from flask import Flask, request, Response
import os
from dotenv import load_dotenv
from rag2_0.intent_recognition import IntentRecognizer
import json
import time
import threading
import datetime
import logging

# 加载环境变量
load_dotenv()

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.StreamHandler()
    ]
)
logging.getLogger('httpx').setLevel(logging.WARNING)
logging.getLogger('openai').setLevel(logging.WARNING)

logger = logging.getLogger(__name__)

app = Flask(__name__)

# 创建线程锁，用于保护共享资源
recognizer_lock = threading.Lock()

# 使用单例模式创建意图识别器
class RecognizerSingleton:
    _instance = None
    _lock = threading.Lock()
    
    @classmethod
    def get_instance(cls):
        if cls._instance is None:
            with cls._lock:
                if cls._instance is None:
                    api_key = os.getenv("OPENAI_API_KEY")
                    base_url = os.getenv("OPENAI_API_BASE")
                    model_name = os.getenv("LLM_MODEL_NAME", "gpt-3.5-turbo")
                    cls._instance = IntentRecognizer(api_key=api_key, base_url=base_url, model_name=model_name)
        return cls._instance

@app.route('/intent_recognize', methods=['POST'])
def intent_recognize():
    try:
        data = request.get_json(force=True)
        query = data.get('query')
        conversation_context = data.get('conversation_context', "")
        chat_history = data.get('chat_history', None)
        previous_slots = data.get('previous_slots', None)
        if not query:
            return Response(json.dumps({"error": "缺少query参数"}, ensure_ascii=False), content_type='application/json; charset=utf-8', status=400)
        
        start_time = time.time()
        
        # 获取单例实例并使用线程锁保护关键操作
        recognizer = RecognizerSingleton.get_instance()
        result = recognizer.process_query(query, conversation_context, chat_history, previous_slots)
            
        end_time = time.time()
        current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S %z")
        logger.info(f"[{os.getpid()}] 意图识别耗时: {end_time - start_time:.2f}秒")

        # 提取分类信息
        classification = result["classification"]
        
        # 提取关键词信息
        keywords = result["keywords"]
        keywords_str = ""
        if keywords and keywords.get("terms"):
            term_details = []
            for term in keywords["terms"]:
                term_info = {
                    "名称": term["name"],
                    # "同义词": ";".join(term["synonymous"]) if term["synonymous"] else [],
                    # "描述": term["description"]
                }
                term_details.append(term_info)
            keywords_str = term_details
        
        # 提取槽位填充信息
        slot_filling = result.get("slot_filling", {})
        
        response_result = {
            "source_query": query,
            "source_query_keys": result["query_keys"],
            "vertical_classification": classification["vertical_classification"],
            "sub_classification": classification["sub_classification"],
            "rewrite_query": result["rewrite"]["rewrite"],
            "keywords": keywords_str,
            "has_slot_filling": len(slot_filling)!=0,
            "slot_filling": {
                "is_complete": slot_filling.get("is_complete", False),
                "missing_slots": slot_filling.get("missing_slots", {}),
                "filled_data": slot_filling.get("filled_data", {})
            }
        }
        return Response(json.dumps(response_result, ensure_ascii=False), content_type='application/json; charset=utf-8')
    except Exception as e:
        print(f"意图识别出错: {str(e)}")
        return Response(json.dumps({"error": str(e)}, ensure_ascii=False), content_type='application/json; charset=utf-8', status=500)

if __name__ == "__main__":
    # 开发环境使用Flask内置服务器
    # 生产环境使用gunicorn支持高并发 poetry run gunicorn -w 10 -k gevent -b 0.0.0.0:8001 rag2_0.dify.intent_recognition_api:app
    app.run(host="0.0.0.0", port=8001, threaded=True)