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import streamlit as st
import pandas as pd
import numpy as np
from PIL import Image
import base64
from io import BytesIO
# --- Page config ---
st.set_page_config(page_title="VeriFact Leaderboard", layout="wide")
# --- Load images ---
@st.cache_data
def load_image(path):
return Image.open(path)
logo = load_image("factrbench.png")
chart = load_image("test.png")
# Display logo
buf = BytesIO()
logo.save(buf, format="PNG")
logo_b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
st.markdown(f"""
<div style="text-align:center; margin-bottom:20px;">
<img src="data:image/png;base64,{logo_b64}" style="width:50%; max-width:700px;"/>
</div>
""", unsafe_allow_html=True)
# Header
st.markdown("""
<div style="text-align:center;">
<p style="font-size:22px;">
VERIFACT: Enhancing Long-Form Factuality Evaluation...
</p>
<p style="font-size:20px;">
# 📑 <a href="">Paper</a> | 💻 <a href="">GitHub</a> | 🤗 <a href="">HuggingFace</a> |
⚙️ <strong>Version</strong>: <strong>V1</strong> | <strong># Models</strong>: 11 | Updated: <strong>April 2025</strong>
</p>
</div>
""", unsafe_allow_html=True)
# --- Load data ---
@st.cache_data
def load_data(path="models.json"):
df = pd.read_json(path, lines=True)
df["Avg"] = df[[f"T{i}" for i in range(1,12)]].mean(axis=1).round(1)
# Compute rank per column
for col in [f"T{i}" for i in range(1,12)] + ["Avg"]:
df[f"{col}_rank"] = df[col].rank(ascending=False, method="min").astype(int)
return df
df = load_data()
# --- Tabs ---
tab1, tab2 = st.tabs(["Leaderboard", "Benchmark Details"])
with tab1:
st.markdown("**Leaderboard:** Higher scores shaded green; best models bolded.")
# Build HTML table
cols = ["Model"] + [f"T{i}" for i in range(1,12)] + ["Avg"]
max_ranks = {col: df[f"{col}_rank"].max() for col in cols if col!="Model"}
html = "<table style='border-collapse:collapse; width:100%;'>"
# header
html += "<tr>" + "".join(f"<th style='padding:4px;'>{c}</th>" for c in cols) + "</tr>"
# rows
for _, row in df.iterrows():
html += "<tr>"
for c in cols:
val = row[c] if c!="Model" else row[c]
if c=="Model":
html += f"<td style='padding:4px;text-align:left;'>{val}</td>"
else:
# color gradient
rank = row[f"{c}_rank"]
norm = 1 - (rank-1)/(max_ranks[c]-1 or 1)
# interpolate green-white
r = int(255 - norm*(255-182))
g = int(255 - norm*(255-243))
b = 255
style = f"background-color:rgb({r},{g},{b}); padding:4px;"
bold = "font-weight:bold;" if rank==1 else ""
html += f"<td style='{style}{bold}'>{val}</td>"
html += "</tr>"
html += "</table>"
st.markdown(html, unsafe_allow_html=True)
with tab2:
buf2 = BytesIO()
chart.save(buf2, format="PNG")
chart_b64 = base64.b64encode(buf2.getvalue()).decode("utf-8")
st.markdown(f"""
<div style="text-align:center;">
<img src="data:image/png;base64,{chart_b64}" style="width:65%;"/>
</div>
""", unsafe_allow_html=True)
st.markdown("### What is VERIFACT?")
st.write("VERIFACT is a factuality evaluation framework...")
st.markdown("### What is FACTRBENCH?")
st.write("FACTRBENCH is the first benchmark for long-form factuality evaluation...")
st.markdown("### Key Findings")
st.write("VERIFACT outperforms prior methods [...]")
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