152 lines
6.2 KiB
Python
152 lines
6.2 KiB
Python
import gradio as gr
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import os
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import shutil
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import knowledge_based_chatglm as kb
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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(kb.embedding_model_dict.keys())
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llm_model_dict_list = list(kb.llm_model_dict.keys())
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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, vector_store, history):
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resp, history = kb.get_knowledge_based_answer(
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query=query, vector_store=vector_store, chat_history=history)
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return history, history
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def get_model_status(history):
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return history + [[None, "模型已完成加载，请选择要加载的文档"]]
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def get_file_status(history):
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return history + [[None, "文档已完成加载，请开始提问"]]
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with gr.Blocks(css="""
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.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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""") as demo:
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gr.Markdown(
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f"""
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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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with gr.Row():
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with gr.Column(scale=2):
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chatbot = gr.Chatbot([[None, """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"step.1: setting"，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"step.2 loading"，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"step.3 asking" """]],
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elem_id="chat-box",
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show_label=False).style(height=600)
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with gr.Column(scale=1):
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with gr.Column():
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llm_model = gr.Radio(llm_model_dict_list,
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label="llm model",
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value="chatglm-6b",
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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=3,
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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 model",
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value="text2vec",
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interactive=True)
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VECTOR_SEARCH_TOP_K = gr.Slider(1,
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20,
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value=6,
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step=1,
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label="vector search top k",
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interactive=True)
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load_model_button = gr.Button("step.1：setting")
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load_model_button.click(lambda *args:
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kb.init_cfg(args[0], args[1], args[2], args[3]),
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show_progress=True,
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api_name="init_cfg",
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inputs=[llm_model, embedding_model, LLM_HISTORY_LEN,VECTOR_SEARCH_TOP_K]
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).then(
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get_model_status, chatbot, chatbot
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)
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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']
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).style(height=100)
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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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history = gr.State([])
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vector_store = gr.State()
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load_button = gr.Button("step.2：loading")
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load_button.click(lambda fileName:
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kb.init_knowledge_vector_store(
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"content/" + fileName),
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show_progress=True,
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api_name="init_knowledge_vector_store",
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inputs=selectFile,
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outputs=vector_store
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).then(
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get_file_status,
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chatbot,
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chatbot,
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show_progress=True,
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)
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with gr.Row():
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with gr.Column(scale=2):
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query = gr.Textbox(show_label=False,
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placeholder="Prompts",
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lines=1,
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value="用200字总结一下"
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).style(container=False)
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with gr.Column(scale=1):
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generate_button = gr.Button("step.3：asking",
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elem_classes="importantButton")
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generate_button.click(get_answer,
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[query, vector_store, chatbot],
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[chatbot, history],
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api_name="get_knowledge_based_answer"
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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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