import streamlit as st
import pandas as pd

# CSS样式
st.markdown("""
<style>
h1 {
    font-size: 2.5em;  /* 标题字体大小 */
}
.stDataFrame {
    font-family: Helvetica;
}
.dataframe th, .dataframe td {
    width: auto;
    min-width: 500px; 
}
</style>
""", unsafe_allow_html=True)

# 标题
st.title('🏆AEOLLM Leaderboard')

# 描述
st.markdown("""
This leaderboard is used to show the performance of the **automatic evaluation methods of LLMs** submitted by the **AEOLLM team** on four tasks:
- Dialogue Generation (DG)
- Text Expansion (TE)
- Summary Generation (SG)
- Non-Factoid QA (NFQA)
            
Details of AEOLLLM can be found at the link: [https://aeollm.github.io/](https://aeollm.github.io/)
""", unsafe_allow_html=True)
# 创建示例数据

# teamId 唯一标识码
DG = {
    "teamId": ["baseline1", "baseline2", "baseline3", "baseline4"],
    "methods": ["chatglm3-6b", "baichuan2-13b", "chatglm-pro", "gpt-4o-mini"],
    "accuracy": [0.5806, 0.5483, 0.6001, 0.6472],
    "kendall's tau": [0.3243, 0.1739, 0.3042, 0.4167],
    "spearman": [0.3505, 0.1857, 0.3264, 0.4512]
}

df1 = pd.DataFrame(DG)
for col in df1.select_dtypes(include=['float64', 'int64']).columns:
    df1[col] = df1[col].apply(lambda x: f"{x:.4f}")

TE = {
    "teamId": ["baseline1", "baseline2", "baseline3", "baseline4"],
    "methods": ["chatglm3-6b", "baichuan2-13b", "chatglm-pro", "gpt-4o-mini"],
    "accuracy": [0.5107, 0.5050, 0.5461, 0.5581],
    "kendall's tau": [0.1281, 0.0635, 0.2716, 0.3864],
    "spearman": [0.1352, 0.0667, 0.2867, 0.4157]
}
df2 = pd.DataFrame(TE)
for col in df2.select_dtypes(include=['float64', 'int64']).columns:
    df2[col] = df2[col].apply(lambda x: f"{x:.4f}")

SG = {
    "teamId": ["baseline1", "baseline2", "baseline3", "baseline4"],
    "methods": ["chatglm3-6b", "baichuan2-13b", "chatglm-pro", "gpt-4o-mini"],
    "accuracy": [0.6504, 0.6014, 0.7162, 0.7441],
    "kendall's tau": [0.3957, 0.2688, 0.5092, 0.5001],
    "spearman": [0.4188, 0.2817, 0.5403, 0.5405],
}
df3 = pd.DataFrame(SG)
for col in df3.select_dtypes(include=['float64', 'int64']).columns:
    df3[col] = df3[col].apply(lambda x: f"{x:.4f}")

NFQA = {
    "teamId": ["baseline1", "baseline2", "baseline3", "baseline4"],
    "methods": ["chatglm3-6b", "baichuan2-13b", "chatglm-pro", "gpt-4o-mini"],
    "accuracy": [0.5935, 0.5817, 0.7000, 0.7203],
    "kendall's tau": [0.2332, 0.2389, 0.4440, 0.4235],
    "spearman": [0.2443, 0.2492, 0.4630, 0.4511]
}
df4 = pd.DataFrame(NFQA)
for col in df4.select_dtypes(include=['float64', 'int64']).columns:
    df4[col] = df4[col].apply(lambda x: f"{x:.4f}")

# 创建标签页
tab1, tab2, tab3, tab4 = st.tabs(["DG", "TE", "SG", "NFQA"])

with tab1:
    st.markdown("""Task: Dialogue Generation; Dataset: DialyDialog""", unsafe_allow_html=True)
    st.dataframe(df1, use_container_width=True)

with tab2:
    st.markdown("""Task: Text Expansion; Dataset: WritingPrompts""", unsafe_allow_html=True)
    st.dataframe(df2, use_container_width=True)

with tab3:
    st.markdown("""Task: Summary Generation; Dataset: Xsum""", unsafe_allow_html=True)
    st.dataframe(df3, use_container_width=True)

with tab4:
    st.markdown("""Task: Non-Factoid QA; Dataset: NF_CATS""", unsafe_allow_html=True)
    st.dataframe(df4, use_container_width=True)