gpt-academic2 / toolbox.py
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import markdown, mdtex2html, threading, importlib, traceback, importlib, inspect, re
from show_math import convert as convert_math
from functools import wraps, lru_cache
def get_reduce_token_percent(text):
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, top_p, temperature, history=[], sys_prompt='', long_connection=True):
"""
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)
"""
import time
from predict import predict_no_ui, 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:
if long_connection:
mutable[0] = predict_no_ui_long_connection(inputs=i_say, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt)
else:
mutable[0] = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt)
break
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 chatbot, history, '正常'
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, 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 = regular_txt_to_markdown(traceback.format_exc())
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 chatbot, history, f'异常 {e}'
return decorated
def HotReload(f):
"""
装饰器函数,实现函数插件热更新
"""
@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>'
if ('$' in txt) and ('```' not in txt):
return pre + markdown.markdown(txt,extensions=['fenced_code','tables']) + '<br><br>' + markdown.markdown(convert_math(txt, splitParagraphs=False),extensions=['fenced_code','tables']) + suf
else:
return pre + markdown.markdown(txt,extensions=['fenced_code','tables']) + suf
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
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.getctime(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, os, time, 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 report_files, 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
@lru_cache
def read_single_conf_with_lru_cache(arg):
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':
# 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}$", r)
if API_MATCH:
print(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功")
else:
assert False, "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('[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('[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