import gradio as gr from init import ( get_secrets, initialize_data, update_dataframe, initialize_repos ) from gen.openllm import GradioMistralChatPPManager from gen.gemini_chat import GradioGeminiChatPPManager from constants.js import ( UPDATE_SEARCH_RESULTS, OPEN_CHAT_IF, CLOSE_CHAT_IF, UPDATE_CHAT_HISTORY ) from datetime import datetime, timedelta from background import process_arxiv_ids from apscheduler.schedulers.background import BackgroundScheduler gemini_api_key, hf_token, dataset_repo_id, request_arxiv_repo_id, restart_repo_id = get_secrets() initialize_repos(dataset_repo_id, request_arxiv_repo_id, hf_token) titles, date_dict, requested_arxiv_ids_df, arxivid2data = initialize_data(dataset_repo_id, request_arxiv_repo_id) from ui import ( get_paper_by_year, get_paper_by_month, get_paper_by_day, set_papers, set_paper, set_date, change_exp_type, add_arxiv_ids_to_queue, before_chat_begin, chat_stream, chat_reset ) if len(date_dict.keys()) > 0: sorted_year = sorted(date_dict.keys()) last_year = sorted_year[-1] if len(sorted_year) > 0 else "" sorted_month = sorted(date_dict[last_year].keys()) last_month = sorted_month[-1] if len(sorted_year) > 0 else "" sorted_day = sorted(date_dict[last_year][last_month].keys()) last_day = sorted_day[-1] if len(sorted_year) > 0 else "" last_papers = date_dict[last_year][last_month][last_day] if len(sorted_year) > 0 else [""] selected_paper = last_papers[0] visible = True else: sorted_year = ["2024"] last_year = sorted_year[-1] sorted_month = ["01"] last_month = sorted_month[-1] sorted_day = ["01"] last_day = sorted_day[-1] selected_paper = {} selected_paper["title"] = "" selected_paper["summary"] = "" selected_paper["arxiv_id"] = "" selected_paper["target_date"] = "2024-01-01" for idx in range(10): selected_paper[f"{idx}_question"] = "" selected_paper[f"{idx}_answers:eli5"] = "" selected_paper[f"{idx}_answers:expert"] = "" selected_paper[f"{idx}_additional_depth_q:follow up question"] = "" selected_paper[f"{idx}_additional_depth_q:answers:eli5"] = "" selected_paper[f"{idx}_additional_depth_q:answers:expert"] = "" selected_paper[f"{idx}_additional_breath_q:follow up question"] = "" selected_paper[f"{idx}_additional_breath_q:answers:eli5"] = "" selected_paper[f"{idx}_additional_breath_q:answers:expert"] = "" last_papers = [selected_paper] visible = False with gr.Blocks(css="constants/styles.css", theme=gr.themes.Soft()) as demo: cur_arxiv_id = gr.Textbox(selected_paper['arxiv_id'], visible=False) local_data = gr.JSON({}, visible=False) chat_state = gr.State({ "ppmanager_type": GradioGeminiChatPPManager # GradioMistralChatPPManager # GradioLLaMA2ChatPPManager }) with gr.Column(elem_id="chatbot-back"): with gr.Column(elem_id="chatbot", elem_classes=["hover-opacity"]): close = gr.Button("𝕏", elem_id="chatbot-right-button") #elem_id="chatbot-right-button") chatbot = gr.Chatbot( label="Gemini 1.0 Pro", show_label=True, show_copy_button=True, show_share_button=True, visible=True, elem_id="chatbot-inside" ) with gr.Row(elem_id="chatbot-bottm"): reset = gr.Button("🗑️ Reset") regen = gr.Button("🔄 Regenerate", visible=False) prompt_txtbox = gr.Textbox(placeholder="Ask anything.....", elem_id="chatbot-txtbox", elem_classes=["textbox-no-label"]) gr.Markdown("# Let's explore papers with auto generated Q&As") with gr.Column(elem_id="control-panel", elem_classes=["group"], visible=visible): with gr.Column(): with gr.Row(): year_dd = gr.Dropdown(sorted_year, value=last_year, label="Year", interactive=True, filterable=False) month_dd = gr.Dropdown(sorted_month, value=last_month, label="Month", interactive=True, filterable=False) day_dd = gr.Dropdown(sorted_day, value=last_day, label="Day", interactive=True, filterable=False) papers_dd = gr.Dropdown( list(set([paper["title"] for paper in last_papers])), value=selected_paper["title"], label="Select paper title", interactive=True, filterable=False ) with gr.Column(elem_classes=["no-gap"]): search_in = gr.Textbox("", placeholder="Enter keywords to search...", elem_classes=["textbox-no-label"]) search_r1 = gr.Button(visible=False, elem_id="search_r1", elem_classes=["no-radius"]) search_r2 = gr.Button(visible=False, elem_id="search_r2", elem_classes=["no-radius"]) search_r3 = gr.Button(visible=False, elem_id="search_r3", elem_classes=["no-radius"]) search_r4 = gr.Button(visible=False, elem_id="search_r4", elem_classes=["no-radius"]) search_r5 = gr.Button(visible=False, elem_id="search_r5", elem_classes=["no-radius"]) search_r6 = gr.Button(visible=False, elem_id="search_r6", elem_classes=["no-radius"]) search_r7 = gr.Button(visible=False, elem_id="search_r7", elem_classes=["no-radius"]) search_r8 = gr.Button(visible=False, elem_id="search_r8", elem_classes=["no-radius"]) search_r9 = gr.Button(visible=False, elem_id="search_r9", elem_classes=["no-radius"]) search_r10 = gr.Button(visible=False, elem_id="search_r10", elem_classes=["no-radius"]) with gr.Column(scale=7, visible=visible): title = gr.Markdown(f"# {selected_paper['title']}", elem_classes=["markdown-center"]) # with gr.Row(): with gr.Row(): arxiv_link = gr.Markdown( "[![arXiv](https://img.shields.io/badge/arXiv-%s-b31b1b.svg?style=for-the-badge)](https://arxiv.org/abs/%s)" % (selected_paper['arxiv_id'], selected_paper['arxiv_id']) + " " "[![Paper page](https://huggingface.co/datasets/huggingface/badges/resolve/main/paper-page-lg.svg)](https://huggingface.co/papers/%s)" % selected_paper['arxiv_id'] + " ", elem_id="link-md", ) chat_button = gr.Button("Chat about any custom questions", interactive=True, elem_id="chat-button") summary = gr.Markdown(f"{selected_paper['summary']}", elem_classes=["small-font"]) with gr.Column(elem_id="qna_block", visible=True): with gr.Row(): with gr.Column(scale=7): gr.Markdown("## Auto generated Questions & Answers") exp_type = gr.Radio(choices=["ELI5", "Technical"], value="ELI5", elem_classes=["exp-type"], scale=3) # 1 with gr.Column(elem_classes=["group"], visible=True) as q_0: basic_q_0 = gr.Markdown(f"### 🙋 {selected_paper['0_question']}") basic_q_eli5_0 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['0_answers:eli5']}", elem_classes=["small-font"]) basic_q_expert_0 = gr.Markdown(f"↪ **(Technical)** {selected_paper['0_answers:expert']}", visible=False, elem_classes=["small-font"]) with gr.Accordion("Additional question #1", open=False, elem_classes=["accordion"]) as aq_0_0: depth_q_0 = gr.Markdown(f"### 🙋🙋 {selected_paper['0_additional_depth_q:follow up question']}") depth_q_eli5_0 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['0_additional_depth_q:answers:eli5']}", elem_classes=["small-font"]) depth_q_expert_0 = gr.Markdown(f"↪ **(Technical)** {selected_paper['0_additional_depth_q:answers:expert']}", visible=False, elem_classes=["small-font"]) with gr.Accordion("Additional question #2", open=False, elem_classes=["accordion"]) as aq_0_1: breath_q_0 = gr.Markdown(f"### 🙋🙋 {selected_paper['0_additional_breath_q:follow up question']}") breath_q_eli5_0 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['0_additional_breath_q:answers:eli5']}", elem_classes=["small-font"]) breath_q_expert_0 = gr.Markdown(f"↪ **(Technical)** {selected_paper['0_additional_breath_q:answers:expert']}", visible=False, elem_classes=["small-font"]) # 2 with gr.Column(elem_classes=["group"], visible=True) as q_1: basic_q_1 = gr.Markdown(f"### 🙋 {selected_paper['1_question']}") basic_q_eli5_1 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['1_answers:eli5']}", elem_classes=["small-font"]) basic_q_expert_1 = gr.Markdown(f"↪ **(Technical)** {selected_paper['1_answers:expert']}", visible=False, elem_classes=["small-font"]) with gr.Accordion("Additional question #1", open=False, elem_classes=["accordion"]) as aq_1_0: depth_q_1 = gr.Markdown(f"### 🙋🙋 {selected_paper['1_additional_depth_q:follow up question']}") depth_q_eli5_1 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['1_additional_depth_q:answers:eli5']}", elem_classes=["small-font"]) depth_q_expert_1 = gr.Markdown(f"↪ **(Technical)** {selected_paper['1_additional_depth_q:answers:expert']}", visible=False, elem_classes=["small-font"]) with gr.Accordion("Additional question #2", open=False, elem_classes=["accordion"]) as aq_1_1: breath_q_1 = gr.Markdown(f"### 🙋🙋 {selected_paper['1_additional_breath_q:follow up question']}") breath_q_eli5_1 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['1_additional_breath_q:answers:eli5']}", elem_classes=["small-font"]) breath_q_expert_1 = gr.Markdown(f"↪ **(Technical)** {selected_paper['1_additional_breath_q:answers:expert']}", visible=False, elem_classes=["small-font"]) # 3 with gr.Column(elem_classes=["group"], visible=True) as q_2: basic_q_2 = gr.Markdown(f"### 🙋 {selected_paper['2_question']}") basic_q_eli5_2 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['2_answers:eli5']}", elem_classes=["small-font"]) basic_q_expert_2 = gr.Markdown(f"↪ **(Technical)** {selected_paper['2_answers:expert']}", visible=False, elem_classes=["small-font"]) with gr.Accordion("Additional question #1", open=False, elem_classes=["accordion"]) as aq_2_0: depth_q_2 = gr.Markdown(f"### 🙋🙋 {selected_paper['2_additional_depth_q:follow up question']}") depth_q_eli5_2 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['2_additional_depth_q:answers:eli5']}", elem_classes=["small-font"]) depth_q_expert_2 = gr.Markdown(f"↪ **(Technical)** {selected_paper['2_additional_depth_q:answers:expert']}", visible=False, elem_classes=["small-font"]) with gr.Accordion("Additional question #2", open=False, elem_classes=["accordion"]) as aq_2_1: breath_q_2 = gr.Markdown(f"### 🙋🙋 {selected_paper['2_additional_breath_q:follow up question']}") breath_q_eli5_2 = gr.Markdown(f"↪ **(ELI5)** {selected_paper['2_additional_breath_q:answers:eli5']}", elem_classes=["small-font"]) breath_q_expert_2 = gr.Markdown(f"↪ **(Technical)** {selected_paper['2_additional_breath_q:answers:expert']}", visible=False, elem_classes=["small-font"]) gr.Markdown("## Request any arXiv ids") arxiv_queue = gr.Dataframe( headers=["Requested arXiv IDs"], col_count=(1, "fixed"), value=update_dataframe, every=180, datatype=["str"], interactive=False, ) arxiv_id_enter = gr.Textbox(placeholder="Enter comma separated arXiv IDs...", elem_classes=["textbox-no-label"]) arxiv_id_enter.submit( add_arxiv_ids_to_queue, [arxiv_queue, arxiv_id_enter], [arxiv_queue, arxiv_id_enter], concurrency_limit=20, ) gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button") gr.Markdown("The target papers are collected from [Hugging Face 🤗 Daily Papers](https://huggingface.co/papers) on a daily basis. " "The entire data is generated by [Google's Gemini 1.0](https://deepmind.google/technologies/gemini/) Pro. " "If you are curious how it is done, visit the [Auto Paper Q&A Generation project repository](https://github.com/deep-diver/auto-paper-analysis) " "Also, the generated dataset is hosted on Hugging Face 🤗 Dataset repository as well([Link](https://huggingface.co/datasets/chansung/auto-paper-qa2)). ") search_r1.click(set_date, search_r1, [year_dd, month_dd, day_dd]).then( set_papers, inputs=[year_dd, month_dd, day_dd, search_r1], outputs=[cur_arxiv_id, papers_dd, search_in], concurrency_limit=20, ) search_r2.click(set_date, search_r2, [year_dd, month_dd, day_dd]).then( set_papers, inputs=[year_dd, month_dd, day_dd, search_r2], outputs=[cur_arxiv_id, papers_dd, search_in], concurrency_limit=20, ) search_r3.click(set_date, search_r3, [year_dd, month_dd, day_dd]).then( set_papers, inputs=[year_dd, month_dd, day_dd, search_r3], outputs=[cur_arxiv_id, papers_dd, search_in], concurrency_limit=20, ) search_r4.click(set_date, search_r4, [year_dd, month_dd, day_dd]).then( set_papers, inputs=[year_dd, month_dd, day_dd, search_r4], outputs=[cur_arxiv_id, papers_dd, search_in], concurrency_limit=20, ) search_r5.click(set_date, search_r5, [year_dd, month_dd, day_dd]).then( set_papers, inputs=[year_dd, month_dd, day_dd, search_r5], outputs=[cur_arxiv_id, papers_dd, search_in], concurrency_limit=20, ) search_r6.click(set_date, search_r6, [year_dd, month_dd, day_dd]).then( set_papers, inputs=[year_dd, month_dd, day_dd, search_r6], outputs=[cur_arxiv_id, papers_dd, search_in], concurrency_limit=20, ) search_r7.click(set_date, search_r7, [year_dd, month_dd, day_dd]).then( set_papers, inputs=[year_dd, month_dd, day_dd, search_r7], outputs=[cur_arxiv_id, papers_dd, search_in], concurrency_limit=20, ) search_r8.click(set_date, search_r8, [year_dd, month_dd, day_dd]).then( set_papers, inputs=[year_dd, month_dd, day_dd, search_r8], outputs=[cur_arxiv_id, papers_dd, search_in], concurrency_limit=20, ) search_r9.click(set_date, search_r9, [year_dd, month_dd, day_dd]).then( set_papers, inputs=[year_dd, month_dd, day_dd, search_r9], outputs=[cur_arxiv_id, papers_dd, search_in], concurrency_limit=20, ) search_r10.click(set_date, search_r10, [year_dd, month_dd, day_dd]).then( set_papers, inputs=[year_dd, month_dd, day_dd, search_r10], outputs=[cur_arxiv_id, papers_dd, search_in], concurrency_limit=20, ) year_dd.input(get_paper_by_year, inputs=[year_dd], outputs=[month_dd, day_dd, papers_dd]).then( set_paper, [year_dd, month_dd, day_dd, papers_dd], [ cur_arxiv_id, title, arxiv_link, summary, basic_q_0, basic_q_eli5_0, basic_q_expert_0, depth_q_0, depth_q_eli5_0, depth_q_expert_0, breath_q_0, breath_q_eli5_0, breath_q_expert_0, basic_q_1, basic_q_eli5_1, basic_q_expert_1, depth_q_1, depth_q_eli5_1, depth_q_expert_1, breath_q_1, breath_q_eli5_1, breath_q_expert_1, basic_q_2, basic_q_eli5_2, basic_q_expert_2, depth_q_2, depth_q_eli5_2, depth_q_expert_2, breath_q_2, breath_q_eli5_2, breath_q_expert_2 ], concurrency_limit=20, ) month_dd.input(get_paper_by_month, inputs=[year_dd, month_dd], outputs=[day_dd, papers_dd]).then( set_paper, [year_dd, month_dd, day_dd, papers_dd], [ cur_arxiv_id, title, arxiv_link, summary, basic_q_0, basic_q_eli5_0, basic_q_expert_0, depth_q_0, depth_q_eli5_0, depth_q_expert_0, breath_q_0, breath_q_eli5_0, breath_q_expert_0, basic_q_1, basic_q_eli5_1, basic_q_expert_1, depth_q_1, depth_q_eli5_1, depth_q_expert_1, breath_q_1, breath_q_eli5_1, breath_q_expert_1, basic_q_2, basic_q_eli5_2, basic_q_expert_2, depth_q_2, depth_q_eli5_2, depth_q_expert_2, breath_q_2, breath_q_eli5_2, breath_q_expert_2 ], concurrency_limit=20, ) day_dd.input(get_paper_by_day, inputs=[year_dd, month_dd, day_dd], outputs=[papers_dd]).then( set_paper, [year_dd, month_dd, day_dd, papers_dd], [ cur_arxiv_id, title, arxiv_link, summary, basic_q_0, basic_q_eli5_0, basic_q_expert_0, depth_q_0, depth_q_eli5_0, depth_q_expert_0, breath_q_0, breath_q_eli5_0, breath_q_expert_0, basic_q_1, basic_q_eli5_1, basic_q_expert_1, depth_q_1, depth_q_eli5_1, depth_q_expert_1, breath_q_1, breath_q_eli5_1, breath_q_expert_1, basic_q_2, basic_q_eli5_2, basic_q_expert_2, depth_q_2, depth_q_eli5_2, depth_q_expert_2, breath_q_2, breath_q_eli5_2, breath_q_expert_2 ], concurrency_limit=20, ) papers_dd.change(set_paper, [year_dd, month_dd, day_dd, papers_dd], [ cur_arxiv_id, title, arxiv_link, summary, basic_q_0, basic_q_eli5_0, basic_q_expert_0, depth_q_0, depth_q_eli5_0, depth_q_expert_0, breath_q_0, breath_q_eli5_0, breath_q_expert_0, basic_q_1, basic_q_eli5_1, basic_q_expert_1, depth_q_1, depth_q_eli5_1, depth_q_expert_1, breath_q_1, breath_q_eli5_1, breath_q_expert_1, basic_q_2, basic_q_eli5_2, basic_q_expert_2, depth_q_2, depth_q_eli5_2, depth_q_expert_2, breath_q_2, breath_q_eli5_2, breath_q_expert_2 ], concurrency_limit=20, ) search_in.change( inputs=[search_in], outputs=[ search_r1, search_r2, search_r3, search_r4, search_r5, search_r6, search_r7, search_r8, search_r9, search_r10 ], js=UPDATE_SEARCH_RESULTS % str(list(titles)), fn=None ) exp_type.select( change_exp_type, exp_type, [ basic_q_eli5_0, basic_q_expert_0, depth_q_eli5_0, depth_q_expert_0, breath_q_eli5_0, breath_q_expert_0, basic_q_eli5_1, basic_q_expert_1, depth_q_eli5_1, depth_q_expert_1, breath_q_eli5_1, breath_q_expert_1, basic_q_eli5_2, basic_q_expert_2, depth_q_eli5_2, depth_q_expert_2, breath_q_eli5_2, breath_q_expert_2 ], concurrency_limit=20, ) chat_button.click(None, [cur_arxiv_id], [local_data, chatbot], js=OPEN_CHAT_IF) chat_event1 = prompt_txtbox.submit( before_chat_begin, None, [reset, regen], concurrency_limit=20, ) chat_event2 = chat_event1.then( chat_stream, [cur_arxiv_id, local_data, prompt_txtbox, chat_state], [prompt_txtbox, chatbot, local_data, reset, regen], concurrency_limit=20, queue=True ) chat_event2.then( None, [cur_arxiv_id, local_data], None, js=UPDATE_CHAT_HISTORY ) close.click( None, None, None, cancels=[chat_event1, chat_event2] ).then( None, None, None,js=CLOSE_CHAT_IF ) reset.click( before_chat_begin, None, [reset, regen], concurrency_limit=20, ).then( chat_reset, [local_data, chat_state], [prompt_txtbox, chatbot, local_data, reset, regen], concurrency_limit=20, ).then( None, [cur_arxiv_id, local_data], None, js=UPDATE_CHAT_HISTORY ) # demo.load(lambda: update_dataframe(request_arxiv_repo_id), None, arxiv_queue, every=180) # demo.load(None, None, [chatbot, local_data], js=GET_LOCAL_STORAGE % idx.value) start_date = datetime.now() + timedelta(minutes=1) scheduler = BackgroundScheduler() scheduler.add_job( process_arxiv_ids, trigger='interval', seconds=300, args=[ gemini_api_key, dataset_repo_id, request_arxiv_repo_id, hf_token, restart_repo_id ], start_date=start_date ) scheduler.start() demo.queue( default_concurrency_limit=20, max_size=256 ).launch( share=True, debug=True )