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52 lines
2.0 KiB
52 lines
2.0 KiB
# from paddlenlp import Taskflow
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#checkPlaceNameServer
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# tagTask1 = Taskflow("ner",device_id=2,precision='fp16')##checkPlaceName
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# from flask import Flask, request, jsonify
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from fastapi import FastAPI, Request
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from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
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import uvicorn
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from fastapi.responses import JSONResponse
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from pydantic import BaseModel
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app = FastAPI()
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import sys
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sys.path.append("..")
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from ner_model import NERModel
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# model = NERModel("bert", "shibing624/bert4ner-base-chinese")
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model = NERModel("bertspan", "shibing624/bertspan4ner-base-chinese",cuda_device=5)
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class RequestData(BaseModel):
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data: dict
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@app.post("/taskflow/checkPlaceNameServer")
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async def process_request(request: RequestData):
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global model
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# 提取文本数据
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text_data = request.data.get('text')
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# 处理文本数据，例如检查错误等
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# 这里您可以添加实际的逻辑来检查文档错误
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predictions, raw_outputs, entities = model.predict(text_data)
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# 返回响应
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return JSONResponse(content={"status": "success", "data": entities}, status_code=200)
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=8191)
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# # 创建一个锁对象
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# import threading
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# app = Flask(__name__)
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# lock = threading.Lock()
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# #多线程但是每次只处理一个请求，多余的请求需要排队
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# @app.route('/taskflow/checkPlaceNameServer', methods=['POST'])
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# def process_request():
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# with lock:
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# data = request.get_json()
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# # print("data",data)
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# # 提取文本数据
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# text_data = data.get('data', {}).get('text')
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# # 处理文本数据，例如检查错误等
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# # 这里您可以添加实际的逻辑来检查文档错误
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# predictions, raw_outputs, entities =model.predict(text_data)
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# # 示例：简单打印接收到的文本
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# # # 返回响应
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# return jsonify({"status": "success", "data": entities}), 200
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# if __name__ == '__main__':
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# app.run(threaded=True,port=8191)
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