ui/leaderboard.py

import pandas as pd
import gradio as gr
from utils import load_leaderboard
import numpy as np
from huggingface_hub import snapshot_download


def make_clickable(url, name):
    return f'<a href="{url}" target="_blank">{name}</a>'

def render_info_html():
    info_text = "With the growing advent of machine-generated speech, the scientific community is responding with exciting resources " \
                 "to detect deep fakes. With research moving at such a rapid pace, it becomes challenging to keep track of generalizability " \
                 "of SOTA DF detection systems. This leaderboard thus presents a comprehensive benchmark of 10 SOTA speech antispoofing " \
                 "systems across 13 popular speech deep fake detection datasets."
    
    # HTML formatted info text
    return gr.Markdown(info_text)

def highlight_min(s, props=''):
    return np.where(s == np.nanmin(s.values), props, '')

def render_leader_board(leaderboard_df, model_citations):

    if not leaderboard_df.empty:
        # Convert model names and dataset names to clickable links
        # leaderboard_df['System'] = leaderboard_df['System'].apply(lambda x: make_clickable(model_citations.get(x, "#"), x))
        # leaderboard_df['Average'] = leaderboard_df.loc[:, ['In-the-wild','ASVSpoof2019','ASVSpoof2021LA','ASVSpoof2021DF','ASVSpoof2024-Dev','ASVSpoof2024-Eval','FakeOrReal','codecfake3','ADD2022','ADD2023','DFADD','LibriVoc','SONAR']].mean(axis=1)
        leaderboard_df.insert(1, 'Average', leaderboard_df.loc[:, ['In-the-wild','ASVSpoof2019','ASVSpoof2021LA','ASVSpoof2021DF','ASVSpoof2024-Dev','ASVSpoof2024-Eval','FakeOrReal','codecfake3','ADD2022','ADD2023','DFADD','LibriVoc','SONAR']].mean(axis=1))

        leaderboard_df = leaderboard_df.sort_values(by="Average", ascending=True).reset_index(drop=True)

        # Assign rank emojis 🥇🥈🥉
        leaderboard_df["System"] = leaderboard_df["System"].apply(lambda x: f"[{x}]({model_citations.get(x, '#')})")

        emojis = ["🥇", "🥈", "🥉"]

        leaderboard_df.System[0] = f"{emojis[0]} {leaderboard_df.System[0]}"
        leaderboard_df.System[1] = f"{emojis[1]} {leaderboard_df.System[1]}"
        leaderboard_df.System[2] = f"{emojis[2]} {leaderboard_df.System[2]}"
        temp = leaderboard_df.loc[:, ['System', 'Training Data', 'Num Parameters', 'Data Augmentation']]
        # styler = leaderboard_df.drop(columns=['Training Data', 'Num Parameters', 'Data Augmentation'], axis=1).style.highlight_min(color = 'lightgreen', axis = 0).format(precision=2)
        styler = (
        leaderboard_df
        .drop(columns=['Training Data', 'Num Parameters', 'Data Augmentation'], axis=1).style \
        .format(precision=2))
        styler.apply(highlight_min, props='color:green', axis=0)

        # styler['System'] = temp['System']
        # styler['Training Data'] = temp['Training Data']
        # styler['Num Parameters'] = temp['Num Parameters'] 
        # styler['Data Augmentation'] = temp['Data Augmentation']  
        return gr.Dataframe(styler, datatype=['markdown'] * 1 + ['number']  * 14)
    return gr.HTML(value="<p>No data available in the leaderboard.</p>")