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12 Commits

Author SHA1 Message Date
binary-husky
8a10db618e Merge branch 'master-interact' 2023-07-09 01:05:04 +08:00
binary-husky
1fe66f0291 优化azure的体验 2023-07-09 00:20:58 +08:00
binary-husky
ced977c443 修复双dollar公式匹配bug 2023-07-08 22:23:29 +08:00
binary-husky
6c2ffbae52 Update README.md 2023-07-08 19:17:35 +08:00
binary-husky
be2f54fac9 Update README.md 2023-07-08 18:21:20 +08:00
binary-husky
87b5e56378 Update requirements.txt 2023-07-08 18:10:33 +08:00
binary-husky
3a5764ed34 Update requirements.txt 2023-07-08 17:59:27 +08:00
qingxu fu
67d9051890 update error message 2023-07-07 17:41:43 +08:00
binary-husky
be96232127 Merge pull request #933 from binary-husky/master-latex-patch
Latex File Name Bug Patch
2023-07-07 16:57:58 +08:00
binary-husky
3b5bc7a784 Update use_azure.md 2023-07-07 10:55:22 +08:00
binary-husky
5e92f437a1 Update use_azure.md 2023-07-07 10:54:21 +08:00
binary-husky
5c0d34793e Latex File Name Bug Patch 2023-07-07 00:09:50 +08:00
15 changed files with 266 additions and 318 deletions

View File

@@ -1,6 +1,8 @@
> **Note**
>
> 2023.7.5: Gradio依赖进行了调整。请及时**更新代码**。安装依赖时,请严格选择`requirements.txt`中**指定的版本**
> 2023.7.5: Gradio依赖调整。请及时**更新代码**
>
> 2023.7.8: pydantic出现兼容问题已修改 `requirements.txt`。安装依赖时,请严格选择`requirements.txt`中**指定的版本**
>
> `pip install -r requirements.txt`

View File

@@ -8,7 +8,7 @@
"""
# [step 1]>> API_KEY = "sk-123456789xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx123456789"。极少数情况下还需要填写组织格式如org-123456789abcdefghijklmno的请向下翻找 API_ORG 设置项
API_KEY = "sk-此处填API密钥" # 可同时填写多个API-KEY用英文逗号分割例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey1,fkxxxx-api2dkey2"
API_KEY = "此处填API密钥" # 可同时填写多个API-KEY用英文逗号分割例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey3,azure-apikey4"
# [step 2]>> 改为True应用代理如果直接在海外服务器部署此处不修改
@@ -110,9 +110,8 @@ SLACK_CLAUDE_USER_TOKEN = ''
# 如果需要使用AZURE 详情请见额外文档 docs\use_azure.md
AZURE_ENDPOINT = "https://你亲手写的api名称.openai.azure.com/"
AZURE_API_KEY = "填入azure openai api的密钥"
AZURE_API_VERSION = "2023-05-15" # 一般不修改
AZURE_ENGINE = "填入你亲手写的部署名" # 读 docs\use_azure.md
AZURE_API_KEY = "填入azure openai api的密钥" # 建议直接在API_KEY处填写该选项即将被弃用
AZURE_ENGINE = "填入你亲手写的部署名" # 读 docs\use_azure.md
# 使用Newbing

View File

@@ -352,6 +352,18 @@ def get_crazy_functions():
})
except:
print('Load function plugin failed')
try:
from crazy_functions.交互功能函数模板 import 交互功能模板函数
function_plugins.update({
"交互功能模板函数": {
"Color": "stop",
"AsButton": False,
"Function": HotReload(交互功能模板函数)
}
})
except:
print('Load function plugin failed')
try:
from crazy_functions.Latex输出PDF结果 import Latex英文纠错加PDF对比

View File

@@ -189,6 +189,18 @@ def rm_comments(main_file):
main_file = re.sub(r'(?<!\\)%.*', '', main_file) # 使用正则表达式查找半行注释, 并替换为空字符串
return main_file
def find_tex_file_ignore_case(fp):
dir_name = os.path.dirname(fp)
base_name = os.path.basename(fp)
if not base_name.endswith('.tex'): base_name+='.tex'
if os.path.exists(pj(dir_name, base_name)): return pj(dir_name, base_name)
# go case in-sensitive
import glob
for f in glob.glob(dir_name+'/*.tex'):
base_name_s = os.path.basename(fp)
if base_name_s.lower() == base_name.lower(): return f
return None
def merge_tex_files_(project_foler, main_file, mode):
"""
Merge Tex project recrusively
@@ -197,15 +209,11 @@ def merge_tex_files_(project_foler, main_file, mode):
for s in reversed([q for q in re.finditer(r"\\input\{(.*?)\}", main_file, re.M)]):
f = s.group(1)
fp = os.path.join(project_foler, f)
if os.path.exists(fp):
# e.g., \input{srcs/07_appendix.tex}
with open(fp, 'r', encoding='utf-8', errors='replace') as fx:
c = fx.read()
else:
# e.g., \input{srcs/07_appendix}
assert os.path.exists(fp+'.tex'), f'即找不到{fp},也找不到{fp}.texTex源文件缺失'
with open(fp+'.tex', 'r', encoding='utf-8', errors='replace') as fx:
c = fx.read()
fp = find_tex_file_ignore_case(fp)
if fp:
with open(fp, 'r', encoding='utf-8', errors='replace') as fx: c = fx.read()
else:
raise RuntimeError(f'找不到{fp}Tex源文件缺失')
c = merge_tex_files_(project_foler, c, mode)
main_file = main_file[:s.span()[0]] + c + main_file[s.span()[1]:]
return main_file
@@ -324,7 +332,7 @@ def split_subprocess(txt, project_folder, return_dict, opts):
# 吸收在42行以内的begin-end组合
text, mask = set_forbidden_text_begin_end(text, mask, r"\\begin\{([a-z\*]*)\}(.*?)\\end\{\1\}", re.DOTALL, limit_n_lines=42)
# 吸收匿名公式
text, mask = set_forbidden_text(text, mask, [ r"\$\$(.*?)\$\$", r"\\\[.*?\\\]" ], re.DOTALL)
text, mask = set_forbidden_text(text, mask, [ r"\$\$([^$]+)\$\$", r"\\\[.*?\\\]" ], re.DOTALL)
# 吸收其他杂项
text, mask = set_forbidden_text(text, mask, [ r"\\section\{(.*?)\}", r"\\section\*\{(.*?)\}", r"\\subsection\{(.*?)\}", r"\\subsubsection\{(.*?)\}" ])
text, mask = set_forbidden_text(text, mask, [ r"\\bibliography\{(.*?)\}", r"\\bibliographystyle\{(.*?)\}" ])

View File

@@ -0,0 +1,63 @@
from toolbox import CatchException, update_ui
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
@CatchException
def 交互功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
"""
txt 输入栏用户输入的文本,例如需要翻译的一段话,再例如一个包含了待处理文件的路径
llm_kwargs gpt模型参数, 如温度和top_p等, 一般原样传递下去就行
plugin_kwargs 插件模型的参数, 如温度和top_p等, 一般原样传递下去就行
chatbot 聊天显示框的句柄,用于显示给用户
history 聊天历史,前情提要
system_prompt 给gpt的静默提醒
web_port 当前软件运行的端口号
"""
history = [] # 清空历史,以免输入溢出
chatbot.append(("这是什么功能?", "交互功能函数模板。在执行完成之后, 可以将自身的状态存储到cookie中, 等待用户的再次调用。"))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
state = chatbot._cookies.get('plugin_state_0001', None) # 初始化插件状态
if state is None:
chatbot._cookies['lock_plugin'] = 'crazy_functions.交互功能函数模板->交互功能模板函数' # 赋予插件锁定 锁定插件回调路径,当下一次用户提交时,会直接转到该函数
chatbot._cookies['plugin_state_0001'] = 'wait_user_keyword' # 赋予插件状态
chatbot.append(("第一次调用:", "请输入关键词, 我将为您查找相关壁纸, 建议使用英文单词, 插件锁定中,请直接提交即可。"))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
if state == 'wait_user_keyword':
chatbot._cookies['lock_plugin'] = None # 解除插件锁定,避免遗忘导致死锁
chatbot._cookies['plugin_state_0001'] = None # 解除插件状态,避免遗忘导致死锁
# 解除插件锁定
chatbot.append((f"获取关键词:{txt}", ""))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
page_return = get_image_page_by_keyword(txt)
inputs=inputs_show_user=f"Extract all image urls in this html page, pick the first 5 images and show them with markdown format: \n\n {page_return}"
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
inputs=inputs, inputs_show_user=inputs_show_user,
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
sys_prompt="When you want to show an image, use markdown format. e.g. ![image_description](image_url). If there are no image url provided, answer 'no image url provided'"
)
chatbot[-1] = [chatbot[-1][0], gpt_say]
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
# ---------------------------------------------------------------------------------
def get_image_page_by_keyword(keyword):
import requests
from bs4 import BeautifulSoup
response = requests.get(f'https://wallhaven.cc/search?q={keyword}', timeout=2)
res = "image urls: \n"
for image_element in BeautifulSoup(response.content, 'html.parser').findAll("img"):
try:
res += image_element["data-src"]
res += "\n"
except:
pass
return res

View File

@@ -14,17 +14,19 @@ def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot
doc = Document(fp)
file_content = "\n".join([para.text for para in doc.paragraphs])
else:
import win32com.client
word = win32com.client.Dispatch("Word.Application")
word.visible = False
# 打开文件
print('fp', os.getcwd())
doc = word.Documents.Open(os.getcwd() + '/' + fp)
# file_content = doc.Content.Text
doc = word.ActiveDocument
file_content = doc.Range().Text
doc.Close()
word.Quit()
try:
import win32com.client
word = win32com.client.Dispatch("Word.Application")
word.visible = False
# 打开文件
doc = word.Documents.Open(os.getcwd() + '/' + fp)
# file_content = doc.Content.Text
doc = word.ActiveDocument
file_content = doc.Range().Text
doc.Close()
word.Quit()
except:
raise RuntimeError('请先将.doc文档转换为.docx文档。')
print(file_content)
# private_upload里面的文件名在解压zip后容易出现乱码rar和7z格式正常故可以只分析文章内容不输入文件名

View File

@@ -100,10 +100,12 @@
# 修改 config.py
```
AZURE_ENDPOINT = "填入终结点"
LLM_MODEL = "azure-gpt-3.5" # 指定启动时的默认模型当然事后从下拉菜单选也ok
AZURE_ENDPOINT = "填入终结点" # 见上述图片
AZURE_API_KEY = "填入azure openai api的密钥"
AZURE_API_VERSION = "2023-05-15" # 默认使用 2023-05-15 版本,无需修改
AZURE_ENGINE = "填入部署名" # 见上
AZURE_ENGINE = "填入部署名" # 见上述图片
```

39
main.py
View File

@@ -4,10 +4,10 @@ def main():
import gradio as gr
if gr.__version__ not in ['3.28.3','3.32.2']: assert False, "需要特殊依赖,请务必用 pip install -r requirements.txt 指令安装依赖详情信息见requirements.txt"
from request_llm.bridge_all import predict
from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith
from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, load_chat_cookies, 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
proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = \
get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = \
get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
# 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
@@ -45,23 +45,23 @@ def main():
proxy_info = check_proxy(proxies)
gr_L1 = lambda: gr.Row().style()
gr_L2 = lambda scale: gr.Column(scale=scale)
gr_L2 = lambda scale, elem_id: gr.Column(scale=scale, elem_id=elem_id)
if LAYOUT == "TOP-DOWN":
gr_L1 = lambda: DummyWith()
gr_L2 = lambda scale: gr.Row()
gr_L2 = lambda scale, elem_id: gr.Row()
CHATBOT_HEIGHT /= 2
cancel_handles = []
with gr.Blocks(title="ChatGPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo:
gr.HTML(title_html)
cookies = gr.State({'api_key': API_KEY, 'llm_model': LLM_MODEL})
cookies = gr.State(load_chat_cookies())
with gr_L1():
with gr_L2(scale=2):
chatbot = gr.Chatbot(label=f"当前模型:{LLM_MODEL}")
chatbot.style(height=CHATBOT_HEIGHT)
with gr_L2(scale=2, elem_id="gpt-chat"):
chatbot = gr.Chatbot(label=f"当前模型:{LLM_MODEL}", elem_id="gpt-chatbot")
if LAYOUT == "TOP-DOWN": chatbot.style(height=CHATBOT_HEIGHT)
history = gr.State([])
with gr_L2(scale=1):
with gr.Accordion("输入区", open=True) as area_input_primary:
with gr_L2(scale=1, elem_id="gpt-panel"):
with gr.Accordion("输入区", open=True, elem_id="input-panel") as area_input_primary:
with gr.Row():
txt = gr.Textbox(show_label=False, placeholder="Input question here.").style(container=False)
with gr.Row():
@@ -71,14 +71,14 @@ def main():
stopBtn = gr.Button("停止", variant="secondary"); stopBtn.style(size="sm")
clearBtn = gr.Button("清除", variant="secondary", visible=False); clearBtn.style(size="sm")
with gr.Row():
status = gr.Markdown(f"Tip: 按Enter提交, 按Shift+Enter换行。当前模型: {LLM_MODEL} \n {proxy_info}")
with gr.Accordion("基础功能区", open=True) as area_basic_fn:
status = gr.Markdown(f"Tip: 按Enter提交, 按Shift+Enter换行。当前模型: {LLM_MODEL} \n {proxy_info}", elem_id="state-panel")
with gr.Accordion("基础功能区", open=True, elem_id="basic-panel") as area_basic_fn:
with gr.Row():
for k in functional:
if ("Visible" in functional[k]) and (not functional[k]["Visible"]): continue
variant = functional[k]["Color"] if "Color" in functional[k] else "secondary"
functional[k]["Button"] = gr.Button(k, variant=variant)
with gr.Accordion("函数插件区", open=True) as area_crazy_fn:
with gr.Accordion("函数插件区", open=True, elem_id="plugin-panel") as area_crazy_fn:
with gr.Row():
gr.Markdown("注意:以下“红颜色”标识的函数插件需从输入区读取路径作为参数.")
with gr.Row():
@@ -100,7 +100,7 @@ def main():
with gr.Row():
with gr.Accordion("点击展开“文件上传区”。上传本地文件可供红色函数插件调用。", open=False) as area_file_up:
file_upload = gr.Files(label="任何文件, 但推荐上传压缩文件(zip, tar)", file_count="multiple")
with gr.Accordion("更换模型 & SysPrompt & 交互界面布局", open=(LAYOUT == "TOP-DOWN")):
with gr.Accordion("更换模型 & SysPrompt & 交互界面布局", open=(LAYOUT == "TOP-DOWN"), elem_id="interact-panel"):
system_prompt = gr.Textbox(show_label=True, placeholder=f"System Prompt", label="System prompt", value=initial_prompt)
top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",)
@@ -109,7 +109,7 @@ def main():
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)
with gr.Accordion("备选输入区", open=True, visible=False) as area_input_secondary:
with gr.Accordion("备选输入区", open=True, visible=False, elem_id="input-panel2") as area_input_secondary:
with gr.Row():
txt2 = gr.Textbox(show_label=False, placeholder="Input question here.", label="输入区2").style(container=False)
with gr.Row():
@@ -176,16 +176,17 @@ def main():
return {chatbot: gr.update(label="当前模型:"+k)}
md_dropdown.select(on_md_dropdown_changed, [md_dropdown], [chatbot] )
# 随变按钮的回调函数注册
def route(k, *args, **kwargs):
def route(request: gr.Request, k, *args, **kwargs):
if k in [r"打开插件列表", r"请先从插件列表中选择"]: return
yield from ArgsGeneralWrapper(crazy_fns[k]["Function"])(*args, **kwargs)
yield from ArgsGeneralWrapper(crazy_fns[k]["Function"])(request, *args, **kwargs)
click_handle = switchy_bt.click(route,[switchy_bt, *input_combo, gr.State(PORT)], output_combo)
click_handle.then(on_report_generated, [cookies, file_upload, chatbot], [cookies, file_upload, chatbot])
cancel_handles.append(click_handle)
# 终止按钮的回调函数注册
stopBtn.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
stopBtn2.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
demo.load(lambda: 0, inputs=None, outputs=None, _js='()=>{ChatBotHeight();}')
# 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 auto_opentab_delay():
import threading, webbrowser, time

View File

@@ -16,9 +16,6 @@ from toolbox import get_conf, trimmed_format_exc
from .bridge_chatgpt import predict_no_ui_long_connection as chatgpt_noui
from .bridge_chatgpt import predict as chatgpt_ui
from .bridge_azure_test import predict_no_ui_long_connection as azure_noui
from .bridge_azure_test import predict as azure_ui
from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui
from .bridge_chatglm import predict as chatglm_ui
@@ -48,10 +45,11 @@ class LazyloadTiktoken(object):
return encoder.decode(*args, **kwargs)
# Endpoint 重定向
API_URL_REDIRECT, = get_conf("API_URL_REDIRECT")
API_URL_REDIRECT, AZURE_ENDPOINT, AZURE_ENGINE = get_conf("API_URL_REDIRECT", "AZURE_ENDPOINT", "AZURE_ENGINE")
openai_endpoint = "https://api.openai.com/v1/chat/completions"
api2d_endpoint = "https://openai.api2d.net/v1/chat/completions"
newbing_endpoint = "wss://sydney.bing.com/sydney/ChatHub"
azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
# 兼容旧版的配置
try:
API_URL, = get_conf("API_URL")
@@ -122,9 +120,9 @@ model_info = {
# azure openai
"azure-gpt-3.5":{
"fn_with_ui": azure_ui,
"fn_without_ui": azure_noui,
"endpoint": get_conf("AZURE_ENDPOINT"),
"fn_with_ui": chatgpt_ui,
"fn_without_ui": chatgpt_noui,
"endpoint": azure_endpoint,
"max_token": 4096,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,

View File

@@ -1,237 +0,0 @@
"""
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高级实验性功能模块调用不会实时显示在界面上参数简单可以多线程并行方便实现复杂的功能逻辑
3. predict_no_ui_long_connection在实验过程中发现调用predict_no_ui处理长文档时和openai的连接容易断掉这个函数用stream的方式解决这个问题同样支持多线程
"""
import logging
import traceback
import importlib
import openai
import time
import requests
import json
# 读取config.py文件中关于AZURE OPENAI API的信息
from toolbox import get_conf, update_ui, clip_history, trimmed_format_exc
TIMEOUT_SECONDS, MAX_RETRY, AZURE_ENGINE, AZURE_ENDPOINT, AZURE_API_VERSION, AZURE_API_KEY = \
get_conf('TIMEOUT_SECONDS', 'MAX_RETRY',"AZURE_ENGINE","AZURE_ENDPOINT", "AZURE_API_VERSION", "AZURE_API_KEY")
def get_full_error(chunk, stream_response):
"""
获取完整的从Openai返回的报错
"""
while True:
try:
chunk += next(stream_response)
except:
break
return chunk
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
"""
发送至azure openai api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 additional_fn is not None:
import core_functional
importlib.reload(core_functional) # 热更新prompt
core_functional = core_functional.get_core_functions()
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数(如果有的话)
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
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
payload = generate_azure_payload(inputs, llm_kwargs, history, system_prompt, stream)
history.append(inputs); history.append("")
retry = 0
while True:
try:
openai.api_type = "azure"
openai.api_version = AZURE_API_VERSION
openai.api_base = AZURE_ENDPOINT
openai.api_key = AZURE_API_KEY
response = openai.ChatCompletion.create(timeout=TIMEOUT_SECONDS, **payload);break
except openai.error.AuthenticationError:
tb_str = '```\n' + trimmed_format_exc() + '```'
chatbot[-1] = [chatbot[-1][0], tb_str]
yield from update_ui(chatbot=chatbot, history=history, msg="openai返回错误") # 刷新界面
return
except:
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}) ……')
gpt_replying_buffer = ""
is_head_of_the_stream = True
if stream:
stream_response = response
while True:
try:
chunk = next(stream_response)
except StopIteration:
from toolbox import regular_txt_to_markdown; tb_str = '```\n' + trimmed_format_exc() + '```'
chatbot[-1] = (chatbot[-1][0], f"[Local Message] 远程返回错误: \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk)}")
yield from update_ui(chatbot=chatbot, history=history, msg="远程返回错误:" + chunk) # 刷新界面
return
if is_head_of_the_stream and (r'"object":"error"' not in chunk):
# 数据流的第一帧不携带content
is_head_of_the_stream = False; continue
if chunk:
#print(chunk)
try:
if "delta" in chunk["choices"][0]:
if chunk["choices"][0]["finish_reason"] == "stop":
logging.info(f'[response] {gpt_replying_buffer}')
break
status_text = f"finish_reason: {chunk['choices'][0]['finish_reason']}"
gpt_replying_buffer = gpt_replying_buffer + chunk["choices"][0]["delta"]["content"]
history[-1] = gpt_replying_buffer
chatbot[-1] = (history[-2], history[-1])
yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
except Exception as e:
traceback.print_exc()
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)
error_msg = chunk
yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
return
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
"""
发送至AZURE OPENAI API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
chatGPT的内部调优参数
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
payload = generate_azure_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
retry = 0
while True:
try:
openai.api_type = "azure"
openai.api_version = AZURE_API_VERSION
openai.api_base = AZURE_ENDPOINT
openai.api_key = AZURE_API_KEY
response = openai.ChatCompletion.create(timeout=TIMEOUT_SECONDS, **payload);break
except:
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
result = ''
while True:
try: chunk = next(stream_response)
except StopIteration:
break
except:
chunk = next(stream_response) # 失败了,重试一次?再失败就没办法了。
if len(chunk)==0: continue
json_data = json.loads(str(chunk))['choices'][0]
delta = json_data["delta"]
if len(delta) == 0:
break
if "role" in delta:
continue
if "content" in delta:
result += delta["content"]
if not console_slience: print(delta["content"], 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] += delta["content"]
# 看门狗,如果超过期限没有喂狗,则终止
if len(observe_window) >= 2000:
if (time.time()-observe_window[1]) > watch_dog_patience:
raise RuntimeError("用户取消了程序。")
else:
raise RuntimeError("意外Json结构"+delta)
if json_data['finish_reason'] == 'content_filter':
raise RuntimeError("由于提问含不合规内容被Azure过滤。")
if json_data['finish_reason'] == 'length':
raise ConnectionAbortedError("正常结束但显示Token不足导致输出不完整请削减单次输入的文本量。")
return result
def generate_azure_payload(inputs, llm_kwargs, history, system_prompt, stream):
"""
整合所有信息选择LLM模型生成 azure openai api请求为发送请求做准备
"""
conversation_cnt = len(history) // 2
messages = [{"role": "system", "content": 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["content"] = history[index]
what_gpt_answer = {}
what_gpt_answer["role"] = "assistant"
what_gpt_answer["content"] = history[index+1]
if what_i_have_asked["content"] != "":
if what_gpt_answer["content"] == "": continue
messages.append(what_i_have_asked)
messages.append(what_gpt_answer)
else:
messages[-1]['content'] = what_gpt_answer['content']
what_i_ask_now = {}
what_i_ask_now["role"] = "user"
what_i_ask_now["content"] = inputs
messages.append(what_i_ask_now)
payload = {
"model": llm_kwargs['llm_model'],
"messages": 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,
"engine": AZURE_ENGINE
}
try:
print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
except:
print('输入中可能存在乱码。')
return payload

View File

@@ -22,8 +22,8 @@ import importlib
# 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, trimmed_format_exc
proxies, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, API_ORG = \
get_conf('proxies', 'API_KEY', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG')
proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG = \
get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG')
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。'
@@ -101,6 +101,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
if (time.time()-observe_window[1]) > watch_dog_patience:
raise RuntimeError("用户取消了程序。")
else: raise RuntimeError("意外Json结构"+delta)
if json_data['finish_reason'] == 'content_filter':
raise RuntimeError("由于提问含不合规内容被Azure过滤。")
if json_data['finish_reason'] == 'length':
raise ConnectionAbortedError("正常结束但显示Token不足导致输出不完整请削减单次输入的文本量。")
return result
@@ -247,6 +249,7 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
"Authorization": f"Bearer {api_key}"
}
if API_ORG.startswith('org-'): headers.update({"OpenAI-Organization": API_ORG})
if llm_kwargs['llm_model'].startswith('azure-'): headers.update({"api-key": api_key})
conversation_cnt = len(history) // 2

View File

@@ -1,4 +1,5 @@
./docs/gradio-3.32.2-py3-none-any.whl
pydantic==1.10.11
tiktoken>=0.3.3
requests[socks]
transformers
@@ -15,4 +16,4 @@ pymupdf
openai
numpy
arxiv
rich
rich

View File

@@ -1,6 +1,6 @@
import gradio as gr
from toolbox import get_conf
CODE_HIGHLIGHT, ADD_WAIFU = get_conf('CODE_HIGHLIGHT', 'ADD_WAIFU')
CODE_HIGHLIGHT, ADD_WAIFU, LAYOUT = get_conf('CODE_HIGHLIGHT', 'ADD_WAIFU', 'LAYOUT')
# gradio可用颜色列表
# gr.themes.utils.colors.slate (石板色)
# gr.themes.utils.colors.gray (灰色)
@@ -82,20 +82,76 @@ def adjust_theme():
button_cancel_text_color_dark="white",
)
# Layout = "LEFT-RIGHT"
js = """
<script>
function ChatBotHeight() {
function update_height(){
var { panel_height_target, chatbot_height, chatbot } = get_elements();
if (panel_height_target!=chatbot_height)
{
var pixelString = panel_height_target.toString() + 'px';
chatbot.style.maxHeight = pixelString; chatbot.style.height = pixelString;
}
}
function update_height_slow(){
var { panel_height_target, chatbot_height, chatbot } = get_elements();
if (panel_height_target!=chatbot_height)
{
new_panel_height = (panel_height_target - chatbot_height)*0.5 + chatbot_height;
if (Math.abs(new_panel_height - panel_height_target) < 10){
new_panel_height = panel_height_target;
}
// console.log(chatbot_height, panel_height_target, new_panel_height);
var pixelString = new_panel_height.toString() + 'px';
chatbot.style.maxHeight = pixelString; chatbot.style.height = pixelString;
}
}
update_height();
setInterval(function() {
update_height_slow()
}, 50); // 每100毫秒执行一次
}
function get_elements() {
var chatbot = document.querySelector('#gpt-chatbot > div.wrap.svelte-18telvq');
if (!chatbot) {
chatbot = document.querySelector('#gpt-chatbot');
}
const panel1 = document.querySelector('#input-panel');
const panel2 = document.querySelector('#basic-panel');
const panel3 = document.querySelector('#plugin-panel');
const panel4 = document.querySelector('#interact-panel');
const panel5 = document.querySelector('#input-panel2');
const panel_active = document.querySelector('#state-panel');
var panel_height_target = (20-panel_active.offsetHeight) + panel1.offsetHeight + panel2.offsetHeight + panel3.offsetHeight + panel4.offsetHeight + panel5.offsetHeight + 21;
var panel_height_target = parseInt(panel_height_target);
var chatbot_height = chatbot.style.height;
var chatbot_height = parseInt(chatbot_height);
return { panel_height_target, chatbot_height, chatbot };
}
</script>
"""
if LAYOUT=="TOP-DOWN":
js = ""
# 添加一个萌萌的看板娘
if ADD_WAIFU:
js = """
js += """
<script src="file=docs/waifu_plugin/jquery.min.js"></script>
<script src="file=docs/waifu_plugin/jquery-ui.min.js"></script>
<script src="file=docs/waifu_plugin/autoload.js"></script>
"""
gradio_original_template_fn = gr.routes.templates.TemplateResponse
def gradio_new_template_fn(*args, **kwargs):
res = gradio_original_template_fn(*args, **kwargs)
res.body = res.body.replace(b'</html>', f'{js}</html>'.encode("utf8"))
res.init_headers()
return res
gr.routes.templates.TemplateResponse = gradio_new_template_fn # override gradio template
gradio_original_template_fn = gr.routes.templates.TemplateResponse
def gradio_new_template_fn(*args, **kwargs):
res = gradio_original_template_fn(*args, **kwargs)
res.body = res.body.replace(b'</html>', f'{js}</html>'.encode("utf8"))
res.init_headers()
return res
gr.routes.templates.TemplateResponse = gradio_new_template_fn # override gradio template
except:
set_theme = None
print('gradio版本较旧, 不能自定义字体和颜色')

View File

@@ -4,6 +4,7 @@ import time
import inspect
import re
import os
import gradio
from latex2mathml.converter import convert as tex2mathml
from functools import wraps, lru_cache
pj = os.path.join
@@ -40,7 +41,7 @@ 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。
"""
def decorated(cookies, max_length, llm_model, txt, txt2, top_p, temperature, chatbot, history, system_prompt, plugin_advanced_arg, *args):
def decorated(request: gradio.Request, 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
@@ -54,13 +55,21 @@ def ArgsGeneralWrapper(f):
'top_p':top_p,
'max_length': max_length,
'temperature':temperature,
'client_ip': request.client.host,
}
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)
if cookies.get('lock_plugin', None) is None:
# 正常状态
yield from f(txt_passon, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, *args)
else:
# 处理个别特殊插件的锁定状态
module, fn_name = cookies['lock_plugin'].split('->')
f_hot_reload = getattr(importlib.import_module(module, fn_name), fn_name)
yield from f_hot_reload(txt_passon, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, *args)
return decorated
@@ -68,8 +77,21 @@ 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
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。"
cookies = chatbot.get_cookies()
# 解决插件锁定时的界面显示问题
if cookies.get('lock_plugin', None):
label = cookies.get('llm_model', "") + " | " + "正在锁定插件" + cookies.get('lock_plugin', None)
chatbot_gr = gradio.update(value=chatbot, label=label)
if cookies.get('label', "") != label: cookies['label'] = label # 记住当前的label
elif cookies.get('label', None):
chatbot_gr = gradio.update(value=chatbot, label=cookies.get('llm_model', ""))
cookies['label'] = None # 清空label
else:
chatbot_gr = chatbot
yield cookies, chatbot_gr, history, msg
def update_ui_lastest_msg(lastmsg, chatbot, history, delay=1): # 刷新界面
"""
@@ -505,16 +527,24 @@ def on_report_generated(cookies, files, chatbot):
chatbot.append(['报告如何远程获取?', f'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。{file_links}'])
return cookies, report_files, chatbot
def load_chat_cookies():
API_KEY, LLM_MODEL, AZURE_API_KEY = get_conf('API_KEY', 'LLM_MODEL', 'AZURE_API_KEY')
if is_any_api_key(AZURE_API_KEY):
if is_any_api_key(API_KEY): API_KEY = API_KEY + ',' + AZURE_API_KEY
else: API_KEY = AZURE_API_KEY
return {'api_key': API_KEY, 'llm_model': LLM_MODEL}
def is_openai_api_key(key):
API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
return bool(API_MATCH_ORIGINAL)
def is_azure_api_key(key):
API_MATCH_AZURE = re.match(r"[a-zA-Z0-9]{32}$", key)
return bool(API_MATCH_ORIGINAL) or bool(API_MATCH_AZURE)
return bool(API_MATCH_AZURE)
def is_api2d_key(key):
if key.startswith('fk') and len(key) == 41:
return True
else:
return False
API_MATCH_API2D = re.match(r"fk[a-zA-Z0-9]{6}-[a-zA-Z0-9]{32}$", key)
return bool(API_MATCH_API2D)
def is_any_api_key(key):
if ',' in key:
@@ -523,10 +553,10 @@ 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)
return is_openai_api_key(key) or is_api2d_key(key) or is_azure_api_key(key)
def what_keys(keys):
avail_key_list = {'OpenAI Key':0, "API2D Key":0}
avail_key_list = {'OpenAI Key':0, "Azure Key":0, "API2D Key":0}
key_list = keys.split(',')
for k in key_list:
@@ -537,7 +567,11 @@ def what_keys(keys):
if is_api2d_key(k):
avail_key_list['API2D Key'] += 1
return f"检测到: OpenAI Key {avail_key_list['OpenAI Key']}API2D Key {avail_key_list['API2D Key']}"
for k in key_list:
if is_azure_api_key(k):
avail_key_list['Azure Key'] += 1
return f"检测到: OpenAI Key {avail_key_list['OpenAI Key']} 个, Azure Key {avail_key_list['Azure Key']} 个, API2D Key {avail_key_list['API2D Key']}"
def select_api_key(keys, llm_model):
import random
@@ -552,8 +586,12 @@ def select_api_key(keys, llm_model):
for k in key_list:
if is_api2d_key(k): avail_key_list.append(k)
if llm_model.startswith('azure-'):
for k in key_list:
if is_azure_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")
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和api2d请求源")
api_key = random.choice(avail_key_list) # 随机负载均衡
return api_key

View File

@@ -1,5 +1,5 @@
{
"version": 3.43,
"version": 3.44,
"show_feature": true,
"new_feature": "修复Azure接口的BUG <-> 完善多语言模块 <-> 完善本地Latex矫错和翻译功能 <-> 增加gpt-3.5-16k的支持 <-> 新增最强Arxiv论文翻译插件 <-> 修复gradio复制按钮BUG <-> 修复PDF翻译的BUG, 新增HTML中英双栏对照 <-> 添加了OpenAI图片生成插件"
"new_feature": "改善UI <-> 修复Azure接口的BUG <-> 完善多语言模块 <-> 完善本地Latex矫错和翻译功能 <-> 增加gpt-3.5-16k的支持 <-> 新增最强Arxiv论文翻译插件 <-> 修复gradio复制按钮BUG <-> 修复PDF翻译的BUG, 新增HTML中英双栏对照 <-> 添加了OpenAI图片生成插件"
}