crazy_functions/批量翻译PDF文档_多线程.py

from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down
import re
import unicodedata


def is_paragraph_break(match):
    """
    根据给定的匹配结果来判断换行符是否表示段落分隔。
    如果换行符前为句子结束标志（句号，感叹号，问号），且下一个字符为大写字母，则换行符更有可能表示段落分隔。
    也可以根据之前的内容长度来判断段落是否已经足够长。
    """
    prev_char, next_char = match.groups()

    # 句子结束标志
    sentence_endings = ".!?"

    # 设定一个最小段落长度阈值
    min_paragraph_length = 140

    if prev_char in sentence_endings and next_char.isupper() and len(match.string[:match.start(1)]) > min_paragraph_length:
        return "\n\n"
    else:
        return " "


def normalize_text(text):
    """
    通过把连字（ligatures）等文本特殊符号转换为其基本形式来对文本进行归一化处理。
    例如，将连字 "fi" 转换为 "f" 和 "i"。
    """
    # 对文本进行归一化处理，分解连字
    normalized_text = unicodedata.normalize("NFKD", text)

    # 替换其他特殊字符
    cleaned_text = re.sub(r'[^\x00-\x7F]+', '', normalized_text)

    return cleaned_text


def clean_text(raw_text):
    """
    对从 PDF 提取出的原始文本进行清洗和格式化处理。
    1. 对原始文本进行归一化处理。
    2. 替换跨行的连词，例如 “Espe-\ncially” 转换为 “Especially”。
    3. 根据 heuristic 规则判断换行符是否是段落分隔，并相应地进行替换。
    """
    # 对文本进行归一化处理
    normalized_text = normalize_text(raw_text)

    # 替换跨行的连词
    text = re.sub(r'(\w+-\n\w+)',
                  lambda m: m.group(1).replace('-\n', ''), normalized_text)

    # 根据前后相邻字符的特点，找到原文本中的换行符
    newlines = re.compile(r'(\S)\n(\S)')

    # 根据 heuristic 规则，用空格或段落分隔符替换原换行符
    final_text = re.sub(newlines, lambda m: m.group(
        1) + is_paragraph_break(m) + m.group(2), text)

    return final_text.strip()

def read_and_clean_pdf_text(fp):
    import fitz, re
    import numpy as np
    # file_content = ""
    with fitz.open(fp) as doc:
        meta_txt = []
        meta_font = []
        for index, page in enumerate(doc):
            # file_content += page.get_text()
            text_areas = page.get_text("dict")  # 获取页面上的文本信息

            # 块元提取                           for each word segment with in line                       for each line         cross-line words                          for each block
            meta_txt.extend( [   " ".join(["".join(     [wtf['text'] for wtf in l['spans'] ])          for l in t['lines']   ]).replace('- ','')               for t in text_areas['blocks'] if 'lines' in t])
            meta_font.extend([   np.mean( [     np.mean([wtf['size'] for wtf in l['spans'] ])          for l in t['lines']   ])                                for t in text_areas['blocks'] if 'lines' in t])
            if index==0:
                page_one_meta = [" ".join(["".join(     [wtf['text'] for wtf in l['spans'] ])          for l in t['lines']   ]).replace('- ','')               for t in text_areas['blocks'] if 'lines' in t]

        def 把字符太少的块清除为回车(meta_txt):
            for index, block_txt in enumerate(meta_txt):
                if len(block_txt) < 100:
                    meta_txt[index] = '\n'
            return meta_txt
        meta_txt = 把字符太少的块清除为回车(meta_txt)

        def 清理多余的空行(meta_txt):
            for index in reversed(range(1, len(meta_txt))):
                if meta_txt[index] == '\n' and meta_txt[index-1] == '\n':
                    meta_txt.pop(index)
            return meta_txt
        meta_txt = 清理多余的空行(meta_txt)

        def 合并小写开头的段落块(meta_txt):
            def starts_with_lowercase_word(s):
                pattern = r"^[a-z]+"
                match = re.match(pattern, s)
                if match:
                    return True
                else:
                    return False
            for _ in range(100):
                for index, block_txt in enumerate(meta_txt):
                    if starts_with_lowercase_word(block_txt):
                        if meta_txt[index-1]!='\n': meta_txt[index-1] += ' '
                        else: meta_txt[index-1] = ''
                        meta_txt[index-1] += meta_txt[index]
                        meta_txt[index] = '\n'
            return meta_txt
        meta_txt = 合并小写开头的段落块(meta_txt)
        meta_txt = 清理多余的空行(meta_txt)

        meta_txt = '\n'.join(meta_txt)
        # 清除重复的换行
        for _ in range(5):
            meta_txt = meta_txt.replace('\n\n','\n')

        # 换行 -> 双换行
        meta_txt = meta_txt.replace('\n', '\n\n')

    return meta_txt, page_one_meta

@CatchException
def 批量翻译PDF文档(txt, top_p, temperature, chatbot, history, sys_prompt, WEB_PORT):
    import glob
    import os

    # 基本信息：功能、贡献者
    chatbot.append([
        "函数插件功能？",
        "批量总结PDF文档。函数插件贡献者: Binary-Husky（二进制哈士奇）"])
    yield chatbot, history, '正常'

    # 尝试导入依赖，如果缺少依赖，则给出安装建议
    try:
        import fitz, tiktoken
    except:
        report_execption(chatbot, history,
                         a=f"解析项目: {txt}",
                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf tiktoken```。")
        yield chatbot, history, '正常'
        return

    # 清空历史，以免输入溢出
    history = []

    # 检测输入参数，如没有给定输入参数，直接退出
    if os.path.exists(txt):
        project_folder = txt
    else:
        if txt == "":
            txt = '空空如也的输入栏'
        report_execption(chatbot, history,
                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
        yield chatbot, history, '正常'
        return

    # 搜索需要处理的文件清单
    file_manifest = [f for f in glob.glob(
        f'{project_folder}/**/*.pdf', recursive=True)]

    # 如果没找到任何文件
    if len(file_manifest) == 0:
        report_execption(chatbot, history,
                         a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
        yield chatbot, history, '正常'
        return

    # 开始正式执行任务
    yield from 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, sys_prompt)


def request_gpt_model_in_new_thread_with_ui_alive(inputs, inputs_show_user, top_p, temperature, chatbot, history, sys_prompt, refresh_interval=0.2):
    import time
    from concurrent.futures import ThreadPoolExecutor
    from request_llm.bridge_chatgpt import predict_no_ui_long_connection
    # 用户反馈
    chatbot.append([inputs_show_user, ""]); msg = '正常'
    yield chatbot, [], msg
    executor = ThreadPoolExecutor(max_workers=16)
    mutable = ["", time.time()]
    future = executor.submit(lambda:
        predict_no_ui_long_connection(inputs=inputs, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt, observe_window=mutable)
    )
    while True:
        # yield一次以刷新前端页面
        time.sleep(refresh_interval)
        # “喂狗”（看门狗）
        mutable[1] = time.time()
        if future.done(): break
        chatbot[-1] = [chatbot[-1][0], mutable[0]]; msg = "正常"
        yield chatbot, [], msg
    return future.result()

def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(inputs_array, inputs_show_user_array, top_p, temperature, chatbot, history_array, sys_prompt_array, refresh_interval=0.2, max_workers=10, scroller_max_len=30):
    import time
    from concurrent.futures import ThreadPoolExecutor
    from request_llm.bridge_chatgpt import predict_no_ui_long_connection
    assert len(inputs_array) == len(history_array)
    assert len(inputs_array) == len(sys_prompt_array)
    executor = ThreadPoolExecutor(max_workers=max_workers)
    n_frag = len(inputs_array)
    # 异步原子
    mutable = [["", time.time()] for _ in range(n_frag)]
    def _req_gpt(index, inputs, history, sys_prompt):
        gpt_say = predict_no_ui_long_connection(
            inputs=inputs, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt, observe_window=mutable[index]
        )
        return gpt_say
    # 异步任务开始
    futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
    cnt = 0
    while True:
        # yield一次以刷新前端页面
        time.sleep(refresh_interval); cnt += 1
        worker_done = [h.done() for h in futures]
        if all(worker_done): executor.shutdown(); break
        # 更好的UI视觉效果
        observe_win = []
        # 每个线程都要“喂狗”（看门狗）
        for thread_index, _ in enumerate(worker_done): mutable[thread_index][1] = time.time()
        # 在前端打印些好玩的东西
        for thread_index, _ in enumerate(worker_done): 
            print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\
                replace('\n','').replace('```','...').replace(' ','.').replace('<br/>','.....').replace('$','.')+"`... ]"
            observe_win.append(print_something_really_funny)
        stat_str = ''.join([f'执行中: {obs}\n\n' if not done else '已完成\n\n' for done, obs in zip(worker_done, observe_win)])
        chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt%10+1))]; msg = "正常"
        yield chatbot, [], msg
    # 异步任务结束
    gpt_response_collection = []
    for inputs_show_user, f in zip(inputs_show_user_array, futures):
        gpt_res = f.result()
        gpt_response_collection.extend([inputs_show_user, gpt_res])
    return gpt_response_collection

def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, sys_prompt):
    import time
    import glob
    import os
    import fitz
    import tiktoken
    TOKEN_LIMIT_PER_FRAGMENT = 1600
    
    for index, fp in enumerate(file_manifest):
        # 读取PDF文件
        file_content, page_one = read_and_clean_pdf_text(fp)
        # 递归地切割PDF文件
        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
        enc = tiktoken.get_encoding("gpt2")
        get_token_num = lambda txt: len(enc.encode(txt))
        # 分解文本
        paper_fragments    = breakdown_txt_to_satisfy_token_limit_for_pdf(
            txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
        page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
            txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
        # 为了更好的效果，我们剥离Introduction之后的部分
        paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
        # 单线，获取文章meta信息
        paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
            inputs=f"以下是一篇学术论文的基础信息，请从中提取出“标题”、“收录会议或期刊”、“作者”、“摘要”、“编号”、“作者邮箱”这六个部分。请用markdown格式输出，最后用中文翻译摘要部分。请提取：{paper_meta}", 
            inputs_show_user=f"请从{fp}中提取出“标题”、“收录会议或期刊”等基本信息。", 
            top_p=top_p, temperature=temperature,
            chatbot=chatbot, history=[],
            sys_prompt="Your job is to collect information from materials。",
        )
        # 多线，翻译
        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
            inputs_array = [f"以下是你需要翻译的文章段落：\n{frag}" for frag in paper_fragments],
            inputs_show_user_array = [f""  for _ in paper_fragments],
            top_p=top_p, temperature=temperature,
            chatbot=chatbot,
            history_array=[[paper_meta] for _ in paper_fragments],
            sys_prompt_array=["请你作为一个学术翻译，把整个段落翻译成中文，要求语言简洁，禁止重复输出原文。" for _ in paper_fragments],
            max_workers=16 # OpenAI所允许的最大并行过载
        )

        final = ["", paper_meta_info + '\n\n---\n\n---\n\n---\n\n']
        final.extend(gpt_response_collection)
        res = write_results_to_file(final)
        chatbot.append((f"{fp}完成了吗？", res)); msg = "完成"
        yield chatbot, history, msg