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| import gradio as gr | |
| import os | |
| import openai | |
| from auto_backgrounds import generate_backgrounds, generate_draft | |
| from utils.file_operations import hash_name | |
| from references_generator import generate_top_k_references | |
| # todo: | |
| # generation.log sometimes disappears | |
| # 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 | |
| # 5. 从提供的bib文件中 找到cite和citedby的文章, 计算embeddings; 从整个paper list中 根据cos距离进行排序; 选取max_refs的文章 | |
| # future: | |
| # 4. add auto_polishing function | |
| # 12. Change link to more appealing color # after the website is built; | |
| # 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 | |
| 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 | |
| openai.Model.list() | |
| 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: | |
| # 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 | |
| else: | |
| # 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) | |
| 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. 等待大概十分钟. | |
| 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. | |
| ***2023-05-17 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=["ICLR2022"], value="ICLR2022", | |
| interactive=False, | |
| info="生成论文的参考模板. (暂不支持修改)") | |
| 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) | |
| gr.Examples( | |
| examples=["latex_templates/example_references.bib"], | |
| inputs=bibtex_file | |
| ) | |
| 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(''' | |
| <h1 style="text-align: center;">Coming soon!</h1> | |
| ''') | |
| with gr.Tab("Github文档 (Coming soon!)"): | |
| gr.Markdown(''' | |
| <h1 style="text-align: center;">Coming soon!</h1> | |
| ''') | |
| 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`: <span style="{style_mapping[IS_OPENAI_API_KEY_AVAILABLE]}">{availability_mapping[IS_OPENAI_API_KEY_AVAILABLE]}</span>. `Cache`: <span style="{style_mapping[IS_CACHE_AVAILABLE]}">{availability_mapping[IS_CACHE_AVAILABLE]}</span>.''') | |
| 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() | |