# glm
from zhipuai import ZhipuAI
import re
import pandas as pd

'''
经过换算，需要135000左右的token
'''
# def zhipuQA(prompt):
#     client = ZhipuAI(api_key="434790cf952335f18b6347e7b6de9777.V50p55zfk8Ye4ojV") # 请填写您自己的APIKey
#     response = client.chat.completions.create(
#         model = "glm-4",
#         messages = [
#             {"role":"user","content":prompt}
#         ],
#     )
#     content = response.choices[0].message.content
#     print('\n',content)
#     res = re.sub(r'\s+', '', content) #处理空格
#     return res

# 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。"

# question  = []
# answers = []
# with open("/home/zhangxj/WorkFile/LCA-GPT/QA/filters/question.txt","r",encoding="utf-8") as file:
#     for line in file.readlines():
#         question.append(line.strip())

# for ques in question:
#     message = (instruction+ques)
#     ans = zhipuQA(message)
#     answers.append(ans)

# data = {"ans":answers}
# df = pd.DataFrame(data)
# df.to_csv("/home/zhangxj/WorkFile/LCA-GPT/QA/eval/GLM.csv",index=False)

df = pd.read_excel("/home/zhangxj/WorkFile/LCA-GPT/QA/eval/GLMoutput.xlsx")
answers = df['content'].values.tolist()

with open("/home/zhangxj/WorkFile/LCA-GPT/QA/eval/GLMpred.txt","w",encoding="utf-8") as file:
    for ans in answers:
        line = re.sub(r'\s+', '', ans)
        file.write(line+"\n")