Add 2 plugins

crazy_functional.py

@@ -603,7 +603,35 @@ def get_crazy_functions():
     except:
         print(trimmed_format_exc())
         print('Load function plugin failed')
+    try:
+        from crazy_functions.批量总结PDF文档_初步 import 批量总结PDF文档_初步
+        function_plugins.update({
+            "批量总结PDF文档_初步": {
+                "Group": "学术",
+                "Color": "stop",
+                "AsButton": False,
+                "Info": "批量总结PDF文档的内容(仅做初步提炼) | 输入参数为路径",
+                "Function": HotReload(批量总结PDF文档_初步)
+            }
+        })
+    except:
+        print(trimmed_format_exc())
+        print('Load function plugin failed')
 
+    try:
+        from crazy_functions.批量总结Markdown文档_进阶 import 批量总结Markdown文档_进阶
+        function_plugins.update({
+            "批量总结Markdown文档_进阶": {
+                "Group": "学术",
+                "Color": "stop",
+                "AsButton": False,
+                "Info": "批量总结Markdown文档的内容(在初步提炼的基础上进一步总结) | 输入参数为路径",
+                "Function": HotReload(批量总结Markdown文档_进阶)
+            }
+        })
+    except:
+        print(trimmed_format_exc())
+        print('Load function plugin failed')
     # try:
     #     from crazy_functions.chatglm微调工具 import 微调数据集生成
     #     function_plugins.update({

crazy_functions/批量总结Markdown文档_进阶.py

@@ -0,0 +1,127 @@
+import logging, os
+from toolbox import update_ui, promote_file_to_downloadzone, gen_time_str, get_log_folder
+from toolbox import CatchException, report_exception, trimmed_format_exc
+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 request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+from .crazy_utils import input_clipping
+
+
+def 总结Markdown(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
+    file_write_buffer = []
+    SUMMARY_WORD_LIMIT = 800
+    meta_inputs_array = []
+    meta_inputs_show_user_array = []
+    meta_sys_prompt_array = []
+    inputs_array = []
+    inputs_show_user_array = []
+    sys_prompt_array = []
+    file_name_array = []
+    for idx, file_name in enumerate(file_manifest):
+        print('begin analysis on:', file_name)
+        file_name_array.append(f'# {idx}.{os.path.basename(file_name)}')
+
+        with open(file_name, 'r', encoding='utf-8', errors='replace') as f:
+            file_content = f.read()
+
+        _ = file_content.split('## metadata')
+        if len(_) >= 2:
+            file_meta = _[-2]
+            file_content = _[-1]
+        else:
+            file_meta = file_name
+
+        meta_inputs_array.append(
+            "我需要你从一段文本中识别并提取出这篇文章的1.标题、2.作者、3.作者单位、4.关键词。"
+            "其中，1.标题和4.关键词需要给出中文和英文的双语结果，2.作者和3.作者单位按原文语言给出。"
+            "以下是需要你识别的文本: " + file_meta
+        )
+        meta_inputs_show_user_array.append(
+            '开始分析元数据：' + file_name
+        )
+        meta_sys_prompt_array.append("As an academic professional, you need to extract basic informations of the paper from its metadata")
+
+        inputs_array.append(
+            "我需要你根据我提供的文本总结一份Markdown文档，分为四个部分：1.研究背景，2.文章主要内容，3.主要创新点，4.结论。"
+            + f"各部分的题目采用二级标题前缀(## ),内容可适当的分为若干条，总字数不超过{SUMMARY_WORD_LIMIT}个中文字符."
+            + "以下是需要你处理的文本: " + file_content)
+        inputs_show_user_array.append('开始总结：' + file_name)
+        sys_prompt_array.append(f"As an academic professional, you need to summarize the text with less than {SUMMARY_WORD_LIMIT} Chinese characters")
+
+    gpt_meta_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
+        inputs_array=meta_inputs_array,
+        inputs_show_user_array=meta_inputs_show_user_array,
+        llm_kwargs=llm_kwargs,
+        chatbot=chatbot,
+        history_array=[[""] for _ in range(len(inputs_array))],
+        sys_prompt_array=meta_sys_prompt_array,
+        # max_workers=5,  # OpenAI所允许的最大并行过载
+        scroller_max_len=80
+    )
+
+    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(len(inputs_array))],
+        sys_prompt_array=sys_prompt_array,
+        # max_workers=5,  # OpenAI所允许的最大并行过载
+        scroller_max_len=80
+    )
+    try:
+        for idx, (gpt_say_meta, gpt_say) in enumerate(zip(gpt_meta_response_collection[1::2], gpt_response_collection[1::2])):
+            file_write_buffer.append(file_name_array[idx])
+            file_write_buffer.append("## 元数据\n\n" + gpt_say_meta)
+            file_write_buffer.append(gpt_say)
+    except:
+        logging.error(trimmed_format_exc())
+
+    res = write_history_to_file(file_write_buffer, file_basename="result.md", auto_caption=False)
+    promote_file_to_downloadzone(res, chatbot=chatbot)
+    yield from update_ui(chatbot=chatbot, history=gpt_response_collection) # 刷新界面
+
+
+@CatchException
+def 批量总结Markdown文档_进阶(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, 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Markdown文档。函数插件贡献者: ValeriaWong，Eralien，Joshua Reed"])
+    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}/**/*.md', 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.md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 总结Markdown(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/批量总结PDF文档_初步.py

@@ -0,0 +1,131 @@
+import zipfile
+import os
+from toolbox import update_ui, promote_file_to_downloadzone, gen_time_str, get_log_folder
+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
+pj = os.path.join
+
+
+def move_file_to_zip(file_path, zip_file):
+    zip_file.write(file_path, os.path.basename(file_path))
+    os.remove(file_path)
+
+
+def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
+    zip_file_path = pj(get_log_folder(), 'result.zip')
+    with zipfile.ZipFile(zip_file_path, 'w') as zip_file:
+        for file_name in file_manifest:
+            file_write_buffer = []
+            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_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
+            from request_llms.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("## metadata\n\n" + paper_meta + "\n\n## metadata")
+
+            ############################## <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 in Chinese 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)
+            file_write_buffer.extend(final_results)
+
+            ############################## <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,
+                file_basename=os.path.splitext(os.path.basename(file_name))[0] + '.md',
+                auto_caption=False
+            )
+            if len(file_manifest) == 1:
+                promote_file_to_downloadzone(res, chatbot=chatbot)
+                return
+            move_file_to_zip(res, zip_file)
+
+    promote_file_to_downloadzone(zip_file_path, chatbot=chatbot)
+
+
+@CatchException
+def 批量总结PDF文档_初步(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, 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，Joshua Reed"])
+    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)