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Create app.py
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app.py
ADDED
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import gradio as gr
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import copy
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import datasets
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STYLE = """
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.small-font{
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font-size: 12pt !important;
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}
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.small-font:hover {
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font-size: 20px !important;
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transition: font-size 0.3s ease-out;
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transition-delay: 0.8s;
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}
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.group {
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padding-left: 10px;
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padding-right: 10px;
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padding-bottom: 10px;
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border: 2px dashed gray;
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border-radius: 20px;
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box-shadow: 5px 3px 10px 1px rgba(0, 0, 0, 0.4) !important;
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}
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.accordion > button > span{
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font-size: 12pt !important;
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}
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.accordion {
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border-style: dashed !important;
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border-left-width: 2px !important;
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border-bottom-width: 2.5px !important;
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border-top: none !important;
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border-right: none !important;
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box-shadow: none !important;
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}
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"""
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dataset_repo_id = "chansung/auto-paper-qa2"
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ds = datasets.load_dataset(dataset_repo_id)
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date2qna = {}
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longest_qans = 0
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def count_nans(row):
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count = 0
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for _, (k, v) in enumerate(data.items()):
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if v is None:
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count = count + 1
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return count
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for data in ds["train"]:
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date = data["target_date"].strftime("%Y-%m-%d")
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if date in date2qna:
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papers = copy.deepcopy(date2qna[date])
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for paper in papers:
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if paper["title"] == data["title"]:
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if count_nans(paper) > count_nans(data):
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date2qna[date].remove(paper)
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date2qna[date].append(data)
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del papers
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else:
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date2qna[date] = [data]
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sorted_dates = sorted(date2qna.keys())
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last_date = sorted_dates[-1]
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last_papers = date2qna[last_date]
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selected_paper = last_papers[0]
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def get_papers(date):
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papers = [paper["title"] for paper in date2qna[date]]
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return gr.Dropdown(
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papers,
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value=papers[0]
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)
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def set_paper(date, paper_title):
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selected_paper = None
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for paper in date2qna[date]:
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if paper["title"] == paper_title:
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selected_paper = paper
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break
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return (
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gr.Markdown(f"# {selected_paper['title']}"), gr.Markdown(selected_paper["summary"]),
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gr.Markdown(f"## π {selected_paper['0_question']}"),
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gr.Markdown(f"βͺ **(ELI5)** {selected_paper['0_answers:eli5']}"),
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gr.Markdown(f"βͺ **(Technical)** {selected_paper['0_answers:expert']}"),
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gr.Markdown(f"## ππ {selected_paper['0_additional_depth_q:follow up question']}"),
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gr.Markdown(f"βͺ **(ELI5)** {selected_paper['0_additional_depth_q:answers:eli5']}"),
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gr.Markdown(f"βͺ **(Technical)** {selected_paper['0_additional_depth_q:answers:expert']}"),
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gr.Markdown(f"## ππ {selected_paper['0_additional_breath_q:follow up question']}"),
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gr.Markdown(f"βͺ **(ELI5)** {selected_paper['0_additional_breath_q:answers:eli5']}"),
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gr.Markdown(f"βͺ **(Technical)** {selected_paper['0_additional_breath_q:answers:expert']}"),
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gr.Markdown(f"## π {selected_paper['1_question']}"),
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gr.Markdown(f"βͺ **(ELI5)** {selected_paper['1_answers:eli5']}"),
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gr.Markdown(f"βͺ **(Technical)** {selected_paper['1_answers:expert']}"),
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gr.Markdown(f"## ππ {selected_paper['1_additional_depth_q:follow up question']}"),
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gr.Markdown(f"βͺ **(ELI5)** {selected_paper['1_additional_depth_q:answers:eli5']}"),
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gr.Markdown(f"βͺ **(Technical)** {selected_paper['1_additional_depth_q:answers:expert']}"),
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gr.Markdown(f"## ππ {selected_paper['1_additional_breath_q:follow up question']}"),
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gr.Markdown(f"βͺ **(ELI5)** {selected_paper['1_additional_breath_q:answers:eli5']}"),
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gr.Markdown(f"βͺ **(Technical)** {selected_paper['1_additional_breath_q:answers:expert']}"),
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gr.Markdown(f"## π {selected_paper['2_question']}"),
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gr.Markdown(f"βͺ **(ELI5)** {selected_paper['2_answers:eli5']}"),
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gr.Markdown(f"βͺ **(Technical)** {selected_paper['2_answers:expert']}"),
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gr.Markdown(f"## ππ {selected_paper['2_additional_depth_q:follow up question']}"),
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gr.Markdown(f"βͺ **(ELI5)** {selected_paper['2_additional_depth_q:answers:eli5']}"),
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gr.Markdown(f"βͺ **(Technical)** {selected_paper['2_additional_depth_q:answers:expert']}"),
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gr.Markdown(f"## ππ {selected_paper['2_additional_breath_q:follow up question']}"),
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gr.Markdown(f"βͺ **(ELI5)** {selected_paper['2_additional_breath_q:answers:eli5']}"),
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gr.Markdown(f"βͺ **(Technical)** {selected_paper['2_additional_breath_q:answers:expert']}"),
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)
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with gr.Blocks(css=STYLE) as demo:
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date_dd = gr.Dropdown(
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sorted_dates,
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value=last_date,
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label="Select date",
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interactive=True
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)
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papers_dd = gr.Dropdown(
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[paper["title"] for paper in last_papers],
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value=selected_paper["title"],
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label="Select paper title",
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interactive=True
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)
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date_dd.input(
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get_papers,
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date_dd,
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papers_dd
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)
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title = gr.Markdown(f"# {selected_paper['title']}")
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summary = gr.Markdown(f"{selected_paper['summary']}", elem_classes=["small-font"])
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gr.Markdown("## Auto generated Questions & Answers")
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# 1
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with gr.Column(elem_classes=["group"], visible=True) as q_0:
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basic_q_0 = gr.Markdown(f"## π {selected_paper['0_question']}")
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basic_q_eli5_0 = gr.Markdown(f"βͺ **(ELI5)** {selected_paper['0_answers:eli5']}", elem_classes=["small-font"])
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basic_q_expert_0 = gr.Markdown(f"βͺ **(Technical)** {selected_paper['0_answers:expert']}", elem_classes=["small-font"])
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with gr.Accordion("Additional question #1", open=False, elem_classes=["accordion"]) as aq_0_0:
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depth_q_0 = gr.Markdown(f"## ππ {selected_paper['0_additional_depth_q:follow up question']}")
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depth_q_eli5_0 = gr.Markdown(f"βͺ **(ELI5)** {selected_paper['0_additional_depth_q:answers:eli5']}", elem_classes=["small-font"])
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depth_q_expert_0 = gr.Markdown(f"βͺ **(Technical)** {selected_paper['0_additional_depth_q:answers:expert']}", elem_classes=["small-font"])
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with gr.Accordion("Additional question #2", open=False, elem_classes=["accordion"]) as aq_0_1:
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breath_q_0 = gr.Markdown(f"## ππ {selected_paper['0_additional_breath_q:follow up question']}")
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breath_q_eli5_0 = gr.Markdown(f"βͺ **(ELI5)** {selected_paper['0_additional_breath_q:answers:eli5']}", elem_classes=["small-font"])
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breath_q_expert_0 = gr.Markdown(f"βͺ **(Technical)** {selected_paper['0_additional_breath_q:answers:expert']}", elem_classes=["small-font"])
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# 2
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with gr.Column(elem_classes=["group"], visible=True) as q_1:
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basic_q_1 = gr.Markdown(f"## π {selected_paper['1_question']}")
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basic_q_eli5_1 = gr.Markdown(f"βͺ **(ELI5)** {selected_paper['1_answers:eli5']}", elem_classes=["small-font"])
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basic_q_expert_1 = gr.Markdown(f"βͺ **(Technical)** {selected_paper['1_answers:expert']}", elem_classes=["small-font"])
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with gr.Accordion("Additional question #1", open=False, elem_classes=["accordion"]) as aq_1_0:
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depth_q_1 = gr.Markdown(f"## ππ {selected_paper['1_additional_depth_q:follow up question']}")
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depth_q_eli5_1 = gr.Markdown(f"βͺ **(ELI5)** {selected_paper['1_additional_depth_q:answers:eli5']}", elem_classes=["small-font"])
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depth_q_expert_1 = gr.Markdown(f"βͺ **(Technical)** {selected_paper['1_additional_depth_q:answers:expert']}", elem_classes=["small-font"])
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with gr.Accordion("Additional question #2", open=False, elem_classes=["accordion"]) as aq_1_1:
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breath_q_1 = gr.Markdown(f"## ππ {selected_paper['1_additional_breath_q:follow up question']}")
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breath_q_eli5_1 = gr.Markdown(f"βͺ **(ELI5)** {selected_paper['1_additional_breath_q:answers:eli5']}", elem_classes=["small-font"])
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breath_q_expert_1 = gr.Markdown(f"βͺ **(Technical)** {selected_paper['1_additional_breath_q:answers:expert']}", elem_classes=["small-font"])
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# 3
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with gr.Column(elem_classes=["group"], visible=True) as q_2:
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basic_q_2 = gr.Markdown(f"## π {selected_paper['2_question']}")
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basic_q_eli5_2 = gr.Markdown(f"βͺ **(ELI5)** {selected_paper['2_answers:eli5']}", elem_classes=["small-font"])
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basic_q_expert_2 = gr.Markdown(f"βͺ **(Technical)** {selected_paper['2_answers:expert']}", elem_classes=["small-font"])
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with gr.Accordion("Additional question #1", open=False, elem_classes=["accordion"]) as aq_2_0:
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depth_q_2 = gr.Markdown(f"## ππ {selected_paper['2_additional_depth_q:follow up question']}")
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depth_q_eli5_2 = gr.Markdown(f"βͺ **(ELI5)** {selected_paper['2_additional_depth_q:answers:eli5']}", elem_classes=["small-font"])
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depth_q_expert_2 = gr.Markdown(f"βͺ **(Technical)** {selected_paper['2_additional_depth_q:answers:expert']}", elem_classes=["small-font"])
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with gr.Accordion("Additional question #2", open=False, elem_classes=["accordion"]) as aq_2_1:
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breath_q_2 = gr.Markdown(f"## ππ {selected_paper['2_additional_breath_q:follow up question']}")
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breath_q_eli5_2 = gr.Markdown(f"βͺ **(ELI5)** {selected_paper['2_additional_breath_q:answers:eli5']}", elem_classes=["small-font"])
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breath_q_expert_2 = gr.Markdown(f"βͺ **(Technical)** {selected_paper['2_additional_breath_q:answers:expert']}", elem_classes=["small-font"])
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+
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papers_dd.input(
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set_paper,
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[date_dd, papers_dd],
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[
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title, summary,
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basic_q_0, basic_q_eli5_0, basic_q_expert_0,
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depth_q_0, depth_q_eli5_0, depth_q_expert_0,
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breath_q_0, breath_q_eli5_0, breath_q_expert_0,
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+
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basic_q_1, basic_q_eli5_1, basic_q_expert_1,
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depth_q_1, depth_q_eli5_1, depth_q_expert_1,
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breath_q_1, breath_q_eli5_1, breath_q_expert_1,
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+
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basic_q_2, basic_q_eli5_2, basic_q_expert_2,
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depth_q_2, depth_q_eli5_2, depth_q_expert_2,
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breath_q_2, breath_q_eli5_2, breath_q_expert_2
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]
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)
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demo.launch(share=True)
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