import gradio as gr import os import openai from auto_backgrounds import generate_backgrounds, generate_draft from utils.file_operations import hash_name, list_folders from references_generator import generate_top_k_references # todo: # 6. get logs when the procedure is not completed. * # 7. 自己的文件库; 更多的prompts # 2. 实现别的功能 # 3. Check API Key GPT-4 Support. # future: # generation.log sometimes disappears (ignore this) # 1. Check if there are any duplicated citations # 2. Remove potential thebibliography and bibitem in .tex file ####################################################################################################################### # Check if openai and cloud storage available ####################################################################################################################### 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: openai.api_key = openai_key try: openai.Model.list() IS_OPENAI_API_KEY_AVAILABLE = True # except Exception as e: except openai.error.AuthenticationError: IS_OPENAI_API_KEY_AVAILABLE = False DEFAULT_MODEL = "gpt-4" if IS_OPENAI_API_KEY_AVAILABLE else "gpt-3.5-turbo" DEFAULT_SECTIONS = ["introduction", "related works", "backgrounds", "methodology", "experiments", "conclusion", "abstract"] if IS_OPENAI_API_KEY_AVAILABLE \ else ["introduction", "related works"] ####################################################################################################################### # Load the list of templates & knowledge databases ####################################################################################################################### ALL_TEMPLATES = list_folders("latex_templates") ALL_DATABASES = ["(None)"] + list_folders("knowledge_databases") ####################################################################################################################### # Gradio UI ####################################################################################################################### theme = gr.themes.Default(font=gr.themes.GoogleFont("Questrial")) # .set( # background_fill_primary='#E5E4E2', # background_fill_secondary = '#F6F6F6', # button_primary_background_fill="#281A39" # ) ANNOUNCEMENT = """ # Auto-Draft: 学术写作辅助工具 本Demo提供对[Auto-Draft](https://github.com/CCCBora/auto-draft)的auto_draft功能的测试. 通过输入想要生成的论文名称(比如Playing atari with deep reinforcement learning),即可由AI辅助生成论文模板. ***2023-06-10 Update***: pass 如果有更多想法和建议欢迎加入QQ群里交流, 如果我在Space里更新了Key我会第一时间通知大家. 群号: ***249738228***.""" ACADEMIC_PAPER = """## 一键生成论文初稿 1. 在Title文本框中输入想要生成的论文名称(比如Playing Atari with Deep Reinforcement Learning). 2. 点击Submit. 等待大概十五分钟(全文). 3. 在右侧下载.zip格式的输出,在Overleaf上编译浏览. """ REFERENCES = """## 一键搜索相关论文 (此功能已经被整合进一键生成论文初稿) 1. 在Title文本框中输入想要搜索文献的论文(比如Playing Atari with Deep Reinforcement Learning). 2. 点击Submit. 等待大概十分钟. 3. 在右侧JSON处会显示相关文献. """ REFERENCES_INSTRUCTION = """### References 这一栏用于定义AI如何选取参考文献. 目前是两种方式混合: 1. GPT自动根据标题生成关键字,使用Semantic Scholar搜索引擎搜索文献,利用Specter获取Paper Embedding来自动选取最相关的文献作为GPT的参考资料. 2. 用户上传bibtex文件,使用Google Scholar搜索摘要作为GPT的参考资料. 关于有希望利用本地文件来供GPT参考的功能将在未来实装. """ DOMAIN_KNOWLEDGE_INSTRUCTION = """### Domain Knowledge 这一栏用于定义AI的知识库. 将提供两种选择: 1. 各个领域内由专家预先收集资料并构建的的FAISS向量数据库. 目前实装的数据库 * (None): 不使用任何知识库 * ml_textbook_test: 包含两本机器学习教材The Elements of Statistical Learning和Reinforcement Learning Theory and Algorithms. 仅用于测试知识库Pipeline. 2. 自行构建的使用OpenAI text-embedding-ada-002模型创建的FAISS向量数据库. (暂未实装) """ OUTPUTS_INSTRUCTION = """### Outputs 这一栏用于定义输出的内容: * Template: 用于填装内容的LaTeX模板. * Models: 使用GPT-4或者GPT-3.5-Turbo生成内容. * Prompts模式: 不生成内容, 而是生成用于生成内容的Prompts. 可以手动复制到网页版或者其他语言模型中进行使用. """ OTHERS_INSTRUCTION = """### Others """ 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"} STATUS = 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. 需要有GPT-4的API权限. 当`Cache`显示AVAILABLE的时候, 所有的输入和输出会被备份到我的云储存中. 显示NOT AVAILABLE的时候不影响实际使用. `OpenAI API`: {availability_mapping[IS_OPENAI_API_KEY_AVAILABLE]}. `Cache`: {availability_mapping[IS_CACHE_AVAILABLE]}.''' def clear_inputs(*args): return "", "" def clear_inputs_refs(*args): return "", 5 def wrapped_generator( paper_title, paper_description, # main input openai_api_key=None, openai_url=None, # key tldr=True, max_kw_refs=10, bib_refs=None, max_tokens_ref=2048, # references knowledge_database=None, max_tokens_kd=2048, query_counts=10, # domain knowledge paper_template="ICLR2022", selected_sections=None, model="gpt-4", prompts_mode=False, # outputs parameters cache_mode=False # IS_CACHE_AVAILABLE # handle cache mode ): # 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 bib_refs is not None: bib_refs = bib_refs.name if openai_api_key is not None: openai.api_key = openai_api_key try: openai.Model.list() except Exception as e: raise gr.Error(f"Key错误. Error: {e}") if cache_mode: from utils.storage import list_all_files, download_file # check if "title"+"description" have been generated before input_dict = {"title": paper_title, "description": paper_description, "generator": "generate_draft"} file_name = hash_name(input_dict) + ".zip" file_list = list_all_files() # print(f"{file_name} will be generated. Check the file list {file_list}") if file_name in file_list: # download from the cloud storage, return it download_file(file_name) return file_name try: output = generate_draft( paper_title, description=paper_description, # main input tldr=tldr, max_kw_refs=max_kw_refs, bib_refs=bib_refs, max_tokens_ref=max_tokens_ref, # references knowledge_database=knowledge_database, max_tokens_kd=max_tokens_kd, query_counts=query_counts, # domain knowledge sections=selected_sections, model=model, template=paper_template, prompts_mode=prompts_mode, # outputs parameters ) if cache_mode: from utils.storage import upload_file upload_file(output) except Exception as e: raise gr.Error(f"生成失败. Error: {e}") return output def wrapped_references_generator(paper_title, num_refs, openai_api_key=None): if openai_api_key is not None: openai.api_key = openai_api_key openai.Model.list() return generate_top_k_references(paper_title, top_k=num_refs) with gr.Blocks(theme=theme) as demo: gr.Markdown(ANNOUNCEMENT) 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) url = gr.Textbox(value=None, lines=1, max_lines=1, label="URL", visible=False) # 每个功能做一个tab with gr.Tab("学术论文"): gr.Markdown(ACADEMIC_PAPER) title = gr.Textbox(value="Playing Atari with Deep Reinforcement Learning", lines=1, max_lines=1, label="Title", info="论文标题") description_pp = gr.Textbox(lines=5, label="Description (Optional)", visible=True, info="这篇论文的主要贡献和创新点. (生成所有章节时共享这个信息, 保持生成的一致性.)") with gr.Accordion("高级设置", open=False): with gr.Row(): with gr.Column(scale=1): gr.Markdown(OUTPUTS_INSTRUCTION) with gr.Column(scale=2): with gr.Row(): template = gr.Dropdown(label="Template", choices=ALL_TEMPLATES, value="Default", interactive=True, info="生成论文的模板.") model_selection = gr.Dropdown(label="Model", choices=["gpt-4", "gpt-3.5-turbo"], value=DEFAULT_MODEL, interactive=True, info="生成论文用到的语言模型.") prompts_mode = gr.Checkbox(value=False, visible=True, interactive=True, label="Prompts模式", info="只输出用于生成论文的Prompts, 可以复制到别的地方生成论文.") sections = gr.CheckboxGroup( choices=["introduction", "related works", "backgrounds", "methodology", "experiments", "conclusion", "abstract"], type="value", label="生成章节", interactive=True, info="选择生成论文的哪些章节.", value=DEFAULT_SECTIONS) with gr.Row(): with gr.Column(scale=1): gr.Markdown(REFERENCES_INSTRUCTION) with gr.Column(scale=2): max_kw_ref_slider = gr.Slider(minimum=1, maximum=20, value=10, step=1, interactive=True, label="MAX_KW_REFS", info="每个Keyword搜索几篇参考文献", visible=False) max_tokens_ref_slider = gr.Slider(minimum=256, maximum=4096, value=2048, step=2, interactive=True, label="MAX_TOKENS", info="参考文献内容占用Prompts中的Token数") tldr_checkbox = gr.Checkbox(value=True, label="TLDR;", info="选择此筐表示将使用Semantic Scholar的TLDR作为文献的总结.", interactive=True) gr.Markdown(''' 上传.bib文件提供AI需要参考的文献. ''') bibtex_file = gr.File(label="Upload .bib file", file_types=["text"], interactive=True) with gr.Row(): with gr.Column(scale=1): gr.Markdown(DOMAIN_KNOWLEDGE_INSTRUCTION) with gr.Column(scale=2): query_counts_slider = gr.Slider(minimum=1, maximum=20, value=10, step=1, interactive=True, label="QUERY_COUNTS", info="从知识库内检索多少条内容", visible=False) max_tokens_kd_slider = gr.Slider(minimum=256, maximum=4096, value=2048, step=2, interactive=True, label="MAX_TOKENS", info="知识库内容占用Prompts中的Token数") # template = gr.Dropdown(label="Template", choices=ALL_TEMPLATES, value="Default", # interactive=True, # info="生成论文的参考模板.") domain_knowledge = gr.Dropdown(label="预载知识库", choices=ALL_DATABASES, value="(None)", interactive=True, info="使用预先构建的知识库.") local_domain_knowledge = gr.File(label="本地知识库 (暂未实装)", interactive=False) with gr.Row(): clear_button_pp = gr.Button("Clear") submit_button_pp = gr.Button("Submit", variant="primary") with gr.Tab("文献搜索"): gr.Markdown(REFERENCES) title_refs = gr.Textbox(value="Playing Atari with Deep Reinforcement Learning", lines=1, max_lines=1, label="Title", info="论文标题") slider_refs = gr.Slider(minimum=1, maximum=100, value=5, step=1, interactive=True, label="最相关的参考文献数目") with gr.Row(): clear_button_refs = gr.Button("Clear") submit_button_refs = gr.Button("Submit", variant="primary") with gr.Tab("文献综述 (Coming soon!)"): gr.Markdown('''

Coming soon!

''') with gr.Tab("Github文档 (Coming soon!)"): gr.Markdown('''

Coming soon!

''') with gr.Column(scale=1): gr.Markdown(STATUS) file_output = gr.File(label="Output") json_output = gr.JSON(label="References") # def wrapped_generator( # paper_title, paper_description, # main input # openai_api_key=None, openai_url=None, # key # tldr=True, max_kw_refs=10, bib_refs=None, max_tokens_ref=2048, # references # knowledge_database=None, max_tokens_kd=2048, query_counts=10, # domain knowledge # paper_template="ICLR2022", selected_sections=None, model="gpt-4", prompts_mode=False, # outputs parameters # cache_mode=IS_CACHE_AVAILABLE # handle cache mode # ): clear_button_pp.click(fn=clear_inputs, inputs=[title, description_pp], outputs=[title, description_pp]) submit_button_pp.click(fn=wrapped_generator, inputs=[title, description_pp, key, url, tldr_checkbox, max_kw_ref_slider, bibtex_file, max_tokens_ref_slider, domain_knowledge, max_tokens_kd_slider, query_counts_slider, template, sections, model_selection, prompts_mode], outputs=file_output) clear_button_refs.click(fn=clear_inputs_refs, inputs=[title_refs, slider_refs], outputs=[title_refs, slider_refs]) submit_button_refs.click(fn=wrapped_references_generator, inputs=[title_refs, slider_refs, key], outputs=json_output) demo.queue(concurrency_count=1, max_size=5, api_open=False) demo.launch(show_error=True)