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 # 8. Decide on how to generate the main part of a paper * (Langchain/AutoGPT # 1. 把paper改成纯JSON? # 2. 实现别的功能 # 3. Check API Key GPT-4 Support. # 8. Re-build some components using `langchain` # - in `gpt_interation`, use LLM # 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: IS_OPENAI_API_KEY_AVAILABLE = False ALL_TEMPLATES = list_folders("latex_templates") def clear_inputs(*args): return "", "" def clear_inputs_refs(*args): return "", 5 def wrapped_generator(paper_title, paper_description, openai_api_key=None, paper_template="ICLR2022", tldr=True, selected_sections=None, bib_refs=None, model="gpt-4", cache_mode=IS_CACHE_AVAILABLE): # 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, upload_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 else: try: # generate the result. # output = fake_generate_backgrounds(title, description, openai_key) output = generate_draft(paper_title, paper_description, template=paper_template, tldr=tldr, sections=selected_sections, bib_refs=bib_refs, model=model) # output = generate_draft(paper_title, paper_description, template, "gpt-4") upload_file(output) return output except Exception as e: raise gr.Error(f"生成失败. Error {e.__name__}: {e}") else: try: # output = fake_generate_backgrounds(title, description, openai_key) output = generate_draft(paper_title, paper_description, template=paper_template, tldr=tldr, sections=selected_sections, bib_refs=bib_refs, model=model) 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) theme = gr.themes.Default(font=gr.themes.GoogleFont("Questrial")) # .set( # background_fill_primary='#E5E4E2', # background_fill_secondary = '#F6F6F6', # button_primary_background_fill="#281A39" # ) 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 这一行用于定义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 (暂未实装) 这一行用于定义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. 每个数据库内包含了数百万页经过同行评议的论文和专业经典书籍. 2. 自行构建的使用OpenAI text-embedding-ada-002模型创建的FAISS向量数据库. """ OTHERS_INSTRUCTION = """### Others """ with gr.Blocks(theme=theme) as demo: gr.Markdown(''' # Auto-Draft: 文献整理辅助工具 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. ***2023-06-08 Update***: * 目前对英文的生成效果更好. 如果需要中文文章可以使用[GPT学术优化](https://github.com/binary-husky/gpt_academic)的`Latex全文翻译、润色`功能. * GPT3.5模型可能会因为Token数不够导致一部分章节为空. 可以在高级设置里减少生成的章节. ***2023-05-17 Update***: 我的API的余额用完了, 所以这个月不再能提供GPT-4的API Key. 这里为大家提供了一个位置输入OpenAI API Key. 同时也提供了GPT-3.5的兼容. 欢迎大家自行体验. 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***. ''') 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) # generator = gr.Dropdown(choices=["学术论文", "文献总结"], value="文献总结", # label="Selection", info="目前支持生成'学术论文'和'文献总结'.", interactive=True) # 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") with gr.Accordion("高级设置", open=False): with gr.Row(): description_pp = gr.Textbox(lines=5, label="Description (Optional)", visible=True, info="对希望生成的论文的一些描述. 包括这篇论文的创新点, 主要贡献, 等.") with gr.Row(): template = gr.Dropdown(label="Template", choices=ALL_TEMPLATES, value="Default", interactive=True, info="生成论文的参考模板.") model_selection = gr.Dropdown(label="Model", choices=["gpt-4", "gpt-3.5-turbo"], value="gpt-3.5-turbo", interactive=True, info="生成论文用到的语言模型.") sections = gr.CheckboxGroup( choices=["introduction", "related works", "backgrounds", "methodology", "experiments", "conclusion", "abstract"], type="value", label="生成章节", interactive=True, value=["introduction", "related works"]) with gr.Row(): with gr.Column(scale=1): gr.Markdown(REFERENCES_INSTRUCTION) with gr.Column(scale=2): search_engine = gr.Dropdown(label="Search Engine", choices=["ArXiv", "Semantic Scholar", "Google Scholar", "None"], value="Semantic Scholar", interactive=False, visible=False, info="用于决定GPT用什么搜索引擎来搜索文献. (暂不支持修改)") 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): domain_knowledge = gr.Dropdown(label="预载知识库", choices=["(None)", "Machine Learning"], value="(None)", interactive=False, info="使用预先构建的知识库. (暂未实装)") 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): 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. 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]}.''') file_output = gr.File(label="Output") json_output = gr.JSON(label="References") 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, template, tldr_checkbox, sections, bibtex_file, model_selection], 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)