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| from toolbox import update_ui, promote_file_to_downloadzone, gen_time_str | |
| from toolbox import CatchException, report_exception | |
| from toolbox import write_history_to_file, promote_file_to_downloadzone | |
| from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive | |
| from .crazy_utils import read_and_clean_pdf_text | |
| from .crazy_utils import input_clipping | |
| def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt): | |
| file_write_buffer = [] | |
| for file_name in file_manifest: | |
| print('begin analysis on:', file_name) | |
| ############################## <第 0 步，切割PDF> ################################## | |
| # 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割） | |
| # 的长度必须小于 2500 个 Token | |
| file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF | |
| file_content = file_content.encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars | |
| page_one = str(page_one).encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars | |
| TOKEN_LIMIT_PER_FRAGMENT = 2500 | |
| from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit | |
| paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model']) | |
| page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=str(page_one), limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model']) | |
| # 为了更好的效果，我们剥离Introduction之后的部分（如果有） | |
| paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0] | |
| ############################## <第 1 步，从摘要中提取高价值信息，放到history中> ################################## | |
| final_results = [] | |
| final_results.append(paper_meta) | |
| ############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ################################## | |
| i_say_show_user = f'首先你在中文语境下通读整篇论文。'; gpt_say = "[Local Message] 收到。" # 用户提示 | |
| chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[]) # 更新UI | |
| iteration_results = [] | |
| last_iteration_result = paper_meta # 初始值是摘要 | |
| MAX_WORD_TOTAL = 4096 * 0.7 | |
| n_fragment = len(paper_fragments) | |
| if n_fragment >= 20: print('文章极长，不能达到预期效果') | |
| for i in range(n_fragment): | |
| NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment | |
| i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} Chinese characters: {paper_fragments[i]}" | |
| i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} Chinese characters: {paper_fragments[i][:200]}" | |
| gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问 | |
| llm_kwargs, chatbot, | |
| history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果 | |
| sys_prompt="Extract the main idea of this section with Chinese." # 提示 | |
| ) | |
| iteration_results.append(gpt_say) | |
| last_iteration_result = gpt_say | |
| ############################## <第 3 步，整理history，提取总结> ################################## | |
| final_results.extend(iteration_results) | |
| final_results.append(f'Please conclude this paper discussed above。') | |
| # This prompt is from https://github.com/kaixindelele/ChatPaper/blob/main/chat_paper.py | |
| NUM_OF_WORD = 1000 | |
| i_say = """ | |
| 1. Mark the title of the paper (with Chinese translation) | |
| 2. list all the authors' names (use English) | |
| 3. mark the first author's affiliation (output Chinese translation only) | |
| 4. mark the keywords of this article (use English) | |
| 5. link to the paper, Github code link (if available, fill in Github:None if not) | |
| 6. summarize according to the following four points.Be sure to use Chinese answers (proper nouns need to be marked in English) | |
| - (1):What is the research background of this article? | |
| - (2):What are the past methods? What are the problems with them? Is the approach well motivated? | |
| - (3):What is the research methodology proposed in this paper? | |
| - (4):On what task and what performance is achieved by the methods in this paper? Can the performance support their goals? | |
| Follow the format of the output that follows: | |
| 1. Title: xxx\n\n | |
| 2. Authors: xxx\n\n | |
| 3. Affiliation: xxx\n\n | |
| 4. Keywords: xxx\n\n | |
| 5. Urls: xxx or xxx , xxx \n\n | |
| 6. Summary: \n\n | |
| - (1):xxx;\n | |
| - (2):xxx;\n | |
| - (3):xxx;\n | |
| - (4):xxx.\n\n | |
| Be sure to use Chinese answers (proper nouns need to be marked in English), statements as concise and academic as possible, | |
| do not have too much repetitive information, numerical values using the original numbers. | |
| """ | |
| # This prompt is from https://github.com/kaixindelele/ChatPaper/blob/main/chat_paper.py | |
| file_write_buffer.extend(final_results) | |
| i_say, final_results = input_clipping(i_say, final_results, max_token_limit=2000) | |
| gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive( | |
| inputs=i_say, inputs_show_user='开始最终总结', | |
| llm_kwargs=llm_kwargs, chatbot=chatbot, history=final_results, | |
| sys_prompt= f"Extract the main idea of this paper with less than {NUM_OF_WORD} Chinese characters" | |
| ) | |
| final_results.append(gpt_say) | |
| file_write_buffer.extend([i_say, gpt_say]) | |
| ############################## <第 4 步，设置一个token上限> ################################## | |
| _, final_results = input_clipping("", final_results, max_token_limit=3200) | |
| yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了 | |
| res = write_history_to_file(file_write_buffer) | |
| promote_file_to_downloadzone(res, chatbot=chatbot) | |
| yield from update_ui(chatbot=chatbot, history=final_results) # 刷新界面 | |
| def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request): | |
| 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) # 刷新界面 | |
| # 尝试导入依赖，如果缺少依赖，则给出安装建议 | |
| try: | |
| import fitz | |
| except: | |
| report_exception(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) # 刷新界面 | |
| 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_exception(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) # 刷新界面 | |
| 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_exception(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) # 刷新界面 | |
| return | |
| # 开始正式执行任务 | |
| yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) | |