app.py

import gradio as gr
import shutil

from chains.local_doc_qa import LocalDocQA
from configs.model_config import *
import nltk
import models.shared as shared
from models.loader.args import parser
from models.loader import LoaderCheckPoint
import os
import pandas as pd

nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path


def get_vs_list():
    lst_default = ["python_bot"]
    if not os.path.exists(KB_ROOT_PATH):
        return lst_default
    lst = os.listdir(KB_ROOT_PATH)
    if not lst:
        return lst_default
    lst.sort()
    return lst_default + lst


embedding_model_dict_list = list(embedding_model_dict.keys())

llm_model_dict_list = list(llm_model_dict.keys())

local_doc_qa = LocalDocQA()

flag_csv_logger = gr.CSVLogger()

user = "None"

users = [
    ("wsy", "123456"),
    ("wdy", "654321"),
    ("lhj", "123456"),
    ("hhy", "123456"),
    ("yl", "123456"),
    ("hy", "123456"),
    ]
# mode = "知识库问答"
vs_path = "/home/wsy/Langchain-chat/Langchain-Chatchat/knowledge_base"

def get_answer(query, vs_path, history, mode, score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
               vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_conent: bool = True,
               chunk_size=CHUNK_SIZE, streaming: bool = STREAMING):
    # if mode == "Bing搜索问答":
    #     for resp, history in local_doc_qa.get_search_result_based_answer(
    #             query=query, chat_history=history, streaming=streaming):
    #         source = "\n\n"
    #         source += "".join(
    #             [
    #                 f"""<details> <summary>出处 [{i + 1}] <a href="{doc.metadata["source"]}" target="_blank">{doc.metadata["source"]}</a> </summary>\n"""
    #                 f"""{doc.page_content}\n"""
    #                 f"""</details>"""
    #                 for i, doc in
    #                 enumerate(resp["source_documents"])])
    #         history[-1][-1] += source
    #         yield history, ""
    if mode == "知识库问答" and vs_path is not None and os.path.exists(vs_path) and "index.faiss" in os.listdir(
            vs_path):
        for resp, history in local_doc_qa.get_knowledge_based_answer(
                query=query, vs_path=vs_path, chat_history=history, streaming=streaming):
            source = "\n\n"
            source += "".join(
                [f"""<details> <summary>出处 [{i + 1}] {os.path.split(doc.metadata["source"])[-1]}</summary>\n"""
                 f"""{doc.page_content}\n"""
                 f"""</details>"""
                 for i, doc in
                 enumerate(resp["source_documents"])])
            history[-1][-1] += source
            yield history, ""
    # elif mode == "知识库测试":
    #     if os.path.exists(vs_path):
    #         resp, prompt = local_doc_qa.get_knowledge_based_conent_test(query=query, vs_path=vs_path,
    #                                                                     score_threshold=score_threshold,
    #                                                                     vector_search_top_k=vector_search_top_k,
    #                                                                     chunk_conent=chunk_conent,
    #                                                                     chunk_size=chunk_size)
    #         if not resp["source_documents"]:
    #             yield history + [[query,
    #                               "根据您的设定，没有匹配到任何内容，请确认您设置的知识相关度 Score 阈值是否过小或其他参数是否正确。"]], ""
    #         else:
    #             source = "\n".join(
    #                 [
    #                     f"""<details open> <summary>【知识相关度 Score】：{doc.metadata["score"]} - 【出处{i + 1}】：  {os.path.split(doc.metadata["source"])[-1]} </summary>\n"""
    #                     f"""{doc.page_content}\n"""
    #                     f"""</details>"""
    #                     for i, doc in
    #                     enumerate(resp["source_documents"])])
    #             history.append([query, "以下内容为知识库中满足设置条件的匹配结果：\n\n" + source])
    #             yield history, ""
    #     else:
    #         yield history + [[query,
    #                           "请选择知识库后进行测试，当前未选择知识库。"]], ""
    else:

        answer_result_stream_result = local_doc_qa.llm_model_chain(
            {"prompt": query, "history": history, "streaming": streaming})

        for answer_result in answer_result_stream_result['answer_result_stream']:
            resp = answer_result.llm_output["answer"]
            history = answer_result.history
            history[-1][-1] = resp
            yield history, ""
    logger.info(f"flagging: username={user},query={query},vs_path={vs_path},mode={mode},history={history}")
    flag_csv_logger.flag([query, vs_path, history, mode], username=user)


def init_model():
    args = parser.parse_args()

    args_dict = vars(args)
    shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
    llm_model_ins = shared.loaderLLM()
    llm_model_ins.history_len = LLM_HISTORY_LEN
    try:
        local_doc_qa.init_cfg(llm_model=llm_model_ins)
        answer_result_stream_result = local_doc_qa.llm_model_chain(
            {"prompt": "你好", "history": [], "streaming": False})

        for answer_result in answer_result_stream_result['answer_result_stream']:
            print(answer_result.llm_output)
        reply = """模型已成功加载，可以开始对话"""
        logger.info(reply)
        return reply
    except Exception as e:
        logger.error(e)
        reply = """模型未成功加载，请到页面左上角"模型配置"选项卡中重新选择后点击"加载模型"按钮"""
        if str(e) == "Unknown platform: darwin":
            logger.info("该报错可能因为您使用的是 macOS 操作系统，需先下载模型至本地后执行 Web UI，具体方法请参考项目 README 中本地部署方法及常见问题："
                        " https://github.com/imClumsyPanda/langchain-ChatGLM")
        else:
            logger.info(reply)
        return reply


def reinit_model(llm_model, embedding_model, llm_history_len, no_remote_model, use_ptuning_v2, use_lora, top_k,
                 history):
    try:
        llm_model_ins = shared.loaderLLM(llm_model, no_remote_model, use_ptuning_v2)
        llm_model_ins.history_len = llm_history_len
        local_doc_qa.init_cfg(llm_model=llm_model_ins,
                              embedding_model=embedding_model,
                              top_k=top_k)
        model_status = """模型已成功重新加载"""
        logger.info(model_status)
    except Exception as e:
        logger.error(e)
        model_status = """模型未成功重新加载，请到页面左上角"模型配置"选项卡中重新选择后点击"加载模型"按钮"""
        logger.info(model_status)
    return history + [[None, model_status]]


def get_vector_store(vs_id, files, sentence_size, history, one_conent, one_content_segmentation):
    vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store")
    filelist = []
    if local_doc_qa.llm_model_chain and local_doc_qa.embeddings:
        if isinstance(files, list):
            for file in files:
                filename = os.path.split(file.name)[-1]
                shutil.move(file.name, os.path.join(KB_ROOT_PATH, vs_id, "content", filename))
                filelist.append(os.path.join(KB_ROOT_PATH, vs_id, "content", filename))
            vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, vs_path, sentence_size)
        else:
            vs_path, loaded_files = local_doc_qa.one_knowledge_add(vs_path, files, one_conent, one_content_segmentation,
                                                                   sentence_size)
        if len(loaded_files):
            file_status = f"已添加 {'、'.join([os.path.split(i)[-1] for i in loaded_files if i])} 内容至知识库，并已加载知识库，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
    logger.info(file_status)
    return vs_path, None, history + [[None, file_status]], \
           gr.update(choices=local_doc_qa.list_file_from_vector_store(vs_path) if vs_path else [])


def change_vs_name_input(vs_id, history):
    if vs_id == "新建知识库":
        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), None, history, \
               gr.update(choices=[]), gr.update(visible=False)
    else:
        vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store")
        if "index.faiss" in os.listdir(vs_path):
            file_status = f"已加载知识库{vs_id}，请开始提问"
            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), \
                   vs_path, history + [[None, file_status]], \
                   gr.update(choices=local_doc_qa.list_file_from_vector_store(vs_path), value=[]), \
                   gr.update(visible=True)
        else:
            file_status = f"已选择知识库{vs_id}，当前知识库中未上传文件，请先上传文件后，再开始提问"
            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), \
                   vs_path, history + [[None, file_status]], \
                   gr.update(choices=[], value=[]), gr.update(visible=True, value=[])


knowledge_base_test_mode_info = ("【注意】\n\n"
                                 "1. 您已进入知识库测试模式，您输入的任何对话内容都将用于进行知识库查询，"
                                 "并仅输出知识库匹配出的内容及相似度分值和及输入的文本源路径，查询的内容并不会进入模型查询。\n\n"
                                 "2. 知识相关度 Score 经测试，建议设置为 500 或更低，具体设置情况请结合实际使用调整。"
                                 """3. 使用"添加单条数据"添加文本至知识库时，内容如未分段，则内容越多越会稀释各查询内容与之关联的score阈值。\n\n"""
                                 "4. 单条内容长度建议设置在100-150左右。\n\n"
                                 "5. 本界面用于知识入库及知识匹配相关参数设定，但当前版本中，"
                                 "本界面中修改的参数并不会直接修改对话界面中参数，仍需前往`configs/model_config.py`修改后生效。"
                                 "相关参数将在后续版本中支持本界面直接修改。")


def change_mode(mode, history):
    if mode == "知识库问答":
        return gr.update(visible=True), gr.update(visible=False), history
        # + [[None, "【注意】：您已进入知识库问答模式，您输入的任何查询都将进行知识库查询，然后会自动整理知识库关联内容进入模型查询！！！"]]
    elif mode == "知识库测试":
        return gr.update(visible=True), gr.update(visible=True), [[None,
                                                                   knowledge_base_test_mode_info]]
    else:
        return gr.update(visible=False), gr.update(visible=False), history


def change_chunk_conent(mode, label_conent, history):
    conent = ""
    if "chunk_conent" in label_conent:
        conent = "搜索结果上下文关联"
    elif "one_content_segmentation" in label_conent:  # 这里没用上，可以先留着
        conent = "内容分段入库"

    if mode:
        return gr.update(visible=True), history + [[None, f"【已开启{conent}】"]]
    else:
        return gr.update(visible=False), history + [[None, f"【已关闭{conent}】"]]


def add_vs_name(vs_name, chatbot):
    if vs_name is None or vs_name.strip() == "":
        vs_status = "知识库名称不能为空，请重新填写知识库名称"
        chatbot = chatbot + [[None, vs_status]]
        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(
            visible=False), chatbot, gr.update(visible=False)
    elif vs_name in get_vs_list():
        vs_status = "与已有知识库名称冲突，请重新选择其他名称后提交"
        chatbot = chatbot + [[None, vs_status]]
        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(
            visible=False), chatbot, gr.update(visible=False)
    else:
        # 新建上传文件存储路径
        if not os.path.exists(os.path.join(KB_ROOT_PATH, vs_name, "content")):
            os.makedirs(os.path.join(KB_ROOT_PATH, vs_name, "content"))
        # 新建向量库存储路径
        if not os.path.exists(os.path.join(KB_ROOT_PATH, vs_name, "vector_store")):
            os.makedirs(os.path.join(KB_ROOT_PATH, vs_name, "vector_store"))
        vs_status = f"""已新增知识库"{vs_name}",将在上传文件并载入成功后进行存储。请在开始对话前，先完成文件上传。 """
        chatbot = chatbot + [[None, vs_status]]
        return gr.update(visible=True, choices=get_vs_list(), value=vs_name), gr.update(
            visible=False), gr.update(visible=False), gr.update(visible=True), chatbot, gr.update(visible=True)


# 自动化加载固定文件间中文件
def reinit_vector_store(vs_id, history):
    try:
        shutil.rmtree(os.path.join(KB_ROOT_PATH, vs_id, "vector_store"))
        vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store")
        sentence_size = gr.Number(value=SENTENCE_SIZE, precision=0,
                                  label="文本入库分句长度限制",
                                  interactive=True, visible=True)
        vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(os.path.join(KB_ROOT_PATH, vs_id, "content"),
                                                                         vs_path, sentence_size)
        model_status = """知识库构建成功"""
    except Exception as e:
        logger.error(e)
        model_status = """知识库构建未成功"""
        logger.info(model_status)
    return history + [[None, model_status]]


def refresh_vs_list():
    return gr.update(choices=get_vs_list()), gr.update(choices=get_vs_list())


def delete_file(vs_id, files_to_delete, chatbot):
    vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store")
    content_path = os.path.join(KB_ROOT_PATH, vs_id, "content")
    docs_path = [os.path.join(content_path, file) for file in files_to_delete]
    status = local_doc_qa.delete_file_from_vector_store(vs_path=vs_path,
                                                        filepath=docs_path)
    if "fail" not in status:
        for doc_path in docs_path:
            if os.path.exists(doc_path):
                os.remove(doc_path)
    rested_files = local_doc_qa.list_file_from_vector_store(vs_path)
    if "fail" in status:
        vs_status = "文件删除失败。"
    elif len(rested_files) > 0:
        vs_status = "文件删除成功。"
    else:
        vs_status = f"文件删除成功，知识库{vs_id}中无已上传文件，请先上传文件后，再开始提问。"
    logger.info(",".join(files_to_delete) + vs_status)
    chatbot = chatbot + [[None, vs_status]]
    return gr.update(choices=local_doc_qa.list_file_from_vector_store(vs_path), value=[]), chatbot


def delete_vs(vs_id, chatbot):
    try:
        shutil.rmtree(os.path.join(KB_ROOT_PATH, vs_id))
        status = f"成功删除知识库{vs_id}"
        logger.info(status)
        chatbot = chatbot + [[None, status]]
        return gr.update(choices=get_vs_list(), value=get_vs_list()[0]), gr.update(visible=True), gr.update(
            visible=True), \
               gr.update(visible=False), chatbot, gr.update(visible=False)
    except Exception as e:
        logger.error(e)
        status = f"删除知识库{vs_id}失败"
        chatbot = chatbot + [[None, status]]
        return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), \
               gr.update(visible=True), chatbot, gr.update(visible=True)


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 = """
# 🎉Welcome Python bot🎉
"""
# default_vs = get_vs_list()[0] if len(get_vs_list()) > 1 else "为空"
init_message = f"""欢迎使用 Python bot!

在下侧对话框输入问题后,按下Shift+回车即可换行继续输入，按下回车即可获得回复！


若想询问程序报错相关问题，将报错信息最后的报错原因贴上来即可。

"""

# 初始化消息
model_status = init_model()

default_theme_args = dict(
    font=["Source Sans Pro", 'ui-sans-serif', 'system-ui', 'sans-serif'],
    font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'],
)

with gr.Blocks(css=block_css, theme=gr.themes.Default(**default_theme_args)) as demo:
    vs_path, file_status, model_status = gr.State(
        os.path.join(KB_ROOT_PATH, get_vs_list()[0], "vector_store") if len(get_vs_list()) > 1 else ""), gr.State(
        ""), gr.State(
        model_status)
    gr.Markdown(webui_title)
    with gr.Tab("对话"):
        with gr.Row():
            with gr.Column(scale=10):
                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=5):
                mode = gr.Radio(["知识库问答"],
                                show_label=False,
                                value="知识库问答" )                          
            #     knowledge_set = gr.Accordion("知识库设定", visible=False)
            #     vs_setting = gr.Accordion("配置知识库")
            #     mode.change(fn=change_mode,
            #                 inputs=[mode, chatbot],
            #                 outputs=[vs_setting, knowledge_set, chatbot])
            #     with vs_setting:
            #         vs_refresh = gr.Button("更新已有知识库选项")
            #         select_vs = gr.Dropdown(get_vs_list(),
            #                                 label="请选择要加载的知识库",
            #                                 interactive=True,
            #                                 value=get_vs_list()[0] if len(get_vs_list()) > 0 else None
            #                                 )
            #         vs_name = gr.Textbox(label="请输入新建知识库名称，当前知识库命名暂不支持中文",
            #                              lines=1,
            #                              interactive=True,
            #                              visible=True)
                    # vs_add = gr.Button(value="添加至知识库选项", visible=True)
                    # vs_delete = gr.Button("删除本知识库", visible=False)
                    # file2vs = gr.Column(visible=False)
                    # with file2vs:
                        # load_vs = gr.Button("加载知识库")
                        # gr.Markdown("向知识库中添加文件")
                        # sentence_size = gr.Number(value=SENTENCE_SIZE, precision=0,
                        #                           label="文本入库分句长度限制",
                        #                           interactive=True, visible=True)
                        # with gr.Tab("上传文件"):
                        #     files = gr.File(label="添加文件",
                        #                     file_types=['.txt', '.md', '.docx', '.pdf', '.png', '.jpg', ".csv"],
                        #                     file_count="multiple",
                        #                     show_label=False)
                        #     load_file_button = gr.Button("上传文件并加载知识库")
                        # with gr.Tab("上传文件夹"):
                        #     folder_files = gr.File(label="添加文件",
                        #                            file_count="directory",
                        #                            show_label=False)
                        #     load_folder_button = gr.Button("上传文件夹并加载知识库")
                        # with gr.Tab("删除文件"):
                        #     files_to_delete = gr.CheckboxGroup(choices=[],
                        #                                        label="请从知识库已有文件中选择要删除的文件",
                        #                                        interactive=True)
                        #     delete_file_button = gr.Button("从知识库中删除选中文件")
                    # vs_refresh.click(fn=refresh_vs_list,
                    #                  inputs=[],
                    #                  outputs=select_vs)
                    # vs_add.click(fn=add_vs_name,
                    #              inputs=[vs_name, chatbot],
                    #              outputs=[select_vs, vs_name, vs_add, file2vs, chatbot, vs_delete])
                    # vs_delete.click(fn=delete_vs,
                    #                 inputs=[select_vs, chatbot],
                    #                 outputs=[select_vs, vs_name, vs_add, file2vs, chatbot, vs_delete])
                    # select_vs.change(fn=change_vs_name_input,
                                    #  inputs=[select_vs, chatbot],
                                    #  outputs=[vs_name, file2vs, vs_path, chatbot])
                    # load_file_button.click(get_vector_store,
                    #                        show_progress=True,
                    #                        inputs=[select_vs, files, sentence_size, chatbot],
                    #                        outputs=[vs_path, files, chatbot, files_to_delete], )
                    # load_folder_button.click(get_vector_store,
                    #                          show_progress=True,
                    #                          inputs=[select_vs, folder_files, sentence_size, chatbot, vs_add,
                    #                                  vs_add],
                    #                          outputs=[vs_path, folder_files, chatbot, files_to_delete], )
                flag_csv_logger.setup([query, vs_path, chatbot, mode], "student_log")
                query.submit(get_answer,
                                [query, vs_path, chatbot, mode],
                                [chatbot, query])
                    # delete_file_button.click(delete_file,
                    #                          show_progress=True,
                    #                          inputs=[select_vs, files_to_delete, chatbot],
                    #                          outputs=[files_to_delete, chatbot])
    # with gr.Tab("知识库测试 Beta"):
    #     with gr.Row():
    #         with gr.Column(scale=10):
    #             chatbot = gr.Chatbot([[None, knowledge_base_test_mode_info]],
    #                                  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=5):
    #             mode = gr.Radio(["知识库测试"],  # "知识库问答",
    #                             label="请选择使用模式",
    #                             value="知识库测试",
    #                             visible=False)
    #             knowledge_set = gr.Accordion("知识库设定", visible=True)
    #             vs_setting = gr.Accordion("配置知识库", visible=True)
    #             mode.change(fn=change_mode,
    #                         inputs=[mode, chatbot],
    #                         outputs=[vs_setting, knowledge_set, chatbot])
    #             with knowledge_set:
    #                 score_threshold = gr.Number(value=VECTOR_SEARCH_SCORE_THRESHOLD,
    #                                             label="知识相关度 Score 阈值，分值越低匹配度越高",
    #                                             precision=0,
    #                                             interactive=True)
    #                 vector_search_top_k = gr.Number(value=VECTOR_SEARCH_TOP_K, precision=0,
    #                                                 label="获取知识库内容条数", interactive=True)
    #                 chunk_conent = gr.Checkbox(value=False,
    #                                            label="是否启用上下文关联",
    #                                            interactive=True)
    #                 chunk_sizes = gr.Number(value=CHUNK_SIZE, precision=0,
    #                                         label="匹配单段内容的连接上下文后最大长度",
    #                                         interactive=True, visible=False)
    #                 chunk_conent.change(fn=change_chunk_conent,
    #                                     inputs=[chunk_conent, gr.Textbox(value="chunk_conent", visible=False), chatbot],
    #                                     outputs=[chunk_sizes, chatbot])
    #             with vs_setting:
    #                 vs_refresh = gr.Button("更新已有知识库选项")
    #                 select_vs_test = gr.Dropdown(get_vs_list(),
    #                                              label="请选择要加载的知识库",
    #                                              interactive=True,
    #                                              value=get_vs_list()[0] if len(get_vs_list()) > 0 else None)
    #                 vs_name = gr.Textbox(label="请输入新建知识库名称，当前知识库命名暂不支持中文",
    #                                      lines=1,
    #                                      interactive=True,
    #                                      visible=True)
    #                 vs_add = gr.Button(value="添加至知识库选项", visible=True)
    #                 file2vs = gr.Column(visible=False)
    #                 with file2vs:
    #                     # load_vs = gr.Button("加载知识库")
    #                     gr.Markdown("向知识库中添加单条内容或文件")
    #                     sentence_size = gr.Number(value=SENTENCE_SIZE, precision=0,
    #                                               label="文本入库分句长度限制",
    #                                               interactive=True, visible=True)
    #                     with gr.Tab("上传文件"):
    #                         files = gr.File(label="添加文件",
    #                                         file_types=['.txt', '.md', '.docx', '.pdf'],
    #                                         file_count="multiple",
    #                                         show_label=False
    #                                         )
    #                         load_file_button = gr.Button("上传文件并加载知识库")
    #                     with gr.Tab("上传文件夹"):
    #                         folder_files = gr.File(label="添加文件",
    #                                                # file_types=['.txt', '.md', '.docx', '.pdf'],
    #                                                file_count="directory",
    #                                                show_label=False)
    #                         load_folder_button = gr.Button("上传文件夹并加载知识库")
    #                     with gr.Tab("添加单条内容"):
    #                         one_title = gr.Textbox(label="标题", placeholder="请输入要添加单条段落的标题", lines=1)
    #                         one_conent = gr.Textbox(label="内容", placeholder="请输入要添加单条段落的内容", lines=5)
    #                         one_content_segmentation = gr.Checkbox(value=True, label="禁止内容分句入库",
    #                                                                interactive=True)
    #                         load_conent_button = gr.Button("添加内容并加载知识库")
    #                 # 将上传的文件保存到content文件夹下,并更新下拉框
    #                 vs_refresh.click(fn=refresh_vs_list,
    #                                  inputs=[],
    #                                  outputs=[select_vs, select_vs_test])
    #                 vs_add.click(fn=add_vs_name,
    #                              inputs=[vs_name, chatbot],
    #                              outputs=[select_vs_test, vs_name, vs_add, file2vs, chatbot])
    #                 select_vs_test.change(fn=change_vs_name_input,
    #                                       inputs=[select_vs_test, chatbot],
    #                                       outputs=[vs_name, vs_add, file2vs, vs_path, chatbot])
    #                 load_file_button.click(get_vector_store,
    #                                        show_progress=True,
    #                                        inputs=[select_vs_test, files, sentence_size, chatbot, vs_add, vs_add],
    #                                        outputs=[vs_path, files, chatbot], )
    #                 load_folder_button.click(get_vector_store,
    #                                          show_progress=True,
    #                                          inputs=[select_vs_test, folder_files, sentence_size, chatbot, vs_add,
    #                                                  vs_add],
    #                                          outputs=[vs_path, folder_files, chatbot], )
    #                 load_conent_button.click(get_vector_store,
    #                                          show_progress=True,
    #                                          inputs=[select_vs_test, one_title, sentence_size, chatbot,
    #                                                  one_conent, one_content_segmentation],
    #                                          outputs=[vs_path, files, chatbot], )
    #                 flag_csv_logger.setup([query, vs_path, chatbot, mode], "flagged")
    #                 query.submit(get_answer,
    #                              [query, vs_path, chatbot, mode, score_threshold, vector_search_top_k, chunk_conent,
    #                               chunk_sizes],
    #                              [chatbot, query])
    # with gr.Tab("模型配置"):
    #     llm_model = gr.Radio(llm_model_dict_list,
    #                          label="LLM 模型",
    #                          value=LLM_MODEL,
    #                          interactive=True)
    #     no_remote_model = gr.Checkbox(shared.LoaderCheckPoint.no_remote_model,
    #                                   label="加载本地模型",
    #                                   interactive=True)

    #     llm_history_len = gr.Slider(0, 10,
    #                                 value=LLM_HISTORY_LEN,
    #                                 step=1,
    #                                 label="LLM 对话轮数",
    #                                 interactive=True)
    #     use_ptuning_v2 = gr.Checkbox(USE_PTUNING_V2,
    #                                  label="使用p-tuning-v2微调过的模型",
    #                                  interactive=True)
    #     use_lora = gr.Checkbox(USE_LORA,
    #                            label="使用lora微调的权重",
    #                            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")
    #     load_model_button.click(reinit_model, show_progress=True,
    #                             inputs=[llm_model, embedding_model, llm_history_len, no_remote_model, use_ptuning_v2,
    #                                     use_lora, top_k, chatbot], outputs=chatbot)
        # load_knowlege_button = gr.Button("重新构建知识库")
        # load_knowlege_button.click(reinit_vector_store, show_progress=True,
        #                            inputs=[select_vs, chatbot], outputs=chatbot)
    
def gradio_callback(inputs, outputs):  
    # 获取用户输入的用户名  
    username = inputs['username']
    # 在这里处理用户名，例如打印出来  
    print("Current username:", username)

def student():
    hy1_path = "/home/wsy/Langchain-chat/Langchain-Chatchat/stuendt/hy_student1.xlsx"
    hy2_path = "/home/wsy/Langchain-chat/Langchain-Chatchat/stuendt/hy_student2.xlsx"
    lhj_path = "/home/wsy/Langchain-chat/Langchain-Chatchat/stuendt/lhj_student.xlsx"
    ygc_path = "/home/wsy/Langchain-chat/Langchain-Chatchat/stuendt/ygc_student.xlsx"
    yl_path = "/home/wsy/Langchain-chat/Langchain-Chatchat/stuendt/yl_student.xlsx"
    zsg1_path = "/home/wsy/Langchain-chat/Langchain-Chatchat/stuendt/zsg_student1.xlsx"
    zsg2_path = "/home/wsy/Langchain-chat/Langchain-Chatchat/stuendt/zsg_student2.xlsx"

    hy1_student_data = pd.DataFrame(pd.read_excel(hy1_path))
    hy2_student_data = pd.DataFrame(pd.read_excel(hy2_path))
    lhj_student_data = pd.DataFrame(pd.read_excel(lhj_path))
    ygc_student_data = pd.DataFrame(pd.read_excel(ygc_path))
    yl_student_data = pd.DataFrame(pd.read_excel(yl_path))
    zsg1_student_data = pd.DataFrame(pd.read_excel(zsg1_path))
    zsg2_student_data = pd.DataFrame(pd.read_excel(zsg2_path))

    hy1_student = list(hy1_student_data[['姓名', '学号']].apply(tuple, axis=1))
    hy2_student = list(hy2_student_data[['姓名', '学号']].apply(tuple, axis=1))
    lhj_student = list(lhj_student_data[['姓名', '学号']].apply(tuple, axis=1))
    ygc_student = list(ygc_student_data[['姓名', '学号']].apply(tuple, axis=1))
    yl_student = list(yl_student_data[['姓名', '学号']].apply(tuple, axis=1))
    zsg1_student = list(zsg1_student_data[['姓名', '学号']].apply(tuple, axis=1))
    zsg2_student = list(zsg2_student_data[['姓名', '学号']].apply(tuple, axis=1))

    student = hy1_student + hy2_student + lhj_student + ygc_student + yl_student + zsg1_student + zsg2_student
    for i in range(len(student)):
        password = student[i][1]
        student[i] = (student[i][0], str(password))

    return student

def login(x, y):
        users = student()
        for username, password in users:
            if username == x and password == y:
                global user
                user = username
                return x, y

    # demo.load(
    #     fn=refresh_vs_list,
    #     inputs=None,
    #     outputs=[select_vs],
    #     queue=True,
    #     show_progress=False,
    # )

(demo
 .queue(concurrency_count=30) #test
 .launch(server_name='0.0.0.0',
         server_port=7860,
         show_api=False,
         share=False,
         inbrowser=False,
         auth=login)
)