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| from request_llm.bridge_chatgpt import predict_no_ui | |
| from toolbox import update_ui | |
| from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down | |
| import re | |
| import unicodedata | |
| fast_debug = False | |
| 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 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt): | |
| import time, glob, os, fitz | |
| print('begin analysis on:', file_manifest) | |
| for index, fp in enumerate(file_manifest): | |
| with fitz.open(fp) as doc: | |
| file_content = "" | |
| for page in doc: | |
| file_content += page.get_text() | |
| file_content = clean_text(file_content) | |
| print(file_content) | |
| prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else "" | |
| i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```' | |
| i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}' | |
| chatbot.append((i_say_show_user, "[Local Message] waiting gpt response.")) | |
| yield from update_ui(chatbot=chatbot, history=history) | |
| if not fast_debug: | |
| msg = '正常' | |
| # ** gpt request ** | |
| gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[]) # 带超时倒计时 | |
| chatbot[-1] = (i_say_show_user, gpt_say) | |
| history.append(i_say_show_user); history.append(gpt_say) | |
| yield from update_ui(chatbot=chatbot, history=chatbot, msg=msg) | |
| if not fast_debug: time.sleep(2) | |
| all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)]) | |
| i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。' | |
| chatbot.append((i_say, "[Local Message] waiting gpt response.")) | |
| yield from update_ui(chatbot=chatbot, history=history) | |
| if not fast_debug: | |
| msg = '正常' | |
| # ** gpt request ** | |
| gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say, chatbot, top_p, temperature, history=history) # 带超时倒计时 | |
| chatbot[-1] = (i_say, gpt_say) | |
| history.append(i_say); history.append(gpt_say) | |
| yield from update_ui(chatbot=chatbot, history=chatbot, msg=msg) | |
| res = write_results_to_file(history) | |
| chatbot.append(("完成了吗？", res)) | |
| yield from update_ui(chatbot=chatbot, history=chatbot, msg=msg) | |
| def 批量总结PDF文档(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT): | |
| import glob, 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: ValeriaWong，Eralien"]) | |
| yield from update_ui(chatbot=chatbot, history=history) | |
| # 尝试导入依赖，如果缺少依赖，则给出安装建议 | |
| try: | |
| import fitz | |
| 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```。") | |
| yield from update_ui(chatbot=chatbot, history=history) | |
| 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 from update_ui(chatbot=chatbot, history=history) | |
| 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)] # + \ | |
| # [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \ | |
| # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \ | |
| # [f for f in glob.glob(f'{project_folder}/**/*.c', 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 from update_ui(chatbot=chatbot, history=history) | |
| return | |
| # 开始正式执行任务 | |
| yield from 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt) | |