app.py

from utils import *
import utils_v2 as v2

global data_component

def update_table(query, min_size, max_size, selected_tasks=None):
    df = get_df()
    filtered_df = search_and_filter_models(df, query, min_size, max_size)
    if selected_tasks and len(selected_tasks) > 0:
        selected_columns = BASE_COLS + selected_tasks
        filtered_df = filtered_df[selected_columns]
    return filtered_df

def update_table_v2(query, min_size, max_size):
    df = v2.get_df()
    filtered_df = v2.search_and_filter_models(df, query, min_size, max_size)
    return filtered_df

with gr.Blocks() as block:
    gr.Markdown(LEADERBOARD_INTRODUCTION)
    
    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        # Table 1, the main leaderboard of overall scores
        with gr.TabItem("📊 MMEB (V2)", elem_id="qa-tab-table1", id=1):
            with gr.Row():
                with gr.Accordion("Citation", open=False):
                    citation_button2 = gr.Textbox(
                        value=v2.CITATION_BUTTON_TEXT,
                        label=CITATION_BUTTON_LABEL,
                        elem_id="citation-button",
                        lines=10,
                    )
            gr.Markdown(v2.TABLE_INTRODUCTION)

            with gr.Row():
                search_bar2 = gr.Textbox(
                    placeholder="Search models...",
                    show_label=False,
                    elem_id="search-bar"
                )
            
            df = get_df()
            df2 = v2.get_df()
            min_size2, max_size2 = get_size_range(df2)

            with gr.Row():
                min_size_slider2 = gr.Slider(
                    minimum=min_size2,
                    maximum=max_size2,
                    value=min_size2,
                    step=0.1,
                    label="Minimum number of parameters (B)",
                )
                max_size_slider2 = gr.Slider(
                    minimum=min_size2,
                    maximum=max_size2,
                    value=max_size2,
                    step=0.1,
                    label="Maximum number of parameters (B)",
                )
            data_component2 = gr.components.Dataframe(
                value=df2[v2.COLUMN_NAMES],
                headers=v2.COLUMN_NAMES,
                type="pandas",
                datatype=v2.DATA_TITLE_TYPE,
                interactive=False,
                visible=True,
                max_height=2400, 
            )
            
            refresh_button2 = gr.Button("Refresh")
            
            def update_with_tasks_v2(*args):
                return update_table_v2(*args)

            search_bar2.change(
                fn=update_with_tasks_v2, 
                inputs=[search_bar2, min_size_slider2, max_size_slider2], 
                outputs=data_component2
            )
            min_size_slider2.change(
                fn=update_with_tasks_v2, 
                inputs=[search_bar2, min_size_slider2, max_size_slider2], 
                outputs=data_component2
            )
            max_size_slider2.change(
                fn=update_with_tasks_v2, 
                inputs=[search_bar2, min_size_slider2, max_size_slider2], 
                outputs=data_component2
            )
            refresh_button2.click(fn=v2.refresh_data, outputs=data_component2)


        def get_special_processed_df2():
            """Temporary special processing to merge v1 scores with v2 image scores.
            Will be removed later after v2 is fully adopted."""
            df2_i = df2[v2.COLUMN_NAMES_I]
            df1 = df.rename(columns={'V1-Overall': 'Image-Overall'})
            df1 = df1[v2.BASE_COLS + v2.SUB_TASKS_I + ['Image-Overall']]
            combined_df = pd.concat([df1, df2_i], ignore_index=True)
            for task in v2.TASKS_I:
                combined_df[task] = combined_df[task].apply(lambda score: '-' if pd.isna(score) else score)
            combined_df = v2.rank_models(combined_df, 'Image-Overall')
            return combined_df[v2.COLUMN_NAMES_I]

        # table 2, image scores only
        with gr.TabItem("🖼️ Image (Previously MMEB-V1)", elem_id="qa-tab-table1", id=2):
            gr.Markdown(v2.TABLE_INTRODUCTION_I)
            df2_i = get_special_processed_df2()
            data_component3 = gr.components.Dataframe(
                value=df2_i,
                headers=v2.COLUMN_NAMES_I,
                type="pandas",
                datatype=v2.DATA_TITLE_TYPE_I,
                interactive=False,
                visible=True,
                max_height=2400, 
            )

        # table 3, video scores only
        with gr.TabItem("💽 Video", elem_id="qa-tab-table1", id=3):
            gr.Markdown(v2.TABLE_INTRODUCTION_V)
            data_component4 = gr.components.Dataframe(
                value=v2.rank_models(df2[v2.COLUMN_NAMES_V], 'Video-Overall'),
                headers=v2.COLUMN_NAMES_V,
                type="pandas",
                datatype=v2.DATA_TITLE_TYPE_V,
                interactive=False,
                visible=True,
                max_height=2400, 
            )

        # table 4, visual document scores only
        with gr.TabItem("📑 Visual Doc", elem_id="qa-tab-table1", id=4):
            gr.Markdown(v2.TABLE_INTRODUCTION_D)
            data_component5 = gr.components.Dataframe(
                value=v2.rank_models(df2[v2.COLUMN_NAMES_D], 'Visdoc-Overall'),
                headers=v2.COLUMN_NAMES_D,
                type="pandas",
                datatype=v2.DATA_TITLE_TYPE_D,
                interactive=False,
                visible=True,
                max_height=2400, 
            )

        # table 5
        with gr.TabItem("📝 About", elem_id="qa-tab-table2", id=5):
            gr.Markdown(LEADERBOARD_INFO, elem_classes="markdown-text")
            gr.Image("overview.png", width=900, label="Dataset Overview")

        # table 6
        with gr.TabItem("🚀 Submit here! ", elem_id="submit-tab", id=6):
            with gr.Row():
                gr.Markdown(SUBMIT_INTRODUCTION, elem_classes="markdown-text")


block.launch(share=True)