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另外一种方式实现ipadder

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This commit is contained in:
w_xiaolizu
2023-04-19 19:34:31 +08:00
parent ee73573655
commit fe6850a0bb
2 changed files with 11 additions and 6 deletions
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11
__main__.py
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@ -7,9 +7,9 @@ from toolbox import format_io, find_free_port, on_file_uploaded, on_report_gener
DummyWith DummyWith
# 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 # 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
proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \ proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY, AVAIL_LLM_MODELS = \
get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT',
'API_KEY') 'API_KEY', 'AVAIL_LLM_MODELS')
# 如果WEB_PORT是-1, 则随机选取WEB端口 # 如果WEB_PORT是-1, 则随机选取WEB端口
PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
@ -125,10 +125,15 @@ with gr.Blocks(title="ChatGPT 学术优化", theme=set_theme, analytics_enabled=
label="Top-p (nucleus sampling)", ) label="Top-p (nucleus sampling)", )
temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True,
label="Temperature", ) label="Temperature", )
max_length_sl = gr.Slider(minimum=256, maximum=4096, value=512, step=1, interactive=True,
label="Local LLM MaxLength", )
models_box = gr.CheckboxGroup(["input加密", "prompt提示"], models_box = gr.CheckboxGroup(["input加密", "prompt提示"],
value=["input加密", "prompt提示"], label="对话模式") value=["input加密", "prompt提示"], label="对话模式")
checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区"], checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区"],
value=["基础功能区", "函数插件区"], label="显示/隐藏功能区") value=["基础功能区", "函数插件区"], label="显示/隐藏功能区")
md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(
container=False)
gr.Markdown(description) gr.Markdown(description)
@ -148,7 +153,7 @@ with gr.Blocks(title="ChatGPT 学术优化", theme=set_theme, analytics_enabled=
[area_basic_fn, area_crazy_fn, area_input_primary, txt]) [area_basic_fn, area_crazy_fn, area_input_primary, txt])
# 整理反复出现的控件句柄组合 # 整理反复出现的控件句柄组合
# submitBtn.info # submitBtn.info
input_combo = [cookies, txt, top_p, temperature, chatbot, history, system_prompt, models_box] input_combo = [cookies, max_length_sl, md_dropdown, txt, top_p, temperature, chatbot, history, system_prompt, models_box]
output_combo = [cookies, chatbot, history, status] output_combo = [cookies, chatbot, history, status]
predict_args = dict(fn=ArgsGeneralWrapper(predict), inputs=input_combo, outputs=output_combo) predict_args = dict(fn=ArgsGeneralWrapper(predict), inputs=input_combo, outputs=output_combo)
# 提交按钮、重置按钮 # 提交按钮、重置按钮

6
toolbox.py
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@ -27,7 +27,7 @@ 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。 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, *args): def decorated(cookies, max_length, llm_model, txt, top_p, temperature, chatbot, history, system_prompt, models, ipaddr:gr.Request, *args):
txt_passon = txt txt_passon = txt
if 'input加密' in models: txt_passon = func_box.encryption_str(txt) if 'input加密' in models: txt_passon = func_box.encryption_str(txt)
# 引入一个有cookie的chatbot # 引入一个有cookie的chatbot
@ -40,14 +40,14 @@ def ArgsGeneralWrapper(f):
'llm_model': llm_model, 'llm_model': llm_model,
'top_p':top_p, 'top_p':top_p,
'max_length': max_length, 'max_length': max_length,
'temperature':temperature, 'temperature': temperature,
'ipaddr': ipaddr.client.host
} }
plugin_kwargs = { plugin_kwargs = {
# 目前还没有 # 目前还没有
} }
chatbot_with_cookie = ChatBotWithCookies(cookies) chatbot_with_cookie = ChatBotWithCookies(cookies)
chatbot_with_cookie.write_list(chatbot) chatbot_with_cookie.write_list(chatbot)
# logging.info(f'[user_click]_{adder.client.host} {txt_passon} ----')
yield from f(txt_passon, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, *args) yield from f(txt_passon, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, *args)
return decorated return decorated