gradio_tabs.py

from graph_visualizations import *
from text_visualization import WordCloudExtractor
import logging
from functools import partial
import gradio as gr


def display_representations(repos_df, repo, representation1, representation2):
    repo_data = repos_df[repos_df["repo_name"] == repo]
    tasks = repo_data["tasks"].iloc[0]
    logging.info(f"repo_data: {repo_data}")
    text1 = (
        repo_data[repo_data["representation"] == representation1]["text"].iloc[0]
        if not repo_data[repo_data["representation"] == representation1].empty
        else "No data available"
    )
    text2 = (
        repo_data[repo_data["representation"] == representation2]["text"].iloc[0]
        if not repo_data[repo_data["representation"] == representation2].empty
        else "No data available"
    )

    return tasks, text1, text2


def get_representation_wordclouds(representations, repos_df):
    wordclouds = dict()
    for representation in representations:
        texts = list(repos_df[repos_df["representation"] == representation]["text"])
        wordclouds[representation] = WordCloudExtractor().extract_wordcloud_image(texts)
    return wordclouds


def load_embeddings_description():
    return


def display_wordclouds(representation_types, repos_df):
    wordcloud_dict = get_representation_wordclouds(representation_types, repos_df)
    gr.Markdown("# Wordclouds")
    gr.Gallery(
        [
            (wordcloud, representation_type)
            for representation_type, wordcloud in wordcloud_dict.items()
        ],
        columns=[3],
        rows=[4],
        height=300,
    )


def setup_repository_representations_tab(repos_df, repos, representation_types):

    gr.Markdown("# Comparing repository representations")
    gr.Markdown("Select a repository and two representation types to compare them.")
    with gr.Row():
        repo = gr.Dropdown(choices=repos, label="Repository", value=repos[0])
        representation1 = gr.Dropdown(
            choices=representation_types, label="Representation 1", value="readme"
        )
        representation2 = gr.Dropdown(
            choices=representation_types,
            label="Representation 2",
            value="code2doc_generated_readme",
        )

    displayed_tasks = gr.Markdown(elem_id="tasks")
    with gr.Row():
        with gr.Column(
            elem_id="column1",
            variant="panel",
            scale=1,
            min_width=300,
        ):
            text1 = gr.Markdown()
        with gr.Column(
            elem_id="column2",
            variant="panel",
            scale=1,
            min_width=300,
        ):
            text2 = gr.Markdown()

    def update_representations(repo, representation1, representation2):
        tasks, text1_content, text2_content = display_representations(
            repos_df, repo, representation1, representation2
        )
        tasks_display = tasks if tasks is not None else "No tasks available"
        return (
            "## Repository PapersWithCode tasks:\n" + " ".join(tasks_display),
            f"### Representation 1: {representation1}\n\n{text1_content}",
            f"### Representation 2: {representation2}\n\n{text2_content}",
        )

    # Initial call to populate textboxes with default values
    displayed_tasks.value, text1.value, text2.value = update_representations(
        repos[0], "readme", "code2doc_generated_readme"
    )

    for component in [repo, representation1, representation2]:
        component.change(
            fn=update_representations,
            inputs=[repo, representation1, representation2],
            outputs=[displayed_tasks, text1, text2],
        )

    display_wordclouds(representation_types, repos_df)


def setup_tasks_tab(descriptions, task_visualizations):

    gr.Markdown(descriptions["task_counts_description"])

    with gr.Row():
        min_task_counts_slider_all = gr.Slider(
            minimum=50,
            maximum=1000,
            value=150,
            step=50,
            label="Minimum Task Count (All Repositories)",
        )
        update_button = gr.Button("Update Plots")
        min_task_counts_slider_selected = gr.Slider(
            minimum=10,
            maximum=100,
            value=50,
            step=10,
            label="Minimum Task Count (Selected Repositories)",
        )
        update_selected_button = gr.Button("Update Plots")

    with gr.Row("Task Counts"):
        all_repos_tasks_plot = gr.Plot(label="All Repositories")
        selected_repos_tasks_plot = gr.Plot(label="Selected Repositories")

    update_button.click(
        fn=partial(task_visualizations.get_tasks_sunburst, which_df="all"),
        inputs=[min_task_counts_slider_all],
        outputs=[all_repos_tasks_plot],
    )

    update_selected_button.click(
        fn=partial(task_visualizations.get_tasks_sunburst, which_df="selected"),
        inputs=[min_task_counts_slider_selected],
        outputs=[selected_repos_tasks_plot],
    )


def setup_embeddings_tab(descriptions, embedding_visualizer):
    tab_elems = [
        gr.Markdown("## Tasks by area"),
        gr.Markdown(descriptions["intro"]),
        gr.Plot(embedding_visualizer.make_task_area_scatterplot()),
    ]

    embedding_plots = embedding_visualizer.make_embedding_plots(
        color_col="representation"
    )
    for plot_name in [
        "Basic representations",
        "Dependency graph based representations",
        "READMEs",
    ]:
        tab_elems.append(gr.Markdown(f"## {plot_name}"))
        if descriptions.get(plot_name):
            tab_elems.append(gr.Markdown(descriptions[plot_name]))
        tab_elems.append(gr.Plot(embedding_plots[plot_name]))
    gr.Column(tab_elems)


def setup_graph_tab():
    gr.Markdown("# Dependency Graph Visualization")
    gr.Markdown("Select a repository to visualize its dependency graph.")
    graphs_dict = init_graphs()
    repo_names = list(graphs_dict.keys())

    def plot_selected_repo(repo_name, layout_type, *edge_type_checkboxes):
        # Convert checkbox values to selected edge types
        edge_types = (
            get_available_edge_types(graphs_dict[repo_name])
            if repo_name in graphs_dict
            else []
        )
        selected_edge_types = set()
        for i, is_selected in enumerate(edge_type_checkboxes):
            if is_selected and i < len(edge_types):
                selected_edge_types.add(edge_types[i])

        fig, stats = visualize_graph(
            repo_name, graphs_dict, layout_type, selected_edge_types
        )
        return fig, stats

    def update_edge_checkboxes(repo_name):
        """Update edge type checkboxes when repository changes"""
        if repo_name not in graphs_dict:
            return [gr.Checkbox(visible=False)] * 8

        edge_types = get_available_edge_types(graphs_dict[repo_name])
        checkboxes = []

        # Create checkboxes for each edge type (up to 8)
        for i in range(8):
            if i < len(edge_types):
                edge_type = edge_types[i]
                # function-function should be unchecked by default
                default_value = edge_type != "function-function"
                checkboxes.append(
                    gr.Checkbox(label=edge_type, value=default_value, visible=True)
                )
            else:
                checkboxes.append(gr.Checkbox(visible=False))

        return checkboxes

    # Get initial edge types for the first repository
    initial_edge_types = []
    if repo_names:
        initial_edge_types = get_available_edge_types(graphs_dict[repo_names[0]])

    with gr.Row():
        with gr.Column(scale=1):
            repo_dropdown = gr.Dropdown(
                choices=repo_names,
                label="Select Repository",
                value=repo_names[0] if repo_names else None,
            )

            layout_dropdown = gr.Dropdown(
                choices=[
                    ("Spring Layout (Force-directed)", "spring"),
                    ("Circular Layout", "circular"),
                    ("Kamada-Kawai Layout", "kamada_kawai"),
                    ("Fruchterman-Reingold Layout", "fruchterman_reingold"),
                    ("Shell Layout", "shell"),
                    ("Spectral Layout", "spectral"),
                    ("Planar Layout", "planar"),
                ],
                label="Select Layout",
                value="spring",
            )

            gr.Markdown("### Edge Type Filters")
            gr.Markdown("Select which edge types to display:")

            # Create checkboxes for edge types with initial values
            edge_checkboxes = []
            for i in range(8):  # Support up to 8 edge types
                if i < len(initial_edge_types):
                    checkbox = gr.Checkbox(
                        label=initial_edge_types[i], value=True, visible=True
                    )
                else:
                    checkbox = gr.Checkbox(label=f"Edge Type {i+1}", visible=False)
                edge_checkboxes.append(checkbox)

            visualize_btn = gr.Button("Visualize Graph", variant="primary")

            stats_text = gr.Textbox(
                label="Graph Statistics", lines=6, interactive=False
            )

        with gr.Column(scale=2):
            graph_plot = gr.Plot(label="Interactive Dependency Graph")

    # Set up event handlers
    all_inputs = [repo_dropdown, layout_dropdown] + edge_checkboxes

    visualize_btn.click(
        fn=plot_selected_repo,
        inputs=all_inputs,
        outputs=[graph_plot, stats_text],
    )

    # Update checkboxes when repository changes
    repo_dropdown.change(
        fn=update_edge_checkboxes,
        inputs=[repo_dropdown],
        outputs=edge_checkboxes,
    )

    # Auto-visualize on dropdown change
    repo_dropdown.change(
        fn=plot_selected_repo,
        inputs=all_inputs,
        outputs=[graph_plot, stats_text],
    )

    # Auto-visualize on layout change
    layout_dropdown.change(
        fn=plot_selected_repo,
        inputs=all_inputs,
        outputs=[graph_plot, stats_text],
    )

    # Auto-visualize on checkbox changes
    for checkbox in edge_checkboxes:
        checkbox.change(
            fn=plot_selected_repo,
            inputs=all_inputs,
            outputs=[graph_plot, stats_text],
        )