import os
import re
import gradio as gr
from PIL import Image
from pprint import pprint
from qwen_agent.agents import Assistant
import sys
os.chdir(sys.path[0])

os.environ['TMPDIR'] = "/home/zhangxj/WorkFile/LCA-GPT/LCARAG/DataAnalysis/tmp"

llm_cfg = {
    'model': 'qwen1.5-72b-chat',
    'model_server': 'dashscope',
    'api_key': "sk-c5f441f863f44094b0ddb96c831b5002",
}

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，
                        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。
                        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。'''

tools = ['code_interpreter']  # `code_interpreter` is a built-in tool for executing code.
messages = []  # This stores the chat history.

files = ["/home/zhangxj/WorkFile/LCA-GPT/DataAnalysis/tmp/2021北京.csv","/home/zhangxj/WorkFile/LCA-GPT/DataAnalysis/报告案例1.md"]

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，
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。'''
messages.append({'role': 'user', 'content': user_input})
bot = Assistant(llm=llm_cfg,
                    system_message=system_instruction,
                    function_list=tools,
                    files=files)
# Get response from bot
response = []
for response in bot.run(messages=messages):
    continue

pprint(response)
messages.extend(response)
# Convert bot response to string
res_str = ""

for res in response:
    res_str += res['content']
try:
    with open("./result.md", "w", encoding="utf-8") as f:
        f.write(res_str)
except IOError as e:
    print(f"An error occurred: {e}")

print(res_str)