import gradio as gr
import os
import shutil

from chains.local_doc_qa import LocalDocQA
from configs.model_config import *
import nltk
from models.base import (BaseAnswer,
                         AnswerResult,
                         AnswerResultStream,
                         AnswerResultQueueSentinelTokenListenerQueue)
import models.shared as shared
from models.loader.args import parser
from models.loader import LoaderCheckPoint

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


def get_vs_list():
    lst_default = ["新建知识库"]
    if not os.path.exists(VS_ROOT_PATH):
        return lst_default
    lst = os.listdir(VS_ROOT_PATH)
    if not lst:
        return lst_default
    lst.sort()
    return lst_default + lst


vs_list = get_vs_list()

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()


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 == "知识库问答" and vs_path is not None and os.path.exists(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:
        for answer_result in local_doc_qa.llm.generatorAnswer(prompt=query, history=history,
                                                              streaming=streaming):

            resp = answer_result.llm_output["answer"]
            history = answer_result.history
            history[-1][-1] = resp + (
                "\n\n当前知识库为空，如需基于知识库进行问答，请先加载知识库后，再进行提问。" if mode == "知识库问答" else "")
            yield history, ""
    logger.info(f"flagging: username={FLAG_USER_NAME},query={query},vs_path={vs_path},mode={mode},history={history}")
    flag_csv_logger.flag([query, vs_path, history, mode], username=FLAG_USER_NAME)


def init_model(llm_model: BaseAnswer = None):
    try:
        local_doc_qa.init_cfg(llm_model=llm_model)
        generator = local_doc_qa.llm.generatorAnswer("你好")
        for answer_result in generator:
            print(answer_result.llm_output)
        reply = """模型已成功加载，可以开始对话，或从右侧选择模式后开始对话"""
        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，可以开始对话，或从右侧选择模式后开始对话"""
        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(VS_ROOT_PATH, vs_id)
    filelist = []
    if not os.path.exists(os.path.join(UPLOAD_ROOT_PATH, vs_id)):
        os.makedirs(os.path.join(UPLOAD_ROOT_PATH, vs_id))
    if local_doc_qa.llm 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(UPLOAD_ROOT_PATH, vs_id, filename))
                filelist.append(os.path.join(UPLOAD_ROOT_PATH, vs_id, 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]]


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
    else:
        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), os.path.join(VS_ROOT_PATH,
                                                                                                         vs_id), history + [
                   [None, file_status]]


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, vs_list, chatbot):
    if vs_name in 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), vs_list, gr.update(visible=True), gr.update(visible=True), gr.update(
            visible=False), chatbot
    else:
        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=[vs_name] + vs_list, value=vs_name), [vs_name] + vs_list, gr.update(
            visible=False), gr.update(visible=False), gr.update(visible=True), chatbot


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)
"""
default_vs = vs_list[0] if len(vs_list) > 1 else "为空"
init_message = f"""欢迎使用 langchain-ChatGLM Web UI！

请在右侧切换模式，目前支持直接与 LLM 模型对话或基于本地知识库问答。

知识库问答模式，选择知识库名称后，即可开始问答，当前知识库{default_vs}，如有需要可以在选择知识库名称后上传文件/文件夹至知识库。

知识库暂不支持文件删除，该功能将在后续版本中推出。
"""

# 初始化消息
args = None
args = parser.parse_args()

args_dict = vars(args)
shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
llm_model_ins = shared.loaderLLM()
llm_model_ins.set_history_len(LLM_HISTORY_LEN)

model_status = init_model(llm_model=llm_model_ins)


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, vs_list = gr.State(
        os.path.join(VS_ROOT_PATH, vs_list[0]) if len(vs_list) > 1 else ""), gr.State(""), gr.State(
        model_status), gr.State(vs_list)

    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(["LLM 对话", "知识库问答"],
                                label="请选择使用模式",
                                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:
                    select_vs = gr.Dropdown(vs_list.value,
                                            label="请选择要加载的知识库",
                                            interactive=True,
                                            value=vs_list.value[0] if len(vs_list.value) > 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")
                    vs_add.click(fn=add_vs_name,
                                 inputs=[vs_name, vs_list, chatbot],
                                 outputs=[select_vs, vs_list, vs_name, vs_add, file2vs, chatbot])
                    select_vs.change(fn=change_vs_name_input,
                                     inputs=[select_vs, chatbot],
                                     outputs=[vs_name, vs_add, file2vs, vs_path, chatbot])
                    load_file_button.click(get_vector_store,
                                           show_progress=True,
                                           inputs=[select_vs, 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, folder_files, sentence_size, chatbot, vs_add,
                                                     vs_add],
                                             outputs=[vs_path, folder_files, chatbot], )
                    flag_csv_logger.setup([query, vs_path, chatbot, mode], "flagged")
                    query.submit(get_answer,
                                 [query, vs_path, chatbot, mode],
                                 [chatbot, query])
    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:
                    select_vs = gr.Dropdown(vs_list.value,
                                            label="请选择要加载的知识库",
                                            interactive=True,
                                            value=vs_list.value[0] if len(vs_list.value) > 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_add.click(fn=add_vs_name,
                                 inputs=[vs_name, vs_list, chatbot],
                                 outputs=[select_vs, vs_list, vs_name, vs_add, file2vs, chatbot])
                    select_vs.change(fn=change_vs_name_input,
                                     inputs=[select_vs, chatbot],
                                     outputs=[vs_name, vs_add, file2vs, vs_path, chatbot])
                    load_file_button.click(get_vector_store,
                                           show_progress=True,
                                           inputs=[select_vs, 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, 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, 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)

(demo
 .queue(concurrency_count=3)
 .launch(server_name='0.0.0.0',
         server_port=7860,
         show_api=False,
         share=False,
         inbrowser=False))
