Refactor UI layout in create_interface function to use tabs for better organization and accessibility of content

app.py

@@ -551,253 +551,258 @@ def create_interface():
         display: none;
     }"""
     ) as demo:
-        gr.HTML(
-            """
-            <div style="text-align: center; margin-bottom: 1rem">
-                <h1>Inkling</h1>
-                <p>Discover papers with deep conceptual connections to your research</p>
-                <p>An experiment in AI-assisted research discovery and insight generation</p>
-            </div>
-        """
-        )
-
-        with gr.Accordion(label="Instructions and Privacy Policy", open=False):
-            gr.Markdown(
+        with gr.Tabs():
+            with gr.Tab("Home"):
+                gr.HTML(
+                    """
+                    <div style="text-align: center; margin-bottom: 1rem">
+                        <h1>Inkling</h1>
+                        <p>Discover papers with deep conceptual connections to your research</p>
+                        <p>An experiment in AI-assisted research discovery and insight generation</p>
+                    </div>
                 """
-                This tool helps you uncover research papers with **deep, meaningful connections** to your ideas.
-                It uses AI to go beyond keyword or semantic similarity — analyzing how papers relate **conceptually** and **contextually**, 
-                even when the surface topics differ.
-
-                The focus is on surfacing *novel insights* — connections that may not be obvious at a glance, 
-                but could **spark new perspectives**, **deepen understanding**, or **highlight relationships that might otherwise be overlooked**.
+                )
 
-                It’s designed to act more like a research collaborator than a search engine — helping you explore conceptual bridges and 
-                unexpected pathways in the literature.
+                with gr.Accordion(label="Instructions and Privacy Policy", open=False):
+                    gr.Markdown(
+                        """
+                        This tool helps you uncover research papers with **deep, meaningful connections** to your ideas.
+                        It uses AI to go beyond keyword or semantic similarity — analyzing how papers relate **conceptually** and **contextually**, 
+                        even when the surface topics differ.
 
-                Please ask any questions or provide feedback on the tool to help us improve it by starting a discussion on
-                the [Community Tab](https://huggingface.co/spaces/nomadicsynth/inkling/discussions).
+                        The focus is on surfacing *novel insights* — connections that may not be obvious at a glance, 
+                        but could **spark new perspectives**, **deepen understanding**, or **highlight relationships that might otherwise be overlooked**.
 
-                **Privacy Policy**: Each query and the results returned will be logged for research and development purposes.
-                Additionally, the abstract or research description you provide will be included in any feedback
-                you submit and may be used to improve the model, and published in a public dataset.
-                Please ensure that you have the right to share this information.
-                By submitting a query and/or feedback, you agree to the use of this information for research purposes.
-                Do not include personally identifiable, proprietary, or sensitive information.
-                """
-            )
-            gr.Markdown(
-                """
-                1. **Enter Abstract**: Paste an abstract or describe your research question or idea in the text box.
-                2. **Find Related Papers**: Click the button to explore conceptually related research.
-                3. **Select a Paper**: Click on a row in the results table to view more details.
-                4. **Analyze Connection**: Click the analysis button to explore the potential connection between the papers.
-                5. **Insight Analysis**: Review the model’s reasoning about how and why these papers may relate meaningfully.
-                """
-            )
+                        It’s designed to act more like a research collaborator than a search engine — helping you explore conceptual bridges and 
+                        unexpected pathways in the literature.
 
-        abstract_input = gr.Textbox(
-            label="Paper Abstract or Description",
-            placeholder="Paste an abstract or describe research details...",
-            lines=8,
-            key="abstract",
-        )
-        search_btn = gr.Button("Find Related Papers", variant="primary")
-
-        # Store full paper data
-        paper_data_state = gr.State([])
-
-        # Store query abstract
-        query_abstract_state = gr.State("")
-
-        # Store selected paper
-        selected_paper_state = gr.State(None)
-
-        # Use Dataframe for results
-        results_df = gr.Dataframe(
-            headers=["Title", "Authors", "Categories", "Date", "Match Score"],
-            datatype=["markdown", "markdown", "str", "date", "str"],
-            latex_delimiters=latex_delimiters,
-            label="Related Papers",
-            interactive=False,
-            wrap=False,
-            line_breaks=False,
-            column_widths=["40%", "20%", "20%", "10%", "10%", "0%"],  # Hide ID column
-            key="results",
-        )
+                        Please ask any questions or provide feedback on the tool to help us improve it by starting a discussion on
+                        the [Community Tab](https://huggingface.co/spaces/nomadicsynth/inkling/discussions).
 
-        with gr.Row():
-            with gr.Column(scale=1):
-                paper_details_output = gr.Markdown(
-                    value="# Paper Details",
-                    label="Paper Details",
-                    latex_delimiters=latex_delimiters,
-                    show_copy_button=True,
-                    key="paper_details",
-                )
-                analyze_btn = gr.Button("Analyze Connection", variant="primary", visible=False)
-                with gr.Accordion(label="Feedback and Flagging", open=True, visible=False) as paper_feedback_accordion:
-                    gr.Markdown(
-                        """
-                        Please provide feedback on the relevance of this paper to your input.
-                        This helps us improve how well the system identifies meaningful research connections.
+                        **Privacy Policy**: Each query and the results returned will be logged for research and development purposes.
+                        Additionally, the abstract or research description you provide will be included in any feedback
+                        you submit and may be used to improve the model, and published in a public dataset.
+                        Please ensure that you have the right to share this information.
+                        By submitting a query and/or feedback, you agree to the use of this information for research purposes.
+                        Do not include personally identifiable, proprietary, or sensitive information.
                         """
                     )
-                    paper_feedback = gr.Radio(
-                        ["👍 Good Match", "👎 Poor Match"],
-                        label="Is this paper meaningfully related to your query?",
-                    )
-                    paper_expert = gr.Checkbox(label="I am an expert in this field", value=False)
-                    paper_comment = gr.Textbox(label="Additional feedback on this match (optional)")
-                    flag_paper_btn = gr.Button("Submit Paper Feedback")
-
-            with gr.Column(scale=1):
-                analysis_output = gr.Markdown(
-                    value="# Connection Analysis",
-                    label="Connection Analysis",
-                    latex_delimiters=latex_delimiters,
-                    show_copy_button=True,
-                    key="analysis_output",
-                )
-                with gr.Accordion(
-                    label="Feedback and Flagging", open=True, visible=False
-                ) as analysis_feedback_accordion:
                     gr.Markdown(
                         """
-                        This connection analysis was generated by an AI model trained to reason about conceptual links between research papers.
-                        If you find the explanation helpful, unclear, or off-base, your feedback will help refine the model’s reasoning process.
+                        1. **Enter Abstract**: Paste an abstract or describe your research question or idea in the text box.
+                        2. **Find Related Papers**: Click the button to explore conceptually related research.
+                        3. **Select a Paper**: Click on a row in the results table to view more details.
+                        4. **Analyze Connection**: Click the analysis button to explore the potential connection between the papers.
+                        5. **Insight Analysis**: Review the model’s reasoning about how and why these papers may relate meaningfully.
                         """
                     )
-                    analysis_feedback = gr.Radio(
-                        ["👍 Helpful", "👎 Not Helpful"],
-                        label="Was this explanation useful in understanding the connection?",
-                    )
-                    analysis_expert = gr.Checkbox(label="I am an expert in this field", value=False)
-                    analysis_comment = gr.Textbox(label="Additional feedback on the analysis (optional)")
-                    flag_analysis_btn = gr.Button("Submit Analysis Feedback")
-
-        # Hidden UI elements for API endpoint
-        abstract_input_hidden = gr.Textbox(visible=False, label="Abstract Input", key="abstract_hidden")
-        synergistic_papers_output = gr.Textbox(
-            visible=False, label="Synergistic Papers", key="synergistic_papers_output"
-        )
-        search_btn_hidden = gr.Button(visible=False, key="search_hidden")
-
-        # API endpoint for find_synergistic_papers
-        search_btn_hidden.click(
-            format_search_results_json,
-            inputs=[abstract_input_hidden],
-            outputs=[synergistic_papers_output],
-            api_name="find_synergistic_papers",
-        )
 
-        # Set up logging directories
-        flagged_paper_matches_path = data_path + "/flagged_paper_matches"
-        flagged_analyses_path = data_path + "/flagged_analyses"
-        os.makedirs(flagged_paper_matches_path, exist_ok=True)
-        os.makedirs(flagged_analyses_path, exist_ok=True)
+                abstract_input = gr.Textbox(
+                    label="Paper Abstract or Description",
+                    placeholder="Paste an abstract or describe research details...",
+                    lines=8,
+                    key="abstract",
+                )
+                search_btn = gr.Button("Find Related Papers", variant="primary")
 
-        # Set up loggers
-        paper_match_logger.setup(
-            [abstract_input, paper_details_output, paper_feedback, paper_expert, paper_comment],
-            flagged_paper_matches_path,
-        )
-        analysis_logger.setup(
-            [
-                abstract_input,
-                paper_details_output,
-                analysis_output,
-                analysis_feedback,
-                analysis_expert,
-                analysis_comment,
-            ],
-            flagged_analyses_path,
-        )
+                # Store full paper data
+                paper_data_state = gr.State([])
 
-        # Display paper details when row is selected
-        def on_select(evt: gr.SelectData, papers, query):
-            selected_index = evt.index[0]  # Get the row index
-            selected = papers[selected_index]
-
-            # Format paper details
-            details_md = format_paper_as_markdown(selected)
-
-            return details_md, selected
-
-        # Connect search button to the search function
-        search_btn.click(
-            format_search_results,
-            inputs=[abstract_input],
-            outputs=[results_df, paper_data_state],
-            api_name="search",
-        ).then(
-            lambda x: x,  # Identity function to pass through the abstract
-            inputs=[abstract_input],
-            outputs=[query_abstract_state],
-            api_name=False,
-        ).then(
-            lambda: None,  # Reset selected paper
-            outputs=[selected_paper_state],
-            api_name=False,
-        ).then(
-            lambda: (
-                gr.update(visible=False),
-                gr.update(visible=False),
-                gr.update(visible=False),
-            ),  # Hide analyze button and feedback accordions
-            outputs=[analyze_btn, paper_feedback_accordion, analysis_feedback_accordion],
-            api_name=False,
-        ).then(
-            lambda: ("# Paper Details", "# Synergy Analysis"),  # Clear previous outputs
-            outputs=[paper_details_output, analysis_output],
-            api_name=False,
-        )
+                # Store query abstract
+                query_abstract_state = gr.State("")
 
-        # Use built-in select event from Dataframe
-        results_df.select(
-            on_select,
-            inputs=[paper_data_state, query_abstract_state],
-            outputs=[paper_details_output, selected_paper_state],
-            api_name=False,
-        ).then(
-            lambda: (gr.update(visible=True), gr.update(visible=True)),  # Show analyze button and feedback accordion
-            outputs=[analyze_btn, paper_feedback_accordion],
-            api_name=False,
-        )
+                # Store selected paper
+                selected_paper_state = gr.State(None)
 
-        # Connect analyze button to run analysis
-        analyze_btn.click(
-            analyse_abstracts,
-            inputs=[query_abstract_state, selected_paper_state],
-            outputs=[analysis_output],
-            show_progress_on=[paper_details_output, analysis_output],
-            api_name=False,
-        ).then(
-            lambda: gr.update(visible=True),  # Show feedback accordion
-            outputs=[analysis_feedback_accordion],
-            api_name=False,
-        )
+                # Use Dataframe for results
+                results_df = gr.Dataframe(
+                    headers=["Title", "Authors", "Categories", "Date", "Match Score"],
+                    datatype=["markdown", "markdown", "str", "date", "str"],
+                    latex_delimiters=latex_delimiters,
+                    label="Related Papers",
+                    interactive=False,
+                    wrap=False,
+                    line_breaks=False,
+                    column_widths=["40%", "20%", "20%", "10%", "10%", "0%"],  # Hide ID column
+                    key="results",
+                )
 
-        # Add flagging handlers
-        flag_paper_btn.click(
-            lambda *args: paper_match_logger.flag(list(args)),
-            inputs=[abstract_input, paper_details_output, paper_feedback, paper_expert, paper_comment],
-            preprocess=False,
-            api_name=False,
-        )
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        paper_details_output = gr.Markdown(
+                            value="# Paper Details",
+                            label="Paper Details",
+                            latex_delimiters=latex_delimiters,
+                            show_copy_button=True,
+                            key="paper_details",
+                        )
+                        analyze_btn = gr.Button("Analyze Connection", variant="primary", visible=False)
+                        with gr.Accordion(label="Feedback and Flagging", open=True, visible=False) as paper_feedback_accordion:
+                            gr.Markdown(
+                                """
+                                Please provide feedback on the relevance of this paper to your input.
+                                This helps us improve how well the system identifies meaningful research connections.
+                                """
+                            )
+                            paper_feedback = gr.Radio(
+                                ["👍 Good Match", "👎 Poor Match"],
+                                label="Is this paper meaningfully related to your query?",
+                            )
+                            paper_expert = gr.Checkbox(label="I am an expert in this field", value=False)
+                            paper_comment = gr.Textbox(label="Additional feedback on this match (optional)")
+                            flag_paper_btn = gr.Button("Submit Paper Feedback")
+
+                    with gr.Column(scale=1):
+                        analysis_output = gr.Markdown(
+                            value="# Connection Analysis",
+                            label="Connection Analysis",
+                            latex_delimiters=latex_delimiters,
+                            show_copy_button=True,
+                            key="analysis_output",
+                        )
+                        with gr.Accordion(
+                            label="Feedback and Flagging", open=True, visible=False
+                        ) as analysis_feedback_accordion:
+                            gr.Markdown(
+                                """
+                                This connection analysis was generated by an AI model trained to reason about conceptual links between research papers.
+                                If you find the explanation helpful, unclear, or off-base, your feedback will help refine the model’s reasoning process.
+                                """
+                            )
+                            analysis_feedback = gr.Radio(
+                                ["👍 Helpful", "👎 Not Helpful"],
+                                label="Was this explanation useful in understanding the connection?",
+                            )
+                            analysis_expert = gr.Checkbox(label="I am an expert in this field", value=False)
+                            analysis_comment = gr.Textbox(label="Additional feedback on the analysis (optional)")
+                            flag_analysis_btn = gr.Button("Submit Analysis Feedback")
+
+                # Hidden UI elements for API endpoint
+                abstract_input_hidden = gr.Textbox(visible=False, label="Abstract Input", key="abstract_hidden")
+                synergistic_papers_output = gr.Textbox(
+                    visible=False, label="Synergistic Papers", key="synergistic_papers_output"
+                )
+                search_btn_hidden = gr.Button(visible=False, key="search_hidden")
+
+                # API endpoint for find_synergistic_papers
+                search_btn_hidden.click(
+                    format_search_results_json,
+                    inputs=[abstract_input_hidden],
+                    outputs=[synergistic_papers_output],
+                    api_name="find_synergistic_papers",
+                )
 
-        flag_analysis_btn.click(
-            lambda *args: analysis_logger.flag(list(args)),
-            inputs=[
-                abstract_input,
-                paper_details_output,
-                analysis_output,
-                analysis_feedback,
-                analysis_expert,
-                analysis_comment,
-            ],
-            preprocess=False,
-            api_name=False,
-        )
+                # Set up logging directories
+                flagged_paper_matches_path = data_path + "/flagged_paper_matches"
+                flagged_analyses_path = data_path + "/flagged_analyses"
+                os.makedirs(flagged_paper_matches_path, exist_ok=True)
+                os.makedirs(flagged_analyses_path, exist_ok=True)
+
+                # Set up loggers
+                paper_match_logger.setup(
+                    [abstract_input, paper_details_output, paper_feedback, paper_expert, paper_comment],
+                    flagged_paper_matches_path,
+                )
+                analysis_logger.setup(
+                    [
+                        abstract_input,
+                        paper_details_output,
+                        analysis_output,
+                        analysis_feedback,
+                        analysis_expert,
+                        analysis_comment,
+                    ],
+                    flagged_analyses_path,
+                )
+
+                # Display paper details when row is selected
+                def on_select(evt: gr.SelectData, papers, query):
+                    selected_index = evt.index[0]  # Get the row index
+                    selected = papers[selected_index]
+
+                    # Format paper details
+                    details_md = format_paper_as_markdown(selected)
+
+                    return details_md, selected
+
+                # Connect search button to the search function
+                search_btn.click(
+                    format_search_results,
+                    inputs=[abstract_input],
+                    outputs=[results_df, paper_data_state],
+                    api_name="search",
+                ).then(
+                    lambda x: x,  # Identity function to pass through the abstract
+                    inputs=[abstract_input],
+                    outputs=[query_abstract_state],
+                    api_name=False,
+                ).then(
+                    lambda: None,  # Reset selected paper
+                    outputs=[selected_paper_state],
+                    api_name=False,
+                ).then(
+                    lambda: (
+                        gr.update(visible=False),
+                        gr.update(visible=False),
+                        gr.update(visible=False),
+                    ),  # Hide analyze button and feedback accordions
+                    outputs=[analyze_btn, paper_feedback_accordion, analysis_feedback_accordion],
+                    api_name=False,
+                ).then(
+                    lambda: ("# Paper Details", "# Synergy Analysis"),  # Clear previous outputs
+                    outputs=[paper_details_output, analysis_output],
+                    api_name=False,
+                )
+
+                # Use built-in select event from Dataframe
+                results_df.select(
+                    on_select,
+                    inputs=[paper_data_state, query_abstract_state],
+                    outputs=[paper_details_output, selected_paper_state],
+                    api_name=False,
+                ).then(
+                    lambda: (gr.update(visible=True), gr.update(visible=True)),  # Show analyze button and feedback accordion
+                    outputs=[analyze_btn, paper_feedback_accordion],
+                    api_name=False,
+                )
+
+                # Connect analyze button to run analysis
+                analyze_btn.click(
+                    analyse_abstracts,
+                    inputs=[query_abstract_state, selected_paper_state],
+                    outputs=[analysis_output],
+                    show_progress_on=[paper_details_output, analysis_output],
+                    api_name=False,
+                ).then(
+                    lambda: gr.update(visible=True),  # Show feedback accordion
+                    outputs=[analysis_feedback_accordion],
+                    api_name=False,
+                )
+
+                # Add flagging handlers
+                flag_paper_btn.click(
+                    lambda *args: paper_match_logger.flag(list(args)),
+                    inputs=[abstract_input, paper_details_output, paper_feedback, paper_expert, paper_comment],
+                    preprocess=False,
+                    api_name=False,
+                )
+
+                flag_analysis_btn.click(
+                    lambda *args: analysis_logger.flag(list(args)),
+                    inputs=[
+                        abstract_input,
+                        paper_details_output,
+                        analysis_output,
+                        analysis_feedback,
+                        analysis_expert,
+                        analysis_comment,
+                    ],
+                    preprocess=False,
+                    api_name=False,
+                )
+
+            with gr.Tab("About"):
+                gr.Markdown(value=open("README.md", "r").read(), label="About")
 
     return demo