import streamlit as st
import pandas as pd
from PIL import Image
import base64
from io import BytesIO

# ─── Page config ──────────────────────────────────────────────────────────────
st.set_page_config(page_title="ExpertLongBench Leaderboard", layout="wide")


logo_image = Image.open("src/ExpertLongBench.png")

# Display logo
buffered = BytesIO()
logo_image.save(buffered, format="PNG")
img_data = base64.b64encode(buffered.getvalue()).decode("utf-8")

st.markdown(
    f"""
    <div class="logo-container" style="display:flex; justify-content: center;">
        <img src="data:image/png;base64,{img_data}" style="width:50%; max-width:700px;"/>
    </div>
    """,
    unsafe_allow_html=True
)

st.markdown(
    '''
    <div class="header">
        <br/>
        <p style="font-size:22px;">
        ExpertLongBench: Benchmarking Language Models on Expert-Level Long-Form Generation with Structured Checklists
        </p>
        <p style="font-size:20px;">
             📑 <a href="">Paper</a> | 💻 <a href="">GitHub</a> | <strong>K</strong> <a href="">Kaggle</a> <add links later>
            ⚙️ <strong>Version</strong>: <strong>V1</strong> | <strong># Models</strong>: 12 | Updated: <strong>May 2025</strong>
        </p>
    </div>
    ''',
    unsafe_allow_html=True
)
# ─── 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 = "<table style='border-collapse:collapse; width:100%; font-size:14px;'>"
    # header
    html += "<tr>" + "".join(f"<th style='padding:6px;'>{col}</th>" for col in cols) + "</tr>"
    # rows
    for _, row in df.iterrows():
        html += "<tr>"
        for col in cols:
            val = row[col]
            if col == "Model":
                html += f"<td style='padding:6px; text-align:left;'>{val}</td>"
            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"<td style='{style}'>{val}</td>"
        html += "</tr>"
    html += "</table>"
    st.markdown(html, unsafe_allow_html=True)

with tab2:
    st.markdown("## Abstract")
    st.write(
        "<add final abstract here>"
    )
    st.markdown("## Pipeline")
    st.write(
        "<add final pipeline figure here>"
    )