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import markdown | |
import importlib | |
import traceback | |
import inspect | |
import re | |
import os | |
from latex2mathml.converter import convert as tex2mathml | |
from functools import wraps, lru_cache | |
""" | |
======================================================================== | |
第一部分 | |
函数插件输入输出接驳区 | |
- ChatBotWithCookies: 带Cookies的Chatbot类,为实现更多强大的功能做基础 | |
- ArgsGeneralWrapper: 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 | |
- update_ui: 刷新界面用 yield from update_ui(chatbot, history) | |
- CatchException: 将插件中出的所有问题显示在界面上 | |
- HotReload: 实现插件的热更新 | |
- trimmed_format_exc: 打印traceback,为了安全而隐藏绝对地址 | |
======================================================================== | |
""" | |
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, max_length, llm_model, txt, txt2, top_p, temperature, chatbot, history, system_prompt, plugin_advanced_arg, *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': llm_model, | |
'top_p':top_p, | |
'max_length': max_length, | |
'temperature':temperature, | |
} | |
plugin_kwargs = { | |
"advanced_arg": plugin_advanced_arg, | |
} | |
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 trimmed_format_exc(): | |
import os, traceback | |
str = traceback.format_exc() | |
current_path = os.getcwd() | |
replace_path = "." | |
return str.replace(current_path, replace_path) | |
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。 | |
""" | |
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' + trimmed_format_exc() + '```' | |
if len(chatbot) == 0: | |
chatbot.clear() | |
chatbot.append(["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。 | |
""" | |
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 | |
""" | |
======================================================================== | |
第二部分 | |
其他小工具: | |
- write_results_to_file: 将结果写入markdown文件中 | |
- regular_txt_to_markdown: 将普通文本转换为Markdown格式的文本。 | |
- report_execption: 向chatbot中添加简单的意外错误信息 | |
- text_divide_paragraph: 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。 | |
- markdown_convertion: 用多种方式组合,将markdown转化为好看的html | |
- format_io: 接管gradio默认的markdown处理方式 | |
- on_file_uploaded: 处理文件的上传(自动解压) | |
- on_report_generated: 将生成的报告自动投射到文件上传区 | |
- clip_history: 当历史上下文过长时,自动截断 | |
- get_conf: 获取设置 | |
- select_api_key: 根据当前的模型类别,抽取可用的api-key | |
======================================================================== | |
""" | |
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 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: | |
if type(content) != str: content = str(content) | |
except: | |
continue | |
if i % 2 == 0: | |
f.write('## ') | |
try: | |
f.write(content) | |
except: | |
# remove everything that cannot be handled by utf8 | |
f.write(content.encode('utf-8', 'ignore').decode()) | |
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 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(" ", " ") | |
text = "</br>".join(lines) | |
return text | |
# 使用 lru缓存 加快转换速度 | |
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 txt.startswith(pre) and txt.endswith(suf): | |
# print('警告,输入了已经经过转化的字符串,二次转化可能出问题') | |
return txt # 已经被转化过,不需要再次转化 | |
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$包裹begin命令时多余<script>) | |
""" | |
content = content.replace('<script type="math/tex">\n<script type="math/tex; mode=display">', '<script type="math/tex; mode=display">') | |
content = content.replace('</script>\n</script>', '</script>') | |
return content | |
def no_code(txt): | |
if '```' not in txt: | |
return True | |
else: | |
if '```reference' in txt: return True # newbing | |
else: return False | |
if ('$' in txt) and no_code(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, txt2, checkboxes): | |
""" | |
当文件被上传时的回调函数 | |
""" | |
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)] | |
if "底部输入区" in checkboxes: | |
txt = "" | |
txt2 = f'private_upload/{time_tag}' | |
else: | |
txt = f'private_upload/{time_tag}' | |
txt2 = "" | |
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“红颜色”标识的函数插件时,以上文件将被作为输入参数'+err_msg]) | |
return chatbot, txt, txt2 | |
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(['报告如何远程获取?', '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): | |
API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$", key) | |
API_MATCH_AZURE = re.match(r"[a-zA-Z0-9]{32}$", key) | |
return bool(API_MATCH_ORIGINAL) or bool(API_MATCH_AZURE) | |
def is_api2d_key(key): | |
if key.startswith('fk') and len(key) == 41: | |
return True | |
else: | |
return False | |
def is_any_api_key(key): | |
if ',' in key: | |
keys = key.split(',') | |
for k in keys: | |
if is_any_api_key(k): return True | |
return False | |
else: | |
return is_openai_api_key(key) or is_api2d_key(key) | |
def what_keys(keys): | |
avail_key_list = {'OpenAI Key':0, "API2D Key":0} | |
key_list = keys.split(',') | |
for k in key_list: | |
if is_openai_api_key(k): | |
avail_key_list['OpenAI Key'] += 1 | |
for k in key_list: | |
if is_api2d_key(k): | |
avail_key_list['API2D Key'] += 1 | |
return f"检测到: OpenAI Key {avail_key_list['OpenAI Key']} 个,API2D Key {avail_key_list['API2D Key']} 个" | |
def select_api_key(keys, llm_model): | |
import random | |
avail_key_list = [] | |
key_list = keys.split(',') | |
if llm_model.startswith('gpt-'): | |
for k in key_list: | |
if is_openai_api_key(k): avail_key_list.append(k) | |
if llm_model.startswith('api2d-'): | |
for k in key_list: | |
if is_api2d_key(k): avail_key_list.append(k) | |
if len(avail_key_list) == 0: | |
raise RuntimeError(f"您提供的api-key不满足要求,不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源。") | |
api_key = random.choice(avail_key_list) # 随机负载均衡 | |
return api_key | |
def read_env_variable(arg, default_value): | |
""" | |
环境变量可以是 `GPT_ACADEMIC_CONFIG`(优先),也可以直接是`CONFIG` | |
例如在windows cmd中,既可以写: | |
set USE_PROXY=True | |
set API_KEY=sk-j7caBpkRoxxxxxxxxxxxxxxxxxxxxxxxxxxxx | |
set proxies={"http":"http://127.0.0.1:10085", "https":"http://127.0.0.1:10085",} | |
set AVAIL_LLM_MODELS=["gpt-3.5-turbo", "chatglm"] | |
set AUTHENTICATION=[("username", "password"), ("username2", "password2")] | |
也可以写: | |
set GPT_ACADEMIC_USE_PROXY=True | |
set GPT_ACADEMIC_API_KEY=sk-j7caBpkRoxxxxxxxxxxxxxxxxxxxxxxxxxxxx | |
set GPT_ACADEMIC_proxies={"http":"http://127.0.0.1:10085", "https":"http://127.0.0.1:10085",} | |
set GPT_ACADEMIC_AVAIL_LLM_MODELS=["gpt-3.5-turbo", "chatglm"] | |
set GPT_ACADEMIC_AUTHENTICATION=[("username", "password"), ("username2", "password2")] | |
""" | |
from colorful import print亮红, print亮绿 | |
arg_with_prefix = "GPT_ACADEMIC_" + arg | |
if arg_with_prefix in os.environ: | |
env_arg = os.environ[arg_with_prefix] | |
elif arg in os.environ: | |
env_arg = os.environ[arg] | |
else: | |
raise KeyError | |
print(f"[ENV_VAR] 尝试加载{arg},默认值:{default_value} --> 修正值:{env_arg}") | |
try: | |
if isinstance(default_value, bool): | |
env_arg = env_arg.strip() | |
if env_arg == 'True': r = True | |
elif env_arg == 'False': r = False | |
else: print('enter True or False, but have:', env_arg); r = default_value | |
elif isinstance(default_value, int): | |
r = int(env_arg) | |
elif isinstance(default_value, float): | |
r = float(env_arg) | |
elif isinstance(default_value, str): | |
r = env_arg.strip() | |
elif isinstance(default_value, dict): | |
r = eval(env_arg) | |
elif isinstance(default_value, list): | |
r = eval(env_arg) | |
elif default_value is None: | |
assert arg == "proxies" | |
r = eval(env_arg) | |
else: | |
print亮红(f"[ENV_VAR] 环境变量{arg}不支持通过环境变量设置! ") | |
raise KeyError | |
except: | |
print亮红(f"[ENV_VAR] 环境变量{arg}加载失败! ") | |
raise KeyError(f"[ENV_VAR] 环境变量{arg}加载失败! ") | |
print亮绿(f"[ENV_VAR] 成功读取环境变量{arg}") | |
return r | |
def read_single_conf_with_lru_cache(arg): | |
from colorful import print亮红, print亮绿, print亮蓝 | |
try: | |
# 优先级1. 获取环境变量作为配置 | |
default_ref = getattr(importlib.import_module('config'), arg) # 读取默认值作为数据类型转换的参考 | |
r = read_env_variable(arg, default_ref) | |
except: | |
try: | |
# 优先级2. 获取config_private中的配置 | |
r = getattr(importlib.import_module('config_private'), arg) | |
except: | |
# 优先级3. 获取config中的配置 | |
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': | |
print亮蓝(f"[API_KEY] 本项目现已支持OpenAI和API2D的api-key。也支持同时填写多个api-key,如API_KEY=\"openai-key1,openai-key2,api2d-key3\"") | |
print亮蓝(f"[API_KEY] 您既可以在config.py中修改api-key(s),也可以在问题输入区输入临时的api-key(s),然后回车键提交后即可生效。") | |
if is_any_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'开头的51位密钥(OpenAI),或者 'fk'开头的41位密钥,请在config文件中修改API密钥之后再运行。") | |
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OpenAI家族的模型。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……就是不起作用,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 | |
def run_gradio_in_subpath(demo, auth, port, custom_path): | |
""" | |
把gradio的运行地址更改到指定的二次路径上 | |
""" | |
def is_path_legal(path: str)->bool: | |
''' | |
check path for sub url | |
path: path to check | |
return value: do sub url wrap | |
''' | |
if path == "/": return True | |
if len(path) == 0: | |
print("ilegal custom path: {}\npath must not be empty\ndeploy on root url".format(path)) | |
return False | |
if path[0] == '/': | |
if path[1] != '/': | |
print("deploy on sub-path {}".format(path)) | |
return True | |
return False | |
print("ilegal custom path: {}\npath should begin with \'/\'\ndeploy on root url".format(path)) | |
return False | |
if not is_path_legal(custom_path): raise RuntimeError('Ilegal custom path') | |
import uvicorn | |
import gradio as gr | |
from fastapi import FastAPI | |
app = FastAPI() | |
if custom_path != "/": | |
def read_main(): | |
return {"message": f"Gradio is running at: {custom_path}"} | |
app = gr.mount_gradio_app(app, demo, path=custom_path) | |
uvicorn.run(app, host="0.0.0.0", port=port) # , auth=auth | |
def clip_history(inputs, history, tokenizer, max_token_limit): | |
""" | |
reduce the length of history by clipping. | |
this function search for the longest entries to clip, little by little, | |
until the number of token of history is reduced under threshold. | |
通过裁剪来缩短历史记录的长度。 | |
此函数逐渐地搜索最长的条目进行剪辑, | |
直到历史记录的标记数量降低到阈值以下。 | |
""" | |
import numpy as np | |
from request_llm.bridge_all import model_info | |
def get_token_num(txt): | |
return len(tokenizer.encode(txt, disallowed_special=())) | |
input_token_num = get_token_num(inputs) | |
if input_token_num < max_token_limit * 3 / 4: | |
# 当输入部分的token占比小于限制的3/4时,裁剪时 | |
# 1. 把input的余量留出来 | |
max_token_limit = max_token_limit - input_token_num | |
# 2. 把输出用的余量留出来 | |
max_token_limit = max_token_limit - 128 | |
# 3. 如果余量太小了,直接清除历史 | |
if max_token_limit < 128: | |
history = [] | |
return history | |
else: | |
# 当输入部分的token占比 > 限制的3/4时,直接清除历史 | |
history = [] | |
return history | |
everything = [''] | |
everything.extend(history) | |
n_token = get_token_num('\n'.join(everything)) | |
everything_token = [get_token_num(e) for e in everything] | |
# 截断时的颗粒度 | |
delta = max(everything_token) // 16 | |
while n_token > max_token_limit: | |
where = np.argmax(everything_token) | |
encoded = tokenizer.encode(everything[where], disallowed_special=()) | |
clipped_encoded = encoded[:len(encoded)-delta] | |
everything[where] = tokenizer.decode(clipped_encoded)[:-1] # -1 to remove the may-be illegal char | |
everything_token[where] = get_token_num(everything[where]) | |
n_token = get_token_num('\n'.join(everything)) | |
history = everything[1:] | |
return history | |
""" | |
======================================================================== | |
第三部分 | |
其他小工具: | |
- zip_folder: 把某个路径下所有文件压缩,然后转移到指定的另一个路径中(gpt写的) | |
======================================================================== | |
""" | |
def zip_folder(source_folder, dest_folder, zip_name): | |
import zipfile | |
import os | |
# Make sure the source folder exists | |
if not os.path.exists(source_folder): | |
print(f"{source_folder} does not exist") | |
return | |
# Make sure the destination folder exists | |
if not os.path.exists(dest_folder): | |
print(f"{dest_folder} does not exist") | |
return | |
# Create the name for the zip file | |
zip_file = os.path.join(dest_folder, zip_name) | |
# Create a ZipFile object | |
with zipfile.ZipFile(zip_file, 'w', zipfile.ZIP_DEFLATED) as zipf: | |
# Walk through the source folder and add files to the zip file | |
for foldername, subfolders, filenames in os.walk(source_folder): | |
for filename in filenames: | |
filepath = os.path.join(foldername, filename) | |
zipf.write(filepath, arcname=os.path.relpath(filepath, source_folder)) | |
# Move the zip file to the destination folder (if it wasn't already there) | |
if os.path.dirname(zip_file) != dest_folder: | |
os.rename(zip_file, os.path.join(dest_folder, os.path.basename(zip_file))) | |
zip_file = os.path.join(dest_folder, os.path.basename(zip_file)) | |
print(f"Zip file created at {zip_file}") | |