 분석 API 호출 실패: {e}")
        traceback.print_exc()
        return f"AI 분석 중 오류가 발생했습니다: {e}"

# --- Together AI 번역 (SDK -> requests 직접 호출로 변경) ---
def Gemma3_AI_Translate(query_txt):
    query_txt = str(query_txt)
    prompt = lexi_prompts.use_prompt(lexi_prompts.query_translator, query_txt=query_txt)

    headers = {
        "Authorization": f"Bearer {TOGETHER_API_KEY}",
        "Content-Type": "application/json"
    }
    payload = {
        "model": "meta-llama/Llama-3.2-3B-Instruct-Turbo",
        "messages": [{"role": "user", "content": prompt}],
        "max_tokens": 512,
        "temperature": 0.3
    }
    
    try:
        response = requests.post("https://api.together.xyz/v1/chat/completions", headers=headers, json=payload, timeout=60)
        response.raise_for_status() # HTTP 오류가 발생하면 예외 발생

        data = response.json()
        return data["choices"][0]["message"]["content"]
    except requests.exceptions.RequestException as e:
        print(f"Together AI 번역 API 호출 실패: {e}")
        traceback.print_exc()
        return query_txt # 번역 실패 시 원래 쿼리를 사용 (최소한의 기능 유지)

# --- 검색 ---
def search_DB_from_multiple_regions(query, selected_regions, region_rag_objects):
    selected_regions = selected_regions or list(region_rag_objects.keys())
    query = Gemma3_AI_Translate(query)
    print(f"번역된 쿼리: {query}")

    combined_results = []
    for region in selected_regions:
        rag = region_rag_objects.get(region)
        if not rag:
            continue

        retriever = rag["ensemble_retriever"]
        vectorstore = rag["vectorstore"]
        sqlite_conn = rag["sqlite_conn"]

        if Filtered_search:
            results = reg_embedding_system.search_with_metadata_filter(
                ensemble_retriever=retriever,
                vectorstore=vectorstore,
                query=query,
                k=search_document_number,
                metadata_filter=filters,
                sqlite_conn=sqlite_conn
            )
        else:
            results = reg_embedding_system.smart_search_vectorstore(
                retriever=retriever,
                query=query,
                k=search_document_number,
                vectorstore=vectorstore,
                sqlite_conn=sqlite_conn,
                enable_detailed_search=True
            )
        print(f"[{region}] 검색: {len(results)}건")
        combined_results.extend(results)

    return combined_results

# --- 최종 AI ---
def RegAI(query, Rag_Results, ResultFile_FolderAddress):
    gc.collect()
    AI_Result = "검색 결과가 없습니다." if not Rag_Results else Gemma3_AI_analysis(query, Rag_Results)

    with open(ResultFile_FolderAddress, 'w', encoding='utf-8') as f:
        print("검색된 문서:", file=f)
        for i, doc in enumerate(Rag_Results):
            print(f"문서 {i+1}: {doc.page_content[:200]}... (메타: {doc.metadata})", file=f)
        print("\n답변:", file=f)
        print(AI_Result, file=f)

    return AI_Result

# --- 실행 ---
if __name__ == '__main__':
    # 로컬 개발용
    threading.Thread(target=load_rag_objects, daemon=True).start()
    time.sleep(2)
    socketio.emit('message', {'message': '데이터 로딩 시작...'})
    socketio.run(app, host='0.0.0.0', port=7860, debug=False)
else:
    # Gunicorn용: 워커 시작 후 로딩
    import atexit
    loading_thread = threading.Thread(target=load_rag_objects, daemon=True)
    loading_thread.start()
    atexit.register(cleanup_connections)