src/leaderboard.py

import pandas as pd
from typing import List, Optional
from .data_loader import DataLoader

class Leaderboard:
    def __init__(self, data_loader: DataLoader):
        self.data_loader = data_loader

    def update_leaderboard(self, metric: str = "Average", top_k: int = 25,
                          model_filter: str = "", open_source_filter: str = "All",
                          year_filter: str = "All", category_filter: str = "All",
                          sort_mode: str = "Auto",
                          selected_metrics: Optional[List[str]] = None) -> pd.DataFrame:
        df = self.data_loader.filter_data(model_filter, open_source_filter,
                                         year_filter, category_filter)
        if df.empty:
            return pd.DataFrame()

        if sort_mode == "Auto":
            ascending = False
        elif sort_mode == "Ascending (low → high)":
            ascending = True
        else:
            ascending = False

        if metric in df.columns:
            df = df.sort_values(by=metric, ascending=ascending)

        df = df.head(top_k).reset_index(drop=True)
        df.insert(0, "Rank", range(1, len(df) + 1))

        base_cols = ["Rank", "Model", "Category"]
        if selected_metrics is None:
            selected_metrics = ["Average"]

        display_cols = base_cols.copy()
        for m in selected_metrics:
            if m in df.columns and m not in display_cols:
                display_cols.append(m)

        # Add optional link columns if they exist
        link_cols = []
        if "Paper" in df.columns:
            link_cols.append("Paper")
        if "Code" in df.columns:
            link_cols.append("Code")
        display_cols.extend(link_cols)

        result_df = df[display_cols].copy()

        # Format numeric values
        for col in result_df.columns:
            if col not in ["Rank", "Model", "Category", "Paper", "Code", "Open Source", "Year"]:
                result_df[col] = result_df[col].apply(
                    lambda x: f"{x:.4f}" if pd.notna(x) and isinstance(x, (int, float)) else "-"
                )

        # Do NOT generate HTML links; keep as plain text
        # (If you want links, you can add them here, but for sorting they are fine as text)

        return result_df