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from toolbox import CatchException, update_ui, gen_time_str
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
from .crazy_utils import input_clipping
import copy, json
def get_fn_lib():
return {
"BatchTranslatePDFDocuments_MultiThreaded": {
"module": "crazy_functions.批量翻译PDF文档_多线程",
"function": "批量翻译PDF文档",
"description": "Translate PDF Documents",
"arg_1_description": "A path containing pdf files.",
},
"SummarizingWordDocuments": {
"module": "crazy_functions.总结word文档",
"function": "总结word文档",
"description": "Summarize Word Documents",
"arg_1_description": "A path containing Word files.",
},
"ImageGeneration": {
"module": "crazy_functions.图片生成",
"function": "图片生成",
"description": "Generate a image that satisfies some description.",
"arg_1_description": "Descriptions about the image to be generated.",
},
"TranslateMarkdownFromEnglishToChinese": {
"module": "crazy_functions.批量Markdown翻译",
"function": "Markdown中译英",
"description": "Translate Markdown Documents from English to Chinese.",
"arg_1_description": "A path containing Markdown files.",
},
"SummaryAudioVideo": {
"module": "crazy_functions.总结音视频",
"function": "总结音视频",
"description": "Get text from a piece of audio and summarize this audio.",
"arg_1_description": "A path containing audio files.",
},
}
functions = [
{
"name": k,
"description": v['description'],
"parameters": {
"type": "object",
"properties": {
"plugin_arg_1": {
"type": "string",
"description": v['arg_1_description'],
},
},
"required": ["plugin_arg_1"],
},
} for k, v in get_fn_lib().items()
]
def inspect_dependency(chatbot, history):
return True
def eval_code(code, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
import importlib
try:
tmp = get_fn_lib()[code['name']]
fp, fn = tmp['module'], tmp['function']
fn_plugin = getattr(importlib.import_module(fp, fn), fn)
arg = json.loads(code['arguments'])['plugin_arg_1']
yield from fn_plugin(arg, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port)
except:
from toolbox import trimmed_format_exc
chatbot.append(["执行错误", f"\n```\n{trimmed_format_exc()}\n```\n"])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
def get_code_block(reply):
import re
pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
matches = re.findall(pattern, reply) # find all code blocks in text
if len(matches) != 1:
raise RuntimeError("GPT is not generating proper code.")
return matches[0].strip('python') # code block
@CatchException
def 终端(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, 暂时没有用武之地
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
"""
# 清空历史, 以免输入溢出
history = []
# 基本信息：功能、贡献者
chatbot.append(["虚空终端插件的功能？", "根据自然语言的描述, 执行任意插件的命令."])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
# 输入
i_say = txt
# 开始
llm_kwargs_function_call = copy.deepcopy(llm_kwargs)
llm_kwargs_function_call['llm_model'] = 'gpt-call-fn' # 修改调用函数
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
inputs=i_say, inputs_show_user=txt,
llm_kwargs=llm_kwargs_function_call, chatbot=chatbot, history=[],
sys_prompt=functions
)
# 将代码转为动画
res = json.loads(gpt_say)['choices'][0]
if res['finish_reason'] == 'function_call':
code = json.loads(gpt_say)['choices'][0]
yield from eval_code(code['message']['function_call'], llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port)
else:
chatbot.append(["无法调用相关功能", res])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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