app.py

import gradio as gr
import pandas as pd

# Define the columns for the UGI Leaderboard
UGI_COLS = [
    '#P', 'Model', 'UGI 🏆', 'Willingness👍', 'QuActivities', 'Internet', 'CrimeStats', 'Stories/Jokes', 'Pol Contro'
]

# Load the leaderboard data from a CSV file
def load_leaderboard_data(csv_file_path):
    try:
        df = pd.read_csv(csv_file_path)
        # Create hyperlinks in the Model column using HTML <a> tags with inline CSS for styling
        df['Model'] = df.apply(lambda row: f'<a href="{row["Link"]}" target="_blank" style="color: blue; text-decoration: none;">{row["Model"]}</a>' if pd.notna(row["Link"]) else row["Model"], axis=1)
        # Drop the 'Link' column as it's no longer needed
        df.drop(columns=['Link'], inplace=True)
        return df
    except Exception as e:
        print(f"Error loading CSV file: {e}")
        return pd.DataFrame(columns=UGI_COLS)  # Return an empty dataframe with the correct columns

# Update the leaderboard table based on the search query and parameter range filters
def update_table(df: pd.DataFrame, query: str, param_ranges: dict) -> pd.DataFrame:
    filtered_df = df
    if any(param_ranges.values()):
        conditions = []
        for param_range, checked in param_ranges.items():
            if checked:
                if param_range == '~1.5':
                    conditions.append((filtered_df['Params'] < 2.5))
                elif param_range == '~3':
                    conditions.append(((filtered_df['Params'] >= 2.5) & (filtered_df['Params'] < 6)))
                elif param_range == '~7':
                    conditions.append(((filtered_df['Params'] >= 6) & (filtered_df['Params'] < 9.5)))
                elif param_range == '~13':
                    conditions.append(((filtered_df['Params'] >= 9.5) & (filtered_df['Params'] < 16)))
                elif param_range == '~20':
                    conditions.append(((filtered_df['Params'] >= 16) & (filtered_df['Params'] < 28)))
                elif param_range == '~34':
                    conditions.append(((filtered_df['Params'] >= 28) & (filtered_df['Params'] < 40)))
                elif param_range == '~50':
                    conditions.append(((filtered_df['Params'] >= 40) & (filtered_df['Params'] < 60)))
                elif param_range == '~70+':
                    conditions.append((filtered_df['Params'] >= 60))
        
        if all(param_ranges.values()):
            conditions.append(filtered_df['Params'].isna())
        
        filtered_df = filtered_df[pd.concat(conditions, axis=1).any(axis=1)]
    
    if query:
        filtered_df = filtered_df[filtered_df.apply(lambda row: query.lower() in row.to_string().lower(), axis=1)]
    
    return filtered_df[UGI_COLS]  # Return only the columns defined in UGI_COLS

# Define the Gradio interface
demo = gr.Blocks()

with demo:
    gr.Markdown("## UGI Leaderboard", elem_classes="text-lg")
    with gr.Column():
        with gr.Row():
            search_bar = gr.Textbox(placeholder=" 🔍 Search for a model...", show_label=False)
        with gr.Row():
            gr.Markdown("Model sizes (in billions of parameters)", elem_classes="text-sm")
            param_range_1 = gr.Checkbox(label="~1.5", value=False)
            param_range_2 = gr.Checkbox(label="~3", value=False)
            param_range_3 = gr.Checkbox(label="~7", value=False)
            param_range_4 = gr.Checkbox(label="~13", value=False)
            param_range_5 = gr.Checkbox(label="~20", value=False)
            param_range_6 = gr.Checkbox(label="~34", value=False)
            param_range_7 = gr.Checkbox(label="~50", value=False)
            param_range_8 = gr.Checkbox(label="~70+", value=False)
    
    # Load the initial leaderboard data
    leaderboard_df = load_leaderboard_data("ugi-leaderboard-data.csv")
    
    # Define the datatypes for each column, setting 'Model' column to 'html'
    datatypes = ['html' if col == 'Model' else 'str' for col in UGI_COLS]
    
    leaderboard_table = gr.Dataframe(
        value=leaderboard_df[UGI_COLS],
        datatype=datatypes,  # Specify the datatype for each column
        interactive=False,  # Set to False to make the leaderboard non-editable
        visible=True,
        elem_classes="text-sm"  # Increase the font size of the leaderboard data
    )

    # Define the search and filter functionality
    inputs = [
        search_bar,
        param_range_1,
        param_range_2,
        param_range_3,
        param_range_4,
        param_range_5,
        param_range_6,
        param_range_7,
        param_range_8
    ]
    
    outputs = leaderboard_table
    
    search_bar.change(
        fn=lambda query, r1, r2, r3, r4, r5, r6, r7, r8: update_table(leaderboard_df, query, {
            '~1.5': r1,
            '~3': r2,
            '~7': r3,
            '~13': r4,
            '~20': r5,
            '~34': r6,
            '~50': r7,
            '~70+': r8
        }),
        inputs=inputs,
        outputs=outputs
    )
    
    for param_range in inputs[1:]:
        param_range.change(
            fn=lambda query, r1, r2, r3, r4, r5, r6, r7, r8: update_table(leaderboard_df, query, {
                '~1.5': r1,
                '~3': r2,
                '~7': r3,
                '~13': r4,
                '~20': r5,
                '~34': r6,
                '~50': r7,
                '~70+': r8
            }),
            inputs=inputs,
            outputs=outputs
        )

# Launch the Gradio app
demo.launch()