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()