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shaocongma
Add a generator wrapper using configuration file. Edit the logic of searching references. Add Gradio UI for testing Knowledge database.
94dc00e
| import uuid | |
| import gradio as gr | |
| import os | |
| import openai | |
| import yaml | |
| from utils.file_operations import list_folders, urlify | |
| from huggingface_hub import snapshot_download | |
| from wrapper import generator_wrapper | |
| # 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 | |
| # future: | |
| # generation.log sometimes disappears (ignore this) | |
| # 1. Check if there are any duplicated citations | |
| # 2. Remove potential thebibliography and bibitem in .tex file | |
| ####################################################################################################################### | |
| # Environment Variables | |
| ####################################################################################################################### | |
| # OPENAI_API_KEY: OpenAI API key for GPT models | |
| # OPENAI_API_BASE: (Optional) Support alternative OpenAI minors | |
| # GPT4_ENABLE: (Optional) Set it to 1 to enable GPT-4 model. | |
| # AWS_ACCESS_KEY_ID: (Optional) | |
| # Access AWS cloud storage (you need to edit `BUCKET_NAME` in `utils/storage.py` if you need to use this function) | |
| # AWS_SECRET_ACCESS_KEY: (Optional) | |
| # Access AWS cloud storage (you need to edit `BUCKET_NAME` in `utils/storage.py` if you need to use this function) | |
| # KDB_REPO: (Optional) A Huggingface dataset hosting Knowledge Databases | |
| # HF_TOKEN: (Optional) Access to KDB_REPO | |
| ####################################################################################################################### | |
| # Check if openai and cloud storage available | |
| ####################################################################################################################### | |
| openai_key = os.getenv("OPENAI_API_KEY") | |
| openai_api_base = os.getenv("OPENAI_API_BASE") | |
| if openai_api_base is not None: | |
| openai.api_base = openai_api_base | |
| GPT4_ENABLE = os.getenv("GPT4_ENABLE") # disable GPT-4 for public repo | |
| 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 GPT4_ENABLE else 'gpt-3.5-turbo-16k' | |
| GPT4_INTERACTIVE = True if GPT4_ENABLE else False | |
| DEFAULT_SECTIONS = ["introduction", "related works", "backgrounds", "methodology", "experiments", | |
| "conclusion", "abstract"] if GPT4_ENABLE \ | |
| else ["introduction", "related works"] | |
| MODEL_LIST = ['gpt-4', 'gpt-3.5-turbo', 'gpt-3.5-turbo-16k'] | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| REPO_ID = os.getenv("KDB_REPO") | |
| if HF_TOKEN is not None and REPO_ID is not None: | |
| snapshot_download(REPO_ID, repo_type="dataset", local_dir="knowledge_databases/", | |
| local_dir_use_symlinks=False, token=HF_TOKEN) | |
| KDB_LIST = ["(None)"] + list_folders("knowledge_databases") | |
| ####################################################################################################################### | |
| # 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)的学术论文模板生成功能的测试. 学术综述和Github文档功能正在开发中. | |
| ## 主要功能 | |
| 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. | |
| ***2023-06-13 Update***: | |
| - 增加了最新的gpt-3.5-turbo-16k模型的支持. | |
| ***2023-06-13 Update***: | |
| 1. 新增‘高级选项-Prompts模式’. 这个模式仅会输出用于生成论文的Prompts而不会生成论文本身. 可以根据自己的需求修改Prompts, 也可以把Prompts复制给其他语言模型. | |
| 2. 把默认的ICLR 2022模板改成了Default模板. 不再显示ICLR的页眉页尾. | |
| 3. 中文支持: 暂不支持. 建议使用英文生成论文, 然后把输出结果送入[GPT 学术优化](https://github.com/binary-husky/gpt_academic)中的Latex全文翻译、润色功能即可. | |
| 4. 使用GPT-4模型： | |
| - 点击Duplicate this Space, 进入Settings-> Repository secrets, 点击New Secret添加OPENAI_API_KEY为自己的OpenAI API Key. 添加GPT4_ENBALE为1. | |
| - 或者可以访问[Auto-Draft-Private](https://huggingface.co/spaces/auto-academic/auto-draft-private). | |
| 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. 用户通过输入文章标题(用英文逗号隔开), AI会自动搜索文献作为参考资料. | |
| 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. (放在输出的ZIP文件的prompts.json文件中) | |
| """ | |
| 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`: <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>.''' | |
| 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, # key | |
| tldr=True, max_kw_refs=10, 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 | |
| ): | |
| file_name_upload = urlify(paper_title) + "_" + uuid.uuid1().hex + ".zip" | |
| # load the default configuration file | |
| with open("configurations/default.yaml", 'r') as file: | |
| config = yaml.safe_load(file) | |
| config["paper"]["title"] = paper_title | |
| config["paper"]["description"] = paper_description | |
| config["references"]["tldr"] = tldr | |
| config["references"]["max_kw_refs"] = max_kw_refs | |
| config["references"]["refs"] = refs | |
| config["references"]["max_tokens_ref"] = max_tokens_ref | |
| config["domain_knowledge"]["knowledge_database"] = knowledge_database | |
| config["domain_knowledge"]["max_tokens_kd"] = max_tokens_kd | |
| config["domain_knowledge"]["query_counts"] = query_counts | |
| config["output"]["selected_sections"] = selected_sections | |
| config["output"]["model"] = model | |
| config["output"]["template"] = paper_template | |
| config["output"]["prompts_mode"] = prompts_mode | |
| 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}") | |
| try: | |
| output = generator_wrapper(config) | |
| if cache_mode: | |
| from utils.storage import upload_file | |
| upload_file(output, target_name=file_name_upload) | |
| except Exception as e: | |
| raise gr.Error(f"生成失败. Error: {e}") | |
| return output | |
| 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) | |
| # 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=MODEL_LIST, | |
| value=DEFAULT_MODEL, | |
| interactive=GPT4_INTERACTIVE, | |
| 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=8192, 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) | |
| text_ref = gr.Textbox(lines=5, label="References (Optional)", visible=True, | |
| info="交给AI参考的文献的标题, 用英文逗号`,`隔开.") | |
| gr.Examples( | |
| examples = ["Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle," | |
| "Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis," | |
| "Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity"], | |
| inputs=text_ref, | |
| cache_examples=False | |
| ) | |
| 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=8192, value=2048, step=2, | |
| interactive=True, label="MAX_TOKENS", | |
| info="知识库内容占用Prompts中的Token数") | |
| 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): | |
| gr.Markdown(STATUS) | |
| 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, | |
| tldr_checkbox, max_kw_ref_slider, text_ref, max_tokens_ref_slider, | |
| domain_knowledge, max_tokens_kd_slider, query_counts_slider, | |
| template, sections, model_selection, prompts_mode], outputs=file_output) | |
| demo.queue(concurrency_count=1, max_size=5, api_open=False) | |
| demo.launch(show_error=True) | |