49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
# glm
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from zhipuai import ZhipuAI
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import re
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import pandas as pd
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'''
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经过换算，需要135000左右的token
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'''
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# def zhipuQA(prompt):
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# client = ZhipuAI(api_key="434790cf952335f18b6347e7b6de9777.V50p55zfk8Ye4ojV") # 请填写您自己的APIKey
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# response = client.chat.completions.create(
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# model = "glm-4",
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# messages = [
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# {"role":"user","content":prompt}
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# ],
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# )
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# content = response.choices[0].message.content
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# print('\n',content)
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# res = re.sub(r'\s+', '', content) #处理空格
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# return res
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# instruction = "你是生命周期领域富有经验和知识的专家。文件的每一行都是一个问题，根据你所掌握的知识回答问题；不要列出几点来回答，不需要换行，只需要用1句话回答问题。"
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# question = []
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# answers = []
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# with open("/home/zhangxj/WorkFile/LCA-GPT/QA/filters/question.txt","r",encoding="utf-8") as file:
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# for line in file.readlines():
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# question.append(line.strip())
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# for ques in question:
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# message = (instruction+ques)
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# ans = zhipuQA(message)
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# answers.append(ans)
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# data = {"ans":answers}
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# df = pd.DataFrame(data)
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# df.to_csv("/home/zhangxj/WorkFile/LCA-GPT/QA/eval/GLM.csv",index=False)
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df = pd.read_excel("/home/zhangxj/WorkFile/LCA-GPT/QA/eval/GLMoutput.xlsx")
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answers = df['content'].values.tolist()
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with open("/home/zhangxj/WorkFile/LCA-GPT/QA/eval/GLMpred.txt","w",encoding="utf-8") as file:
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for ans in answers:
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line = re.sub(r'\s+', '', ans)
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file.write(line+"\n")
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