# from paddlenlp import Taskflow
#checkPlaceNameServer
# tagTask1 = Taskflow("ner",device_id=2,precision='fp16')##checkPlaceName
# from flask import Flask, request, jsonify
from fastapi import FastAPI, Request
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
import uvicorn
from fastapi.responses import JSONResponse
from pydantic import BaseModel
app = FastAPI()

import sys 
sys.path.append("..") 
from ner_model import NERModel
# model = NERModel("bert", "shibing624/bert4ner-base-chinese")
model = NERModel("bertspan", "shibing624/bertspan4ner-base-chinese",cuda_device=5)
class RequestData(BaseModel):
    data: dict
@app.post("/taskflow/checkPlaceNameServer")
async def process_request(request: RequestData):
    global model
    # 提取文本数据
    text_data = request.data.get('text')
    # 处理文本数据，例如检查错误等
    # 这里您可以添加实际的逻辑来检查文档错误
    predictions, raw_outputs, entities = model.predict(text_data)
    
    # 返回响应
    return JSONResponse(content={"status": "success", "data": entities}, status_code=200)
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8191)
# # 创建一个锁对象
# import threading
# app = Flask(__name__)
# lock = threading.Lock()
# #多线程但是每次只处理一个请求，多余的请求需要排队
# @app.route('/taskflow/checkPlaceNameServer', methods=['POST'])
# def process_request():
#     with lock:
#         data = request.get_json()
#         # print("data",data)
#         # 提取文本数据
#         text_data = data.get('data', {}).get('text')
#         # 处理文本数据，例如检查错误等
#         # 这里您可以添加实际的逻辑来检查文档错误
#         predictions, raw_outputs, entities =model.predict(text_data)
#         # 示例：简单打印接收到的文本
#         # # 返回响应
#         return jsonify({"status": "success", "data": entities}), 200

# if __name__ == '__main__':
#     app.run(threaded=True,port=8191)