def trulens_leaderboard(app_ids: Optional[List[str]] = None):
    """
    Render the leaderboard page.

    Args:

        app_ids List[str]: A list of application IDs (default is None)

    Example:
        ```python
        from trulens.core import streamlit as trulens_st

        trulens_st.trulens_leaderboard()
        ```
    """
    session = core_session.TruSession()

    lms = session.connector.db
    df, feedback_col_names = lms.get_records_and_feedback(app_ids=app_ids)
    feedback_defs = lms.get_feedback_defs()
    feedback_directions = {
        (
            row.feedback_json.get("supplied_name", "")
            or row.feedback_json["implementation"]["name"]
        ): row.feedback_json.get("higher_is_better", True)
        for _, row in feedback_defs.iterrows()
    }

    if df.empty:
        st.write("No records yet...")
        return

    df.sort_values(by="app_id", inplace=True)

    if df.empty:
        st.write("No records yet...")

    def get_data():
        return lms.get_records_and_feedback([])

    def get_apps():
        return list(lms.get_apps())

    records, feedback_col_names = get_data()
    records = records.sort_values(by="app_id")

    apps = get_apps()

    for app in apps:
        app_df = records.loc[records.app_id == app]
        if app_df.empty:
            continue
        app_json = app_df["app_json"].iloc[0]
        app_name = app_json["app_name"]
        app_version = app_json["app_version"]
        app_name_version = f"{app_name} - {app_version}"
        metadata = app_json.get("metadata")
        tags = app_json.get("tags")
        st.header(
            app_name_version,
            help=dashboard_components.draw_metadata_and_tags(metadata, tags),
        )
        app_feedback_col_names = [
            col_name
            for col_name in feedback_col_names
            if not app_df[col_name].isna().all()
        ]
        col1, col2, col3, col4, *feedback_cols = st_columns(
            5 + len(app_feedback_col_names)
        )
        latency_mean = (
            app_df["latency"]
            .apply(
                lambda td: td
                if td != legacy_migration.MIGRATION_UNKNOWN_STR
                else None
            )
            .mean()
        )

        col1.metric("Records", len(app_df))
        col2.metric(
            "Average Latency (Seconds)",
            (
                f"{text_utils.format_quantity(round(latency_mean, 5), precision=2)}"
                if not math.isnan(latency_mean)
                else "nan"
            ),
        )
        col3.metric(
            "Total Cost (USD)",
            f"${text_utils.format_quantity(round(sum(cost for cost in app_df.total_cost if cost is not None), 5), precision=2)}",
        )
        col4.metric(
            "Total Tokens",
            text_utils.format_quantity(
                sum(
                    tokens
                    for tokens in app_df.total_tokens
                    if tokens is not None
                ),
                precision=2,
            ),
        )

        for i, col_name in enumerate(app_feedback_col_names):
            mean = app_df[col_name].mean()

            st.write(
                dashboard_styles.stmetricdelta_hidearrow,
                unsafe_allow_html=True,
            )

            higher_is_better = feedback_directions.get(col_name, True)

            if "distance" in col_name:
                cat = dashboard_styles.CATEGORY.of_score(
                    mean, higher_is_better=higher_is_better, is_distance=True
                )
                feedback_cols[i].metric(
                    label=col_name,
                    value=f"{round(mean, 2)}",
                    delta_color="normal",
                )
            else:
                cat = dashboard_styles.CATEGORY.of_score(
                    mean, higher_is_better=higher_is_better
                )
                feedback_cols[i].metric(
                    label=col_name,
                    value=f"{round(mean, 2)}",
                    delta=f"{cat.icon} {cat.adjective}",
                    delta_color=(
                        "normal"
                        if cat.compare(
                            mean,
                            dashboard_styles.CATEGORY.PASS[
                                cat.direction
                            ].threshold,
                        )
                        else "inverse"
                    ),
                )

        st.markdown("""---""")

    Render the leaderboard page.

    Args:

        app_ids List[str]: A list of application IDs (default is None)

    Example:
        ```python
        from trulens.core import streamlit as trulens_st

        trulens_st.trulens_leaderboard()
        ```
    
src/dashboard/trulens/dashboard/streamlit.py