294 lines
12 KiB
Python
294 lines
12 KiB
Python
import markdown, mdtex2html, threading, importlib, traceback
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from show_math import convert as convert_math
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from functools import wraps
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def predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[], sys_prompt=''):
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"""
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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
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"""
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import time
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from predict import predict_no_ui
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from toolbox import get_conf
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TIMEOUT_SECONDS, MAX_RETRY = get_conf('TIMEOUT_SECONDS', 'MAX_RETRY')
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# 多线程的时候，需要一个mutable结构在不同线程之间传递信息
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# 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
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mutable = [None, '']
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# multi-threading worker
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def mt(i_say, history):
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while True:
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try:
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mutable[0] = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt)
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break
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except ConnectionAbortedError as e:
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if len(history) > 0:
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history = [his[len(his)//2:] for his in history if his is not None]
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mutable[1] = 'Warning! History conversation is too long, cut into half. '
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else:
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i_say = i_say[:len(i_say)//2]
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mutable[1] = 'Warning! Input file is too long, cut into half. '
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except TimeoutError as e:
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mutable[0] = '[Local Message] Failed with timeout.'
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raise TimeoutError
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# 创建新线程发出http请求
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thread_name = threading.Thread(target=mt, args=(i_say, history)); thread_name.start()
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# 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
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cnt = 0
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while thread_name.is_alive():
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cnt += 1
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chatbot[-1] = (i_say_show_user, f"[Local Message] {mutable[1]}waiting gpt response {cnt}/{TIMEOUT_SECONDS*2*(MAX_RETRY+1)}"+''.join(['.']*(cnt%4)))
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yield chatbot, history, '正常'
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time.sleep(1)
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# 把gpt的输出从mutable中取出来
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gpt_say = mutable[0]
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if gpt_say=='[Local Message] Failed with timeout.': raise TimeoutError
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return gpt_say
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def write_results_to_file(history, file_name=None):
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"""
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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。
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"""
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import os, time
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if file_name is None:
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# file_name = time.strftime("chatGPT分析报告%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
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file_name = 'chatGPT分析报告' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
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os.makedirs('./gpt_log/', exist_ok=True)
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with open(f'./gpt_log/{file_name}', 'w', encoding = 'utf8') as f:
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f.write('# chatGPT 分析报告\n')
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for i, content in enumerate(history):
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if i%2==0: f.write('## ')
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f.write(content)
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f.write('\n\n')
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res = '以上材料已经被写入' + os.path.abspath(f'./gpt_log/{file_name}')
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print(res)
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return res
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def regular_txt_to_markdown(text):
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"""
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将普通文本转换为Markdown格式的文本。
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"""
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text = text.replace('\n', '\n\n')
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text = text.replace('\n\n\n', '\n\n')
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text = text.replace('\n\n\n', '\n\n')
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return text
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def CatchException(f):
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"""
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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。
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"""
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@wraps(f)
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def decorated(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
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try:
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yield from f(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT)
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except Exception as e:
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from check_proxy import check_proxy
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from toolbox import get_conf
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proxies, = get_conf('proxies')
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tb_str = regular_txt_to_markdown(traceback.format_exc())
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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)}")
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yield chatbot, history, f'异常 {e}'
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return decorated
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def report_execption(chatbot, history, a, b):
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"""
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向chatbot中添加错误信息
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"""
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chatbot.append((a, b))
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history.append(a); history.append(b)
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def text_divide_paragraph(text):
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"""
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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。
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"""
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if '```' in text:
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# careful input
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return text
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else:
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# wtf input
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lines = text.split("\n")
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for i, line in enumerate(lines):
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lines[i] = lines[i].replace(" ", " ")
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text = "</br>".join(lines)
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return text
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def markdown_convertion(txt):
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"""
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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。
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"""
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if ('$' in txt) and ('```' not in txt):
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return markdown.markdown(txt,extensions=['fenced_code','tables']) + '<br><br>' + \
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markdown.markdown(convert_math(txt, splitParagraphs=False),extensions=['fenced_code','tables'])
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else:
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return markdown.markdown(txt,extensions=['fenced_code','tables'])
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def format_io(self, y):
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"""
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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。
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"""
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if y is None or y == []: return []
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i_ask, gpt_reply = y[-1]
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i_ask = text_divide_paragraph(i_ask) # 输入部分太自由，预处理一波
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y[-1] = (
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None if i_ask is None else markdown.markdown(i_ask, extensions=['fenced_code','tables']),
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None if gpt_reply is None else markdown_convertion(gpt_reply)
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)
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return y
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def find_free_port():
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"""
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返回当前系统中可用的未使用端口。
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"""
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import socket
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from contextlib import closing
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with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
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s.bind(('', 0))
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s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
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return s.getsockname()[1]
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def extract_archive(file_path, dest_dir):
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import zipfile
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import tarfile
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import os
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# Get the file extension of the input file
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file_extension = os.path.splitext(file_path)[1]
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# Extract the archive based on its extension
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if file_extension == '.zip':
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with zipfile.ZipFile(file_path, 'r') as zipobj:
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zipobj.extractall(path=dest_dir)
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print("Successfully extracted zip archive to {}".format(dest_dir))
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elif file_extension in ['.tar', '.gz', '.bz2']:
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with tarfile.open(file_path, 'r:*') as tarobj:
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tarobj.extractall(path=dest_dir)
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print("Successfully extracted tar archive to {}".format(dest_dir))
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else:
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return
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def find_recent_files(directory):
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"""
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me: find files that is created with in one minutes under a directory with python, write a function
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gpt: here it is!
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"""
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import os
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import time
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current_time = time.time()
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one_minute_ago = current_time - 60
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recent_files = []
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for filename in os.listdir(directory):
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file_path = os.path.join(directory, filename)
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if file_path.endswith('.log'): continue
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created_time = os.path.getctime(file_path)
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if created_time >= one_minute_ago:
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if os.path.isdir(file_path): continue
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recent_files.append(file_path)
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return recent_files
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def on_file_uploaded(files, chatbot, txt):
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if len(files) == 0: return chatbot, txt
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import shutil, os, time, glob
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from toolbox import extract_archive
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try: shutil.rmtree('./private_upload/')
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except: pass
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time_tag = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
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os.makedirs(f'private_upload/{time_tag}', exist_ok=True)
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for file in files:
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file_origin_name = os.path.basename(file.orig_name)
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shutil.copy(file.name, f'private_upload/{time_tag}/{file_origin_name}')
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extract_archive(f'private_upload/{time_tag}/{file_origin_name}',
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dest_dir=f'private_upload/{time_tag}/{file_origin_name}.extract')
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moved_files = [fp for fp in glob.glob('private_upload/**/*', recursive=True)]
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txt = f'private_upload/{time_tag}'
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moved_files_str = '\t\n\n'.join(moved_files)
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chatbot.append(['我上传了文件，请查收',
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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现在您点击任意实验功能时，以上文件将被作为输入参数'])
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return chatbot, txt
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def on_report_generated(files, chatbot):
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from toolbox import find_recent_files
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report_files = find_recent_files('gpt_log')
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if len(report_files) == 0: return report_files, chatbot
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# files.extend(report_files)
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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。'])
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return report_files, chatbot
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def get_conf(*args):
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# 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
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res = []
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for arg in args:
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try: r = getattr(importlib.import_module('config_private'), arg)
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except: r = getattr(importlib.import_module('config'), arg)
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res.append(r)
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# 在读取API_KEY时，检查一下是不是忘了改config
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if arg=='API_KEY' and len(r) != 51:
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assert False, "正确的API_KEY密钥是51位，请在config文件中修改API密钥, 添加海外代理之后再运行。" + \
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"（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）"
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return res
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def clear_line_break(txt):
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txt = txt.replace('\n', ' ')
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txt = txt.replace(' ', ' ')
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txt = txt.replace(' ', ' ')
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return txt
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import re
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import unicodedata
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def is_paragraph_break(match):
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"""
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根据给定的匹配结果来判断换行符是否表示段落分隔。
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如果换行符前为句子结束标志（句号，感叹号，问号），且下一个字符为大写字母，则换行符更有可能表示段落分隔。
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也可以根据之前的内容长度来判断段落是否已经足够长。
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"""
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prev_char, next_char = match.groups()
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# 句子结束标志
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sentence_endings = ".!?"
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# 设定一个最小段落长度阈值
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min_paragraph_length = 140
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if prev_char in sentence_endings and next_char.isupper() and len(match.string[:match.start(1)]) > min_paragraph_length:
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return "\n\n"
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else:
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return " "
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def normalize_text(text):
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"""
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通过把连字（ligatures）等文本特殊符号转换为其基本形式来对文本进行归一化处理。
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例如，将连字 "fi" 转换为 "f" 和 "i"。
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"""
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# 对文本进行归一化处理，分解连字
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normalized_text = unicodedata.normalize("NFKD", text)
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# 替换其他特殊字符
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cleaned_text = re.sub(r'[^\x00-\x7F]+', '', normalized_text)
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return cleaned_text
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def clean_text(raw_text):
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"""
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对从 PDF 提取出的原始文本进行清洗和格式化处理。
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1. 对原始文本进行归一化处理。
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2. 替换跨行的连词，例如 “Espe-\ncially” 转换为 “Especially”。
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3. 根据 heuristic 规则判断换行符是否是段落分隔，并相应地进行替换。
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"""
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# 对文本进行归一化处理
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normalized_text = normalize_text(raw_text)
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# 替换跨行的连词
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text = re.sub(r'(\w+-\n\w+)', lambda m: m.group(1).replace('-\n', ''), normalized_text)
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# 根据前后相邻字符的特点，找到原文本中的换行符
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newlines = re.compile(r'(\S)\n(\S)')
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# 根据 heuristic 规则，用空格或段落分隔符替换原换行符
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final_text = re.sub(newlines, lambda m: m.group(1) + is_paragraph_break(m) + m.group(2), text)
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return final_text.strip() |