Delete app.py

app.py

@@ -1,242 +0,0 @@
-# app.py
-from flask import Flask, render_template, jsonify, request
-from flask_socketio import SocketIO
-import threading
-import os
-import sqlite3
-import gc
-import time
-import re
-
-# --- 외부 모듈 임포트 ---
-import reg_embedding_system
-import leximind_prompts
-
-# --- Together AI SDK ---
-from together import Together
-
-# --- 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 ---
-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 클라이언트 (Hugging Face Secrets에서 API 키 가져오기) ---
-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)
-
-# --- RAG 로딩 ---
-def load_rag_objects():
-    global region_rag_objects
-    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..."})
-            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)
-            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 ---
-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)
-
-    response = client.chat.completions.create(
-        model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
-        messages=[{"role": "user", "content": prompt}],
-        max_tokens=1024,
-        temperature=0.7
-    )
-    return response.choices[0].message.content
-
-# --- Together AI 번역 ---
-def Gemma3_AI_Translate(query_txt):
-    query_txt = str(query_txt)
-    prompt = lexi_prompts.use_prompt(lexi_prompts.query_translator, query_txt=query_txt)
-
-    response = client.chat.completions.create(
-        model="meta-llama/Llama-3.2-3B-Instruct-Turbo",
-        messages=[{"role": "user", "content": prompt}],
-        max_tokens=512,
-        temperature=0.3
-    )
-    return response.choices[0].message.content
-
-# --- 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__':
-    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)