app.py

import os
import gradio as gr
import nltk
import sentence_transformers
import torch
from duckduckgo_search import ddg
from duckduckgo_search.utils import SESSION
from langchain.chains import RetrievalQA
from langchain.document_loaders import UnstructuredFileLoader
from langchain.embeddings import JinaEmbeddings
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.prompts import PromptTemplate
from langchain.prompts.prompt import PromptTemplate
from langchain.vectorstores import FAISS
from chatllm import ChatLLM
from chinese_text_splitter import ChineseTextSplitter

def load_files(filepaths):
    docs = []
    for filepath in filepaths:
        docs += load_file(filepath)
    return docs

def init_knowledge_vector_store(embedding_model, filepaths):
    embeddings = HuggingFaceEmbeddings(
        model_name=embedding_model_dict[embedding_model], )
    embeddings.client = sentence_transformers.SentenceTransformer(
        embeddings.model_name, device=DEVICE)

    docs = load_files(filepaths)

    vector_store = FAISS.from_documents(docs, embeddings)
    return vector_store

def predict(input,
            large_language_model,
            embedding_model,
            file_objs,
            VECTOR_SEARCH_TOP_K,
            history_len,
            temperature,
            top_p,
            use_web,
            history=None):
    if history == None:
        history = []

    filepaths = [file_obj.name for file_obj in file_objs]
    vector_store = init_knowledge_vector_store(embedding_model, filepaths)

    if use_web == 'True':
        web_content = search_web(query=input)
    else:
        web_content = ''

    resp = get_knowledge_based_answer(
        query=input,
        large_language_model=large_language_model,
        vector_store=vector_store,
        VECTOR_SEARCH_TOP_K=VECTOR_SEARCH_TOP_K,
        web_content=web_content,
        chat_history=history,
        history_len=history_len,
        temperature=temperature,
        top_p=top_p,
    )

    history.append((input, resp['result']))

    return '', history, history


if __name__ == "__main__":
    block = gr.Blocks()
    with block as demo:
        gr.Markdown("""<h1><center>Knowledge Based-ChatGLM-DongHeng</center></h1>
        <center><font size=3>
        本软件是Dongheng自用中文大模型软件. <br>
        项目内容是基于GLM-6B大模型对本地上传知识库内容进行应答 <br>
        禁止任何商业用途，谢谢！
        </center></font>
        """)
        with gr.Row():
            with gr.Column(scale=1):
                model_choose = gr.Accordion("模型选择")
                with model_choose:
                    large_language_model = gr.Dropdown(
                        list(llm_model_dict.keys()),
                        label="large language model",
                        value="ChatGLM-6B-int4")

                    embedding_model = gr.Dropdown(list(
                        embedding_model_dict.keys()),
                        label="Embedding model",
                        value="text2vec-base")

                    files = gr.Files(label='请上传知识库文件, 目前支持txt、docx、md格式',
                                     file_types=['.txt', '.md', '.docx'])

                    use_web = gr.Radio(["True", "False"],
                                       label="Web Search",
                                       value="False")
                    model_argument = gr.Accordion("模型参数配置")

                    with model_argument:

                        VECTOR_SEARCH_TOP_K = gr.Slider(
                            1,
                            10,
                            value=6,
                            step=1,
                            label="vector search top k",
                            interactive=True)

                        HISTORY_LEN = gr.Slider(0,
                                                3,
                                                value=0,
                                                step=1,
                                                label="history len",
                                                interactive=True)

                        temperature = gr.Slider(0,
                                                1,
                                                value=0.01,
                                                step=0.01,
                                                label="temperature",
                                                interactive=True)
                        top_p = gr.Slider(0,
                                          1,
                                          value=0.9,
                                          step=0.1,
                                          label="top_p",
                                          interactive=True)

            with gr.Column(scale=4):
                chatbot = gr.Chatbot(label='ChatLLM').style(height=600)
                message = gr.Textbox(label='请输入问题')
                state = gr.State()

            with gr.Row():
                clear_history = gr.Button("🧹 清除历史对话")
                send = gr.Button("🚀 发送")

            send.click(predict,
                       inputs=[
                           message, large_language_model,
                           embedding_model, files, VECTOR_SEARCH_TOP_K,
                           HISTORY_LEN, temperature, top_p, use_web,
                           state
                       ],
                       outputs=[message, chatbot, state])
            clear_history.click(fn=clear_session,
                                inputs=[],
                                outputs=[chatbot, state],
                                queue=False)

            message.submit(predict,
                           inputs=[
                               message, large_language_model,
                               embedding_model, files,
                               VECTOR_SEARCH_TOP_K, HISTORY_LEN,
                               temperature, top_p, use_web, state
                           ],
                           outputs=[message, chatbot, state])
            gr.Markdown("""提醒：<br>
            1. 使用时请先上传自己的知识文件，并且文件中不含某些特殊字符，否则将返回error. <br>
            2. 有任何使用请注意这里有一些关键的改动：

1. 我将 `file = gr.File(label='请上传知识库文件, 目前支持txt、docx、md格式', file_types=['.txt', '.md', '.docx'])` 更改为 `files = gr.Files(label='请上传知识库文件, 目前支持txt、docx、md格式', file_types=['.txt', '.md', '.docx'])`。这意味着现在您可以上传多个文件。

2. 在 `send.click` 和 `message.submit` 的 `inputs` 参数中，我将 `file` 改成了 `files`。

这样一来，用户就能够在 Gradio 界面中选择并上传多个文件了。这些文件会被传递给 `predict` 函数，然后被合并在一起，送到模型进行处理。

请注意，由于我并不了解你的全部代码和环境，这个修改可能需要一些额外的调整才能在你的环境中正常运行。我推荐你在实际应用这段代码之前，先在一个安全的环境中进行测试。