439 lines
15 KiB
Python
439 lines
15 KiB
Python
from toolbox import CatchException, update_ui, report_exception
|
||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||
from crazy_functions.plugin_template.plugin_class_template import (
|
||
GptAcademicPluginTemplate,
|
||
)
|
||
from crazy_functions.plugin_template.plugin_class_template import ArgProperty
|
||
|
||
# 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，注意需要使用双引号将内容括起来。
|
||
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，注意需要使用双引号将内容括起来。
|
||
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，注意需要使用双引号将内容括起来。
|
||
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，注意需要使用双引号将内容括起来。
|
||
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，注意需要使用双引号将内容括起来。
|
||
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，注意需要使用双引号将内容括起来。
|
||
mermaid语法举例：
|
||
```mermaid
|
||
mindmap
|
||
root((mindmap))
|
||
("Origins")
|
||
("Long history")
|
||
::icon(fa fa-book)
|
||
("Popularisation")
|
||
("British popular psychology author Tony Buzan")
|
||
::icon(fa fa-user)
|
||
("Research")
|
||
("On effectiveness<br/>and features")
|
||
::icon(fa fa-search)
|
||
("On Automatic creation")
|
||
::icon(fa fa-robot)
|
||
("Uses")
|
||
("Creative techniques")
|
||
::icon(fa fa-lightbulb-o)
|
||
("Strategic planning")
|
||
::icon(fa fa-flag)
|
||
("Argument mapping")
|
||
::icon(fa fa-comments)
|
||
("Tools")
|
||
("Pen and paper")
|
||
::icon(fa fa-pencil)
|
||
("Mermaid")
|
||
::icon(fa fa-code)
|
||
```
|
||
"""
|
||
|
||
|
||
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> ##################################
|
||
gpt_say = str(plugin_kwargs) # 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
|
||
|
||
|
||
@CatchException
|
||
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
|
||
)
|
||
|
||
|
||
class Mermaid_Gen(GptAcademicPluginTemplate):
|
||
def __init__(self):
|
||
pass
|
||
|
||
def define_arg_selection_menu(self):
|
||
gui_definition = {
|
||
"Type_of_Mermaid": ArgProperty(
|
||
title="绘制的Mermaid图表类型",
|
||
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",
|
||
],
|
||
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 生成多种Mermaid图表(
|
||
txt,
|
||
llm_kwargs,
|
||
plugin_kwargs,
|
||
chatbot,
|
||
history,
|
||
system_prompt,
|
||
user_request,
|
||
)
|