import gradio as gr
import os
import openai
from auto_backgrounds import generate_backgrounds, fake_generator
from auto_draft import generate_draft
openai_key = os.getenv("OPENAI_API_KEY")
access_key_id = os.getenv('AWS_ACCESS_KEY_ID')
secret_access_key = os.getenv('AWS_SECRET_ACCESS_KEY')
if access_key_id is None or secret_access_key is None:
print("Access keys are not provided. Outputs cannot be saved to AWS Cloud Storage.\n")
IS_CACHE_AVAILABLE = False
else:
IS_CACHE_AVAILABLE = True
if openai_key is None:
print("OPENAI_API_KEY is not found in environment variables. The output may not be generated.\n")
IS_OPENAI_API_KEY_AVAILABLE = False
else:
# todo: check if this key is available or not
openai.api_key = openai_key
try:
openai.Model.list()
IS_OPENAI_API_KEY_AVAILABLE = True
except Exception as e:
IS_OPENAI_API_KEY_AVAILABLE = False
def clear_inputs(text1, text2):
return "", ""
def wrapped_generator(title, description, openai_key = None,
template = "ICLR2022",
cache_mode = IS_CACHE_AVAILABLE, generator=None):
# if `cache_mode` is True, then follow the following steps:
# check if "title"+"description" have been generated before
# if so, download from the cloud storage, return it
# if not, generate the result.
if generator is None:
generator = generate_backgrounds
if openai_key is not None:
openai.api_key = openai_key
openai.Model.list()
if cache_mode:
from utils.storage import list_all_files, hash_name, download_file, upload_file
# check if "title"+"description" have been generated before
file_name = hash_name(title, description) + ".zip"
file_list = list_all_files()
if file_name in file_list:
# download from the cloud storage, return it
download_file(file_name)
return file_name
else:
# generate the result.
# output = fake_generate_backgrounds(title, description, openai_key)
output = generate_backgrounds(title, description, template, "gpt-4")
upload_file(file_name)
return output
else:
# output = fake_generate_backgrounds(title, description, openai_key)
output = generate_backgrounds(title, description, template, "gpt-4")
return output
theme = gr.themes.Monochrome(font=gr.themes.GoogleFont("Questrial")).set(
background_fill_primary='#F6F6F6',
button_primary_background_fill="#281A39",
input_background_fill='#E5E4E2'
)
with gr.Blocks(theme=theme) as demo:
gr.Markdown('''
# Auto-Draft: 文献整理辅助工具-限量免费使用
本Demo提供对[Auto-Draft](https://github.com/CCCBora/auto-draft)的auto_backgrounds功能的测试。通过输入一个领域的名称(比如Deep Reinforcement Learning),即可自动对这个领域的相关文献进行归纳总结.
***2023-04-30 Update***: 如果有更多想法和建议欢迎加入群里交流, 群号: ***249738228***.
***2023-04-26 Update***: 我本月的余额用完了, 感谢乐乐老师帮忙宣传, 也感觉大家的体验和反馈! 我会按照大家的意见对功能进行改进. 下个月会把Space的访问权限限制在Huggingface的Organization里, 欢迎有兴趣的同学通过下面的链接加入! [AUTO-ACADEMIC](https://huggingface.co/organizations/auto-academic/share/HPjgazDSlkwLNCWKiAiZoYtXaJIatkWDYM)
## 用法
输入一个领域的名称(比如Deep Reinforcement Learning), 点击Submit, 等待大概十分钟, 下载output.zip,在Overleaf上编译浏览.
''')
with gr.Row():
with gr.Column(scale=2):
key = gr.Textbox(value=openai_key, lines=1, max_lines=1, label="OpenAI Key", visible=not IS_OPENAI_API_KEY_AVAILABLE)
# key = gr.Textbox(value=openai_key, lines=1, max_lines=1, label="OpenAI Key", visible=False)
title = gr.Textbox(value="Deep Reinforcement Learning", lines=1, max_lines=1, label="Title")
description = gr.Textbox(lines=5, label="Description (Optional)")
with gr.Row():
clear_button = gr.Button("Clear")
submit_button = gr.Button("Submit")
with gr.Column(scale=1):
style_mapping = {True: "color:white;background-color:green", False: "color:white;background-color:red"} #todo: to match website's style
availability_mapping = {True: "AVAILABLE", False: "NOT AVAILABLE"}
gr.Markdown(f'''## Huggingface Space Status
当`OpenAI API`显示AVAILABLE的时候这个Space可以直接使用.
当`OpenAI API`显示NOT AVAILABLE的时候这个Space可以通过在左侧输入OPENAI KEY来使用.
`OpenAI API`: {availability_mapping[IS_OPENAI_API_KEY_AVAILABLE]}. `Cache`: {availability_mapping[IS_CACHE_AVAILABLE]}.''')
file_output = gr.File(label="Output")
clear_button.click(fn=clear_inputs, inputs=[title, description], outputs=[title, description])
submit_button.click(fn=wrapped_generator, inputs=[title, description, key], outputs=file_output)
demo.queue(concurrency_count=1, max_size=5, api_open=False)
demo.launch()