import streamlit as st import pandas as pd # ─── Page config ────────────────────────────────────────────────────────────── st.set_page_config(page_title="ExpertLongBench Leaderboard", layout="wide") # ─── Load data ──────────────────────────────────────────────────────────────── @st.cache_data def load_data(path="src/models.json"): df = pd.read_json(path, lines=True) score_cols = [f"T{i}" for i in range(1, 12)] df["Avg"] = df[score_cols].mean(axis=1).round(1) # Compute rank per column (1 = best) for col in score_cols + ["Avg"]: df[f"{col}_rank"] = df[col].rank(ascending=False, method="min").astype(int) return df df = load_data() # Precompute max ranks for color scaling score_cols = [f"T{i}" for i in range(1, 12)] + ["Avg"] max_ranks = {col: df[f"{col}_rank"].max() for col in score_cols} # ─── Tabs ────────────────────────────────────────────────────────────────────── tab1, tab2 = st.tabs(["Leaderboard", "Benchmark Details"]) with tab1: st.markdown("**Leaderboard:** higher scores shaded green; best models bolded.") # Build raw HTML table cols = ["Model"] + [f"T{i}" for i in range(1,12)] + ["Avg"] html = "
{col} | " for col in cols) + "|
---|---|
{val} | " else: rank = int(row[f"{col}_rank"]) norm = 1 - (rank - 1) / ((max_ranks[col] - 1) or 1) # interpolate green (182,243,182) → white (255,255,255) r = int(255 - norm*(255-182)) g = int(255 - norm*(255-243)) b = 255 bold = "font-weight:bold;" if rank == 1 else "" style = f"background-color:rgb({r},{g},{b}); padding:6px; {bold}" html += f"{val} | " html += "