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