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Version 3.74: Merge latest updates on dev branch (frontier) (#1621)

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* Update version to 3.74

* Add support for Yi Model API (#1635)

* 更新以支持零一万物模型

* 删除newbing

* 修改config

---------

Co-authored-by: binary-husky <qingxu.fu@outlook.com>

* Refactor function signatures in bridge files

* fix qwen api change

* rename and ref functions

* rename and move some cookie functions

* 增加haiku模型，新增endpoint配置说明 (#1626)

* haiku added

* 新增haiku，新增endpoint配置说明

* Haiku added

* 将说明同步至最新Endpoint

---------

Co-authored-by: binary-husky <qingxu.fu@outlook.com>

* private_upload目录下进行文件鉴权 (#1596)

* private_upload目录下进行文件鉴权

* minor fastapi adjustment

* Add logging functionality to enable saving
conversation records

* waiting to fix username retrieve

* support 2rd web path

* allow accessing default user dir

---------

Co-authored-by: binary-husky <qingxu.fu@outlook.com>

* remove yaml deps

* fix favicon

* fix abs path auth problem

* forget to write a return

* add `dashscope` to deps

* fix GHSA-v9q9-xj86-953p

* 用户名重叠越权访问patch (#1681)

* add cohere model api access

* cohere + can_multi_thread

* fix block user access(fail)

* fix fastapi bug

* change cohere api endpoint

* explain version

---------

Co-authored-by: Menghuan1918 <menghuan2003@outlook.com>
Co-authored-by: Skyzayre <120616113+Skyzayre@users.noreply.github.com>
Co-authored-by: XIao <46100050+Kilig947@users.noreply.github.com>
This commit is contained in:
binary-husky 2024-04-08 11:49:30 +08:00 committed by GitHub
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commit bdd46c5dd1
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28 changed files with 868 additions and 253 deletions
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7
config.py
View File

@ -45,7 +45,7 @@ AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-p
# "qwen-turbo", "qwen-plus", "qwen-max", "qwen-local",
# "moonshot-v1-128k", "moonshot-v1-32k", "moonshot-v1-8k",
# "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-turbo-0125"
# "claude-3-sonnet-20240229","claude-3-opus-20240229", "claude-2.1", "claude-instant-1.2",
# "claude-3-haiku-20240307","claude-3-sonnet-20240229","claude-3-opus-20240229", "claude-2.1", "claude-instant-1.2",
# "moss", "llama2", "chatglm_onnx", "internlm", "jittorllms_pangualpha", "jittorllms_llama",
# "yi-34b-chat-0205", "yi-34b-chat-200k"
# ]
@ -157,7 +157,8 @@ ADD_WAIFU = False
AUTHENTICATION = []
# 如果需要在二级路径下运行（常规情况下，不要修改!!）（需要配合修改main.py才能生效!）
# 如果需要在二级路径下运行（常规情况下，不要修改!!）
# （举例 CUSTOM_PATH = "/gpt_academic"，可以让软件运行在 http://ip:port/gpt_academic/ 下。）
CUSTOM_PATH = "/"
@ -377,4 +378,4 @@ NUM_CUSTOM_BASIC_BTN = 4
└── MATHPIX_APPKEY
"""
"""

120
main.py
View File

@ -13,6 +13,17 @@ help_menu_description = \
</br></br>如何语音对话: 请阅读Wiki
</br></br>如何临时更换API_KEY: 在输入区输入临时API_KEY后提交（网页刷新后失效）"""
def enable_log(PATH_LOGGING):
import logging, uuid
admin_log_path = os.path.join(PATH_LOGGING, "admin")
os.makedirs(admin_log_path, exist_ok=True)
log_dir = os.path.join(admin_log_path, "chat_secrets.log")
try:logging.basicConfig(filename=log_dir, level=logging.INFO, encoding="utf-8", format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
except:logging.basicConfig(filename=log_dir, level=logging.INFO, format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
# Disable logging output from the 'httpx' logger
logging.getLogger("httpx").setLevel(logging.WARNING)
print(f"所有对话记录将自动保存在本地目录{log_dir}, 请注意自我隐私保护哦！")
def main():
import gradio as gr
if gr.__version__ not in ['3.32.9']:
@ -31,18 +42,11 @@ def main():
from check_proxy import get_current_version
from themes.theme import adjust_theme, advanced_css, theme_declaration, js_code_clear, js_code_reset, js_code_show_or_hide, js_code_show_or_hide_group2
from themes.theme import js_code_for_css_changing, js_code_for_toggle_darkmode, js_code_for_persistent_cookie_init
from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, init_cookie
from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, assign_user_uuid
title_html = f"<h1 align=\"center\">GPT 学术优化 {get_current_version()}</h1>{theme_declaration}"
# 对话记录, python 版本建议3.9+（越新越好）
import logging, uuid
os.makedirs(PATH_LOGGING, exist_ok=True)
chat_secrets_log = os.path.join(PATH_LOGGING, "chat_secrets.log")
try:logging.basicConfig(filename=chat_secrets_log, level=logging.INFO, encoding="utf-8", format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
except:logging.basicConfig(filename=chat_secrets_log, level=logging.INFO, format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
# Disable logging output from the 'httpx' logger
logging.getLogger("httpx").setLevel(logging.WARNING)
print(f"所有对话记录将自动保存在本地目录 {chat_secrets_log}, 请注意自我隐私保护哦！")
# 对话、日志记录
enable_log(PATH_LOGGING)
# 一些普通功能模块
from core_functional import get_core_functions
@ -75,9 +79,9 @@ def main():
cancel_handles = []
customize_btns = {}
predefined_btns = {}
with gr.Blocks(title="GPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo:
with gr.Blocks(title="GPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as app_block:
gr.HTML(title_html)
secret_css, py_pickle_cookie = gr.Textbox(visible=False), gr.Textbox(visible=False)
secret_css, web_cookie_cache = gr.Textbox(visible=False), gr.Textbox(visible=False)
cookies = gr.State(load_chat_cookies())
with gr_L1():
with gr_L2(scale=2, elem_id="gpt-chat"):
@ -199,64 +203,19 @@ def main():
with gr.Column(scale=1, min_width=70):
basic_fn_confirm = gr.Button("确认并保存", variant="primary"); basic_fn_confirm.style(size="sm")
basic_fn_clean = gr.Button("恢复默认", variant="primary"); basic_fn_clean.style(size="sm")
def assign_btn(persistent_cookie_, cookies_, basic_btn_dropdown_, basic_fn_title, basic_fn_prefix, basic_fn_suffix, clean_up=False):
ret = {}
# 读取之前的自定义按钮
customize_fn_overwrite_ = cookies_['customize_fn_overwrite']
# 更新新的自定义按钮
customize_fn_overwrite_.update({
basic_btn_dropdown_:
{
"Title":basic_fn_title,
"Prefix":basic_fn_prefix,
"Suffix":basic_fn_suffix,
}
}
)
if clean_up:
customize_fn_overwrite_ = {}
cookies_.update(customize_fn_overwrite_) # 更新cookie
visible = (not clean_up) and (basic_fn_title != "")
if basic_btn_dropdown_ in customize_btns:
# 是自定义按钮，不是预定义按钮
ret.update({customize_btns[basic_btn_dropdown_]: gr.update(visible=visible, value=basic_fn_title)})
else:
# 是预定义按钮
ret.update({predefined_btns[basic_btn_dropdown_]: gr.update(visible=visible, value=basic_fn_title)})
ret.update({cookies: cookies_})
try: persistent_cookie_ = from_cookie_str(persistent_cookie_) # persistent cookie to dict
except: persistent_cookie_ = {}
persistent_cookie_["custom_bnt"] = customize_fn_overwrite_ # dict update new value
persistent_cookie_ = to_cookie_str(persistent_cookie_) # persistent cookie to dict
ret.update({py_pickle_cookie: persistent_cookie_}) # write persistent cookie
return ret
from shared_utils.cookie_manager import assign_btn__fn_builder
assign_btn = assign_btn__fn_builder(customize_btns, predefined_btns, cookies, web_cookie_cache)
# update btn
h = basic_fn_confirm.click(assign_btn, [py_pickle_cookie, cookies, basic_btn_dropdown, basic_fn_title, basic_fn_prefix, basic_fn_suffix],
[py_pickle_cookie, cookies, *customize_btns.values(), *predefined_btns.values()])
h.then(None, [py_pickle_cookie], None, _js="""(py_pickle_cookie)=>{setCookie("py_pickle_cookie", py_pickle_cookie, 365);}""")
h = basic_fn_confirm.click(assign_btn, [web_cookie_cache, cookies, basic_btn_dropdown, basic_fn_title, basic_fn_prefix, basic_fn_suffix],
[web_cookie_cache, cookies, *customize_btns.values(), *predefined_btns.values()])
h.then(None, [web_cookie_cache], None, _js="""(web_cookie_cache)=>{setCookie("web_cookie_cache", web_cookie_cache, 365);}""")
# clean up btn
h2 = basic_fn_clean.click(assign_btn, [py_pickle_cookie, cookies, basic_btn_dropdown, basic_fn_title, basic_fn_prefix, basic_fn_suffix, gr.State(True)],
[py_pickle_cookie, cookies, *customize_btns.values(), *predefined_btns.values()])
h2.then(None, [py_pickle_cookie], None, _js="""(py_pickle_cookie)=>{setCookie("py_pickle_cookie", py_pickle_cookie, 365);}""")
h2 = basic_fn_clean.click(assign_btn, [web_cookie_cache, cookies, basic_btn_dropdown, basic_fn_title, basic_fn_prefix, basic_fn_suffix, gr.State(True)],
[web_cookie_cache, cookies, *customize_btns.values(), *predefined_btns.values()])
h2.then(None, [web_cookie_cache], None, _js="""(web_cookie_cache)=>{setCookie("web_cookie_cache", web_cookie_cache, 365);}""")
def persistent_cookie_reload(persistent_cookie_, cookies_):
ret = {}
for k in customize_btns:
ret.update({customize_btns[k]: gr.update(visible=False, value="")})
try: persistent_cookie_ = from_cookie_str(persistent_cookie_) # persistent cookie to dict
except: return ret
customize_fn_overwrite_ = persistent_cookie_.get("custom_bnt", {})
cookies_['customize_fn_overwrite'] = customize_fn_overwrite_
ret.update({cookies: cookies_})
for k,v in persistent_cookie_["custom_bnt"].items():
if v['Title'] == "": continue
if k in customize_btns: ret.update({customize_btns[k]: gr.update(visible=True, value=v['Title'])})
else: ret.update({predefined_btns[k]: gr.update(visible=True, value=v['Title'])})
return ret
# 功能区显示开关与功能区的互动
def fn_area_visibility(a):
@ -376,11 +335,14 @@ def main():
audio_mic.stream(deal_audio, inputs=[audio_mic, cookies])
demo.load(init_cookie, inputs=[cookies], outputs=[cookies])
demo.load(persistent_cookie_reload, inputs = [py_pickle_cookie, cookies],
outputs = [py_pickle_cookie, cookies, *customize_btns.values(), *predefined_btns.values()], _js=js_code_for_persistent_cookie_init)
demo.load(None, inputs=[], outputs=None, _js=f"""()=>init_frontend_with_cookies("{DARK_MODE}","{INIT_SYS_PROMPT}","{ADD_WAIFU}")""") # 配置暗色主题或亮色主题
demo.load(None, inputs=[gr.Textbox(LAYOUT, visible=False)], outputs=None, _js='(LAYOUT)=>{GptAcademicJavaScriptInit(LAYOUT);}')
app_block.load(assign_user_uuid, inputs=[cookies], outputs=[cookies])
from shared_utils.cookie_manager import load_web_cookie_cache__fn_builder
load_web_cookie_cache = load_web_cookie_cache__fn_builder(customize_btns, cookies, predefined_btns)
app_block.load(load_web_cookie_cache, inputs = [web_cookie_cache, cookies],
outputs = [web_cookie_cache, cookies, *customize_btns.values(), *predefined_btns.values()], _js=js_code_for_persistent_cookie_init)
app_block.load(None, inputs=[], outputs=None, _js=f"""()=>GptAcademicJavaScriptInit("{DARK_MODE}","{INIT_SYS_PROMPT}","{ADD_WAIFU}","{LAYOUT}")""") # 配置暗色主题或亮色主题
# gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数
def run_delayed_tasks():
@ -395,19 +357,15 @@ def main():
threading.Thread(target=auto_updates, name="self-upgrade", daemon=True).start() # 查看自动更新
threading.Thread(target=open_browser, name="open-browser", daemon=True).start() # 打开浏览器页面
threading.Thread(target=warm_up_mods, name="warm-up", daemon=True).start() # 预热tiktoken模块
threading.Thread(target=warm_up_mods, name="warm-up", daemon=True).start() # 预热tiktoken模块
# 运行一些异步任务：自动更新、打开浏览器页面、预热tiktoken模块
run_delayed_tasks()
demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
quiet=True,
server_name="0.0.0.0",
ssl_keyfile=None if SSL_KEYFILE == "" else SSL_KEYFILE,
ssl_certfile=None if SSL_CERTFILE == "" else SSL_CERTFILE,
ssl_verify=False,
server_port=PORT,
favicon_path=os.path.join(os.path.dirname(__file__), "docs/logo.png"),
auth=AUTHENTICATION if len(AUTHENTICATION) != 0 else None,
blocked_paths=["config.py","__pycache__","config_private.py","docker-compose.yml","Dockerfile",f"{PATH_LOGGING}/admin", chat_secrets_log])
# 最后，正式开始服务
from shared_utils.fastapi_server import start_app
start_app(app_block, CONCURRENT_COUNT, AUTHENTICATION, PORT, SSL_KEYFILE, SSL_CERTFILE)
if __name__ == "__main__":
main()

55
request_llms/bridge_all.py
View File

@ -34,6 +34,9 @@ from .bridge_google_gemini import predict_no_ui_long_connection as genai_noui
from .bridge_zhipu import predict_no_ui_long_connection as zhipu_noui
from .bridge_zhipu import predict as zhipu_ui
from .bridge_cohere import predict as cohere_ui
from .bridge_cohere import predict_no_ui_long_connection as cohere_noui
colors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044']
class LazyloadTiktoken(object):
@ -64,6 +67,7 @@ newbing_endpoint = "wss://sydney.bing.com/sydney/ChatHub"
gemini_endpoint = "https://generativelanguage.googleapis.com/v1beta/models"
claude_endpoint = "https://api.anthropic.com/v1/messages"
yimodel_endpoint = "https://api.lingyiwanwu.com/v1/chat/completions"
cohere_endpoint = 'https://api.cohere.ai/v1/chat'
if not AZURE_ENDPOINT.endswith('/'): AZURE_ENDPOINT += '/'
azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
@ -82,6 +86,7 @@ if newbing_endpoint in API_URL_REDIRECT: newbing_endpoint = API_URL_REDIRECT[new
if gemini_endpoint in API_URL_REDIRECT: gemini_endpoint = API_URL_REDIRECT[gemini_endpoint]
if claude_endpoint in API_URL_REDIRECT: claude_endpoint = API_URL_REDIRECT[claude_endpoint]
if yimodel_endpoint in API_URL_REDIRECT: yimodel_endpoint = API_URL_REDIRECT[yimodel_endpoint]
if cohere_endpoint in API_URL_REDIRECT: cohere_endpoint = API_URL_REDIRECT[cohere_endpoint]
# 获取tokenizer
tokenizer_gpt35 = LazyloadTiktoken("gpt-3.5-turbo")
@ -310,6 +315,18 @@ model_info = {
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
# cohere
"cohere-command-r-plus": {
"fn_with_ui": cohere_ui,
"fn_without_ui": cohere_noui,
"can_multi_thread": True,
"endpoint": cohere_endpoint,
"max_token": 1024 * 4,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
}
# -=-=-=-=-=-=- 月之暗面 -=-=-=-=-=-=-
from request_llms.bridge_moonshot import predict as moonshot_ui
@ -359,7 +376,7 @@ for model in AVAIL_LLM_MODELS:
# -=-=-=-=-=-=- 以下部分是新加入的模型，可能附带额外依赖 -=-=-=-=-=-=-
# claude家族
claude_models = ["claude-instant-1.2","claude-2.0","claude-2.1","claude-3-sonnet-20240229","claude-3-opus-20240229"]
claude_models = ["claude-instant-1.2","claude-2.0","claude-2.1","claude-3-haiku-20240307","claude-3-sonnet-20240229","claude-3-opus-20240229"]
if any(item in claude_models for item in AVAIL_LLM_MODELS):
from .bridge_claude import predict_no_ui_long_connection as claude_noui
from .bridge_claude import predict as claude_ui
@ -393,6 +410,16 @@ if any(item in claude_models for item in AVAIL_LLM_MODELS):
"token_cnt": get_token_num_gpt35,
},
})
model_info.update({
"claude-3-haiku-20240307": {
"fn_with_ui": claude_ui,
"fn_without_ui": claude_noui,
"endpoint": claude_endpoint,
"max_token": 200000,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
})
model_info.update({
"claude-3-sonnet-20240229": {
"fn_with_ui": claude_ui,
@ -789,7 +816,7 @@ def LLM_CATCH_EXCEPTION(f):
"""
装饰器函数，将错误显示出来
"""
def decorated(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience):
def decorated(inputs:str, llm_kwargs:dict, history:list, sys_prompt:str, observe_window:list, console_slience:bool):
try:
return f(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
except Exception as e:
@ -799,9 +826,9 @@ def LLM_CATCH_EXCEPTION(f):
return decorated
def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, observe_window=[], console_slience=False):
def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list, sys_prompt:str, observe_window:list=[], console_slience:bool=False):
"""
发送至LLM，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
发送至LLM，等待回复，一次性完成，不显示中间过程。但内部（尽可能地）用stream的方法避免中途网线被掐。
inputs：
是本次问询的输入
sys_prompt:
@ -819,7 +846,6 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
model = llm_kwargs['llm_model']
n_model = 1
if '&' not in model:
assert not model.startswith("tgui"), "TGUI不支持函数插件的实现"
# 如果只询问1个大语言模型：
method = model_info[model]["fn_without_ui"]
@ -880,15 +906,22 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
return res
def predict(inputs, llm_kwargs, *args, **kwargs):
def predict(inputs:str, llm_kwargs:dict, *args, **kwargs):
"""
发送至LLM，流式获取输出。
用于基础的对话功能。
inputs 是本次问询的输入
top_p, temperature是LLM的内部调优参数
history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
additional_fn代表点击的哪个按钮，按钮见functional.py
完整参数列表：
predict(
inputs:str, # 是本次问询的输入
llm_kwargs:dict, # 是LLM的内部调优参数
plugin_kwargs:dict, # 是插件的内部参数
chatbot:ChatBotWithCookies, # 原样传递，负责向用户前端展示对话，兼顾前端状态的功能
history:list=[], # 是之前的对话列表
system_prompt:str='', # 系统静默prompt
stream:bool=True, # 是否流式输出（已弃用）
additional_fn:str=None # 基础功能区按钮的附加功能
):
"""
inputs = apply_gpt_academic_string_mask(inputs, mode="show_llm")

3
request_llms/bridge_chatglmft.py
View File

@ -137,7 +137,8 @@ class GetGLMFTHandle(Process):
global glmft_handle
glmft_handle = None
#################################################################################
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
observe_window:list=[], console_slience:bool=False):
"""
多线程方法
函数的说明请见 request_llms/bridge_all.py

6
request_llms/bridge_chatgpt.py
View File

@ -23,6 +23,7 @@ import random
# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history
from toolbox import trimmed_format_exc, is_the_upload_folder, read_one_api_model_name, log_chat
from toolbox import ChatBotWithCookies
proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY')
@ -69,7 +70,7 @@ def verify_endpoint(endpoint):
raise ValueError("Endpoint不正确, 请检查AZURE_ENDPOINT的配置! 当前的Endpoint为:" + endpoint)
return endpoint
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=None, console_slience:bool=False):
"""
发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
inputs：
@ -147,7 +148,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
return result
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
"""
发送至chatGPT，流式获取输出。
用于基础的对话功能。

15
request_llms/bridge_claude.py
View File

@ -13,11 +13,11 @@ import logging
import os
import time
import traceback
from toolbox import get_conf, update_ui, trimmed_format_exc, encode_image, every_image_file_in_path
import json
import requests
from toolbox import get_conf, update_ui, trimmed_format_exc, encode_image, every_image_file_in_path, log_chat
picture_system_prompt = "\n当回复图像时,必须说明正在回复哪张图像。所有图像仅在最后一个问题中提供,即使它们在历史记录中被提及。请使用'这是第X张图像:'的格式来指明您正在描述的是哪张图像。"
Claude_3_Models = ["claude-3-sonnet-20240229", "claude-3-opus-20240229"]
Claude_3_Models = ["claude-3-haiku-20240307", "claude-3-sonnet-20240229", "claude-3-opus-20240229"]
# config_private.py放自己的秘密如API和代理网址
# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
@ -95,7 +95,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
# make a POST request to the API endpoint, stream=False
from .bridge_all import model_info
endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
response = requests.post(endpoint, headers=headers, json=message,
response = requests.post(endpoint, headers=headers, json=message,
proxies=proxies, stream=True, timeout=TIMEOUT_SECONDS);break
except requests.exceptions.ReadTimeout as e:
retry += 1
@ -116,7 +116,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
if need_to_pass:
pass
elif is_last_chunk:
logging.info(f'[response] {result}')
# logging.info(f'[response] {result}')
break
else:
if chunkjson and chunkjson['type'] == 'content_block_delta':
@ -194,7 +194,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
# make a POST request to the API endpoint, stream=True
from .bridge_all import model_info
endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
response = requests.post(endpoint, headers=headers, json=message,
response = requests.post(endpoint, headers=headers, json=message,
proxies=proxies, stream=True, timeout=TIMEOUT_SECONDS);break
except requests.exceptions.ReadTimeout as e:
retry += 1
@ -216,7 +216,8 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
if need_to_pass:
pass
elif is_last_chunk:
logging.info(f'[response] {gpt_replying_buffer}')
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
# logging.info(f'[response] {gpt_replying_buffer}')
break
else:
if chunkjson and chunkjson['type'] == 'content_block_delta':
@ -305,4 +306,4 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, image_paths):
'stream': True,
'system': system_prompt
}
return headers, payload
return headers, payload

328
request_llms/bridge_cohere.py Normal file
View File

@ -0,0 +1,328 @@
# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
"""
该文件中主要包含三个函数
不具备多线程能力的函数：
1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
具备多线程调用能力的函数
2. predict_no_ui_long_connection：支持多线程
"""
import json
import time
import gradio as gr
import logging
import traceback
import requests
import importlib
import random
# config_private.py放自己的秘密如API和代理网址
# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history
from toolbox import trimmed_format_exc, is_the_upload_folder, read_one_api_model_name, log_chat
from toolbox import ChatBotWithCookies
proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY')
timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
'网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
def get_full_error(chunk, stream_response):
"""
获取完整的从Cohere返回的报错
"""
while True:
try:
chunk += next(stream_response)
except:
break
return chunk
def decode_chunk(chunk):
# 提前读取一些信息 （用于判断异常）
chunk_decoded = chunk.decode()
chunkjson = None
has_choices = False
choice_valid = False
has_content = False
has_role = False
try:
chunkjson = json.loads(chunk_decoded)
has_choices = 'choices' in chunkjson
if has_choices: choice_valid = (len(chunkjson['choices']) > 0)
if has_choices and choice_valid: has_content = ("content" in chunkjson['choices'][0]["delta"])
if has_content: has_content = (chunkjson['choices'][0]["delta"]["content"] is not None)
if has_choices and choice_valid: has_role = "role" in chunkjson['choices'][0]["delta"]
except:
pass
return chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role
from functools import lru_cache
@lru_cache(maxsize=32)
def verify_endpoint(endpoint):
"""
检查endpoint是否可用
"""
if "你亲手写的api名称" in endpoint:
raise ValueError("Endpoint不正确, 请检查AZURE_ENDPOINT的配置! 当前的Endpoint为:" + endpoint)
return endpoint
def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=None, console_slience:bool=False):
"""
发送，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
inputs：
是本次问询的输入
sys_prompt:
系统静默prompt
llm_kwargs：
内部调优参数
history：
是之前的对话列表
observe_window = None：
用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
"""
watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
retry = 0
while True:
try:
# make a POST request to the API endpoint, stream=False
from .bridge_all import model_info
endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
response = requests.post(endpoint, headers=headers, proxies=proxies,
json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
except requests.exceptions.ReadTimeout as e:
retry += 1
traceback.print_exc()
if retry > MAX_RETRY: raise TimeoutError
if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
stream_response = response.iter_lines()
result = ''
json_data = None
while True:
try: chunk = next(stream_response)
except StopIteration:
break
except requests.exceptions.ConnectionError:
chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk)
if chunkjson['event_type'] == 'stream-start': continue
if chunkjson['event_type'] == 'text-generation':
result += chunkjson["text"]
if not console_slience: print(chunkjson["text"], end='')
if observe_window is not None:
# 观测窗，把已经获取的数据显示出去
if len(observe_window) >= 1:
observe_window[0] += chunkjson["text"]
# 看门狗，如果超过期限没有喂狗，则终止
if len(observe_window) >= 2:
if (time.time()-observe_window[1]) > watch_dog_patience:
raise RuntimeError("用户取消了程序。")
if chunkjson['event_type'] == 'stream-end': break
return result
def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
"""
发送至chatGPT，流式获取输出。
用于基础的对话功能。
inputs 是本次问询的输入
top_p, temperature是chatGPT的内部调优参数
history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
additional_fn代表点击的哪个按钮，按钮见functional.py
"""
# if is_any_api_key(inputs):
# chatbot._cookies['api_key'] = inputs
# chatbot.append(("输入已识别为Cohere的api_key", what_keys(inputs)))
# yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
# return
# elif not is_any_api_key(chatbot._cookies['api_key']):
# chatbot.append((inputs, "缺少api_key。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。"))
# yield from update_ui(chatbot=chatbot, history=history, msg="缺少api_key") # 刷新界面
# return
user_input = inputs
if additional_fn is not None:
from core_functional import handle_core_functionality
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
raw_input = inputs
# logging.info(f'[raw_input] {raw_input}')
chatbot.append((inputs, ""))
yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
# check mis-behavior
if is_the_upload_folder(user_input):
chatbot[-1] = (inputs, f"[Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。")
yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
time.sleep(2)
try:
headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
except RuntimeError as e:
chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。")
yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
return
# 检查endpoint是否合法
try:
from .bridge_all import model_info
endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
except:
tb_str = '```\n' + trimmed_format_exc() + '```'
chatbot[-1] = (inputs, tb_str)
yield from update_ui(chatbot=chatbot, history=history, msg="Endpoint不满足要求") # 刷新界面
return
history.append(inputs); history.append("")
retry = 0
while True:
try:
# make a POST request to the API endpoint, stream=True
response = requests.post(endpoint, headers=headers, proxies=proxies,
json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
except:
retry += 1
chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
if retry > MAX_RETRY: raise TimeoutError
gpt_replying_buffer = ""
is_head_of_the_stream = True
if stream:
stream_response = response.iter_lines()
while True:
try:
chunk = next(stream_response)
except StopIteration:
# 非Cohere官方接口的出现这样的报错，Cohere和API2D不会走这里
chunk_decoded = chunk.decode()
error_msg = chunk_decoded
# 其他情况，直接返回报错
chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
yield from update_ui(chatbot=chatbot, history=history, msg="非Cohere官方接口返回了错误:" + chunk.decode()) # 刷新界面
return
# 提前读取一些信息 （用于判断异常）
chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk)
if chunkjson:
try:
if chunkjson['event_type'] == 'stream-start':
continue
if chunkjson['event_type'] == 'text-generation':
gpt_replying_buffer = gpt_replying_buffer + chunkjson["text"]
history[-1] = gpt_replying_buffer
chatbot[-1] = (history[-2], history[-1])
yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
if chunkjson['event_type'] == 'stream-end':
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
history[-1] = gpt_replying_buffer
chatbot[-1] = (history[-2], history[-1])
yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
break
except Exception as e:
yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
chunk = get_full_error(chunk, stream_response)
chunk_decoded = chunk.decode()
error_msg = chunk_decoded
chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
print(error_msg)
return
def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
from .bridge_all import model_info
Cohere_website = ' 请登录Cohere查看详情 https://platform.Cohere.com/signup'
if "reduce the length" in error_msg:
if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'],
max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
elif "does not exist" in error_msg:
chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
elif "Incorrect API key" in error_msg:
chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. Cohere以提供了不正确的API_KEY为由, 拒绝服务. " + Cohere_website)
elif "exceeded your current quota" in error_msg:
chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. Cohere以账户额度不足为由, 拒绝服务." + Cohere_website)
elif "account is not active" in error_msg:
chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. Cohere以账户失效为由, 拒绝服务." + Cohere_website)
elif "associated with a deactivated account" in error_msg:
chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. Cohere以账户失效为由, 拒绝服务." + Cohere_website)
elif "API key has been deactivated" in error_msg:
chatbot[-1] = (chatbot[-1][0], "[Local Message] API key has been deactivated. Cohere以账户失效为由, 拒绝服务." + Cohere_website)
elif "bad forward key" in error_msg:
chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
elif "Not enough point" in error_msg:
chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
else:
from toolbox import regular_txt_to_markdown
tb_str = '```\n' + trimmed_format_exc() + '```'
chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
return chatbot, history
def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
"""
整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
"""
# if not is_any_api_key(llm_kwargs['api_key']):
# raise AssertionError("你提供了错误的API_KEY。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。")
api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key}"
}
if API_ORG.startswith('org-'): headers.update({"Cohere-Organization": API_ORG})
if llm_kwargs['llm_model'].startswith('azure-'):
headers.update({"api-key": api_key})
if llm_kwargs['llm_model'] in AZURE_CFG_ARRAY.keys():
azure_api_key_unshared = AZURE_CFG_ARRAY[llm_kwargs['llm_model']]["AZURE_API_KEY"]
headers.update({"api-key": azure_api_key_unshared})
conversation_cnt = len(history) // 2
messages = [{"role": "SYSTEM", "message": system_prompt}]
if conversation_cnt:
for index in range(0, 2*conversation_cnt, 2):
what_i_have_asked = {}
what_i_have_asked["role"] = "USER"
what_i_have_asked["message"] = history[index]
what_gpt_answer = {}
what_gpt_answer["role"] = "CHATBOT"
what_gpt_answer["message"] = history[index+1]
if what_i_have_asked["message"] != "":
if what_gpt_answer["message"] == "": continue
if what_gpt_answer["message"] == timeout_bot_msg: continue
messages.append(what_i_have_asked)
messages.append(what_gpt_answer)
else:
messages[-1]['message'] = what_gpt_answer['message']
model = llm_kwargs['llm_model']
if model.startswith('cohere-'): model = model[len('cohere-'):]
payload = {
"model": model,
"message": inputs,
"chat_history": messages,
"temperature": llm_kwargs['temperature'], # 1.0,
"top_p": llm_kwargs['top_p'], # 1.0,
"n": 1,
"stream": stream,
"presence_penalty": 0,
"frequency_penalty": 0,
}
return headers,payload

4
request_llms/bridge_google_gemini.py
View File

@ -7,6 +7,7 @@ import re
import os
import time
from request_llms.com_google import GoogleChatInit
from toolbox import ChatBotWithCookies
from toolbox import get_conf, update_ui, update_ui_lastest_msg, have_any_recent_upload_image_files, trimmed_format_exc
proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY')
@ -44,7 +45,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
return gpt_replying_buffer
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
# 检查API_KEY
if get_conf("GEMINI_API_KEY") == "":
yield from update_ui_lastest_msg(f"请配置 GEMINI_API_KEY。", chatbot=chatbot, history=history, delay=0)

5
request_llms/bridge_jittorllms_llama.py
View File

@ -1,10 +1,10 @@
from transformers import AutoModel, AutoTokenizer
import time
import threading
import importlib
from toolbox import update_ui, get_conf
from multiprocessing import Process, Pipe
from transformers import AutoModel, AutoTokenizer
load_message = "jittorllms尚未加载，加载需要一段时间。注意，请避免混用多种jittor模型，否则可能导致显存溢出而造成卡顿，取决于`config.py`的配置，jittorllms消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
@ -106,7 +106,8 @@ class GetGLMHandle(Process):
global llama_glm_handle
llama_glm_handle = None
#################################################################################
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
observe_window:list=[], console_slience:bool=False):
"""
多线程方法
函数的说明请见 request_llms/bridge_all.py

5
request_llms/bridge_jittorllms_pangualpha.py
View File

@ -1,10 +1,10 @@
from transformers import AutoModel, AutoTokenizer
import time
import threading
import importlib
from toolbox import update_ui, get_conf
from multiprocessing import Process, Pipe
from transformers import AutoModel, AutoTokenizer
load_message = "jittorllms尚未加载，加载需要一段时间。注意，请避免混用多种jittor模型，否则可能导致显存溢出而造成卡顿，取决于`config.py`的配置，jittorllms消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
@ -106,7 +106,8 @@ class GetGLMHandle(Process):
global pangu_glm_handle
pangu_glm_handle = None
#################################################################################
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
observe_window:list=[], console_slience:bool=False):
"""
多线程方法
函数的说明请见 request_llms/bridge_all.py

3
request_llms/bridge_jittorllms_rwkv.py
View File

@ -106,7 +106,8 @@ class GetGLMHandle(Process):
global rwkv_glm_handle
rwkv_glm_handle = None
#################################################################################
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
observe_window:list=[], console_slience:bool=False):
"""
多线程方法
函数的说明请见 request_llms/bridge_all.py

4
request_llms/bridge_moonshot.py
View File

@ -8,6 +8,7 @@ import time
import logging
from toolbox import get_conf, update_ui, log_chat
from toolbox import ChatBotWithCookies
import requests
@ -146,7 +147,8 @@ def msg_handle_error(llm_kwargs, chunk_decoded):
return error_msg
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
chatbot.append([inputs, ""])
if additional_fn is not None:

3
request_llms/bridge_moss.py
View File

@ -171,7 +171,8 @@ class GetGLMHandle(Process):
global moss_handle
moss_handle = None
#################################################################################
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
observe_window:list=[], console_slience:bool=False):
"""
多线程方法
函数的说明请见 request_llms/bridge_all.py

3
request_llms/bridge_qianfan.py
View File

@ -117,7 +117,8 @@ def generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
raise RuntimeError(dec['error_msg'])
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
observe_window:list=[], console_slience:bool=False):
"""
⭐多线程方法
函数的说明请见 request_llms/bridge_all.py

5
request_llms/bridge_qwen.py
View File

@ -5,7 +5,8 @@ from toolbox import check_packages, report_exception
model_name = 'Qwen'
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
observe_window:list=[], console_slience:bool=False):
"""
⭐多线程方法
函数的说明请见 request_llms/bridge_all.py
@ -47,6 +48,8 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
if additional_fn is not None:
from core_functional import handle_core_functionality
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
chatbot[-1] = (inputs, "")
yield from update_ui(chatbot=chatbot, history=history)
# 开始接收回复
from .com_qwenapi import QwenRequestInstance

3
request_llms/bridge_skylark2.py
View File

@ -9,7 +9,8 @@ def validate_key():
if YUNQUE_SECRET_KEY == '': return False
return True
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
observe_window:list=[], console_slience:bool=False):
"""
⭐ 多线程方法
函数的说明请见 request_llms/bridge_all.py

3
request_llms/bridge_spark.py
View File

@ -13,7 +13,8 @@ def validate_key():
return False
return True
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
observe_window:list=[], console_slience:bool=False):
"""
⭐多线程方法
函数的说明请见 request_llms/bridge_all.py

10
request_llms/bridge_zhipu.py
View File

@ -1,7 +1,8 @@
import time
import os
from toolbox import update_ui, get_conf, update_ui_lastest_msg
from toolbox import update_ui, get_conf, update_ui_lastest_msg, log_chat
from toolbox import check_packages, report_exception, have_any_recent_upload_image_files
from toolbox import ChatBotWithCookies
model_name = '智谱AI大模型'
zhipuai_default_model = 'glm-4'
@ -16,7 +17,8 @@ def make_media_input(inputs, image_paths):
inputs = inputs + f'<br/><br/><div align="center"><img src="file={os.path.abspath(image_path)}"></div>'
return inputs
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="",
observe_window:list=[], console_slience:bool=False):
"""
⭐多线程方法
函数的说明请见 request_llms/bridge_all.py
@ -42,7 +44,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
return response
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
"""
⭐单线程方法
函数的说明请见 request_llms/bridge_all.py
@ -90,4 +93,5 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
chatbot[-1] = [inputs, response]
yield from update_ui(chatbot=chatbot, history=history)
history.extend([inputs, response])
log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response)
yield from update_ui(chatbot=chatbot, history=history)

6
request_llms/local_llm_class.py
View File

@ -1,6 +1,7 @@
import time
import threading
from toolbox import update_ui, Singleton
from toolbox import ChatBotWithCookies
from multiprocessing import Process, Pipe
from contextlib import redirect_stdout
from request_llms.queued_pipe import create_queue_pipe
@ -214,7 +215,7 @@ class LocalLLMHandle(Process):
def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='classic'):
load_message = f"{model_name}尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，{model_name}消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=[], console_slience:bool=False):
"""
refer to request_llms/bridge_all.py
"""
@ -260,7 +261,8 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='cla
raise RuntimeError("程序终止。")
return response
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
"""
refer to request_llms/bridge_all.py
"""

3
requirements.txt
View File

@ -17,6 +17,7 @@ prompt_toolkit
latex2mathml
python-docx
mdtex2html
dashscope
pyautogen
colorama
Markdown
@ -25,4 +26,4 @@ pymupdf
openai
arxiv
numpy
rich
rich

60
shared_utils/cookie_manager.py
View File

@ -1 +1,61 @@
from typing import Callable
def load_web_cookie_cache__fn_builder(customize_btns, cookies, predefined_btns)->Callable:
def load_web_cookie_cache(persistent_cookie_, cookies_):
import gradio as gr
from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, assign_user_uuid
ret = {}
for k in customize_btns:
ret.update({customize_btns[k]: gr.update(visible=False, value="")})
try: persistent_cookie_ = from_cookie_str(persistent_cookie_) # persistent cookie to dict
except: return ret
customize_fn_overwrite_ = persistent_cookie_.get("custom_bnt", {})
cookies_['customize_fn_overwrite'] = customize_fn_overwrite_
ret.update({cookies: cookies_})
for k,v in persistent_cookie_["custom_bnt"].items():
if v['Title'] == "": continue
if k in customize_btns: ret.update({customize_btns[k]: gr.update(visible=True, value=v['Title'])})
else: ret.update({predefined_btns[k]: gr.update(visible=True, value=v['Title'])})
return ret
return load_web_cookie_cache
def assign_btn__fn_builder(customize_btns, predefined_btns, cookies, web_cookie_cache)->Callable:
def assign_btn(persistent_cookie_, cookies_, basic_btn_dropdown_, basic_fn_title, basic_fn_prefix, basic_fn_suffix, clean_up=False):
import gradio as gr
from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, assign_user_uuid
ret = {}
# 读取之前的自定义按钮
customize_fn_overwrite_ = cookies_['customize_fn_overwrite']
# 更新新的自定义按钮
customize_fn_overwrite_.update({
basic_btn_dropdown_:
{
"Title":basic_fn_title,
"Prefix":basic_fn_prefix,
"Suffix":basic_fn_suffix,
}
}
)
if clean_up:
customize_fn_overwrite_ = {}
cookies_.update(customize_fn_overwrite_) # 更新cookie
visible = (not clean_up) and (basic_fn_title != "")
if basic_btn_dropdown_ in customize_btns:
# 是自定义按钮，不是预定义按钮
ret.update({customize_btns[basic_btn_dropdown_]: gr.update(visible=visible, value=basic_fn_title)})
else:
# 是预定义按钮
ret.update({predefined_btns[basic_btn_dropdown_]: gr.update(visible=visible, value=basic_fn_title)})
ret.update({cookies: cookies_})
try: persistent_cookie_ = from_cookie_str(persistent_cookie_) # persistent cookie to dict
except: persistent_cookie_ = {}
persistent_cookie_["custom_bnt"] = customize_fn_overwrite_ # dict update new value
persistent_cookie_ = to_cookie_str(persistent_cookie_) # persistent cookie to dict
ret.update({web_cookie_cache: persistent_cookie_}) # write persistent cookie
return ret
return assign_btn

211
shared_utils/fastapi_server.py Normal file
View File

@ -0,0 +1,211 @@
"""
Tests:
- custom_path false / no user auth:
-- upload file(yes)
-- download file(yes)
-- websocket(yes)
-- block __pycache__ access(yes)
-- rel (yes)
-- abs (yes)
-- block user access(fail) http://localhost:45013/file=gpt_log/admin/chat_secrets.log
-- fix(commit f6bf05048c08f5cd84593f7fdc01e64dec1f584a)-> block successful
- custom_path yes("/cc/gptac") / no user auth:
-- upload file(yes)
-- download file(yes)
-- websocket(yes)
-- block __pycache__ access(yes)
-- block user access(yes)
- custom_path yes("/cc/gptac/") / no user auth:
-- upload file(yes)
-- download file(yes)
-- websocket(yes)
-- block user access(yes)
- custom_path yes("/cc/gptac/") / + user auth:
-- upload file(yes)
-- download file(yes)
-- websocket(yes)
-- block user access(yes)
-- block user-wise access (yes)
- custom_path no + user auth:
-- upload file(yes)
-- download file(yes)
-- websocket(yes)
-- block user access(yes)
-- block user-wise access (yes)
queue cocurrent effectiveness
-- upload file(yes)
-- download file(yes)
-- websocket(yes)
"""
import os, requests, threading, time
import uvicorn
def _authorize_user(path_or_url, request, gradio_app):
from toolbox import get_conf, default_user_name
PATH_PRIVATE_UPLOAD, PATH_LOGGING = get_conf('PATH_PRIVATE_UPLOAD', 'PATH_LOGGING')
sensitive_path = None
path_or_url = os.path.relpath(path_or_url)
if path_or_url.startswith(PATH_LOGGING):
sensitive_path = PATH_LOGGING
if path_or_url.startswith(PATH_PRIVATE_UPLOAD):
sensitive_path = PATH_PRIVATE_UPLOAD
if sensitive_path:
token = request.cookies.get("access-token") or request.cookies.get("access-token-unsecure")
user = gradio_app.tokens.get(token) # get user
allowed_users = [user, 'autogen', default_user_name] # three user path that can be accessed
for user_allowed in allowed_users:
# exact match
if f"{os.sep}".join(path_or_url.split(os.sep)[:2]) == os.path.join(sensitive_path, user_allowed):
return True
return False # "越权访问!"
return True
class Server(uvicorn.Server):
# A server that runs in a separate thread
def install_signal_handlers(self):
pass
def run_in_thread(self):
self.thread = threading.Thread(target=self.run, daemon=True)
self.thread.start()
while not self.started:
time.sleep(1e-3)
def close(self):
self.should_exit = True
self.thread.join()
def start_app(app_block, CONCURRENT_COUNT, AUTHENTICATION, PORT, SSL_KEYFILE, SSL_CERTFILE):
import uvicorn
import fastapi
import gradio as gr
from fastapi import FastAPI
from gradio.routes import App
from toolbox import get_conf
CUSTOM_PATH, PATH_LOGGING = get_conf('CUSTOM_PATH', 'PATH_LOGGING')
# --- --- configurate gradio app block --- ---
app_block:gr.Blocks
app_block.ssl_verify = False
app_block.auth_message = '请登录'
app_block.favicon_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "docs/logo.png")
app_block.auth = AUTHENTICATION if len(AUTHENTICATION) != 0 else None
app_block.blocked_paths = ["config.py", "__pycache__", "config_private.py", "docker-compose.yml", "Dockerfile", f"{PATH_LOGGING}/admin"]
app_block.dev_mode = False
app_block.config = app_block.get_config_file()
app_block.enable_queue = True
app_block.queue(concurrency_count=CONCURRENT_COUNT)
app_block.validate_queue_settings()
app_block.show_api = False
app_block.config = app_block.get_config_file()
max_threads = 40
app_block.max_threads = max(
app_block._queue.max_thread_count if app_block.enable_queue else 0, max_threads
)
app_block.is_colab = False
app_block.is_kaggle = False
app_block.is_sagemaker = False
gradio_app = App.create_app(app_block)
# --- --- replace gradio endpoint to forbid access to sensitive files --- ---
if len(AUTHENTICATION) > 0:
dependencies = []
endpoint = None
for route in list(gradio_app.router.routes):
if route.path == "/file/{path:path}":
gradio_app.router.routes.remove(route)
if route.path == "/file={path_or_url:path}":
dependencies = route.dependencies
endpoint = route.endpoint
gradio_app.router.routes.remove(route)
@gradio_app.get("/file/{path:path}", dependencies=dependencies)
@gradio_app.head("/file={path_or_url:path}", dependencies=dependencies)
@gradio_app.get("/file={path_or_url:path}", dependencies=dependencies)
async def file(path_or_url: str, request: fastapi.Request):
if len(AUTHENTICATION) > 0:
if not _authorize_user(path_or_url, request, gradio_app):
return "越权访问!"
return await endpoint(path_or_url, request)
# --- --- app_lifespan --- ---
from contextlib import asynccontextmanager
@asynccontextmanager
async def app_lifespan(app):
async def startup_gradio_app():
if gradio_app.get_blocks().enable_queue:
gradio_app.get_blocks().startup_events()
async def shutdown_gradio_app():
pass
await startup_gradio_app() # startup logic here
yield # The application will serve requests after this point
await shutdown_gradio_app() # cleanup/shutdown logic here
# --- --- FastAPI --- ---
fastapi_app = FastAPI(lifespan=app_lifespan)
fastapi_app.mount(CUSTOM_PATH, gradio_app)
# --- --- favicon --- ---
if CUSTOM_PATH != '/':
from fastapi.responses import FileResponse
@fastapi_app.get("/favicon.ico")
async def favicon():
return FileResponse(app_block.favicon_path)
# --- --- uvicorn.Config --- ---
ssl_keyfile = None if SSL_KEYFILE == "" else SSL_KEYFILE
ssl_certfile = None if SSL_CERTFILE == "" else SSL_CERTFILE
server_name = "0.0.0.0"
config = uvicorn.Config(
fastapi_app,
host=server_name,
port=PORT,
reload=False,
log_level="warning",
ssl_keyfile=ssl_keyfile,
ssl_certfile=ssl_certfile,
)
server = Server(config)
url_host_name = "localhost" if server_name == "0.0.0.0" else server_name
if ssl_keyfile is not None:
if ssl_certfile is None:
raise ValueError(
"ssl_certfile must be provided if ssl_keyfile is provided."
)
path_to_local_server = f"https://{url_host_name}:{PORT}/"
else:
path_to_local_server = f"http://{url_host_name}:{PORT}/"
if CUSTOM_PATH != '/':
path_to_local_server += CUSTOM_PATH.lstrip('/').rstrip('/') + '/'
# --- --- begin --- ---
server.run_in_thread()
# --- --- after server launch --- ---
app_block.server = server
app_block.server_name = server_name
app_block.local_url = path_to_local_server
app_block.protocol = (
"https"
if app_block.local_url.startswith("https") or app_block.is_colab
else "http"
)
if app_block.enable_queue:
app_block._queue.set_url(path_to_local_server)
forbid_proxies = {
"http": "",
"https": "",
}
requests.get(f"{app_block.local_url}startup-events", verify=app_block.ssl_verify, proxies=forbid_proxies)
app_block.is_running = True
app_block.block_thread()

13
shared_utils/key_pattern_manager.py
View File

@ -28,6 +28,11 @@ def is_api2d_key(key):
return bool(API_MATCH_API2D)
def is_cohere_api_key(key):
API_MATCH_AZURE = re.match(r"[a-zA-Z0-9]{40}$", key)
return bool(API_MATCH_AZURE)
def is_any_api_key(key):
if ',' in key:
keys = key.split(',')
@ -35,7 +40,7 @@ def is_any_api_key(key):
if is_any_api_key(k): return True
return False
else:
return is_openai_api_key(key) or is_api2d_key(key) or is_azure_api_key(key)
return is_openai_api_key(key) or is_api2d_key(key) or is_azure_api_key(key) or is_cohere_api_key(key)
def what_keys(keys):
@ -74,8 +79,12 @@ def select_api_key(keys, llm_model):
for k in key_list:
if is_azure_api_key(k): avail_key_list.append(k)
if llm_model.startswith('cohere-'):
for k in key_list:
if is_cohere_api_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（右下角更换模型菜单中可切换openai,azure,claude,api2d等请求源）。")
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（左上角更换模型菜单中可切换openai,azure,claude,cohere等请求源）。")
api_key = random.choice(avail_key_list) # 随机负载均衡
return api_key

61
tests/test_llms.py
View File

@ -11,28 +11,45 @@ def validate_path():
validate_path() # validate path so you can run from base directory
if __name__ == "__main__":
# from request_llms.bridge_newbingfree import predict_no_ui_long_connection
# from request_llms.bridge_moss import predict_no_ui_long_connection
# from request_llms.bridge_jittorllms_pangualpha import predict_no_ui_long_connection
# from request_llms.bridge_jittorllms_llama import predict_no_ui_long_connection
# from request_llms.bridge_claude import predict_no_ui_long_connection
# from request_llms.bridge_internlm import predict_no_ui_long_connection
# from request_llms.bridge_deepseekcoder import predict_no_ui_long_connection
# from request_llms.bridge_qwen_7B import predict_no_ui_long_connection
from request_llms.bridge_qwen_local import predict_no_ui_long_connection
# from request_llms.bridge_spark import predict_no_ui_long_connection
# from request_llms.bridge_zhipu import predict_no_ui_long_connection
# from request_llms.bridge_chatglm3 import predict_no_ui_long_connection
if "在线模型":
if __name__ == "__main__":
from request_llms.bridge_cohere import predict_no_ui_long_connection
# from request_llms.bridge_spark import predict_no_ui_long_connection
# from request_llms.bridge_zhipu import predict_no_ui_long_connection
# from request_llms.bridge_chatglm3 import predict_no_ui_long_connection
llm_kwargs = {
"llm_model": "command-r-plus",
"max_length": 4096,
"top_p": 1,
"temperature": 1,
}
llm_kwargs = {
"max_length": 4096,
"top_p": 1,
"temperature": 1,
}
result = predict_no_ui_long_connection(
inputs="请问什么是质子？", llm_kwargs=llm_kwargs, history=["你好", "我好！"], sys_prompt="系统"
)
print("final result:", result)
print("final result:", result)
if "本地模型":
if __name__ == "__main__":
# from request_llms.bridge_newbingfree import predict_no_ui_long_connection
# from request_llms.bridge_moss import predict_no_ui_long_connection
# from request_llms.bridge_jittorllms_pangualpha import predict_no_ui_long_connection
# from request_llms.bridge_jittorllms_llama import predict_no_ui_long_connection
# from request_llms.bridge_claude import predict_no_ui_long_connection
# from request_llms.bridge_internlm import predict_no_ui_long_connection
# from request_llms.bridge_deepseekcoder import predict_no_ui_long_connection
# from request_llms.bridge_qwen_7B import predict_no_ui_long_connection
# from request_llms.bridge_qwen_local import predict_no_ui_long_connection
llm_kwargs = {
"max_length": 4096,
"top_p": 1,
"temperature": 1,
}
result = predict_no_ui_long_connection(
inputs="请问什么是质子？", llm_kwargs=llm_kwargs, history=["你好", "我好！"], sys_prompt=""
)
print("final result:", result)
result = predict_no_ui_long_connection(
inputs="请问什么是质子？", llm_kwargs=llm_kwargs, history=["你好", "我好！"], sys_prompt=""
)
print("final result:", result)

154
themes/common.js
View File

@ -2,15 +2,15 @@
// 第 1 部分: 工具函数
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
function push_data_to_gradio_component(DAT, ELEM_ID, TYPE){
function push_data_to_gradio_component(DAT, ELEM_ID, TYPE) {
// type, // type==="str" / type==="float"
if (TYPE=="str"){
if (TYPE == "str") {
// convert dat to string: do nothing
}
else if (TYPE=="no_conversion"){
else if (TYPE == "no_conversion") {
// no nothing
}
else if (TYPE=="float"){
else if (TYPE == "float") {
// convert dat to float
DAT = parseFloat(DAT);
}
@ -24,7 +24,7 @@ function push_data_to_gradio_component(DAT, ELEM_ID, TYPE){
}
async function get_gradio_component(ELEM_ID){
async function get_gradio_component(ELEM_ID) {
function waitFor(ELEM_ID) {
return new Promise((resolve) => {
const myEvent = new CustomEvent('gpt_academic_get_gradio_component_value', {
@ -41,14 +41,13 @@ async function get_gradio_component(ELEM_ID){
}
async function get_data_from_gradio_component(ELEM_ID){
async function get_data_from_gradio_component(ELEM_ID) {
let comp = await get_gradio_component(ELEM_ID);
return comp.props.value;
}
function update_array(arr, item, mode) {
// let p = ["基础功能区", "输入清除键", "函数插件区"];
function update_array(arr, item, mode) {
// // Remove "输入清除键"
// p = updateArray(p, "输入清除键", "remove");
// console.log(p); // Should log: ["基础功能区", "函数插件区"]
@ -60,13 +59,13 @@ function update_array(arr, item, mode) {
const index = arr.indexOf(item);
if (mode === "remove") {
if (index !== -1) {
// Item found, remove it
arr.splice(index, 1);
// Item found, remove it
arr.splice(index, 1);
}
} else if (mode === "add") {
if (index === -1) {
// Item not found, add it
arr.push(item);
// Item not found, add it
arr.push(item);
}
}
return arr;
@ -85,6 +84,7 @@ function gradioApp() {
return elem.shadowRoot ? elem.shadowRoot : elem;
}
function setCookie(name, value, days) {
var expires = "";
@ -97,6 +97,7 @@ function setCookie(name, value, days) {
document.cookie = name + "=" + value + expires + "; path=/";
}
function getCookie(name) {
var decodedCookie = decodeURIComponent(document.cookie);
var cookies = decodedCookie.split(';');
@ -112,6 +113,7 @@ function getCookie(name) {
return null;
}
let toastCount = 0;
function toast_push(msg, duration) {
duration = isNaN(duration) ? 3000 : duration;
@ -134,6 +136,7 @@ function toast_push(msg, duration) {
toastCount++;
}
function toast_up(msg) {
var m = document.getElementById('toast_up');
if (m) {
@ -146,6 +149,7 @@ function toast_up(msg) {
document.body.appendChild(m);
}
function toast_down() {
var m = document.getElementById('toast_up');
if (m) {
@ -153,6 +157,7 @@ function toast_down() {
}
}
function begin_loading_status() {
// Create the loader div and add styling
var loader = document.createElement('div');
@ -327,6 +332,7 @@ function do_something_but_not_too_frequently(min_interval, func) {
}
}
function chatbotContentChanged(attempt = 1, force = false) {
// https://github.com/GaiZhenbiao/ChuanhuChatGPT/tree/main/web_assets/javascript
for (var i = 0; i < attempt; i++) {
@ -343,7 +349,6 @@ function chatbotContentChanged(attempt = 1, force = false) {
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
// 第 3 部分: chatbot动态高度调整
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
function chatbotAutoHeight() {
// 自动调整高度：立即
function update_height() {
@ -375,6 +380,7 @@ function chatbotAutoHeight() {
setInterval(function () { update_height_slow() }, 50); // 每50毫秒执行一次
}
swapped = false;
function swap_input_area() {
// Get the elements to be swapped
@ -394,6 +400,7 @@ function swap_input_area() {
else { swapped = true; }
}
function get_elements(consider_state_panel = false) {
var chatbot = document.querySelector('#gpt-chatbot > div.wrap.svelte-18telvq');
if (!chatbot) {
@ -491,6 +498,7 @@ async function upload_files(files) {
}
}
function register_func_paste(input) {
let paste_files = [];
if (input) {
@ -517,6 +525,7 @@ function register_func_paste(input) {
}
}
function register_func_drag(elem) {
if (elem) {
const dragEvents = ["dragover"];
@ -553,6 +562,7 @@ function register_func_drag(elem) {
}
}
function elem_upload_component_pop_message(elem) {
if (elem) {
const dragEvents = ["dragover"];
@ -582,6 +592,7 @@ function elem_upload_component_pop_message(elem) {
}
}
function register_upload_event() {
locate_upload_elems();
if (elem_upload_float) {
@ -604,6 +615,7 @@ function register_upload_event() {
}
}
function monitoring_input_box() {
register_upload_event();
@ -637,7 +649,6 @@ window.addEventListener("DOMContentLoaded", function () {
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
// 第 5 部分: 音频按钮样式变化
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
function audio_fn_init() {
let audio_component = document.getElementById('elem_audio');
if (audio_component) {
@ -674,6 +685,7 @@ function audio_fn_init() {
}
}
function minor_ui_adjustment() {
let cbsc_area = document.getElementById('cbsc');
cbsc_area.style.paddingTop = '15px';
@ -766,21 +778,6 @@ function limit_scroll_position() {
// 第 7 部分: JS初始化函数
// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
function GptAcademicJavaScriptInit(LAYOUT = "LEFT-RIGHT") {
audio_fn_init();
minor_ui_adjustment();
chatbotIndicator = gradioApp().querySelector('#gpt-chatbot > div.wrap');
var chatbotObserver = new MutationObserver(() => {
chatbotContentChanged(1);
});
chatbotObserver.observe(chatbotIndicator, { attributes: true, childList: true, subtree: true });
if (LAYOUT === "LEFT-RIGHT") { chatbotAutoHeight(); }
if (LAYOUT === "LEFT-RIGHT") { limit_scroll_position(); }
// setInterval(function () { uml("mermaid") }, 5000); // 每50毫秒执行一次
}
function loadLive2D() {
try {
$("<link>").attr({ href: "file=themes/waifu_plugin/waifu.css", rel: "stylesheet", type: "text/css" }).appendTo('head');
@ -802,12 +799,12 @@ function loadLive2D() {
live2d_settings['canTakeScreenshot'] = false;
live2d_settings['canTurnToHomePage'] = false;
live2d_settings['canTurnToAboutPage'] = false;
live2d_settings['showHitokoto'] = false; // 显示一言
live2d_settings['showHitokoto'] = false; // 显示一言
live2d_settings['showF12Status'] = false; // 显示加载状态
live2d_settings['showF12Message'] = false; // 显示看板娘消息
live2d_settings['showF12OpenMsg'] = false; // 显示控制台打开提示
live2d_settings['showCopyMessage'] = false; // 显示 复制内容 提示
live2d_settings['showWelcomeMessage'] = true; // 显示进入面页欢迎词
live2d_settings['showF12OpenMsg'] = false; // 显示控制台打开提示
live2d_settings['showCopyMessage'] = false; // 显示 复制内容 提示
live2d_settings['showWelcomeMessage'] = true; // 显示进入面页欢迎词
/* 在 initModel 前添加 */
initModel("file=themes/waifu_plugin/waifu-tips.json");
}
@ -817,7 +814,8 @@ function loadLive2D() {
} catch (err) { console.log("[Error] JQuery is not defined.") }
}
function get_checkbox_selected_items(elem_id){
function get_checkbox_selected_items(elem_id) {
display_panel_arr = [];
document.getElementById(elem_id).querySelector('[data-testid="checkbox-group"]').querySelectorAll('label').forEach(label => {
// Get the span text
@ -831,62 +829,24 @@ function get_checkbox_selected_items(elem_id){
return display_panel_arr;
}
function set_checkbox(key, bool, set_twice=false) {
set_success = false;
elem_ids = ["cbsc", "cbs"]
elem_ids.forEach(id => {
document.getElementById(id).querySelector('[data-testid="checkbox-group"]').querySelectorAll('label').forEach(label => {
// Get the span text
const spanText = label.querySelector('span').textContent;
if (spanText === key) {
if (bool){
label.classList.add('selected');
} else {
if (label.classList.contains('selected')) {
label.classList.remove('selected');
}
}
if (set_twice){
setTimeout(() => {
if (bool){
label.classList.add('selected');
} else {
if (label.classList.contains('selected')) {
label.classList.remove('selected');
}
}
}, 5000);
}
label.querySelector('input').checked = bool;
set_success = true;
return
}
});
});
if (!set_success){
console.log("设置checkbox失败，没有找到对应的key")
}
}
function gpt_academic_gradio_saveload(
save_or_load, // save_or_load==="save" / save_or_load==="load"
elem_id, // element id
cookie_key, // cookie key
save_value="", // save value
load_type = "str", // type==="str" / type==="float"
load_default=false, // load default value
load_default_value=""
) {
save_or_load, // save_or_load==="save" / save_or_load==="load"
elem_id, // element id
cookie_key, // cookie key
save_value = "", // save value
load_type = "str", // type==="str" / type==="float"
load_default = false, // load default value
load_default_value = ""
) {
if (save_or_load === "load") {
let value = getCookie(cookie_key);
if (value) {
console.log('加载cookie', elem_id, value)
push_data_to_gradio_component(value, elem_id, load_type);
}
else{
if (load_default){
else {
if (load_default) {
console.log('加载cookie的默认值', elem_id, load_default_value)
push_data_to_gradio_component(load_default_value, elem_id, load_type);
}
@ -897,11 +857,24 @@ function gpt_academic_gradio_saveload(
}
}
async function init_frontend_with_cookies(dark, prompt, live2d) {
let searchString = "输入清除键";
let bool_value = "False";
////////////////// darkmode ///////////////////
async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout) {
// 第一部分，布局初始化
audio_fn_init();
minor_ui_adjustment();
chatbotIndicator = gradioApp().querySelector('#gpt-chatbot > div.wrap');
var chatbotObserver = new MutationObserver(() => {
chatbotContentChanged(1);
});
chatbotObserver.observe(chatbotIndicator, { attributes: true, childList: true, subtree: true });
if (layout === "LEFT-RIGHT") { chatbotAutoHeight(); }
if (layout === "LEFT-RIGHT") { limit_scroll_position(); }
// 第二部分，读取Cookie，初始话界面
let searchString = "";
let bool_value = "";
// darkmode 深色模式
if (getCookie("js_darkmode_cookie")) {
dark = getCookie("js_darkmode_cookie")
}
@ -916,12 +889,13 @@ async function init_frontend_with_cookies(dark, prompt, live2d) {
}
}
////////////////////// SysPrompt ///////////////////////////
// SysPrompt 系统静默提示词
gpt_academic_gradio_saveload("load", "elem_prompt", "js_system_prompt_cookie", null, "str");
////////////////////// Temperature ///////////////////////////
// Temperature 大模型温度参数
gpt_academic_gradio_saveload("load", "elem_temperature", "js_temperature_cookie", null, "float");
////////////////////// clearButton ///////////////////////////
// clearButton 自动清除按钮
if (getCookie("js_clearbtn_show_cookie")) {
// have cookie
bool_value = getCookie("js_clearbtn_show_cookie")
@ -949,7 +923,7 @@ async function init_frontend_with_cookies(dark, prompt, live2d) {
}
}
////////////////////// live2d ///////////////////////////
// live2d 显示
if (getCookie("js_live2d_show_cookie")) {
// have cookie
searchString = "添加Live2D形象";

6
themes/theme.py
View File

@ -48,7 +48,7 @@ adjust_theme, advanced_css, theme_declaration, _ = load_dynamic_theme(get_conf("
cookie相关工具函数
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
"""
def init_cookie(cookies):
def assign_user_uuid(cookies):
# 为每一位访问的用户赋予一个独一无二的uuid编码
cookies.update({"uuid": uuid.uuid4()})
return cookies
@ -106,8 +106,8 @@ js_code_for_toggle_darkmode = """() => {
}"""
js_code_for_persistent_cookie_init = """(py_pickle_cookie, cookie) => {
return [getCookie("py_pickle_cookie"), cookie];
js_code_for_persistent_cookie_init = """(web_cookie_cache, cookie) => {
return [getCookie("web_cookie_cache"), cookie];
}
"""

21
toolbox.py
View File

@ -535,17 +535,13 @@ def on_file_uploaded(
def on_report_generated(cookies:dict, files:List[str], chatbot:ChatBotWithCookies):
# from toolbox import find_recent_files
# PATH_LOGGING = get_conf('PATH_LOGGING')
if "files_to_promote" in cookies:
report_files = cookies["files_to_promote"]
cookies.pop("files_to_promote")
else:
report_files = []
# report_files = find_recent_files(PATH_LOGGING)
if len(report_files) == 0:
return cookies, None, chatbot
# files.extend(report_files)
file_links = ""
for f in report_files:
file_links += (
@ -1009,10 +1005,13 @@ def check_repeat_upload(new_pdf_path, pdf_hash):
return False, None
def log_chat(llm_model: str, input_str: str, output_str: str):
if output_str and input_str and llm_model:
uid = str(uuid.uuid4().hex)
logging.info(f"[Model({uid})] {llm_model}")
input_str = input_str.rstrip('\n')
logging.info(f"[Query({uid})]\n{input_str}")
output_str = output_str.rstrip('\n')
logging.info(f"[Response({uid})]\n{output_str}\n\n")
try:
if output_str and input_str and llm_model:
uid = str(uuid.uuid4().hex)
logging.info(f"[Model({uid})] {llm_model}")
input_str = input_str.rstrip('\n')
logging.info(f"[Query({uid})]\n{input_str}")
output_str = output_str.rstrip('\n')
logging.info(f"[Response({uid})]\n{output_str}\n\n")
except:
print(trimmed_format_exc())

4
version
View File

@ -1,5 +1,5 @@
{
"version": 3.73,
"version": 3.74,
"show_feature": true,
"new_feature": "优化oneapi接入方法 <-> 接入月之暗面模型 <-> 支持切换多个智谱ai模型 <-> 用绘图功能增强部分插件 <-> 基础功能区支持自动切换中英提示词 <-> 支持Mermaid绘图库（让大模型绘制脑图）"
"new_feature": "增加多用户文件鉴权验证提高安全性 <-> 优化oneapi接入方法 <-> 接入Cohere和月之暗面模型 <-> 简化挂载二级目录的步骤 <-> 支持Mermaid绘图库（让大模型绘制脑图）"
}