gpt-academicrrrr / toolbox.py
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import markdown
import mdtex2html
import threading
import importlib
import traceback
import inspect
import re
from latex2mathml.converter import convert as tex2mathml
from functools import wraps, lru_cache
############################### 插件输入输出接驳区 #######################################
class ChatBotWithCookies(list):
def __init__(self, cookie):
self._cookies = cookie
def write_list(self, list):
for t in list:
self.append(t)
def get_list(self):
return [t for t in self]
def get_cookies(self):
return self._cookies
def ArgsGeneralWrapper(f):
"""
装饰器函数,用于重组输入参数,改变输入参数的顺序与结构。
"""
def decorated(cookies, txt, txt2, top_p, temperature, chatbot, history, system_prompt, txt_pattern, *args):
txt_passon = txt
if txt == "" and txt2 != "": txt_passon = txt2
# 引入一个有cookie的chatbot
cookies.update({
'top_p':top_p,
'temperature':temperature,
})
llm_kwargs = {
'api_key': cookies['api_key'],
'llm_model': cookies['llm_model'],
'top_p':top_p,
'temperature':temperature,
}
# plugin_kwargs = {
# # 目前还没有
# }
plugin_kwargs = dict(txt_pattern = txt_pattern)
chatbot_with_cookie = ChatBotWithCookies(cookies)
chatbot_with_cookie.write_list(chatbot)
yield from f(txt_passon, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, *args)
return decorated
def update_ui(chatbot, history, msg='正常', **kwargs): # 刷新界面
"""
刷新用户界面
"""
assert isinstance(chatbot, ChatBotWithCookies), "在传递chatbot的过程中不要将其丢弃。必要时,可用clear将其清空,然后用for+append循环重新赋值。"
yield chatbot.get_cookies(), chatbot, history, msg
############################### ################## #######################################
##########################################################################################
def get_reduce_token_percent(text):
"""
* 此函数未来将被弃用
"""
try:
# text = "maximum context length is 4097 tokens. However, your messages resulted in 4870 tokens"
pattern = r"(\d+)\s+tokens\b"
match = re.findall(pattern, text)
EXCEED_ALLO = 500 # 稍微留一点余地,否则在回复时会因余量太少出问题
max_limit = float(match[0]) - EXCEED_ALLO
current_tokens = float(match[1])
ratio = max_limit/current_tokens
assert ratio > 0 and ratio < 1
return ratio, str(int(current_tokens-max_limit))
except:
return 0.5, '不详'
def predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, llm_kwargs, history=[], sys_prompt='', long_connection=True):
"""
* 此函数未来将被弃用(替代函数 request_gpt_model_in_new_thread_with_ui_alive 文件 chatgpt_academic/crazy_functions/crazy_utils)
调用简单的predict_no_ui接口,但是依然保留了些许界面心跳功能,当对话太长时,会自动采用二分法截断
i_say: 当前输入
i_say_show_user: 显示到对话界面上的当前输入,例如,输入整个文件时,你绝对不想把文件的内容都糊到对话界面上
chatbot: 对话界面句柄
top_p, temperature: gpt参数
history: gpt参数 对话历史
sys_prompt: gpt参数 sys_prompt
long_connection: 是否采用更稳定的连接方式(推荐)(已弃用)
"""
import time
from request_llm.bridge_chatgpt import predict_no_ui_long_connection
from toolbox import get_conf
TIMEOUT_SECONDS, MAX_RETRY = get_conf('TIMEOUT_SECONDS', 'MAX_RETRY')
# 多线程的时候,需要一个mutable结构在不同线程之间传递信息
# list就是最简单的mutable结构,我们第一个位置放gpt输出,第二个位置传递报错信息
mutable = [None, '']
# multi-threading worker
def mt(i_say, history):
while True:
try:
mutable[0] = predict_no_ui_long_connection(
inputs=i_say, llm_kwargs=llm_kwargs, history=history, sys_prompt=sys_prompt)
except ConnectionAbortedError as token_exceeded_error:
# 尝试计算比例,尽可能多地保留文本
p_ratio, n_exceed = get_reduce_token_percent(
str(token_exceeded_error))
if len(history) > 0:
history = [his[int(len(his) * p_ratio):]
for his in history if his is not None]
else:
i_say = i_say[: int(len(i_say) * p_ratio)]
mutable[1] = f'警告,文本过长将进行截断,Token溢出数:{n_exceed},截断比例:{(1-p_ratio):.0%}。'
except TimeoutError as e:
mutable[0] = '[Local Message] 请求超时。'
raise TimeoutError
except Exception as e:
mutable[0] = f'[Local Message] 异常:{str(e)}.'
raise RuntimeError(f'[Local Message] 异常:{str(e)}.')
# 创建新线程发出http请求
thread_name = threading.Thread(target=mt, args=(i_say, history))
thread_name.start()
# 原来的线程则负责持续更新UI,实现一个超时倒计时,并等待新线程的任务完成
cnt = 0
while thread_name.is_alive():
cnt += 1
chatbot[-1] = (i_say_show_user,
f"[Local Message] {mutable[1]}waiting gpt response {cnt}/{TIMEOUT_SECONDS*2*(MAX_RETRY+1)}"+''.join(['.']*(cnt % 4)))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
time.sleep(1)
# 把gpt的输出从mutable中取出来
gpt_say = mutable[0]
if gpt_say == '[Local Message] Failed with timeout.':
raise TimeoutError
return gpt_say
def write_results_to_file(history, file_name=None):
"""
将对话记录history以Markdown格式写入文件中。如果没有指定文件名,则使用当前时间生成文件名。
"""
import os
import time
if file_name is None:
# file_name = time.strftime("chatGPT分析报告%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
file_name = 'chatGPT分析报告' + \
time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
os.makedirs('./gpt_log/', exist_ok=True)
with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
f.write('# chatGPT 分析报告\n')
for i, content in enumerate(history):
try: # 这个bug没找到触发条件,暂时先这样顶一下
if type(content) != str:
content = str(content)
except:
continue
if i % 2 == 0:
f.write('## ')
f.write(content)
f.write('\n\n')
res = '以上材料已经被写入' + os.path.abspath(f'./gpt_log/{file_name}')
print(res)
return res
def regular_txt_to_markdown(text):
"""
将普通文本转换为Markdown格式的文本。
"""
text = text.replace('\n', '\n\n')
text = text.replace('\n\n\n', '\n\n')
text = text.replace('\n\n\n', '\n\n')
return text
def CatchException(f):
"""
装饰器函数,捕捉函数f中的异常并封装到一个生成器中返回,并显示到聊天当中。
"""
@wraps(f)
def decorated(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
try:
yield from f(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT)
except Exception as e:
from check_proxy import check_proxy
from toolbox import get_conf
proxies, = get_conf('proxies')
tb_str = '```\n' + traceback.format_exc() + '```'
if chatbot is None or len(chatbot) == 0:
chatbot = [["插件调度异常", "异常原因"]]
chatbot[-1] = (chatbot[-1][0],
f"[Local Message] 实验性函数调用出错: \n\n{tb_str} \n\n当前代理可用性: \n\n{check_proxy(proxies)}")
yield from update_ui(chatbot=chatbot, history=history, msg=f'异常 {e}') # 刷新界面
return decorated
def HotReload(f):
"""
HotReload的装饰器函数,用于实现Python函数插件的热更新。
函数热更新是指在不停止程序运行的情况下,更新函数代码,从而达到实时更新功能。
在装饰器内部,使用wraps(f)来保留函数的元信息,并定义了一个名为decorated的内部函数。
内部函数通过使用importlib模块的reload函数和inspect模块的getmodule函数来重新加载并获取函数模块,
然后通过getattr函数获取函数名,并在新模块中重新加载函数。
最后,使用yield from语句返回重新加载过的函数,并在被装饰的函数上执行。
最终,装饰器函数返回内部函数。这个内部函数可以将函数的原始定义更新为最新版本,并执行函数的新版本。
"""
@wraps(f)
def decorated(*args, **kwargs):
fn_name = f.__name__
f_hot_reload = getattr(importlib.reload(inspect.getmodule(f)), fn_name)
yield from f_hot_reload(*args, **kwargs)
return decorated
def report_execption(chatbot, history, a, b):
"""
向chatbot中添加错误信息
"""
chatbot.append((a, b))
history.append(a)
history.append(b)
def text_divide_paragraph(text):
"""
将文本按照段落分隔符分割开,生成带有段落标签的HTML代码。
"""
if '```' in text:
# careful input
return text
else:
# wtf input
lines = text.split("\n")
for i, line in enumerate(lines):
lines[i] = lines[i].replace(" ", "&nbsp;")
text = "</br>".join(lines)
return text
def markdown_convertion(txt):
"""
将Markdown格式的文本转换为HTML格式。如果包含数学公式,则先将公式转换为HTML格式。
"""
pre = '<div class="markdown-body">'
suf = '</div>'
markdown_extension_configs = {
'mdx_math': {
'enable_dollar_delimiter': True,
'use_gitlab_delimiters': False,
},
}
find_equation_pattern = r'<script type="math/tex(?:.*?)>(.*?)</script>'
def tex2mathml_catch_exception(content, *args, **kwargs):
try:
content = tex2mathml(content, *args, **kwargs)
except:
content = content
return content
def replace_math_no_render(match):
content = match.group(1)
if 'mode=display' in match.group(0):
content = content.replace('\n', '</br>')
return f"<font color=\"#00FF00\">$$</font><font color=\"#FF00FF\">{content}</font><font color=\"#00FF00\">$$</font>"
else:
return f"<font color=\"#00FF00\">$</font><font color=\"#FF00FF\">{content}</font><font color=\"#00FF00\">$</font>"
def replace_math_render(match):
content = match.group(1)
if 'mode=display' in match.group(0):
if '\\begin{aligned}' in content:
content = content.replace('\\begin{aligned}', '\\begin{array}')
content = content.replace('\\end{aligned}', '\\end{array}')
content = content.replace('&', ' ')
content = tex2mathml_catch_exception(content, display="block")
return content
else:
return tex2mathml_catch_exception(content)
def markdown_bug_hunt(content):
"""
解决一个mdx_math的bug(单$包裹begin命令时多余<script>)
"""
content = content.replace('<script type="math/tex">\n<script type="math/tex; mode=display">', '<script type="math/tex; mode=display">')
content = content.replace('</script>\n</script>', '</script>')
return content
if ('$' in txt) and ('```' not in txt): # 有$标识的公式符号,且没有代码段```的标识
# convert everything to html format
split = markdown.markdown(text='---')
convert_stage_1 = markdown.markdown(text=txt, extensions=['mdx_math', 'fenced_code', 'tables', 'sane_lists'], extension_configs=markdown_extension_configs)
convert_stage_1 = markdown_bug_hunt(convert_stage_1)
# re.DOTALL: Make the '.' special character match any character at all, including a newline; without this flag, '.' will match anything except a newline. Corresponds to the inline flag (?s).
# 1. convert to easy-to-copy tex (do not render math)
convert_stage_2_1, n = re.subn(find_equation_pattern, replace_math_no_render, convert_stage_1, flags=re.DOTALL)
# 2. convert to rendered equation
convert_stage_2_2, n = re.subn(find_equation_pattern, replace_math_render, convert_stage_1, flags=re.DOTALL)
# cat them together
return pre + convert_stage_2_1 + f'{split}' + convert_stage_2_2 + suf
else:
return pre + markdown.markdown(txt, extensions=['fenced_code', 'codehilite', 'tables', 'sane_lists']) + suf
def close_up_code_segment_during_stream(gpt_reply):
"""
在gpt输出代码的中途(输出了前面的```,但还没输出完后面的```),补上后面的```
Args:
gpt_reply (str): GPT模型返回的回复字符串。
Returns:
str: 返回一个新的字符串,将输出代码片段的“后面的```”补上。
"""
if '```' not in gpt_reply:
return gpt_reply
if gpt_reply.endswith('```'):
return gpt_reply
# 排除了以上两个情况,我们
segments = gpt_reply.split('```')
n_mark = len(segments) - 1
if n_mark % 2 == 1:
# print('输出代码片段中!')
return gpt_reply+'\n```'
else:
return gpt_reply
def format_io(self, y):
"""
将输入和输出解析为HTML格式。将y中最后一项的输入部分段落化,并将输出部分的Markdown和数学公式转换为HTML格式。
"""
if y is None or y == []:
return []
i_ask, gpt_reply = y[-1]
i_ask = text_divide_paragraph(i_ask) # 输入部分太自由,预处理一波
gpt_reply = close_up_code_segment_during_stream(gpt_reply) # 当代码输出半截的时候,试着补上后个```
y[-1] = (
None if i_ask is None else markdown.markdown(i_ask, extensions=['fenced_code', 'tables']),
None if gpt_reply is None else markdown_convertion(gpt_reply)
)
return y
def find_free_port():
"""
返回当前系统中可用的未使用端口。
"""
import socket
from contextlib import closing
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
s.bind(('', 0))
s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
return s.getsockname()[1]
def extract_archive(file_path, dest_dir):
import zipfile
import tarfile
import os
# Get the file extension of the input file
file_extension = os.path.splitext(file_path)[1]
# Extract the archive based on its extension
if file_extension == '.zip':
with zipfile.ZipFile(file_path, 'r') as zipobj:
zipobj.extractall(path=dest_dir)
print("Successfully extracted zip archive to {}".format(dest_dir))
elif file_extension in ['.tar', '.gz', '.bz2']:
with tarfile.open(file_path, 'r:*') as tarobj:
tarobj.extractall(path=dest_dir)
print("Successfully extracted tar archive to {}".format(dest_dir))
# 第三方库,需要预先pip install rarfile
# 此外,Windows上还需要安装winrar软件,配置其Path环境变量,如"C:\Program Files\WinRAR"才可以
elif file_extension == '.rar':
try:
import rarfile
with rarfile.RarFile(file_path) as rf:
rf.extractall(path=dest_dir)
print("Successfully extracted rar archive to {}".format(dest_dir))
except:
print("Rar format requires additional dependencies to install")
return '\n\n需要安装pip install rarfile来解压rar文件'
# 第三方库,需要预先pip install py7zr
elif file_extension == '.7z':
try:
import py7zr
with py7zr.SevenZipFile(file_path, mode='r') as f:
f.extractall(path=dest_dir)
print("Successfully extracted 7z archive to {}".format(dest_dir))
except:
print("7z format requires additional dependencies to install")
return '\n\n需要安装pip install py7zr来解压7z文件'
else:
return ''
return ''
def find_recent_files(directory):
"""
me: find files that is created with in one minutes under a directory with python, write a function
gpt: here it is!
"""
import os
import time
current_time = time.time()
one_minute_ago = current_time - 60
recent_files = []
for filename in os.listdir(directory):
file_path = os.path.join(directory, filename)
if file_path.endswith('.log'):
continue
created_time = os.path.getmtime(file_path)
if created_time >= one_minute_ago:
if os.path.isdir(file_path):
continue
recent_files.append(file_path)
return recent_files
def on_file_uploaded(files, chatbot, txt):
if len(files) == 0:
return chatbot, txt
import shutil
import os
import time
import glob
from toolbox import extract_archive
try:
shutil.rmtree('./private_upload/')
except:
pass
time_tag = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
os.makedirs(f'private_upload/{time_tag}', exist_ok=True)
err_msg = ''
for file in files:
file_origin_name = os.path.basename(file.orig_name)
shutil.copy(file.name, f'private_upload/{time_tag}/{file_origin_name}')
err_msg += extract_archive(f'private_upload/{time_tag}/{file_origin_name}',
dest_dir=f'private_upload/{time_tag}/{file_origin_name}.extract')
moved_files = [fp for fp in glob.glob(
'private_upload/**/*', recursive=True)]
txt = f'private_upload/{time_tag}'
moved_files_str = '\t\n\n'.join(moved_files)
chatbot.append(['我上传了文件,请查收',
f'[Local Message] 收到以下文件: \n\n{moved_files_str}' +
f'\n\n调用路径参数已自动修正到: \n\n{txt}' +
f'\n\n现在您点击任意实验功能时,以上文件将被作为输入参数'+err_msg])
return chatbot, txt
def on_report_generated(files, chatbot):
from toolbox import find_recent_files
report_files = find_recent_files('gpt_log')
if len(report_files) == 0:
return None, chatbot
# files.extend(report_files)
chatbot.append(['汇总报告如何远程获取?', '汇总报告已经添加到右侧“文件上传区”(可能处于折叠状态),请查收。'])
return report_files, chatbot
def is_openai_api_key(key):
# 正确的 API_KEY 是 "sk-" + 48 位大小写字母数字的组合
API_MATCH = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
return API_MATCH
@lru_cache(maxsize=128)
def read_single_conf_with_lru_cache(arg):
from colorful import print亮红, print亮绿
try:
r = getattr(importlib.import_module('config_private'), arg)
except:
r = getattr(importlib.import_module('config'), arg)
# 在读取API_KEY时,检查一下是不是忘了改config
if arg == 'API_KEY':
if is_openai_api_key(r):
print亮绿(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功")
else:
print亮红( "[API_KEY] 正确的 API_KEY 是 'sk-' + '48 位大小写字母数字' 的组合,请在config文件中修改API密钥, 添加海外代理之后再运行。" + \
"(如果您刚更新过代码,请确保旧版config_private文件中没有遗留任何新增键值)")
if arg == 'proxies':
if r is None:
print亮红('[PROXY] 网络代理状态:未配置。无代理状态下很可能无法访问。建议:检查USE_PROXY选项是否修改。')
else:
print亮绿('[PROXY] 网络代理状态:已配置。配置信息如下:', r)
assert isinstance(r, dict), 'proxies格式错误,请注意proxies选项的格式,不要遗漏括号。'
return r
def get_conf(*args):
# 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
res = []
for arg in args:
r = read_single_conf_with_lru_cache(arg)
res.append(r)
return res
def clear_line_break(txt):
txt = txt.replace('\n', ' ')
txt = txt.replace(' ', ' ')
txt = txt.replace(' ', ' ')
return txt
class DummyWith():
"""
这段代码定义了一个名为DummyWith的空上下文管理器,
它的作用是……额……没用,即在代码结构不变得情况下取代其他的上下文管理器。
上下文管理器是一种Python对象,用于与with语句一起使用,
以确保一些资源在代码块执行期间得到正确的初始化和清理。
上下文管理器必须实现两个方法,分别为 __enter__()和 __exit__()。
在上下文执行开始的情况下,__enter__()方法会在代码块被执行前被调用,
而在上下文执行结束时,__exit__()方法则会被调用。
"""
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
return