diff --git a/crazy_functions/批量总结PDF文档.py b/crazy_functions/批量总结PDF文档.py index ea8c0526..fc65f5c8 100644 --- a/crazy_functions/批量总结PDF文档.py +++ b/crazy_functions/批量总结PDF文档.py @@ -1,121 +1,107 @@ -from toolbox import update_ui +from toolbox import update_ui, promote_file_to_downloadzone, gen_time_str from toolbox import CatchException, report_execption, write_results_to_file -import re -import unicodedata -fast_debug = False 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 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 - 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, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt): - 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) + 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 - 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 + 1}/{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) # 刷新界面 + from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf + from request_llm.bridge_all import model_info + enc = model_info["gpt-3.5-turbo"]['tokenizer'] + def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) + 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（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) - if not fast_debug: - msg = '正常' - # ** gpt request ** - gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive( - inputs=i_say, - inputs_show_user=i_say_show_user, - llm_kwargs=llm_kwargs, - chatbot=chatbot, - history=[], - sys_prompt="总结文章。" - ) # 带超时倒计时 - + ############################## <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 - 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=history, msg=msg) # 刷新界面 - if not fast_debug: time.sleep(2) + 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 - 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) # 刷新界面 - - if not fast_debug: - msg = '正常' - # ** gpt request ** + ############################## <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=i_say, - llm_kwargs=llm_kwargs, - chatbot=chatbot, - history=history, - sys_prompt="总结文章。" - ) # 带超时倒计时 + 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 - chatbot[-1] = (i_say, gpt_say) - history.append(i_say); history.append(gpt_say) - yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面 - res = write_results_to_file(history) - chatbot.append(("完成了吗？", res)) - yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面 + res = write_results_to_file(file_write_buffer, file_name=gen_time_str()) + promote_file_to_downloadzone(res.split('\t')[-1], chatbot=chatbot) + yield from update_ui(chatbot=chatbot, history=final_results) # 刷新界面 @CatchException @@ -151,10 +137,7 @@ def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst 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)] + 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: diff --git a/toolbox.py b/toolbox.py index 4bd84299..3a7d89d3 100644 --- a/toolbox.py +++ b/toolbox.py @@ -214,7 +214,7 @@ def write_results_to_file(history, file_name=None): # remove everything that cannot be handled by utf8 f.write(content.encode('utf-8', 'ignore').decode()) f.write('\n\n') - res = '以上材料已经被写入' + os.path.abspath(f'./gpt_log/{file_name}') + res = '以上材料已经被写入:\t' + os.path.abspath(f'./gpt_log/{file_name}') print(res) return res @@ -467,8 +467,11 @@ def promote_file_to_downloadzone(file, rename_file=None, chatbot=None): import shutil if rename_file is None: rename_file = f'{gen_time_str()}-{os.path.basename(file)}' new_path = os.path.join(f'./gpt_log/', rename_file) + # 如果已经存在，先删除 if os.path.exists(new_path) and not os.path.samefile(new_path, file): os.remove(new_path) + # 把文件复制过去 if not os.path.exists(new_path): shutil.copyfile(file, new_path) + # 将文件添加到chatbot cookie中，避免多用户干扰 if chatbot: if 'file_to_promote' in chatbot._cookies: current = chatbot._cookies['file_to_promote'] else: current = []