app.py

# -*- coding: utf-8 -*-
"""
@author:XuMing(xuming624@qq.com)
@description:
modified from https://github.com/imClumsyPanda/langchain-ChatGLM/blob/master/webui.py
"""
import argparse
import hashlib
import os
import shutil

import gradio as gr
from loguru import logger

from chatpdf import ChatPDF

pwd_path = os.path.abspath(os.path.dirname(__file__))

CONTENT_DIR = os.path.join(pwd_path, "content")
logger.info(f"CONTENT_DIR: {CONTENT_DIR}")
VECTOR_SEARCH_TOP_K = 3
MAX_INPUT_LEN = 2048

embedding_model_dict = {
    "text2vec-base": "shibing624/text2vec-base-chinese",
    "text2vec-multilingual": "shibing624/text2vec-base-multilingual",
    "text2vec-large": "GanymedeNil/text2vec-large-chinese",
    "sentence-transformers": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
    "ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
    "ernie-base": "nghuyong/ernie-3.0-base-zh",
}

# supported LLM models
llm_model_dict = {
    "llama-2-7b": "LinkSoul/Chinese-Llama-2-7b-4bit",
    "baichuan-13b-chat": "baichuan-inc/Baichuan-13B-Chat",
    "chatglm-6b-int4-qe": "THUDM/chatglm-6b-int4-qe",
    "chatglm-2-6b": "THUDM/chatglm2-6b",
    "chatglm-2-6b-int4": "THUDM/chatglm2-6b-int4",
    "chatglm-6b-int4": "THUDM/chatglm-6b-int4",
    "chatglm-6b": "THUDM/chatglm-6b",
    "llama-7b": "shibing624/chinese-alpaca-plus-7b-hf",
    "llama-13b": "shibing624/chinese-alpaca-plus-13b-hf",
}

llm_model_dict_list = list(llm_model_dict.keys())
embedding_model_dict_list = list(embedding_model_dict.keys())

parser = argparse.ArgumentParser()
parser.add_argument("--sim_model", type=str, default="shibing624/text2vec-base-chinese")
parser.add_argument("--gen_model_type", type=str, default="llama")
parser.add_argument("--gen_model", type=str, default="LinkSoul/Chinese-Llama-2-7b-4bit")
parser.add_argument("--lora_model", type=str, default=None)
parser.add_argument("--device", type=str, default="cpu")
parser.add_argument("--int4", action='store_true', help="use int4 quantization")
parser.add_argument("--int8", action='store_true', help="use int8 quantization")
args = parser.parse_args()
print(args)

model = None


def get_file_list():
    if not os.path.exists("content"):
        return []
    return [f for f in os.listdir("content") if
            f.endswith(".txt") or f.endswith(".pdf") or f.endswith(".docx") or f.endswith(".md")]


file_list = get_file_list()


def upload_file(file):
    if not os.path.exists(CONTENT_DIR):
        os.mkdir(CONTENT_DIR)
    filename = os.path.basename(file.name)
    shutil.move(file.name, os.path.join(CONTENT_DIR, filename))
    # file_list首位插入新上传的文件
    file_list.insert(0, filename)
    return gr.Dropdown.update(choices=file_list, value=filename)


def parse_text(text):
    """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
    lines = text.split("\n")
    lines = [line for line in lines if line != ""]
    count = 0
    for i, line in enumerate(lines):
        if "```" in line:
            count += 1
            items = line.split('`')
            if count % 2 == 1:
                lines[i] = f'<pre><code class="language-{items[-1]}">'
            else:
                lines[i] = f'<br></code></pre>'
        else:
            if i > 0:
                if count % 2 == 1:
                    line = line.replace("`", "\`")
                    line = line.replace("<", "&lt;")
                    line = line.replace(">", "&gt;")
                    line = line.replace(" ", "&nbsp;")
                    line = line.replace("*", "&ast;")
                    line = line.replace("_", "&lowbar;")
                    line = line.replace("-", "&#45;")
                    line = line.replace(".", "&#46;")
                    line = line.replace("!", "&#33;")
                    line = line.replace("(", "&#40;")
                    line = line.replace(")", "&#41;")
                    line = line.replace("$", "&#36;")
                lines[i] = "<br>" + line
    text = "".join(lines)
    return text


def get_answer(query, index_path, history, topn=VECTOR_SEARCH_TOP_K, max_input_size=1024, only_chat=False):
    if model is None:
        return [None, "模型还未加载"], query
    if index_path and not only_chat:
        if not model.sim_model.corpus_embeddings:
            model.load_index(index_path)
        response, reference_results = model.predict(
            query=query, topn=topn, context_len=max_input_size)

        logger.debug(f"query: {query}, response with content: {response}")
        for i in range(len(reference_results)):
            r = reference_results[i]
            response += f"\n{r.strip()}"
        response = parse_text(response)
        history = history + [[query, response]]
    else:
        # 未加载文件，仅返回生成模型结果
        instruction = """[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.

                    If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n{} [/INST]"""
        if args.gen_model_type == "llama":
            query = instruction.format(query)
        model.history.append([query, ''])
        response = ""
        for new_text in model.stream_generate_answer(query, context_len=max_input_size):
            response += new_text
        response = response.strip()
        model.history[-1][1] = response
        response = parse_text(response)
        history = history + [[query, response]]
        logger.debug(f"query: {query}, response: {response}")
    return history, ""


def update_status(history, status):
    history = history + [[None, status]]
    logger.info(status)
    return history


def reinit_model(llm_model, embedding_model, history):
    try:
        global model
        if model is not None:
            del model
 
        model = ChatPDF(
            sim_model_name_or_path=embedding_model_dict.get(
                embedding_model,
                "shibing624/text2vec-base-chinese"
            ),
            gen_model_type=llm_model.split('-')[0],
            gen_model_name_or_path=llm_model_dict.get(llm_model, "LinkSoul/Chinese-Llama-2-7b-4bit"),
            lora_model_name_or_path=None,
        )

        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"""
    except Exception as e:
        model = None
        logger.error(e)
        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"""
    return history + [[None, model_status]]


def get_file_hash(fpath):
    return hashlib.md5(open(fpath, 'rb').read()).hexdigest()


def get_vector_store(filepath, history, embedding_model):
    logger.info(filepath, history)
    index_path = None
    file_status = ''
    if model is not None:

        local_file_path = os.path.join(CONTENT_DIR, filepath)

        local_file_hash = get_file_hash(local_file_path)
        index_file_name = f"{filepath}.{embedding_model}.{local_file_hash}.index.json"

        local_index_path = os.path.join(CONTENT_DIR, index_file_name)

        if os.path.exists(local_index_path):
            model.load_index(local_index_path)
            index_path = local_index_path
            file_status = "文件已成功加载，请开始提问"

        elif os.path.exists(local_file_path):
            model.load_doc_files(local_file_path)
            model.save_index(local_index_path)
            index_path = local_index_path
            if index_path:
                file_status = "文件索引并成功加载，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"

    return index_path, history + [[None, file_status]]


def reset_chat(chatbot, state):
    return None, None


def change_max_input_size(input_size):
    if model is not None:
        model.max_input_size = input_size
    return


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 = """
# 🎉ChatPDF WebUI🎉
Link in: [https://github.com/shibing624/ChatPDF](https://github.com/shibing624/ChatPDF)  PS: 2核CPU 16G内存机器，约2min一条😭
"""

init_message = """欢迎使用 ChatPDF Web UI，可以直接提问或上传文件后提问 """

with gr.Blocks(css=block_css) as demo:
    index_path, file_status, model_status = gr.State(""), gr.State(""), gr.State("")
    gr.Markdown(webui_title)
    with gr.Row():
        with gr.Column(scale=2):
            chatbot = gr.Chatbot([[None, init_message], [None, None]],
                                 elem_id="chat-box",
                                 show_label=False).style(height=700)
            query = gr.Textbox(show_label=False,
                               placeholder="请输入提问内容，按回车进行提交",
                               ).style(container=False)
            clear_btn = gr.Button('🔄Clear!', elem_id='clear').style(full_width=True)
        with gr.Column(scale=1):
            llm_model = gr.Radio(llm_model_dict_list,
                                 label="LLM 模型",
                                 value=list(llm_model_dict.keys())[0],
                                 interactive=True)
            embedding_model = gr.Radio(embedding_model_dict_list,
                                       label="Embedding 模型",
                                       value=embedding_model_dict_list[0],
                                       interactive=True)

            load_model_button = gr.Button("重新加载模型")

            with gr.Row():
                only_chat = gr.Checkbox(False, label="不加载文件(纯聊天)")

            with gr.Row():
                topn = gr.Slider(1, 100, 20, step=1, label="最大搜索数量")
                max_input_size = gr.Slider(512, 4096, MAX_INPUT_LEN, step=10, label="摘要最大长度")
            with gr.Tab("select"):
                selectFile = gr.Dropdown(
                    file_list,
                    label="content file",
                    interactive=True,
                    value=file_list[0] if len(file_list) > 0 else None
                )
            with gr.Tab("upload"):
                file = gr.File(
                    label="content file",
                    file_types=['.txt', '.md', '.docx', '.pdf']
                )
            load_file_button = gr.Button("加载文件")
    max_input_size.change(
        change_max_input_size,
        inputs=max_input_size
    )
    load_model_button.click(
        reinit_model,
        show_progress=True,
        inputs=[llm_model, embedding_model, chatbot],
        outputs=chatbot
    )
    # 将上传的文件保存到content文件夹下,并更新下拉框
    file.upload(upload_file, inputs=file, outputs=selectFile)
    load_file_button.click(
        get_vector_store,
        show_progress=True,
        inputs=[selectFile, chatbot, embedding_model],
        outputs=[index_path, chatbot],
    )
    query.submit(
        get_answer,
        [query, index_path, chatbot, topn, max_input_size, only_chat],
        [chatbot, query],
    )
    clear_btn.click(reset_chat, [chatbot, query], [chatbot, query])

demo.queue(concurrency_count=3).launch()