import streamlit as st
import pandas as pd

st.set_page_config(page_title="Cyber Benchmark Hub: SECQA Leaderboard", layout="wide")

st.title("Cyber Benchmark Hub: SECQA Leaderboard")
st.markdown("#### [View the SECQA Dataset](https://huggingface.co/datasets/zefang-liu/secqa)")

with st.sidebar:
    st.image("https://cdn.prod.website-files.com/630f558f2a15ca1e88a2f774/631f1436ad7a0605fecc5e15_Logo.svg", use_container_width=True)
    st.markdown("[Priam.ai](https://www.priam.ai/)")
    st.divider()
    
    dataset_categories = ["Multiple Choice"]
    selected_category = st.selectbox("Select Dataset Category", dataset_categories, index=0)
    
    datasets_by_category = {
        "Multiple Choice": ["secQA"],
    }
    dataset_choice = st.selectbox("Select Dataset", datasets_by_category[selected_category], index=0)
    
    st.divider()
    st.header("Filters & Options")
    dataset_version = st.radio("Select Dataset Version", ["v1", "v2"])
    # For filtering the leaderboard by model type
    # Note: The available model types will come from the CSV, once loaded.
    # We'll load the CSV later and then update this filter accordingly.
    source_filter_placeholder = st.empty()  # placeholder for source filter after data is loaded
    
    st.markdown("---")
    st.header("Test Parameters")
    test_params = pd.DataFrame({
        "Value": [0, 1, 0, 1, 0]
    }, index=["Temperature", "n", "Presence Penalty", "Top_p", "Frequency Penalty"])
    st.table(test_params)

# Determine file path based on dataset choice.
# For now, if dataset_choice is "secQA", we use "Benchmark.csv"
if dataset_choice == "secQA":
    file_path = "Benchmark.csv"  # Ensure this file is uploaded in your Hugging Face Space
else:
    file_path = "Benchmark.csv"  # Placeholder: update with actual file paths for future datasets

# Function to load and clean CSV data
@st.cache_data
def load_data(file_path):
    df = pd.read_csv(file_path)
    
    # Remove any unnamed columns (caused by trailing commas)
    df = df.loc[:, ~df.columns.str.contains('Unnamed', na=False)]
    
    # Standardize column names
    df.columns = df.columns.str.strip()
    df.rename(columns={
        "model name": "Model",
        "source": "Type",
        "v1 metric": "V1 Accuracy",
        "v2 metric": "V2 Accuracy"
    }, inplace=True)
    
    # Convert percentage strings to floats (e.g., "100%" → 1.0)
    for col in ["V1 Accuracy", "V2 Accuracy"]:
        df[col] = df[col].astype(str).str.replace("%", "").str.strip()
        df[col] = pd.to_numeric(df[col], errors='coerce') / 100
    
    return df

# Load dataset
df = load_data(file_path)

# Update the source filter with the actual options from the data
source_filter = source_filter_placeholder.multiselect(
    "Select Model Type",
    options=df["Type"].unique().tolist(),
    default=df["Type"].unique().tolist()
)

# Apply filtering based on the sidebar selections
df_filtered = df[df["Type"].isin(source_filter)] if source_filter else df

# Choose the correct metric version and compute Accuracy
df_filtered["Accuracy"] = df_filtered["V1 Accuracy"] if dataset_version == "v1" else df_filtered["V2 Accuracy"]
df_filtered = df_filtered[["Model", "Type", "Accuracy"]].dropna()  # Drop rows with errors

# Sort by Accuracy descending
df_filtered = df_filtered.sort_values("Accuracy", ascending=False).reset_index(drop=True)

# Compute dense ranking so that models with equal accuracy share the same rank
df_filtered['Rank'] = df_filtered['Accuracy'].rank(method='dense', ascending=False).astype(int)
df_filtered = df_filtered[['Rank', 'Model', 'Type', 'Accuracy']]

# Use columns to display leaderboard and model details side-by-side
col1, col2 = st.columns([2, 1])

with col1:
    st.subheader(f"Leaderboard for {dataset_choice.upper()} Version {dataset_version}")
    st.dataframe(df_filtered.style.hide(axis='index'))

with col2:
    st.subheader("Model Details")
    selected_model = st.selectbox("Select a Model", df_filtered["Model"].tolist())
    model_details = df_filtered[df_filtered["Model"] == selected_model].iloc[0]
    st.write(f"**Model:** {model_details['Model']}")
    st.write(f"**Type:** {model_details['Type']}")
    st.write(f"**Accuracy:** {model_details['Accuracy']:.2%}")
    st.write(f"**Rank:** {model_details['Rank']}")

# Footer
st.markdown("---")
st.info("More dataset benchmarks will be added to this hub in the future.")