54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
import os
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import re
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import gradio as gr
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from PIL import Image
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from pprint import pprint
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from qwen_agent.agents import Assistant
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import sys
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os.chdir(sys.path[0])
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os.environ['TMPDIR'] = "/home/zhangxj/WorkFile/LCA-GPT/LCARAG/DataAnalysis/tmp"
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llm_cfg = {
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'model': 'qwen1.5-72b-chat',
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'model_server': 'dashscope',
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'api_key': "sk-c5f441f863f44094b0ddb96c831b5002",
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}
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system_instruction = '''你是一位专注在生命周期领域做数据分析的助手，在数据分析之后，
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如果有可视化要求，请使用 `plt.show()` 显示图像,并将图像进行保存。
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最后，请对数据分析结果结合生命周期评价领域知识进行解释。'''
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tools = ['code_interpreter'] # `code_interpreter` is a built-in tool for executing code.
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messages = [] # This stores the chat history.
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files = ["/home/zhangxj/WorkFile/LCA-GPT/DataAnalysis/tmp/2021北京.csv","/home/zhangxj/WorkFile/LCA-GPT/DataAnalysis/报告案例1.md"]
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user_input = '''首先分析上传的2021北京.csv的碳排放数据，并处理分析数据和可视化分析，
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请按照报告案例1作为模板，用你掌握的信息进行填充，并且将可视化得到的图像结果插入到报告中并加以分析，以markdown格式输出填充数据信息之后的报告。'''
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messages.append({'role': 'user', 'content': user_input})
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bot = Assistant(llm=llm_cfg,
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system_message=system_instruction,
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function_list=tools,
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files=files)
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# Get response from bot
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response = []
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for response in bot.run(messages=messages):
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continue
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pprint(response)
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messages.extend(response)
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# Convert bot response to string
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res_str = ""
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for res in response:
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res_str += res['content']
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try:
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with open("./result.md", "w", encoding="utf-8") as f:
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f.write(res_str)
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except IOError as e:
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print(f"An error occurred: {e}")
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print(res_str)
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