镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 14:36:48 +00:00
normalize source code names
这个提交包含在：
binary-husky
@@ -0,0 +1,437 @@
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from toolbox import CatchException, update_ui, report_exception
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from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
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from crazy_functions.plugin_template.plugin_class_template import (
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GptAcademicPluginTemplate,
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)
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from crazy_functions.plugin_template.plugin_class_template import ArgProperty
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# 以下是每类图表的PROMPT
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SELECT_PROMPT = """
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“{subject}”
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=============
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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:
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1 流程图
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2 序列图
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3 类图
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4 饼图
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5 甘特图
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6 状态图
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7 实体关系图
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8 象限提示图
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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。
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"""
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# 没有思维导图!!!测试发现模型始终会优先选择思维导图
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# 流程图
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PROMPT_1 = """
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请你给出围绕“{subject}”的逻辑关系图，使用mermaid语法，注意需要使用双引号将内容括起来。
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mermaid语法举例：
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```mermaid
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graph TD
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P("编程") --> L1("Python")
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P("编程") --> L2("C")
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P("编程") --> L3("C++")
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P("编程") --> L4("Javascipt")
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P("编程") --> L5("PHP")
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```
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"""
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# 序列图
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PROMPT_2 = """
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请你给出围绕“{subject}”的序列图，使用mermaid语法。
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mermaid语法举例：
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```mermaid
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sequenceDiagram
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participant A as 用户
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participant B as 系统
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A->>B: 登录请求
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B->>A: 登录成功
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A->>B: 获取数据
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B->>A: 返回数据
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```
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"""
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# 类图
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PROMPT_3 = """
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请你给出围绕“{subject}”的类图，使用mermaid语法。
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mermaid语法举例：
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```mermaid
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classDiagram
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Class01 <|-- AveryLongClass : Cool
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Class03 *-- Class04
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Class05 o-- Class06
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Class07 .. Class08
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Class09 --> C2 : Where am i?
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Class09 --* C3
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Class09 --|> Class07
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Class07 : equals()
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Class07 : Object[] elementData
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Class01 : size()
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Class01 : int chimp
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Class01 : int gorilla
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Class08 <--> C2: Cool label
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```
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"""
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# 饼图
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PROMPT_4 = """
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请你给出围绕“{subject}”的饼图，使用mermaid语法，注意需要使用双引号将内容括起来。
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mermaid语法举例：
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```mermaid
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pie title Pets adopted by volunteers
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"狗" : 386
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"猫" : 85
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"兔子" : 15
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```
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"""
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# 甘特图
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PROMPT_5 = """
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请你给出围绕“{subject}”的甘特图，使用mermaid语法，注意需要使用双引号将内容括起来。
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mermaid语法举例：
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```mermaid
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gantt
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title "项目开发流程"
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dateFormat YYYY-MM-DD
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section "设计"
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"需求分析" :done, des1, 2024-01-06,2024-01-08
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"原型设计" :active, des2, 2024-01-09, 3d
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"UI设计" : des3, after des2, 5d
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section "开发"
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"前端开发" :2024-01-20, 10d
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"后端开发" :2024-01-20, 10d
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```
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"""
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# 状态图
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PROMPT_6 = """
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请你给出围绕“{subject}”的状态图，使用mermaid语法，注意需要使用双引号将内容括起来。
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mermaid语法举例：
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```mermaid
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stateDiagram-v2
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[*] --> "Still"
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"Still" --> [*]
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"Still" --> "Moving"
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"Moving" --> "Still"
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"Moving" --> "Crash"
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"Crash" --> [*]
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```
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"""
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# 实体关系图
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PROMPT_7 = """
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请你给出围绕“{subject}”的实体关系图，使用mermaid语法。
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mermaid语法举例：
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```mermaid
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erDiagram
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CUSTOMER ||--o{ ORDER : places
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ORDER ||--|{ LINE-ITEM : contains
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CUSTOMER {
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string name
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string id
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}
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ORDER {
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string orderNumber
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date orderDate
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string customerID
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}
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LINE-ITEM {
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number quantity
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string productID
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}
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```
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"""
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# 象限提示图
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PROMPT_8 = """
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请你给出围绕“{subject}”的象限图，使用mermaid语法，注意需要使用双引号将内容括起来。
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mermaid语法举例：
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```mermaid
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graph LR
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A["Hard skill"] --> B("Programming")
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A["Hard skill"] --> C("Design")
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D["Soft skill"] --> E("Coordination")
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D["Soft skill"] --> F("Communication")
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```
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"""
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# 思维导图
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PROMPT_9 = """
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{subject}
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==========
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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，注意需要使用双引号将内容括起来。
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mermaid语法举例：
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```mermaid
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mindmap
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root((mindmap))
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("Origins")
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("Long history")
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::icon(fa fa-book)
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("Popularisation")
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("British popular psychology author Tony Buzan")
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::icon(fa fa-user)
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("Research")
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("On effectiveness<br/>and features")
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::icon(fa fa-search)
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("On Automatic creation")
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::icon(fa fa-robot)
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("Uses")
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("Creative techniques")
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::icon(fa fa-lightbulb-o)
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("Strategic planning")
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::icon(fa fa-flag)
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("Argument mapping")
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::icon(fa fa-comments)
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("Tools")
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("Pen and paper")
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::icon(fa fa-pencil)
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("Mermaid")
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::icon(fa fa-code)
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```
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"""
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def 解析历史输入(history, llm_kwargs, file_manifest, chatbot, plugin_kwargs):
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############################## <第 0 步，切割输入> ##################################
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# 借用PDF切割中的函数对文本进行切割
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TOKEN_LIMIT_PER_FRAGMENT = 2500
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txt = (
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str(history).encode("utf-8", "ignore").decode()
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) # avoid reading non-utf8 chars
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from crazy_functions.pdf_fns.breakdown_txt import (
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breakdown_text_to_satisfy_token_limit,
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)
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txt = breakdown_text_to_satisfy_token_limit(
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txt=txt, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs["llm_model"]
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)
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############################## <第 1 步，迭代地历遍整个文章，提取精炼信息> ##################################
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results = []
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MAX_WORD_TOTAL = 4096
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n_txt = len(txt)
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last_iteration_result = "从以下文本中提取摘要。"
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for i in range(n_txt):
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NUM_OF_WORD = MAX_WORD_TOTAL // n_txt
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i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words in Chinese: {txt[i]}"
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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]} ...."
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gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
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i_say,
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i_say_show_user, # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
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llm_kwargs,
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chatbot,
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history=[
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"The main content of the previous section is?",
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last_iteration_result,
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], # 迭代上一次的结果
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sys_prompt="Extracts the main content from the text section where it is located for graphing purposes, answer me with Chinese.", # 提示
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)
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results.append(gpt_say)
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last_iteration_result = gpt_say
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############################## <第 2 步，根据整理的摘要选择图表类型> ##################################
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gpt_say = str(plugin_kwargs) # 将图表类型参数赋值为插件参数
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results_txt = "\n".join(results) # 合并摘要
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if gpt_say not in [
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"1",
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"2",
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"3",
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"4",
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"5",
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"6",
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"7",
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"8",
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"9",
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]: # 如插件参数不正确则使用对话模型判断
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i_say_show_user = (
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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"
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)
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gpt_say = "[Local Message] 收到。" # 用户提示
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chatbot.append([i_say_show_user, gpt_say])
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yield from update_ui(chatbot=chatbot, history=[]) # 更新UI
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i_say = SELECT_PROMPT.format(subject=results_txt)
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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。'
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for i in range(3):
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gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
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inputs=i_say,
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inputs_show_user=i_say_show_user,
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llm_kwargs=llm_kwargs,
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chatbot=chatbot,
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history=[],
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sys_prompt="",
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)
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if gpt_say in [
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"1",
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"2",
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"3",
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"4",
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"5",
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"6",
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"7",
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"8",
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"9",
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]: # 判断返回是否正确
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break
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if gpt_say not in ["1", "2", "3", "4", "5", "6", "7", "8", "9"]:
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gpt_say = "1"
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############################## <第 3 步，根据选择的图表类型绘制图表> ##################################
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if gpt_say == "1":
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i_say = PROMPT_1.format(subject=results_txt)
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elif gpt_say == "2":
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i_say = PROMPT_2.format(subject=results_txt)
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elif gpt_say == "3":
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i_say = PROMPT_3.format(subject=results_txt)
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elif gpt_say == "4":
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i_say = PROMPT_4.format(subject=results_txt)
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elif gpt_say == "5":
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i_say = PROMPT_5.format(subject=results_txt)
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elif gpt_say == "6":
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i_say = PROMPT_6.format(subject=results_txt)
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elif gpt_say == "7":
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i_say = PROMPT_7.replace("{subject}", results_txt) # 由于实体关系图用到了{}符号
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elif gpt_say == "8":
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i_say = PROMPT_8.format(subject=results_txt)
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elif gpt_say == "9":
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i_say = PROMPT_9.format(subject=results_txt)
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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。"
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gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
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inputs=i_say,
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inputs_show_user=i_say_show_user,
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llm_kwargs=llm_kwargs,
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chatbot=chatbot,
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history=[],
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sys_prompt="",
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)
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history.append(gpt_say)
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
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@CatchException
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def Mermaid_Figure_Gen(
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txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port
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):
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"""
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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
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llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
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plugin_kwargs 插件模型的参数，用于灵活调整复杂功能的各种参数
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chatbot 聊天显示框的句柄，用于显示给用户
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history 聊天历史，前情提要
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system_prompt 给gpt的静默提醒
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web_port 当前软件运行的端口号
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"""
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import os
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# 基本信息：功能、贡献者
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chatbot.append(
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[
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"函数插件功能？",
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"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。\
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\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",
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]
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)
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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if os.path.exists(txt): # 如输入区无内容则直接解析历史记录
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from crazy_functions.pdf_fns.parse_word import extract_text_from_files
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file_exist, final_result, page_one, file_manifest, exception = (
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extract_text_from_files(txt, chatbot, history)
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)
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else:
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file_exist = False
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exception = ""
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file_manifest = []
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if exception != "":
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if exception == "word":
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report_exception(
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chatbot,
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history,
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a=f"解析项目: {txt}",
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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。",
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)
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elif exception == "pdf":
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report_exception(
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chatbot,
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history,
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a=f"解析项目: {txt}",
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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```。",
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)
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elif exception == "word_pip":
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report_exception(
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chatbot,
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history,
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a=f"解析项目: {txt}",
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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```。",
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)
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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else:
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if not file_exist:
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history.append(txt) # 如输入区不是文件则将输入区内容加入历史记录
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i_say_show_user = f"首先你从历史记录中提取摘要。"
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gpt_say = "[Local Message] 收到。" # 用户提示
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chatbot.append([i_say_show_user, gpt_say])
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yield from update_ui(chatbot=chatbot, history=history) # 更新UI
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yield from 解析历史输入(
|
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history, llm_kwargs, file_manifest, chatbot, plugin_kwargs
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)
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else:
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file_num = len(file_manifest)
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for i in range(file_num): # 依次处理文件
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i_say_show_user = f"[{i+1}/{file_num}]处理文件{file_manifest[i]}"
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gpt_say = "[Local Message] 收到。" # 用户提示
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chatbot.append([i_say_show_user, gpt_say])
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yield from update_ui(chatbot=chatbot, history=history) # 更新UI
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history = [] # 如输入区内容为文件则清空历史记录
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history.append(final_result[i])
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yield from 解析历史输入(
|
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history, llm_kwargs, file_manifest, chatbot, plugin_kwargs
|
||||
)
|
||||
|
||||
|
||||
class Mermaid_Gen(GptAcademicPluginTemplate):
|
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def __init__(self):
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pass
|
||||
|
||||
def define_arg_selection_menu(self):
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||||
gui_definition = {
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"Type_of_Mermaid": ArgProperty(
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title="绘制的Mermaid图表类型",
|
||||
options=[
|
||||
"由LLM决定",
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"流程图",
|
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"序列图",
|
||||
"类图",
|
||||
"饼图",
|
||||
"甘特图",
|
||||
"状态图",
|
||||
"实体关系图",
|
||||
"象限提示图",
|
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"思维导图",
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||||
],
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default_value="由LLM决定",
|
||||
description="选择'由LLM决定'时将由对话模型判断适合的图表类型(不包括思维导图)，选择其他类型时将直接绘制指定的图表类型。",
|
||||
type="dropdown",
|
||||
).model_dump_json(),
|
||||
}
|
||||
return gui_definition
|
||||
|
||||
def execute(
|
||||
txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request
|
||||
):
|
||||
options = [
|
||||
"由LLM决定",
|
||||
"流程图",
|
||||
"序列图",
|
||||
"类图",
|
||||
"饼图",
|
||||
"甘特图",
|
||||
"状态图",
|
||||
"实体关系图",
|
||||
"象限提示图",
|
||||
"思维导图",
|
||||
]
|
||||
plugin_kwargs = options.index(plugin_kwargs['Type_of_Mermaid'])
|
||||
yield from Mermaid_Figure_Gen(
|
||||
txt,
|
||||
llm_kwargs,
|
||||
plugin_kwargs,
|
||||
chatbot,
|
||||
history,
|
||||
system_prompt,
|
||||
user_request,
|
||||
)
|
||||
在新工单中引用
屏蔽一个用户