gpt-academic-private / crazy_functions /代码重写为全英文_多线程.py
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import threading
from request_llm.bridge_chatgpt import predict_no_ui_long_connection
from toolbox import update_ui
from toolbox import CatchException, write_results_to_file, report_execption
from .crazy_utils import breakdown_txt_to_satisfy_token_limit
def extract_code_block_carefully(txt):
splitted = txt.split('```')
n_code_block_seg = len(splitted) - 1
if n_code_block_seg <= 1: return txt
# 剩下的情况都开头除去 ``` 结尾除去一次 ```
txt_out = '```'.join(splitted[1:-1])
return txt_out
def break_txt_into_half_at_some_linebreak(txt):
lines = txt.split('\n')
n_lines = len(lines)
pre = lines[:(n_lines//2)]
post = lines[(n_lines//2):]
return "\n".join(pre), "\n".join(post)
@CatchException
def 全项目切换英文(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port):
# 第1步:清空历史,以免输入溢出
history = []
# 第2步:尝试导入依赖,如果缺少依赖,则给出安装建议
try:
import tiktoken
except:
report_execption(chatbot, history,
a = f"解析项目: {txt}",
b = f"导入软件依赖失败。使用该模块需要额外依赖,安装方法```pip install --upgrade tiktoken```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
# 第3步:集合文件
import time, glob, os, shutil, re
os.makedirs('gpt_log/generated_english_version', exist_ok=True)
os.makedirs('gpt_log/generated_english_version/crazy_functions', exist_ok=True)
file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \
[f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]
# file_manifest = ['./toolbox.py']
i_say_show_user_buffer = []
# 第4步:随便显示点什么防止卡顿的感觉
for index, fp in enumerate(file_manifest):
# if 'test_project' in fp: continue
with open(fp, 'r', encoding='utf-8') as f:
file_content = f.read()
i_say_show_user =f'[{index}/{len(file_manifest)}] 接下来请将以下代码中包含的所有中文转化为英文,只输出转化后的英文代码,请用代码块输出代码: {os.path.abspath(fp)}'
i_say_show_user_buffer.append(i_say_show_user)
chatbot.append((i_say_show_user, "[Local Message] 等待多线程操作,中间过程不予显示."))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
# 第5步:Token限制下的截断与处理
MAX_TOKEN = 3000
import tiktoken
from toolbox import get_conf
enc = tiktoken.encoding_for_model(*get_conf('LLM_MODEL'))
def get_token_fn(txt): return len(enc.encode(txt))
# 第6步:任务函数
mutable_return = [None for _ in file_manifest]
observe_window = [[""] for _ in file_manifest]
def thread_worker(fp,index):
if index > 10:
time.sleep(60)
print('Openai 限制免费用户每分钟20次请求,降低请求频率中。')
with open(fp, 'r', encoding='utf-8') as f:
file_content = f.read()
i_say_template = lambda fp, file_content: f'接下来请将以下代码中包含的所有中文转化为英文,只输出代码,文件名是{fp},文件代码是 ```{file_content}```'
try:
gpt_say = ""
# 分解代码文件
file_content_breakdown = breakdown_txt_to_satisfy_token_limit(file_content, get_token_fn, MAX_TOKEN)
for file_content_partial in file_content_breakdown:
i_say = i_say_template(fp, file_content_partial)
# # ** gpt request **
gpt_say_partial = predict_no_ui_long_connection(inputs=i_say, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=observe_window[index])
gpt_say_partial = extract_code_block_carefully(gpt_say_partial)
gpt_say += gpt_say_partial
mutable_return[index] = gpt_say
except ConnectionAbortedError as token_exceed_err:
print('至少一个线程任务Token溢出而失败', e)
except Exception as e:
print('至少一个线程任务意外失败', e)
# 第7步:所有线程同时开始执行任务函数
handles = [threading.Thread(target=thread_worker, args=(fp,index)) for index, fp in enumerate(file_manifest)]
for h in handles:
h.daemon = True
h.start()
chatbot.append(('开始了吗?', f'多线程操作已经开始'))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
# 第8步:循环轮询各个线程是否执行完毕
cnt = 0
while True:
cnt += 1
time.sleep(0.2)
th_alive = [h.is_alive() for h in handles]
if not any(th_alive): break
# 更好的UI视觉效果
observe_win = []
for thread_index, alive in enumerate(th_alive):
observe_win.append("[ ..."+observe_window[thread_index][0][-60:].replace('\n','').replace('```','...').replace(' ','.').replace('<br/>','.....').replace('$','.')+"... ]")
stat = [f'执行中: {obs}\n\n' if alive else '已完成\n\n' for alive, obs in zip(th_alive, observe_win)]
stat_str = ''.join(stat)
chatbot[-1] = (chatbot[-1][0], f'多线程操作已经开始,完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt%10+1)))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
# 第9步:把结果写入文件
for index, h in enumerate(handles):
h.join() # 这里其实不需要join了,肯定已经都结束了
fp = file_manifest[index]
gpt_say = mutable_return[index]
i_say_show_user = i_say_show_user_buffer[index]
where_to_relocate = f'gpt_log/generated_english_version/{fp}'
if gpt_say is not None:
with open(where_to_relocate, 'w+', encoding='utf-8') as f:
f.write(gpt_say)
else: # 失败
shutil.copyfile(file_manifest[index], where_to_relocate)
chatbot.append((i_say_show_user, f'[Local Message] 已完成{os.path.abspath(fp)}的转化,\n\n存入{os.path.abspath(where_to_relocate)}'))
history.append(i_say_show_user); history.append(gpt_say)
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
time.sleep(1)
# 第10步:备份一个文件
res = write_results_to_file(history)
chatbot.append(("生成一份任务执行报告", res))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面