gpt-academic2 / toolbox.py
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import markdown, mdtex2html, threading
from show_math import convert as convert_math
from functools import wraps
def predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[], sys_prompt=''):
"""
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
"""
import time
try: from config_private import TIMEOUT_SECONDS, MAX_RETRY
except: from config import TIMEOUT_SECONDS, MAX_RETRY
from predict import predict_no_ui
# 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(inputs=i_say, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt)
break
except ConnectionAbortedError as e:
if len(history) > 0:
history = [his[len(his)//2:] for his in history if his is not None]
mutable[1] = 'Warning! History conversation is too long, cut into half. '
else:
i_say = i_say[:len(i_say)//2]
mutable[1] = 'Warning! Input file is too long, cut into half. '
except TimeoutError as e:
mutable[0] = '[Local Message] Failed with timeout.'
raise TimeoutError
# 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):
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:
import traceback
from check_proxy import check_proxy
try: from config_private import proxies
except: from config import 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 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):
if i!=0: lines[i] = "<p>"+lines[i].replace(" ", "&nbsp;")+"</p>"
text = "".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。
"""
if ('$' in txt) and ('```' not in txt):
return markdown.markdown(txt,extensions=['fenced_code','tables']) + '<br><br>' + \
markdown.markdown(convert_math(txt, splitParagraphs=False),extensions=['fenced_code','tables'])
else:
return markdown.markdown(txt,extensions=['fenced_code','tables'])
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: 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))
else:
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:
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)
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}')
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}\n\n调用路径参数已自动修正到: \n\n{txt}\n\n现在您可以直接选择任意实现性功能'])
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,请查收。'])
return report_files, chatbot