from toolbox import update_ui from toolbox import CatchException, report_execption, write_results_to_file fast_debug = False class PaperFileGroup(): def __init__(self): self.file_paths = [] self.file_contents = [] self.sp_file_contents = [] self.sp_file_index = [] self.sp_file_tag = [] # count_token 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=())) self.get_token_num = get_token_num def run_file_split(self, max_token_limit=1900): """ 将长文本分离开来 """ for index, file_content in enumerate(self.file_contents): if self.get_token_num(file_content) < max_token_limit: self.sp_file_contents.append(file_content) self.sp_file_index.append(index) self.sp_file_tag.append(self.file_paths[index]) else: from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit) for j, segment in enumerate(segments): self.sp_file_contents.append(segment) self.sp_file_index.append(index) self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex") print('Segmentation: done') def 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'): import time, os, re from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency # <-------- 读取Latex文件，删除其中的所有注释 ----------> pfg = PaperFileGroup() for index, fp in enumerate(file_manifest): with open(fp, 'r', encoding='utf-8', errors='replace') as f: file_content = f.read() # 定义注释的正则表达式 comment_pattern = r'%.*' # 使用正则表达式查找注释，并替换为空字符串 clean_tex_content = re.sub(comment_pattern, '', file_content) # 记录删除注释后的文本 pfg.file_paths.append(fp) pfg.file_contents.append(clean_tex_content) # <-------- 拆分过长的latex文件 ----------> pfg.run_file_split(max_token_limit=1024) n_split = len(pfg.sp_file_contents) # <-------- 抽取摘要 ----------> # if language == 'en': # abs_extract_inputs = f"Please write an abstract for this paper" # # 单线，获取文章meta信息 # paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive( # inputs=abs_extract_inputs, # inputs_show_user=f"正在抽取摘要信息。", # llm_kwargs=llm_kwargs, # chatbot=chatbot, history=[], # sys_prompt="Your job is to collect information from materials。", # ) # <-------- 多线程润色开始 ----------> if language == 'en': inputs_array = ["Below is a section from an academic paper, polish this section to meet the academic standard, improve the grammar, clarity and overall readability, do not modify any latex command such as \section, \cite and equations:" + f"\n\n{frag}" for frag in pfg.sp_file_contents] inputs_show_user_array = [f"Polish {f}" for f in pfg.sp_file_tag] sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)] elif language == 'zh': inputs_array = [f"以下是一篇学术论文中的一段内容，请将此部分润色以满足学术标准，提高语法、清晰度和整体可读性，不要修改任何LaTeX命令，例如\section，\cite和方程式：" + f"\n\n{frag}" for frag in pfg.sp_file_contents] inputs_show_user_array = [f"润色 {f}" for f in pfg.sp_file_tag] sys_prompt_array=["你是一位专业的中文学术论文作家。" for _ in range(n_split)] gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( inputs_array=inputs_array, inputs_show_user_array=inputs_show_user_array, llm_kwargs=llm_kwargs, chatbot=chatbot, history_array=[[""] for _ in range(n_split)], sys_prompt_array=sys_prompt_array, # max_workers=5, # 并行任务数量限制，最多同时执行5个，其他的排队等待 scroller_max_len = 80 ) # <-------- 整理结果，退出 ----------> create_report_file_name = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + f"-chatgpt.polish.md" res = write_results_to_file(gpt_response_collection, file_name=create_report_file_name) history = gpt_response_collection chatbot.append((f"{fp}完成了吗？", res)) yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 @CatchException def Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): # 基本信息：功能、贡献者 chatbot.append([ "函数插件功能？", "对整个Latex项目进行润色。函数插件贡献者: Binary-Husky"]) 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 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 tiktoken```。") yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 return history = [] # 清空历史，以免输入溢出 import glob, os 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) # 刷新界面 return file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] if len(file_manifest) == 0: report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}") yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 return yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en') @CatchException def Latex中文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): # 基本信息：功能、贡献者 chatbot.append([ "函数插件功能？", "对整个Latex项目进行润色。函数插件贡献者: Binary-Husky"]) 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 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 tiktoken```。") yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 return history = [] # 清空历史，以免输入溢出 import glob, os 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) # 刷新界面 return file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] if len(file_manifest) == 0: report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}") yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 return yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh')