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| from toolbox import CatchException, update_ui, report_exception | |
| from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive | |
| import datetime | |
| #以下是每类图表的PROMPT | |
| SELECT_PROMPT = """ | |
| “{subject}” | |
| ============= | |
| 以上是从文章中提取的摘要,将会使用这些摘要绘制图表。请你选择一个合适的图表类型: | |
| 1 流程图 | |
| 2 序列图 | |
| 3 类图 | |
| 4 饼图 | |
| 5 甘特图 | |
| 6 状态图 | |
| 7 实体关系图 | |
| 8 象限提示图 | |
| 不需要解释原因，仅需要输出单个不带任何标点符号的数字。 | |
| """ | |
| #没有思维导图!!!测试发现模型始终会优先选择思维导图 | |
| #流程图 | |
| PROMPT_1 = """ | |
| 请你给出围绕“{subject}”的逻辑关系图，使用mermaid语法，mermaid语法举例： | |
| ```mermaid | |
| graph TD | |
| P(编程) --> L1(Python) | |
| P(编程) --> L2(C) | |
| P(编程) --> L3(C++) | |
| P(编程) --> L4(Javascipt) | |
| P(编程) --> L5(PHP) | |
| ``` | |
| """ | |
| #序列图 | |
| PROMPT_2 = """ | |
| 请你给出围绕“{subject}”的序列图，使用mermaid语法，mermaid语法举例： | |
| ```mermaid | |
| sequenceDiagram | |
| participant A as 用户 | |
| participant B as 系统 | |
| A->>B: 登录请求 | |
| B->>A: 登录成功 | |
| A->>B: 获取数据 | |
| B->>A: 返回数据 | |
| ``` | |
| """ | |
| #类图 | |
| PROMPT_3 = """ | |
| 请你给出围绕“{subject}”的类图，使用mermaid语法，mermaid语法举例： | |
| ```mermaid | |
| classDiagram | |
| Class01 <|-- AveryLongClass : Cool | |
| Class03 *-- Class04 | |
| Class05 o-- Class06 | |
| Class07 .. Class08 | |
| Class09 --> C2 : Where am i? | |
| Class09 --* C3 | |
| Class09 --|> Class07 | |
| Class07 : equals() | |
| Class07 : Object[] elementData | |
| Class01 : size() | |
| Class01 : int chimp | |
| Class01 : int gorilla | |
| Class08 <--> C2: Cool label | |
| ``` | |
| """ | |
| #饼图 | |
| PROMPT_4 = """ | |
| 请你给出围绕“{subject}”的饼图，使用mermaid语法，mermaid语法举例： | |
| ```mermaid | |
| pie title Pets adopted by volunteers | |
| "狗" : 386 | |
| "猫" : 85 | |
| "兔子" : 15 | |
| ``` | |
| """ | |
| #甘特图 | |
| PROMPT_5 = """ | |
| 请你给出围绕“{subject}”的甘特图，使用mermaid语法，mermaid语法举例： | |
| ```mermaid | |
| gantt | |
| title 项目开发流程 | |
| dateFormat YYYY-MM-DD | |
| section 设计 | |
| 需求分析 :done, des1, 2024-01-06,2024-01-08 | |
| 原型设计 :active, des2, 2024-01-09, 3d | |
| UI设计 : des3, after des2, 5d | |
| section 开发 | |
| 前端开发 :2024-01-20, 10d | |
| 后端开发 :2024-01-20, 10d | |
| ``` | |
| """ | |
| #状态图 | |
| PROMPT_6 = """ | |
| 请你给出围绕“{subject}”的状态图，使用mermaid语法，mermaid语法举例： | |
| ```mermaid | |
| stateDiagram-v2 | |
| [*] --> Still | |
| Still --> [*] | |
| Still --> Moving | |
| Moving --> Still | |
| Moving --> Crash | |
| Crash --> [*] | |
| ``` | |
| """ | |
| #实体关系图 | |
| PROMPT_7 = """ | |
| 请你给出围绕“{subject}”的实体关系图，使用mermaid语法，mermaid语法举例： | |
| ```mermaid | |
| erDiagram | |
| CUSTOMER ||--o{ ORDER : places | |
| ORDER ||--|{ LINE-ITEM : contains | |
| CUSTOMER { | |
| string name | |
| string id | |
| } | |
| ORDER { | |
| string orderNumber | |
| date orderDate | |
| string customerID | |
| } | |
| LINE-ITEM { | |
| number quantity | |
| string productID | |
| } | |
| ``` | |
| """ | |
| #象限提示图 | |
| PROMPT_8 = """ | |
| 请你给出围绕“{subject}”的象限图，使用mermaid语法，mermaid语法举例： | |
| ```mermaid | |
| graph LR | |
| A[Hard skill] --> B(Programming) | |
| A[Hard skill] --> C(Design) | |
| D[Soft skill] --> E(Coordination) | |
| D[Soft skill] --> F(Communication) | |
| ``` | |
| """ | |
| #思维导图 | |
| PROMPT_9 = """ | |
| {subject} | |
| ========== | |
| 请给出上方内容的思维导图，充分考虑其之间的逻辑，使用mermaid语法，mermaid语法举例： | |
| ```mermaid | |
| mindmap | |
| root((mindmap)) | |
| Origins | |
| Long history | |
| ::icon(fa fa-book) | |
| Popularisation | |
| British popular psychology author Tony Buzan | |
| Research | |
| On effectiveness<br/>and features | |
| On Automatic creation | |
| Uses | |
| Creative techniques | |
| Strategic planning | |
| Argument mapping | |
| Tools | |
| Pen and paper | |
| Mermaid | |
| ``` | |
| """ | |
| def 解析历史输入(history,llm_kwargs,file_manifest,chatbot,plugin_kwargs): | |
| ############################## <第 0 步，切割输入> ################################## | |
| # 借用PDF切割中的函数对文本进行切割 | |
| TOKEN_LIMIT_PER_FRAGMENT = 2500 | |
| txt = str(history).encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars | |
| from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit | |
| txt = breakdown_text_to_satisfy_token_limit(txt=txt, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model']) | |
| ############################## <第 1 步，迭代地历遍整个文章，提取精炼信息> ################################## | |
| results = [] | |
| MAX_WORD_TOTAL = 4096 | |
| n_txt = len(txt) | |
| last_iteration_result = "从以下文本中提取摘要。" | |
| if n_txt >= 20: print('文章极长，不能达到预期效果') | |
| for i in range(n_txt): | |
| NUM_OF_WORD = MAX_WORD_TOTAL // n_txt | |
| i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words in Chinese: {txt[i]}" | |
| i_say_show_user = f"[{i+1}/{n_txt}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {txt[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 content of the previous section is?", last_iteration_result], # 迭代上一次的结果 | |
| sys_prompt="Extracts the main content from the text section where it is located for graphing purposes, answer me with Chinese." # 提示 | |
| ) | |
| results.append(gpt_say) | |
| last_iteration_result = gpt_say | |
| ############################## <第 2 步，根据整理的摘要选择图表类型> ################################## | |
| if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg") | |
| gpt_say = plugin_kwargs.get("advanced_arg", "") #将图表类型参数赋值为插件参数 | |
| results_txt = '\n'.join(results) #合并摘要 | |
| if gpt_say not in ['1','2','3','4','5','6','7','8','9']: #如插件参数不正确则使用对话模型判断 | |
| i_say_show_user = f'接下来将判断适合的图表类型,如连续3次判断失败将会使用流程图进行绘制'; 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 | |
| i_say = SELECT_PROMPT.format(subject=results_txt) | |
| i_say_show_user = f'请判断适合使用的流程图类型,其中数字对应关系为:1-流程图,2-序列图,3-类图,4-饼图,5-甘特图,6-状态图,7-实体关系图,8-象限提示图。由于不管提供文本是什么,模型大概率认为"思维导图"最合适,因此思维导图仅能通过参数调用。' | |
| for i in range(3): | |
| 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="" | |
| ) | |
| if gpt_say in ['1','2','3','4','5','6','7','8','9']: #判断返回是否正确 | |
| break | |
| if gpt_say not in ['1','2','3','4','5','6','7','8','9']: | |
| gpt_say = '1' | |
| ############################## <第 3 步，根据选择的图表类型绘制图表> ################################## | |
| if gpt_say == '1': | |
| i_say = PROMPT_1.format(subject=results_txt) | |
| elif gpt_say == '2': | |
| i_say = PROMPT_2.format(subject=results_txt) | |
| elif gpt_say == '3': | |
| i_say = PROMPT_3.format(subject=results_txt) | |
| elif gpt_say == '4': | |
| i_say = PROMPT_4.format(subject=results_txt) | |
| elif gpt_say == '5': | |
| i_say = PROMPT_5.format(subject=results_txt) | |
| elif gpt_say == '6': | |
| i_say = PROMPT_6.format(subject=results_txt) | |
| elif gpt_say == '7': | |
| i_say = PROMPT_7.replace("{subject}", results_txt) #由于实体关系图用到了{}符号 | |
| elif gpt_say == '8': | |
| i_say = PROMPT_8.format(subject=results_txt) | |
| elif gpt_say == '9': | |
| i_say = PROMPT_9.format(subject=results_txt) | |
| i_say_show_user = f'请根据判断结果绘制相应的图表。如需绘制思维导图请使用参数调用,同时过大的图表可能需要复制到在线编辑器中进行渲染。' | |
| 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="" | |
| ) | |
| history.append(gpt_say) | |
| yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新 | |
| def 生成多种Mermaid图表(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): | |
| """ | |
| txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径 | |
| llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行 | |
| plugin_kwargs 插件模型的参数，用于灵活调整复杂功能的各种参数 | |
| chatbot 聊天显示框的句柄，用于显示给用户 | |
| history 聊天历史，前情提要 | |
| system_prompt 给gpt的静默提醒 | |
| web_port 当前软件运行的端口号 | |
| """ | |
| import os | |
| # 基本信息：功能、贡献者 | |
| chatbot.append([ | |
| "函数插件功能？", | |
| "根据当前聊天历史或指定的路径文件(文件内容优先)绘制多种mermaid图表，将会由对话模型首先判断适合的图表类型，随后绘制图表。\ | |
| \n您也可以使用插件参数指定绘制的图表类型,函数插件贡献者: Menghuan1918"]) | |
| yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 | |
| if os.path.exists(txt): #如输入区无内容则直接解析历史记录 | |
| from crazy_functions.pdf_fns.parse_word import extract_text_from_files | |
| file_exist, final_result, page_one, file_manifest, excption = extract_text_from_files(txt, chatbot, history) | |
| else: | |
| file_exist = False | |
| excption = "" | |
| file_manifest = [] | |
| if excption != "": | |
| if excption == "word": | |
| report_exception(chatbot, history, | |
| a = f"解析项目: {txt}", | |
| b = f"找到了.doc文件，但是该文件格式不被支持，请先转化为.docx格式。") | |
| elif excption == "pdf": | |
| 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```。") | |
| elif excption == "word_pip": | |
| 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 python-docx pywin32```。") | |
| yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 | |
| else: | |
| if not file_exist: | |
| history.append(txt) #如输入区不是文件则将输入区内容加入历史记录 | |
| i_say_show_user = f'首先你从历史记录中提取摘要。'; 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=history) # 更新UI | |
| yield from 解析历史输入(history,llm_kwargs,file_manifest,chatbot,plugin_kwargs) | |
| else: | |
| file_num = len(file_manifest) | |
| for i in range(file_num): #依次处理文件 | |
| i_say_show_user = f"[{i+1}/{file_num}]处理文件{file_manifest[i]}"; 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=history) # 更新UI | |
| history = [] #如输入区内容为文件则清空历史记录 | |
| history.append(final_result[i]) | |
| yield from 解析历史输入(history,llm_kwargs,file_manifest,chatbot,plugin_kwargs) |