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28 Commits

Author SHA1 Message Date
96c1852abc Merge branch 'master' into huggingface 2023-06-30 12:09:25 +08:00
cd145c0794 1 2023-06-29 15:04:03 +08:00
7a4d4ad956 Merge branch 'huggingface' of github.com:binary-husky/chatgpt_academic into huggingface 2023-06-29 12:54:24 +08:00
9f9848c6e9 again 2023-06-29 12:54:19 +08:00
94425c49fd again 2023-05-28 21:34:50 +08:00
e874a16050 try again 2023-05-28 21:33:28 +08:00
c28388c5fe load version 2023-05-28 21:32:10 +08:00
b4a56d391b Merge branch 'huggingface' of github.com:binary-husky/chatgpt_academic into huggingface 2023-05-28 21:30:34 +08:00
7075092f86 fix app 2023-05-28 21:30:29 +08:00
1086ff8092 Merge branch 'huggingface' of github.com:binary-husky/chatgpt_academic into huggingface 2023-05-28 21:27:31 +08:00
3a22446b47 try4 2023-05-28 21:27:25 +08:00
7842cf03cc Merge branch 'master' into huggingface 2023-05-28 21:27:20 +08:00
54f55c32f2 213 2023-05-28 21:25:45 +08:00
94318ff0a2 try3 2023-05-28 21:24:46 +08:00
5be6b83762 try2 2023-05-28 21:24:02 +08:00
6f18d1716e Merge branch 'master' into huggingface 2023-05-28 21:21:12 +08:00
90944bd744 up 2023-05-25 15:04:53 +08:00
752937cb70 Merge branch 'master' into huggingface 2023-05-25 15:01:30 +08:00
c584cbac5b fix ver 2023-05-19 14:08:47 +08:00
309d12b404 Merge branch 'master' into huggingface 2023-05-19 14:05:23 +08:00
52ea0acd61 Merge branch 'master' into huggingface 2023-05-06 23:06:53 +08:00
9f5e3e0fd5 Merge branch 'master' into huggingface 2023-05-05 18:24:36 +08:00
315e78e5d9 Merge branch 'master' into huggingface 2023-04-29 03:53:32 +08:00
b6b4ba684a Merge branch 'master' into huggingface 2023-04-24 18:32:56 +08:00
2281a5ca7f 修改提示 2023-04-24 12:55:53 +08:00
49558686f2 Merge branch 'master' into huggingface 2023-04-24 12:30:59 +08:00
b050ccedb5 Merge branch 'master' into huggingface 2023-04-21 18:48:00 +08:00
ae56cab6f4 huggingface 2023-04-19 18:07:32 +08:00
14 changed files with 210 additions and 43 deletions

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@ -1,3 +1,15 @@
---
title: ChatImprovement
emoji: 😻
colorFrom: blue
colorTo: blue
sdk: gradio
sdk_version: 3.32.0
app_file: app.py
pinned: false
---
# ChatGPT 学术优化
> **Note**
>
> 2023.5.27 对Gradio依赖进行了调整Fork并解决了官方Gradio的若干Bugs。请及时**更新代码**并重新更新pip依赖。安装依赖时请严格选择`requirements.txt`中**指定的版本**

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@ -1,8 +1,10 @@
import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
def main():
import subprocess, sys
subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'gradio-stable-fork'])
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"
if gr.__version__ not in ['3.28.3','3.32.3']: assert False, "用 pip install -r requirements.txt 安装依赖"
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
# 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
@ -54,6 +56,7 @@ def main():
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)
gr.HTML('''<center><a href="https://huggingface.co/spaces/qingxu98/gpt-academic?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>请您打开此页面后务必点击上方的“复制空间”Duplicate Space按钮<font color="#FF00FF">使用时先在输入框填入API-KEY然后回车。</font><br/>切忌在“复制空间”Duplicate Space之前填入API_KEY或进行提问否则您的API_KEY将极可能被空间所有者攫取<br/>支持任意数量的OpenAI的密钥和API2D的密钥共存例如输入"OpenAI密钥1,API2D密钥2",然后提交,即可同时使用两种模型接口。</center>''')
cookies = gr.State({'api_key': API_KEY, 'llm_model': LLM_MODEL})
with gr_L1():
with gr_L2(scale=2):
@ -63,7 +66,7 @@ def main():
with gr_L2(scale=1):
with gr.Accordion("输入区", open=True) as area_input_primary:
with gr.Row():
txt = gr.Textbox(show_label=False, placeholder="Input question here.").style(container=False)
txt = gr.Textbox(show_label=False, lines=2, placeholder="输入问题或API密钥输入多个密钥时用英文逗号间隔。支持OpenAI密钥和API2D密钥共存。").style(container=False)
with gr.Row():
submitBtn = gr.Button("提交", variant="primary")
with gr.Row():
@ -197,10 +200,7 @@ def main():
threading.Thread(target=warm_up_modules, name="warm-up", daemon=True).start()
auto_opentab_delay()
demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
server_name="0.0.0.0", server_port=PORT,
favicon_path="docs/logo.png", auth=AUTHENTICATION,
blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile"])
demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", share=False, favicon_path="docs/logo.png", blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile"])
# 如果需要在二级路径下运行
# CUSTOM_PATH, = get_conf('CUSTOM_PATH')

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@ -45,10 +45,9 @@ WEB_PORT = -1
# 如果OpenAI不响应网络卡顿、代理失败、KEY失效重试的次数限制
MAX_RETRY = 2
# 模型选择是 (注意: LLM_MODEL是默认选中的模型, 同时它必须被包含在AVAIL_LLM_MODELS切换列表中 )
LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
AVAIL_LLM_MODELS = ["gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt35", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss", "newbing", "newbing-free", "stack-claude"]
# P.S. 其他可用的模型还包括 ["gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "newbing-free", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
# OpenAI模型选择是gpt4现在只对申请成功的人开放
LLM_MODEL = "gpt-3.5-turbo" # 可选 "chatglm"
AVAIL_LLM_MODELS = ["newbing-free", "gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "api2d-gpt-3.5-turbo"]
# 本地LLM模型如ChatGLM的执行方式 CPU/GPU
LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"

View File

@ -226,12 +226,20 @@ def get_crazy_functions():
try:
from crazy_functions.联网的ChatGPT import 连接网络回答问题
function_plugins.update({
"连接网络回答问题(输入问题,再点击按钮,需要访问谷歌)": {
"连接网络回答问题(输入问题后点击该插件,需要访问谷歌)": {
"Color": "stop",
"AsButton": False, # 加入下拉菜单中
"Function": HotReload(连接网络回答问题)
}
})
from crazy_functions.联网的ChatGPT_bing版 import 连接bing搜索回答问题
function_plugins.update({
"连接网络回答问题中文Bing版输入问题后点击该插件": {
"Color": "stop",
"AsButton": False, # 加入下拉菜单中
"Function": HotReload(连接bing搜索回答问题)
}
})
except:
print('Load function plugin failed')

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@ -27,6 +27,24 @@ def set_forbidden_text(text, mask, pattern, flags=0):
mask[res.span()[0]:res.span()[1]] = PRESERVE
return text, mask
def reverse_forbidden_text(text, mask, pattern, flags=0, forbid_wrapper=True):
"""
Move area out of preserve area (make text editable for GPT)
count the number of the braces so as to catch compelete text area.
e.g.
\begin{abstract} blablablablablabla. \end{abstract}
"""
if isinstance(pattern, list): pattern = '|'.join(pattern)
pattern_compile = re.compile(pattern, flags)
for res in pattern_compile.finditer(text):
if not forbid_wrapper:
mask[res.span()[0]:res.span()[1]] = TRANSFORM
else:
mask[res.regs[0][0]: res.regs[1][0]] = PRESERVE # '\\begin{abstract}'
mask[res.regs[1][0]: res.regs[1][1]] = TRANSFORM # abstract
mask[res.regs[1][1]: res.regs[0][1]] = PRESERVE # abstract
return text, mask
def set_forbidden_text_careful_brace(text, mask, pattern, flags=0):
"""
Add a preserve text area in this paper (text become untouchable for GPT).
@ -326,6 +344,7 @@ def split_subprocess(txt, project_folder, return_dict, opts):
# reverse 操作必须放在最后
text, mask = reverse_forbidden_text_careful_brace(text, mask, r"\\caption\{(.*?)\}", re.DOTALL, forbid_wrapper=True)
text, mask = reverse_forbidden_text_careful_brace(text, mask, r"\\abstract\{(.*?)\}", re.DOTALL, forbid_wrapper=True)
text, mask = reverse_forbidden_text(text, mask, r"\\begin\{abstract\}(.*?)\\end\{abstract\}", re.DOTALL, forbid_wrapper=True)
root = convert_to_linklist(text, mask)
# 修复括号
@ -672,10 +691,9 @@ def remove_buggy_lines(file_path, log_path, tex_name, tex_name_pure, n_fix, work
print("Fatal error occurred, but we cannot identify error, please download zip, read latex log, and compile manually.")
return False, -1, [-1]
def compile_latex_with_timeout(command, timeout=60):
def compile_latex_with_timeout(command, cwd, timeout=60):
import subprocess
process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=cwd)
try:
stdout, stderr = process.communicate(timeout=timeout)
except subprocess.TimeoutExpired:
@ -699,24 +717,24 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
# https://stackoverflow.com/questions/738755/dont-make-me-manually-abort-a-latex-compile-when-theres-an-error
yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译原始PDF ...', chatbot, history) # 刷新Gradio前端界面
os.chdir(work_folder_original); ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex'); os.chdir(current_dir)
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)
yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译转化后的PDF ...', chatbot, history) # 刷新Gradio前端界面
os.chdir(work_folder_modified); ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex'); os.chdir(current_dir)
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)
if ok and os.path.exists(pj(work_folder_modified, f'{main_file_modified}.pdf')):
# 只有第二步成功,才能继续下面的步骤
yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译BibTex ...', chatbot, history) # 刷新Gradio前端界面
if not os.path.exists(pj(work_folder_original, f'{main_file_original}.bbl')):
os.chdir(work_folder_original); ok = compile_latex_with_timeout(f'bibtex {main_file_original}.aux'); os.chdir(current_dir)
ok = compile_latex_with_timeout(f'bibtex {main_file_original}.aux', work_folder_original)
if not os.path.exists(pj(work_folder_modified, f'{main_file_modified}.bbl')):
os.chdir(work_folder_modified); ok = compile_latex_with_timeout(f'bibtex {main_file_modified}.aux'); os.chdir(current_dir)
ok = compile_latex_with_timeout(f'bibtex {main_file_modified}.aux', work_folder_modified)
yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译文献交叉引用 ...', chatbot, history) # 刷新Gradio前端界面
os.chdir(work_folder_original); ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex'); os.chdir(current_dir)
os.chdir(work_folder_modified); ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex'); os.chdir(current_dir)
os.chdir(work_folder_original); ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex'); os.chdir(current_dir)
os.chdir(work_folder_modified); ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex'); os.chdir(current_dir)
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)
if mode!='translate_zh':
yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 使用latexdiff生成论文转化前后对比 ...', chatbot, history) # 刷新Gradio前端界面
@ -724,13 +742,11 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
ok = compile_latex_with_timeout(f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex')
yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 正在编译对比PDF ...', chatbot, history) # 刷新Gradio前端界面
os.chdir(work_folder); ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error merge_diff.tex'); os.chdir(current_dir)
os.chdir(work_folder); ok = compile_latex_with_timeout(f'bibtex merge_diff.aux'); os.chdir(current_dir)
os.chdir(work_folder); ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error merge_diff.tex'); os.chdir(current_dir)
os.chdir(work_folder); ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error merge_diff.tex'); os.chdir(current_dir)
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
ok = compile_latex_with_timeout(f'bibtex merge_diff.aux', work_folder)
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
# <--------------------->
os.chdir(current_dir)
# <---------- 检查结果 ----------->
results_ = ""
@ -766,7 +782,6 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
yield from update_ui_lastest_msg(f'由于最为关键的转化PDF编译失败, 将根据报错信息修正tex源文件并重试, 当前报错的latex代码处于第{buggy_lines}行 ...', chatbot, history) # 刷新Gradio前端界面
if not can_retry: break
os.chdir(current_dir)
return False # 失败啦

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@ -0,0 +1,102 @@
from toolbox import CatchException, update_ui
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
import requests
from bs4 import BeautifulSoup
from request_llm.bridge_all import model_info
def bing_search(query, proxies=None):
query = query
url = f"https://cn.bing.com/search?q={query}"
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'}
response = requests.get(url, headers=headers, proxies=proxies)
soup = BeautifulSoup(response.content, 'html.parser')
results = []
for g in soup.find_all('li', class_='b_algo'):
anchors = g.find_all('a')
if anchors:
link = anchors[0]['href']
if not link.startswith('http'):
continue
title = g.find('h2').text
item = {'title': title, 'link': link}
results.append(item)
for r in results:
print(r['link'])
return results
def scrape_text(url, proxies) -> str:
"""Scrape text from a webpage
Args:
url (str): The URL to scrape text from
Returns:
str: The scraped text
"""
headers = {
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36',
'Content-Type': 'text/plain',
}
try:
response = requests.get(url, headers=headers, proxies=proxies, timeout=8)
if response.encoding == "ISO-8859-1": response.encoding = response.apparent_encoding
except:
return "无法连接到该网页"
soup = BeautifulSoup(response.text, "html.parser")
for script in soup(["script", "style"]):
script.extract()
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = "\n".join(chunk for chunk in chunks if chunk)
return text
@CatchException
def 连接bing搜索回答问题(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,暂时没有用武之地
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((f"请结合互联网信息回答以下问题:{txt}",
"[Local Message] 请注意,您正在调用一个[函数插件]的模板该模板可以实现ChatGPT联网信息综合。该函数面向希望实现更多有趣功能的开发者它可以作为创建新功能函数的模板。您若希望分享新的功能模组请不吝PR"))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间我们先及时地做一次界面更新
# ------------- < 第1步爬取搜索引擎的结果 > -------------
from toolbox import get_conf
proxies, = get_conf('proxies')
urls = bing_search(txt, proxies)
history = []
# ------------- < 第2步依次访问网页 > -------------
max_search_result = 8 # 最多收纳多少个网页的结果
for index, url in enumerate(urls[:max_search_result]):
res = scrape_text(url['link'], proxies)
history.extend([f"{index}份搜索结果:", res])
chatbot.append([f"{index}份搜索结果:", res[:500]+"......"])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间我们先及时地做一次界面更新
# ------------- < 第3步ChatGPT综合 > -------------
i_say = f"从以上搜索结果中抽取信息,然后回答问题:{txt}"
i_say, history = input_clipping( # 裁剪输入从最长的条目开始裁剪防止爆token
inputs=i_say,
history=history,
max_token_limit=model_info[llm_kwargs['llm_model']]['max_token']*3//4
)
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
inputs=i_say, inputs_show_user=i_say,
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
sys_prompt="请从给定的若干条搜索结果中抽取信息,对最相关的两个搜索结果进行总结,然后回答问题。"
)
chatbot[-1] = (i_say, gpt_say)
history.append(i_say);history.append(gpt_say)
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新

View File

@ -13,11 +13,11 @@ def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt
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((txt, "正在同时咨询ChatGPT和ChatGLM……"))
chatbot.append((txt, "正在同时咨询gpt-3.5和gpt-4……"))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间我们先及时地做一次界面更新
# llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口用&符号分隔
llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo' # 支持任意数量的llm接口用&符号分隔
llm_kwargs['llm_model'] = 'gpt-3.5-turbo&gpt-4' # 支持任意数量的llm接口用&符号分隔
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
inputs=txt, inputs_show_user=txt,
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,

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@ -104,7 +104,7 @@ def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
meta_paper_info_list = meta_paper_info_list[batchsize:]
chatbot.append(["状态?",
"已经全部完成您可以试试让AI写一个Related Works例如您可以继续输入Write a \"Related Works\" section about \"你搜索的研究领域\" for me."])
"已经全部完成您可以试试让AI写一个Related Works例如您可以继续输入Write an academic \"Related Works\" section about \"你搜索的研究领域\" for me."])
msg = '正常'
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
res = write_results_to_file(history)

View File

@ -1,6 +1,7 @@
from toolbox import CatchException, update_ui
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
import datetime
import datetime, re
@CatchException
def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
"""
@ -18,12 +19,34 @@ def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
for i in range(5):
currentMonth = (datetime.date.today() + datetime.timedelta(days=i)).month
currentDay = (datetime.date.today() + datetime.timedelta(days=i)).day
i_say = f'历史中哪些事件发生在{currentMonth}{currentDay}日?列举两条并发送相关图片。发送图片时,使用Markdown将Unsplash API中的PUT_YOUR_QUERY_HERE替换成描述该事件的一个最重要的单词'
i_say = f'历史中哪些事件发生在{currentMonth}{currentDay}日?用中文列举两条,然后分别给出描述事件的两个英文单词。' + '当你给出关键词时,使用以下json格式{"KeyWords":[EnglishKeyWord1,EnglishKeyWord2]}'
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
inputs=i_say, inputs_show_user=i_say,
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
sys_prompt="当你想发送一张照片时请使用Markdown, 并且不要有反斜线, 不要用代码块。使用 Unsplash API (https://source.unsplash.com/1280x720/? < PUT_YOUR_QUERY_HERE >)。"
sys_prompt='输出格式示例1908年美国消防救援事业发展的“美国消防协会”成立。关键词{"KeyWords":["Fire","American"]}。'
)
gpt_say = get_images(gpt_say)
chatbot[-1] = (i_say, gpt_say)
history.append(i_say);history.append(gpt_say)
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
def get_images(gpt_say):
def get_image_by_keyword(keyword):
import requests
from bs4 import BeautifulSoup
response = requests.get(f'https://wallhaven.cc/search?q={keyword}', timeout=2)
for image_element in BeautifulSoup(response.content, 'html.parser').findAll("img"):
if "data-src" in image_element: break
return image_element["data-src"]
for keywords in re.findall('{"KeyWords":\[(.*?)\]}', gpt_say):
keywords = [n.strip('"') for n in keywords.split(',')]
try:
description = keywords[0]
url = get_image_by_keyword(keywords[0])
img_tag = f"\n\n![{description}]({url})"
gpt_say += img_tag
except:
continue
return gpt_say

Binary file not shown.

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@ -152,7 +152,7 @@ model_info = {
"token_cnt": get_token_num_gpt4,
},
# chatglm
# chatglm 直接对齐到 chatglm2
"chatglm": {
"fn_with_ui": chatglm_ui,
"fn_without_ui": chatglm_noui,
@ -161,6 +161,15 @@ model_info = {
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"chatglm2": {
"fn_with_ui": chatglm_ui,
"fn_without_ui": chatglm_noui,
"endpoint": None,
"max_token": 1024,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
# newbing
"newbing": {
"fn_with_ui": newbing_ui,

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@ -40,12 +40,12 @@ class GetGLMHandle(Process):
while True:
try:
if self.chatglm_model is None:
self.chatglm_tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
self.chatglm_tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
device, = get_conf('LOCAL_MODEL_DEVICE')
if device=='cpu':
self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float()
self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).float()
else:
self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).half().cuda()
self.chatglm_model = self.chatglm_model.eval()
break
else:

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@ -1,4 +1,3 @@
./docs/gradio-3.32.2-py3-none-any.whl
tiktoken>=0.3.3
requests[socks]
transformers
@ -15,4 +14,4 @@ pymupdf
openai
numpy
arxiv
rich
rich

View File

@ -498,7 +498,7 @@ def on_report_generated(cookies, files, chatbot):
else:
report_files = find_recent_files('gpt_log')
if len(report_files) == 0:
return None, chatbot
return cookies, None, chatbot
# files.extend(report_files)
file_links = ''
for f in report_files: file_links += f'<br/><a href="file={os.path.abspath(f)}" target="_blank">{f}</a>'
@ -842,4 +842,4 @@ def objload(file='objdump.tmp'):
return
with open(file, 'rb') as f:
return pickle.load(f)