107 lines
3.8 KiB
Python
107 lines
3.8 KiB
Python
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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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'''
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数据分析助手问答
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不知道为什么在服务器上跑就出现
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PermissionError: [Errno 13] Permission denied: '/tmp/gradio/872ec5dfa2067f6f2cafe865d734a9e4ab00234b'
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但是在自己机器上跑就没有问题。。。应该是权限问题吧 i think
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'''
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# 重置了临时路径 也还是不行。
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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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# Function to extract image path from response
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def getImage(response):
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pattern = r'workspace.*?\.png'
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path = None
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for res in response:
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content = str(res['content'])
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matches = re.findall(pattern, content)
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# print("*********",res['content'])
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if len(matches):
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path = matches[0]
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print("###### path #####,", path)
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return path
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# Function to handle user input and generate chatbot response
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def chatbot_interface(user_input, uploaded_file_path):
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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=[uploaded_file_path])
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# Add user input to messages
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messages.append({'role': 'user', 'content': user_input})
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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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# Get image path from response
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tmp_path = getImage(response)
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image_path = None
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if tmp_path:
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image_path = os.path.join("/home/zhangxj/WorkFile/LCA-GPT/LCARAG/DataAnalysis/tmp", tmp_path)
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print("image path", image_path)
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# Check if image path exists and open image
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if image_path and os.path.exists(image_path):
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image = Image.open(image_path)
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return res_str, image
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else:
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return res_str, None
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# Function to handle file upload
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def upload_file(file):
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return f"文件上传成功{file}"
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# Creating the Gradio interface
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with gr.Blocks() as demo:
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with gr.Column():
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with gr.Row():
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chatbot_input = gr.Textbox(label="LCA-Data-Assistant", placeholder="Enter your message here...")
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with gr.Row():
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with gr.Column():
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chatbot_output_markdown = gr.Markdown()
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chatbot_output_image = gr.Image(type="pil")
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with gr.Row():
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file_input = gr.File(label="Upload a file")
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file_input.GRADIO_CACHE = "/home/zhangxj/WorkFile/LCA-GPT/LCARAG/DataAnalysis/tmp"
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# Handle user input and file upload events
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def update_output(user_input, uploaded_file_path):
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response_str, image = chatbot_interface(user_input, uploaded_file_path)
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markdown_output = response_str
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image_output = image if image else None
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return markdown_output, image_output
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chatbot_input.submit(fn=update_output, inputs=[chatbot_input, file_input],
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outputs=[chatbot_output_markdown, chatbot_output_image])
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file_input.change(fn=upload_file, inputs=file_input, outputs=[])
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# Launch the Gradio app
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demo.launch()
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