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- import gradio as gr
- import os
- import shutil
- from chains.local_doc_qa import LocalDocQA
- from configs.model_config import *
- import nltk
- nltk.data.path = [os.path.join(os.path.dirname(__file__), "nltk_data")] + nltk.data.path
- # return top-k text chunk from vector store
- VECTOR_SEARCH_TOP_K = 6
- # LLM input history length
- LLM_HISTORY_LEN = 3
- def get_file_list():
- if not os.path.exists("content"):
- return []
- return [f for f in os.listdir("content")]
- file_list = get_file_list()
- embedding_model_dict_list = list(embedding_model_dict.keys())
- llm_model_dict_list = list(llm_model_dict.keys())
- local_doc_qa = LocalDocQA()
- def upload_file(file):
- if not os.path.exists("content"):
- os.mkdir("content")
- filename = os.path.basename(file.name)
- shutil.move(file.name, "content/" + filename)
- # file_list首位插入新上传的文件
- file_list.insert(0, filename)
- return gr.Dropdown.update(choices=file_list, value=filename)
- def get_answer(query, vs_path, history):
- if vs_path:
- resp, history = local_doc_qa.get_knowledge_based_answer(
- query=query, vs_path=vs_path, chat_history=history)
- else:
- history = history + [[None, "请先加载文件后,再进行提问。"]]
- return history, ""
- def update_status(history, status):
- history = history + [[None, status]]
- print(status)
- return history
- def init_model():
- try:
- local_doc_qa.init_cfg()
- return """模型已成功加载,请选择文件后点击"加载文件"按钮"""
- except Exception as e:
- print(e)
- return """模型未成功加载,请重新选择后点击"加载模型"按钮"""
- def reinit_model(llm_model, embedding_model, llm_history_len, top_k, history):
- try:
- local_doc_qa.init_cfg(llm_model=llm_model,
- embedding_model=embedding_model,
- llm_history_len=llm_history_len,
- top_k=top_k)
- model_status = """模型已成功重新加载,请选择文件后点击"加载文件"按钮"""
- except Exception as e:
- print(e)
- model_status = """模型未成功重新加载,请重新选择后点击"加载模型"按钮"""
- return history + [[None, model_status]]
- def get_vector_store(filepath, history):
- if local_doc_qa.llm and local_doc_qa.embeddings:
- vs_path = local_doc_qa.init_knowledge_vector_store(["content/" + filepath])
- if vs_path:
- file_status = "文件已成功加载,请开始提问"
- else:
- file_status = "文件未成功加载,请重新上传文件"
- else:
- file_status = "模型未完成加载,请先在加载模型后再导入文件"
- vs_path = None
- return vs_path, history + [[None, file_status]]
- block_css = """.importantButton {
- background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
- border: none !important;
- }
- .importantButton:hover {
- background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important;
- border: none !important;
- }"""
- webui_title = """
- # 🎉langchain-ChatGLM WebUI🎉
- 👍 [https://github.com/imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM)
- """
- init_message = """欢迎使用 langchain-ChatGLM Web UI,开始提问前,请依次如下 3 个步骤:
- 1. 选择语言模型、Embedding 模型及相关参数后点击"重新加载模型",并等待加载完成提示
- 2. 上传或选择已有文件作为本地知识文档输入后点击"重新加载文档",并等待加载完成提示
- 3. 输入要提交的问题后,点击回车提交 """
- model_status = init_model()
- with gr.Blocks(css=block_css) as demo:
- vs_path, file_status, model_status = gr.State(""), gr.State(""), gr.State(model_status)
- gr.Markdown(webui_title)
- with gr.Row():
- with gr.Column(scale=2):
- chatbot = gr.Chatbot([[None, init_message], [None, model_status.value]],
- elem_id="chat-box",
- show_label=False).style(height=750)
- query = gr.Textbox(show_label=False,
- placeholder="请输入提问内容,按回车进行提交",
- ).style(container=False)
- with gr.Column(scale=1):
- llm_model = gr.Radio(llm_model_dict_list,
- label="LLM 模型",
- value=LLM_MODEL,
- interactive=True)
- llm_history_len = gr.Slider(0,
- 10,
- value=LLM_HISTORY_LEN,
- step=1,
- label="LLM history len",
- interactive=True)
- embedding_model = gr.Radio(embedding_model_dict_list,
- label="Embedding 模型",
- value=EMBEDDING_MODEL,
- interactive=True)
- top_k = gr.Slider(1,
- 20,
- value=VECTOR_SEARCH_TOP_K,
- step=1,
- label="向量匹配 top k",
- interactive=True)
- load_model_button = gr.Button("重新加载模型")
- # with gr.Column():
- with gr.Tab("select"):
- selectFile = gr.Dropdown(file_list,
- label="content file",
- interactive=True,
- value=file_list[0] if len(file_list) > 0 else None)
- with gr.Tab("upload"):
- file = gr.File(label="content file",
- file_types=['.txt', '.md', '.docx', '.pdf']
- ) # .style(height=100)
- load_file_button = gr.Button("加载文件")
- load_model_button.click(reinit_model,
- show_progress=True,
- inputs=[llm_model, embedding_model, llm_history_len, top_k, chatbot],
- outputs=chatbot
- )
- # 将上传的文件保存到content文件夹下,并更新下拉框
- file.upload(upload_file,
- inputs=file,
- outputs=selectFile)
- load_file_button.click(get_vector_store,
- show_progress=True,
- inputs=[selectFile, chatbot],
- outputs=[vs_path, chatbot],
- )
- query.submit(get_answer,
- [query, vs_path, chatbot],
- [chatbot, query],
- )
- demo.queue(concurrency_count=3).launch(
- server_name='0.0.0.0', share=False, inbrowser=False)
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