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| import gradio as gr | |
| import copy | |
| import datasets | |
| STYLE = """ | |
| .main { | |
| width: 90% !important; | |
| margin: 0 auto; /* Center the container */ | |
| } | |
| .small-font{ | |
| font-size: 12pt !important; | |
| } | |
| .small-font:hover { | |
| font-size: 20px !important; | |
| transition: font-size 0.3s ease-out; | |
| transition-delay: 0.8s; | |
| } | |
| .group { | |
| padding-top: 10px; | |
| padding-left: 10px; | |
| padding-right: 10px; | |
| padding-bottom: 10px; | |
| border: 2px dashed gray; | |
| border-radius: 20px; | |
| box-shadow: 5px 3px 10px 1px rgba(0, 0, 0, 0.4) !important; | |
| } | |
| .accordion > button > span{ | |
| font-size: 12pt !important; | |
| } | |
| .accordion { | |
| border-style: dashed !important; | |
| border-left-width: 2px !important; | |
| border-bottom-width: 2.5px !important; | |
| border-top: none !important; | |
| border-right: none !important; | |
| box-shadow: none !important; | |
| } | |
| .no-gap { | |
| gap: 0px; | |
| } | |
| .no-radius { | |
| border-radius: 0px; | |
| } | |
| #search_input > label > span { | |
| display: none; | |
| } | |
| #exp-type > span { | |
| display: none; | |
| } | |
| """ | |
| dataset_repo_id = "chansung/auto-paper-qa2" | |
| ds = datasets.load_dataset(dataset_repo_id) | |
| title2qna = {} | |
| date2qna = {} | |
| longest_qans = 0 | |
| def count_nans(row): | |
| count = 0 | |
| for _, (k, v) in enumerate(data.items()): | |
| if v is None: | |
| count = count + 1 | |
| return count | |
| for data in ds["train"]: | |
| date = data["target_date"].strftime("%Y-%m-%d") | |
| if date in date2qna: | |
| papers = copy.deepcopy(date2qna[date]) | |
| for paper in papers: | |
| if paper["title"] == data["title"]: | |
| if count_nans(paper) > count_nans(data): | |
| date2qna[date].remove(paper) | |
| date2qna[date].append(data) | |
| del papers | |
| else: | |
| date2qna[date] = [data] | |
| for date in date2qna: | |
| papers = date2qna[date] | |
| for paper in papers: | |
| title2qna[paper["title"]] = paper | |
| titles = title2qna.keys() | |
| sorted_dates = sorted(date2qna.keys()) | |
| last_date = sorted_dates[-1] | |
| last_papers = date2qna[last_date] | |
| selected_paper = last_papers[0] | |
| def get_papers(date): | |
| papers = [paper["title"] for paper in date2qna[date]] | |
| return gr.Dropdown( | |
| papers, | |
| value=papers[0] | |
| ) | |
| def set_paper(date, paper_title): | |
| selected_paper = None | |
| for paper in date2qna[date]: | |
| if paper["title"] == paper_title: | |
| selected_paper = paper | |
| break | |
| return ( | |
| gr.Markdown(f"# {selected_paper['title']}"), gr.Markdown(selected_paper["summary"]), | |
| gr.Markdown(f"## 🙋 {selected_paper['0_question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['0_answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['0_answers:expert']}"), | |
| gr.Markdown(f"## 🙋🙋 {selected_paper['0_additional_depth_q:follow up question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['0_additional_depth_q:answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['0_additional_depth_q:answers:expert']}"), | |
| gr.Markdown(f"## 🙋🙋 {selected_paper['0_additional_breath_q:follow up question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['0_additional_breath_q:answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['0_additional_breath_q:answers:expert']}"), | |
| gr.Markdown(f"## 🙋 {selected_paper['1_question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['1_answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['1_answers:expert']}"), | |
| gr.Markdown(f"## 🙋🙋 {selected_paper['1_additional_depth_q:follow up question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['1_additional_depth_q:answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['1_additional_depth_q:answers:expert']}"), | |
| gr.Markdown(f"## 🙋🙋 {selected_paper['1_additional_breath_q:follow up question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['1_additional_breath_q:answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['1_additional_breath_q:answers:expert']}"), | |
| gr.Markdown(f"## 🙋 {selected_paper['2_question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['2_answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['2_answers:expert']}"), | |
| gr.Markdown(f"## 🙋🙋 {selected_paper['2_additional_depth_q:follow up question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['2_additional_depth_q:answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['2_additional_depth_q:answers:expert']}"), | |
| gr.Markdown(f"## 🙋🙋 {selected_paper['2_additional_breath_q:follow up question']}"), | |
| gr.Markdown(f"↪ **(ELI5)** {selected_paper['2_additional_breath_q:answers:eli5']}"), | |
| gr.Markdown(f"↪ **(Technical)** {selected_paper['2_additional_breath_q:answers:expert']}"), | |
| ) | |
| def change_exp_type(exp_type): | |
| if exp_type == "ELI5": | |
| return ( | |
| gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), | |
| gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), | |
| gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), | |
| ) | |
| else: | |
| return ( | |
| gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), | |
| gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), | |
| gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), | |
| ) | |
| def search(search_in, max_results=3): | |
| results = [] | |
| for title in titles: | |
| if len(results) > 3: | |
| break | |
| else: | |
| if search_in in title: | |
| results.append(title) | |
| return ( | |
| gr.Textbox( | |
| visible=True if len(results) > 0 else False, | |
| value=results[0] if len(results) > 0 else "" | |
| ), | |
| gr.Textbox( | |
| visible=True if len(results) > 1 else False, | |
| value=results[1] if len(results) > 1 else "" | |
| ), | |
| gr.Textbox( | |
| visible=True if len(results) > 2 else False, | |
| value=results[2] if len(results) > 2 else "" | |
| ) | |
| ) | |
| UPDATE_SEARCH_RESULTS = f""" | |
| function search(searchIn, maxResults = 3) {{ | |
| if (searchIn.trim().length > 0) {{ | |
| const results = []; | |
| let titles = {list(titles)}; | |
| for (const title of titles) {{ // Assuming 'titles' is an array defined elsewhere | |
| if (results.length > 3) {{ | |
| break; | |
| }} else {{ | |
| if (title.toLowerCase().includes(searchIn.toLowerCase())) {{ // JavaScript's equivalent to Python's 'in' | |
| results.push(title); | |
| }} | |
| }} | |
| }} | |
| // Handle UI elements (Explanation below) | |
| const resultElements = [1, 2, 3].map(index => {{ | |
| return results[index - 1] || ''; | |
| }}); | |
| if (resultElements[0] == '') {{ | |
| document.getElementById('search_r1').style.display = 'none'; | |
| }} else {{ | |
| document.getElementById('search_r1').style.display = 'block'; | |
| }} | |
| if (resultElements[1] == '') {{ | |
| document.getElementById('search_r2').style.display = 'none'; | |
| }} else {{ | |
| document.getElementById('search_r2').style.display = 'block'; | |
| }} | |
| if (resultElements[2] == '') {{ | |
| document.getElementById('search_r3').style.display = 'none'; | |
| }} else {{ | |
| document.getElementById('search_r3').style.display = 'block'; | |
| }} | |
| return resultElements; | |
| }} else {{ | |
| document.getElementById('search_r1').style.display = 'none'; | |
| document.getElementById('search_r2').style.display = 'none'; | |
| document.getElementById('search_r3').style.display = 'none'; | |
| return ['', '', ''] | |
| }} | |
| }} | |
| """ | |
| def set_date(title): | |
| paper = title2qna[title] | |
| date = paper["target_date"].strftime("%Y-%m-%d") | |
| return date | |
| def set_papers(date, title): | |
| papers = [paper["title"] for paper in date2qna[date]] | |
| return ( | |
| gr.Dropdown(choices=papers, value=title), | |
| gr.Textbox("") | |
| ) | |
| with gr.Blocks(css=STYLE) as demo: | |
| gr.Markdown("# Let's explore papers with auto generated Q&As") | |
| with gr.Column(elem_classes=["group"]): | |
| with gr.Row(): | |
| date_dd = gr.Dropdown( | |
| sorted_dates, | |
| value=last_date, | |
| label="Select date", | |
| interactive=True, | |
| scale=3, | |
| ) | |
| papers_dd = gr.Dropdown( | |
| [paper["title"] for paper in last_papers], | |
| value=selected_paper["title"], | |
| label="Select paper title", | |
| interactive=True, | |
| scale=7, | |
| ) | |
| with gr.Column(elem_classes=["no-gap"]): | |
| search_in = gr.Textbox("", placeholder="Enter keywords to search...", elem_id="search_input") | |
| 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"]) | |
| title = gr.Markdown(f"# {selected_paper['title']}") | |
| summary = gr.Markdown(f"{selected_paper['summary']}", elem_classes=["small-font"]) | |
| with gr.Row(): | |
| with gr.Column(scale=7): | |
| gr.Markdown("## Auto generated Questions & Answers") | |
| exp_type = gr.Radio(choices=["ELI5", "Technical"], value="ELI5", elem_id="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("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, | |
| date_dd | |
| ).then( | |
| set_papers, | |
| inputs=[date_dd, search_r1], | |
| outputs=[papers_dd, search_in] | |
| ) | |
| search_r2.click( | |
| set_date, | |
| search_r2, | |
| date_dd | |
| ).then( | |
| set_papers, | |
| inputs=[date_dd, search_r2], | |
| outputs=[papers_dd, search_in] | |
| ) | |
| search_r3.click( | |
| set_date, | |
| search_r3, | |
| date_dd | |
| ).then( | |
| set_papers, | |
| inputs=[date_dd, search_r3], | |
| outputs=[papers_dd, search_in] | |
| ) | |
| date_dd.input( | |
| get_papers, | |
| date_dd, | |
| papers_dd | |
| ).then( | |
| set_paper, | |
| [date_dd, papers_dd], | |
| [ | |
| title, 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 | |
| ] | |
| ) | |
| papers_dd.change( | |
| set_paper, | |
| [date_dd, papers_dd], | |
| [ | |
| title, 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 | |
| ] | |
| ) | |
| search_in.change( | |
| inputs=[search_in], | |
| outputs=[search_r1, search_r2, search_r3], | |
| js=UPDATE_SEARCH_RESULTS, | |
| 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 | |
| ] | |
| ) | |
| demo.launch(share=True) |