59 lines
2.2 KiB
Python
59 lines
2.2 KiB
Python
import pandas as pd
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import os
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from langchain_community.llms import QianfanLLMEndpoint,Tongyi
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from langchain.chains import LLMChain
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from langchain_core.prompts import ChatPromptTemplate
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from langchain.prompts import PromptTemplate
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import re
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api = "xxxxxx"
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sk = "xxxxxx"
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llm = QianfanLLMEndpoint(model="ERNIE-4.0-8K",qianfan_ak=api,qianfan_sk=sk)
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def classify(text):
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prompt = PromptTemplate(
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input_variables=["prompt"],
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template='''我国国民经济行业共有20个门类，如下：
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农、林、牧、渔业；采矿业；制造业；电力、热力、燃气及水生产和供应业；建筑业；批发和零售业；交通运输、仓储和邮政业；
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住宿和餐饮业；信息传输、软件和信息技术服务业；金融业；房地产业；租赁和商务服务业；科学研究和技术服务业；
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水利、环境和公共设施管理业；居民服务、修理和其他服务业；教育；卫生和社会工作；文化、体育和娱乐业；公共管理、社会保障和社会组织；国际组织。
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输出下面内容属于哪个行业类别：{prompt}。
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答案用#表示，输出格式如下：
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#科学研究和技术服务业#
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'''
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)
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chain = LLMChain(llm = llm,prompt=prompt)
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response = chain.run(
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{"prompt":text}
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)
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# print(response)
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return response
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def normalize(text):
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clean_text = re.sub(r'[\r\n]+', '', text)
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pattern = r'(?<=#)(.*?)(?=#)'
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matches = re.findall(pattern, clean_text)
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return matches[-1]
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if __name__ == "__main__":
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data = pd.read_excel("/home/zhangxj/WorkFile/LCA-GPT/QA/QA.xlsx")
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class_list = []
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for idx,item in data.iterrows():
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ques = item['question']
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ans = item['answer']
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query = "问题："+ques+"\n答案："+ans
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res = classify(query)
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res = normalize(res)
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print(res)
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class_list.append(res)
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question_list = data['question'].tolist()
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ans_list = data['answer'].tolist()
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df = pd.DataFrame({'question': question_list, 'answer': ans_list, '类别': class_list})
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df.to_csv("/home/zhangxj/WorkFile/LCA-GPT/QA/classify.csv",index = False) |