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Update src/streamlit_app.py
Browse files- src/streamlit_app.py +30 -68
src/streamlit_app.py
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import streamlit as st
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import pandas as pd
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import numpy as np
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from PIL import Image
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import base64
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from io import BytesIO
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#
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st.set_page_config(page_title="VeriFact Leaderboard", layout="wide")
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#
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@st.cache_data
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def load_image(path):
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return Image.open(path)
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# logo = load_image("factrbench.png")
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# chart = load_image("test.png")
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# Display logo
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buf = BytesIO()
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logo.save(buf, format="PNG")
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logo_b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
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st.markdown(f"""
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<div style="text-align:center; margin-bottom:20px;">
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<img src="data:image/png;base64,{logo_b64}" style="width:50%; max-width:700px;"/>
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</div>
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""", unsafe_allow_html=True)
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# Header
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st.markdown("""
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<div style="text-align:center;">
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<p style="font-size:22px;">
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VERIFACT: Enhancing Long-Form Factuality Evaluation...
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</p>
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<p style="font-size:20px;">
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# π <a href="">Paper</a> | π» <a href="">GitHub</a> | π€ <a href="">HuggingFace</a> |
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βοΈ <strong>Version</strong>: <strong>V1</strong> | <strong># Models</strong>: 11 | Updated: <strong>April 2025</strong>
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</p>
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</div>
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""", unsafe_allow_html=True)
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# --- Load data ---
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@st.cache_data
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def load_data(path="models.json"):
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df = pd.read_json(path, lines=True)
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df[f"{col}_rank"] = df[col].rank(ascending=False, method="min").astype(int)
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return df
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df = load_data()
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#
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tab1, tab2 = st.tabs(["Leaderboard", "Benchmark Details"])
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with tab1:
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st.markdown("**Leaderboard:**
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# Build HTML table
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cols = ["Model"] + [f"T{i}" for i in range(1,12)] + ["Avg"]
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html = "<table style='border-collapse:collapse; width:100%;'>"
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# header
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html += "<tr>" + "".join(f"<th style='padding:
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# rows
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for _, row in df.iterrows():
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html += "<tr>"
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for
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val = row[
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if
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html += f"<td style='padding:
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else:
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# interpolate green-white
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r = int(255 - norm*(255-182))
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g = int(255 - norm*(255-243))
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b = 255
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html += f"<td style='{style}
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html += "</tr>"
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html += "</table>"
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st.markdown(html, unsafe_allow_html=True)
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with tab2:
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<img src="data:image/png;base64,{chart_b64}" style="width:65%;"/>
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</div>
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""", unsafe_allow_html=True)
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import streamlit as st
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import pandas as pd
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# βββ Page config ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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st.set_page_config(page_title="VeriFact Leaderboard", layout="wide")
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# βββ Load data ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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@st.cache_data
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def load_data(path="models.json"):
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df = pd.read_json(path, lines=True)
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score_cols = [f"T{i}" for i in range(1, 12)]
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df["Avg"] = df[score_cols].mean(axis=1).round(1)
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# Compute rank per column (1 = best)
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for col in score_cols + ["Avg"]:
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df[f"{col}_rank"] = df[col].rank(ascending=False, method="min").astype(int)
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return df
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df = load_data()
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# Precompute max ranks for color scaling
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score_cols = [f"T{i}" for i in range(1, 12)] + ["Avg"]
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max_ranks = {col: df[f"{col}_rank"].max() for col in score_cols}
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# βββ Tabs ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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tab1, tab2 = st.tabs(["Leaderboard", "Benchmark Details"])
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with tab1:
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st.markdown("**Leaderboard:** higher scores shaded green; best models bolded.")
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# Build raw HTML table
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cols = ["Model"] + [f"T{i}" for i in range(1,12)] + ["Avg"]
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html = "<table style='border-collapse:collapse; width:100%; font-size:14px;'>"
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# header
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html += "<tr>" + "".join(f"<th style='padding:6px;'>{col}</th>" for col in cols) + "</tr>"
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# rows
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for _, row in df.iterrows():
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html += "<tr>"
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for col in cols:
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val = row[col]
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if col == "Model":
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html += f"<td style='padding:6px; text-align:left;'>{val}</td>"
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else:
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rank = int(row[f"{col}_rank"])
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norm = 1 - (rank - 1) / ((max_ranks[col] - 1) or 1)
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# interpolate green (182,243,182) β white (255,255,255)
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r = int(255 - norm*(255-182))
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g = int(255 - norm*(255-243))
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b = 255
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bold = "font-weight:bold;" if rank == 1 else ""
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style = f"background-color:rgb({r},{g},{b}); padding:6px; {bold}"
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html += f"<td style='{style}'>{val}</td>"
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html += "</tr>"
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html += "</table>"
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st.markdown(html, unsafe_allow_html=True)
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with tab2:
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st.markdown("### Benchmark Details")
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st.write(
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"VERIFACT is a factuality evaluation framework for longβform LLM outputs. "
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"FACTRBENCH provides reference fact sets and external evidence across realβworld prompts."
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)
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