Updated leaderboard

.gitignore

@@ -20,4 +20,5 @@ eval-*/
 logs/
 data/
 outputs/
-hf_cache/
+hf_cache/
+demos/

app.py

@@ -9,6 +9,7 @@ from loguru import logger
 import populate
 from about import LEADERBOARD_INTRODUCTION_TEXT, LEADERBOARD_TITLE
 from app_configs import DEFAULT_SELECTIONS, THEME
+from components.leaderboard import create_leaderboard_interface
 from components.quizbowl.bonus import BonusInterface
 from components.quizbowl.tossup import TossupInterface
 from components.typed_dicts import PipelineInterfaceDefaults, TossupInterfaceDefaults
@@ -55,18 +56,6 @@ def download_dataset_snapshot(repo_id, local_dir):
 download_dataset_snapshot(QUEUE_REPO, EVAL_REQUESTS_PATH)
 
 
-def fetch_tossup_leaderboard():
-    logger.info("Tossup leaderboard fetched...")
-    download_dataset_snapshot(RESULTS_REPO, EVAL_RESULTS_PATH)
-    return populate.get_tossups_leaderboard_df(EVAL_RESULTS_PATH, "tiny_eval")
-
-
-def fetch_bonus_leaderboard():
-    logger.info("Bonus leaderboard fetched...")
-    download_dataset_snapshot(RESULTS_REPO, EVAL_RESULTS_PATH)
-    return populate.get_bonuses_leaderboard_df(EVAL_RESULTS_PATH, "tiny_eval")
-
-
 def load_dataset(mode: str):
     if mode == "tossup":
         ds = datasets.load_dataset(PLAYGROUND_DATASET_NAMES["tossup"], split="eval")
@@ -155,30 +144,7 @@ if __name__ == "__main__":
                 leaderboard_timer = gr.Timer(LEADERBOARD_REFRESH_INTERVAL)
                 gr.Markdown("<a id='leaderboard' href='#leaderboard'>QANTA Leaderboard</a>")
                 gr.Markdown(LEADERBOARD_INTRODUCTION_TEXT)
-                refresh_btn = gr.Button("🔄 Refresh")
-
-                gr.Markdown("## 📚 Tossup Round Leaderboard")
-                tossup_leaderboard = gr.Dataframe(
-                    value=fetch_tossup_leaderboard,
-                    every=leaderboard_timer,
-                    headers=[c.name for c in fields(AutoEvalColumn)],
-                    datatype=[c.type for c in fields(AutoEvalColumn)],
-                    elem_id="tossup-table",
-                    interactive=False,
-                    visible=True,
-                )
-
-                gr.Markdown("## 📚 Bonus Round Leaderboard")
-                bonus_leaderboard = gr.Dataframe(
-                    value=fetch_bonus_leaderboard,
-                    every=leaderboard_timer,
-                    headers=[c.name for c in fields(AutoEvalColumn)],
-                    datatype=[c.type for c in fields(AutoEvalColumn)],
-                    elem_id="bonus-table",
-                )
-
-                refresh_btn.click(fn=fetch_tossup_leaderboard, inputs=[], outputs=tossup_leaderboard)
-                refresh_btn.click(fn=fetch_bonus_leaderboard, inputs=[], outputs=bonus_leaderboard)
+                create_leaderboard_interface(demo)
             with gr.Tab("❓ Help", id="help"):
                 with gr.Row():
                     with gr.Column():

shared/workflows

@@ -1 +1 @@
-Subproject commit 9d8bfae31f4db8b25165c950742d13e6c4e80de8
+Subproject commit a3347cef2dbbf020dcd01029efa4969e851b393a

src/components/leaderboard.py

@@ -0,0 +1,86 @@
+# This file is kept for reference only and is not used in the enhanced implementation
+# The actual implementation is in enhanced_leaderboard.py
+
+import gradio as gr
+import pandas as pd
+from gradio_leaderboard import Leaderboard
+
+import populate
+from envs import EVAL_RESULTS_PATH, LEADERBOARD_REFRESH_INTERVAL
+
+
+def fetch_tossup_leaderboard(style: bool = True):
+    # download_dataset_snapshot(RESULTS_REPO, EVAL_RESULTS_PATH)
+    df = populate.get_tossups_leaderboard_df(EVAL_RESULTS_PATH, "tiny_eval")
+
+    def colour_pos_neg(v):
+        """Return a CSS rule for the cell that called the function."""
+        if pd.isna(v):  # keep NaNs unstyled
+            return ""
+        return "color: green;" if v > 0 else "color: red;"
+
+    # Apply formatting and styling
+    styled_df = df.style.format(
+        {
+            "Avg Score ⬆️": "{:5.2f}",
+            "Buzz Accuracy": "{:>6.1%}",
+            "Buzz Position": "{:>6.2f}",
+            "Win Rate w/ Humans": "{:>6.1%}",
+            "Win Rate w/ Humans (Aggressive)": "{:>6.1%}",
+        }
+    ).map(colour_pos_neg, subset=["Avg Score ⬆️"])
+
+    return styled_df if style else df
+
+
+def fetch_bonus_leaderboard(style: bool = True):
+    # download_dataset_snapshot(RESULTS_REPO, EVAL_RESULTS_PATH)
+    df = populate.get_bonuses_leaderboard_df(EVAL_RESULTS_PATH, "tiny_eval")
+
+    # Apply formatting and styling
+    styled_df = df.style.format(
+        {
+            "Question Accuracy": "{:>6.1%}",
+            "Part Accuracy": "{:>6.1%}",
+        }
+    )
+
+    return styled_df if style else df
+
+
+def create_leaderboard_interface(app):
+    leaderboard_timer = gr.Timer(LEADERBOARD_REFRESH_INTERVAL)
+    refresh_btn = gr.Button("🔄 Refresh")
+
+    gr.Markdown("## 📚 Tossup Round Leaderboard")
+    tossup_df = fetch_tossup_leaderboard(style=False)
+    tossup_leaderboard = Leaderboard(
+        value=tossup_df,
+        search_columns=["Submission"],
+        datatype=["str", "number", "number", "number", "number", "number"],
+        elem_id="tossup-table",
+        interactive=False,  # Ensure it's not interactive
+    )
+
+    gr.Markdown("## 📚 Bonus Round Leaderboard")
+    bonus_df = fetch_bonus_leaderboard(style=False)
+    bonus_leaderboard = Leaderboard(
+        value=bonus_df,
+        search_columns=["Submission"],
+        datatype=["str", "number", "number"],
+        elem_id="bonus-table",
+        interactive=False,  # Ensure it's not interactive
+    )
+
+    gr.on(
+        triggers=[leaderboard_timer.tick, refresh_btn.click, app.load],
+        fn=fetch_tossup_leaderboard,
+        inputs=[],
+        outputs=tossup_leaderboard,
+    )
+    gr.on(
+        triggers=[leaderboard_timer.tick, refresh_btn.click, app.load],
+        fn=fetch_bonus_leaderboard,
+        inputs=[],
+        outputs=bonus_leaderboard,
+    )

src/display/custom_css.py

@@ -109,6 +109,9 @@ input[type=range][disabled] {
     font-size: 20px;
 }
 
+.table td .cell-wrap span {
+    white-space: pre;
+}
 
 .step-container {
     background-color: var(--card-bg-color);

src/leaderboard/gradio_leaderboard.py

@@ -0,0 +1,386 @@
+"""gr.Leaderboard() component"""
+
+from __future__ import annotations
+
+import warnings
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Union
+
+import pandas as pd
+import semantic_version
+from gradio.components import Component
+from gradio.data_classes import GradioModel
+from gradio.events import Events
+from pandas.api.types import (
+    is_bool_dtype,
+    is_numeric_dtype,
+    is_object_dtype,
+    is_string_dtype,
+)
+from pandas.io.formats.style import Styler
+
+
+@dataclass
+class SearchColumns:
+    primary_column: str
+    secondary_columns: Optional[List[str]]
+    label: Optional[str] = None
+    placeholder: Optional[str] = None
+
+
+@dataclass
+class SelectColumns:
+    default_selection: Optional[list[str]] = field(default_factory=list)
+    cant_deselect: Optional[list[str]] = field(default_factory=list)
+    allow: bool = True
+    label: Optional[str] = None
+    show_label: bool = True
+    info: Optional[str] = None
+
+
+@dataclass
+class ColumnFilter:
+    column: str
+    type: Literal["slider", "dropdown", "checkboxgroup", "boolean"] = None
+    default: Optional[Union[int, float, List[Tuple[str, str]]]] = None
+    choices: Optional[Union[int, float, List[Tuple[str, str]]]] = None
+    label: Optional[str] = None
+    info: Optional[str] = None
+    show_label: bool = True
+    min: Optional[Union[int, float]] = None
+    max: Optional[Union[int, float]] = None
+
+
+class DataframeData(GradioModel):
+    headers: List[str]
+    data: Union[List[List[Any]], List[Tuple[Any, ...]]]
+    metadata: Optional[Dict[str, Optional[List[Any]]]] = None
+
+
+class Leaderboard(Component):
+    """
+    This component displays a table of value spreadsheet-like component. Can be used to display data as an output component, or as an input to collect data from the user.
+    Demos: filter_records, matrix_transpose, tax_calculator, sort_records
+    """
+
+    EVENTS = [Events.change, Events.input, Events.select]
+
+    data_model = DataframeData
+
+    def __init__(
+        self,
+        value: pd.DataFrame | None = None,
+        *,
+        datatype: str | list[str] = "str",
+        search_columns: list[str] | SearchColumns | None = None,
+        select_columns: list[str] | SelectColumns | None = None,
+        filter_columns: list[str | ColumnFilter] | None = None,
+        bool_checkboxgroup_label: str | None = None,
+        hide_columns: list[str] | None = None,
+        latex_delimiters: list[dict[str, str | bool]] | None = None,
+        label: str | None = None,
+        show_label: bool | None = None,
+        every: float | None = None,
+        height: int = 500,
+        scale: int | None = None,
+        min_width: int = 160,
+        interactive: bool | None = None,
+        visible: bool = True,
+        elem_id: str | None = None,
+        elem_classes: list[str] | str | None = None,
+        render: bool = True,
+        wrap: bool = False,
+        line_breaks: bool = True,
+        column_widths: list[str | int] | None = None,
+    ):
+        """
+        Parameters:
+            value: Default value to display in the DataFrame. Must be a pandas DataFrame.
+            datatype: Datatype of values in sheet. Can be provided per column as a list of strings, or for the entire sheet as a single string. Valid datatypes are "str", "number", "bool", "date", and "markdown".
+            search_columns: See Configuration section of docs for details.
+            select_columns: See Configuration section of docs for details.
+            filter_columns: See Configuration section of docs for details.
+            bool_checkboxgroup_label: Label for the checkboxgroup filter for boolean columns.
+            hide_columns: List of columns to hide by default. They will not be displayed in the table but they can still be used for searching, filtering.
+            label: The label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.
+            latex_delimiters: A list of dicts of the form {"left": open delimiter (str), "right": close delimiter (str), "display": whether to display in newline (bool)} that will be used to render LaTeX expressions. If not provided, `latex_delimiters` is set to `[{ "left": "$$", "right": "$$", "display": True }]`, so only expressions enclosed in $$ delimiters will be rendered as LaTeX, and in a new line. Pass in an empty list to disable LaTeX rendering. For more information, see the [KaTeX documentation](https://katex.org/docs/autorender.html). Only applies to columns whose datatype is "markdown".
+            label: The label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.
+            show_label: if True, will display label.
+            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
+            height: The maximum height of the dataframe, specified in pixels if a number is passed, or in CSS units if a string is passed. If more rows are created than can fit in the height, a scrollbar will appear.
+            scale: relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.
+            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
+            interactive: if True, will allow users to edit the dataframe; if False, can only be used to display data. If not provided, this is inferred based on whether the component is used as an input or output.
+            visible: If False, component will be hidden.
+            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
+            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
+            render: If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.
+            wrap: If True, the text in table cells will wrap when appropriate. If False and the `column_width` parameter is not set, the column widths will expand based on the cell contents and the table may need to be horizontally scrolled. If `column_width` is set, then any overflow text will be hidden.
+            line_breaks: If True (default), will enable Github-flavored Markdown line breaks in chatbot messages. If False, single new lines will be ignored. Only applies for columns of type "markdown."
+            column_widths: An optional list representing the width of each column. The elements of the list should be in the format "100px" (ints are also accepted and converted to pixel values) or "10%". If not provided, the column widths will be automatically determined based on the content of the cells. Setting this parameter will cause the browser to try to fit the table within the page width.
+        """
+        if value is None:
+            raise ValueError("Leaderboard component must have a value set.")
+        self.wrap = wrap
+        self.headers = [str(s) for s in value.columns]
+        self.datatype = datatype
+        self.search_columns = self._get_search_columns(search_columns)
+        self.bool_checkboxgroup_label = bool_checkboxgroup_label
+        self.select_columns_config = self._get_select_columns(select_columns, value)
+        self.filter_columns = self._get_column_filter_configs(filter_columns, value)
+        self.raise_error_if_incorrect_config()
+
+        self.hide_columns = hide_columns or []
+        self.col_count = (len(self.headers), "fixed")
+        if isinstance(value, Styler):
+            self.row_count = (value.data.shape[0], "fixed")
+        else:
+            self.row_count = (value.shape[0], "fixed")
+
+        if latex_delimiters is None:
+            latex_delimiters = [{"left": "$$", "right": "$$", "display": True}]
+        self.latex_delimiters = latex_delimiters
+        self.height = height
+        self.line_breaks = line_breaks
+        self.column_widths = [w if isinstance(w, str) else f"{w}px" for w in (column_widths or [])]
+        super().__init__(
+            label=label,
+            every=every,
+            show_label=show_label,
+            scale=scale,
+            min_width=min_width,
+            interactive=interactive,
+            visible=visible,
+            elem_id=elem_id,
+            elem_classes=elem_classes,
+            render=render,
+            value=value,
+        )
+
+    def raise_error_if_incorrect_config(self):
+        for col in [self.search_columns.primary_column, *self.search_columns.secondary_columns]:
+            if col not in self.headers:
+                raise ValueError(f"Column '{col}' not found in the DataFrame headers.")
+        for col in self.select_columns_config.default_selection + self.select_columns_config.cant_deselect:
+            if col not in self.headers:
+                raise ValueError(f"Column '{col}' not found in the DataFrame headers.")
+        for col in [col.column for col in self.filter_columns]:
+            if col not in self.headers:
+                raise ValueError(f"Column '{col}' not found in the DataFrame headers.")
+
+    @staticmethod
+    def _get_best_filter_type(
+        column: str, value: pd.DataFrame
+    ) -> Literal["slider", "checkboxgroup", "dropdown", "checkbox"]:
+        if is_bool_dtype(value[column]):
+            return "checkbox"
+        if is_numeric_dtype(value[column]):
+            return "slider"
+        if is_string_dtype(value[column]) or is_object_dtype(value[column]):
+            return "checkboxgroup"
+        warnings.warn(
+            f"{column}'s type is not numeric or string, defaulting to checkboxgroup filter type.",
+            UserWarning,
+        )
+        return "checkboxgroup"
+
+    @staticmethod
+    def _get_column_filter_configs(columns: list[str | ColumnFilter] | None, value: pd.DataFrame) -> list[ColumnFilter]:
+        if columns is None:
+            return []
+        if not isinstance(columns, list):
+            raise ValueError("Columns must be a list of strings or ColumnFilter objects")
+        return [Leaderboard._get_column_filter_config(column, value) for column in columns]
+
+    @staticmethod
+    def _get_column_filter_config(column: str | ColumnFilter, value: pd.DataFrame):
+        column_name = column if isinstance(column, str) else column.column
+        best_filter_type = Leaderboard._get_best_filter_type(column_name, value)
+        min_val = None
+        max_val = None
+        if best_filter_type == "slider":
+            default = [
+                value[column_name].quantile(0.25),
+                value[column_name].quantile(0.70),
+            ]
+            min_val = value[column_name].min()
+            max_val = value[column_name].max()
+            choices = None
+        elif best_filter_type == "checkbox":
+            default = False
+            choices = None
+        else:
+            default = value[column_name].unique().tolist()
+            default = [(s, s) for s in default]
+            choices = default
+        if isinstance(column, ColumnFilter):
+            if column.type == "boolean":
+                column.type = "checkbox"
+            if not column.type:
+                column.type = best_filter_type
+            if column.default is None:
+                column.default = default
+            if not column.choices:
+                column.choices = choices
+            if min_val is not None and max_val is not None:
+                column.min = min_val
+                column.max = max_val
+            return column
+        if isinstance(column, str):
+            return ColumnFilter(
+                column=column,
+                type=best_filter_type,
+                default=default,
+                choices=choices,
+                min=min_val,
+                max=max_val,
+            )
+        raise ValueError(f"Columns {column} must be a string or a ColumnFilter object")
+
+    @staticmethod
+    def _get_search_columns(
+        search_columns: list[str] | SearchColumns | None,
+    ) -> SearchColumns:
+        if search_columns is None:
+            return SearchColumns(primary_column=None, secondary_columns=[])
+        if isinstance(search_columns, SearchColumns):
+            return search_columns
+        if isinstance(search_columns, list):
+            return SearchColumns(primary_column=search_columns[0], secondary_columns=search_columns[1:])
+        raise ValueError("search_columns must be a list of strings or a SearchColumns object")
+
+    @staticmethod
+    def _get_select_columns(
+        select_columns: list[str] | SelectColumns | None,
+        value: pd.DataFrame,
+    ) -> SelectColumns:
+        if select_columns is None:
+            return SelectColumns(allow=False)
+        if isinstance(select_columns, SelectColumns):
+            if not select_columns.default_selection:
+                select_columns.default_selection = value.columns.tolist()
+            return select_columns
+        if isinstance(select_columns, list):
+            return SelectColumns(default_selection=select_columns, allow=True)
+        raise ValueError("select_columns must be a list of strings or a SelectColumns object")
+
+    def get_config(self):
+        return {
+            "row_count": self.row_count,
+            "col_count": self.col_count,
+            "headers": self.headers,
+            "select_columns_config": self.select_columns_config,
+            **super().get_config(),
+        }
+
+    def preprocess(self, payload: DataframeData) -> pd.DataFrame:
+        """
+        Parameters:
+            payload: the uploaded spreadsheet data as an object with `headers` and `data` attributes
+        Returns:
+            Passes the uploaded spreadsheet data as a `pandas.DataFrame`, `numpy.array`, `polars.DataFrame`, or native 2D Python `list[list]` depending on `type`
+        """
+        import pandas as pd
+
+        if payload.headers is not None:
+            return pd.DataFrame(
+                [] if payload.data == [[]] else payload.data,
+                columns=payload.headers,
+            )
+        else:
+            return pd.DataFrame(payload.data)
+
+    def postprocess(self, value: pd.DataFrame) -> DataframeData:
+        """
+        Parameters:
+            value: Expects data any of these formats: `pandas.DataFrame`, `pandas.Styler`, `numpy.array`, `polars.DataFrame`, `list[list]`, `list`, or a `dict` with keys 'data' (and optionally 'headers'), or `str` path to a csv, which is rendered as the spreadsheet.
+        Returns:
+            the uploaded spreadsheet data as an object with `headers` and `data` attributes
+        """
+        import pandas as pd
+        from pandas.io.formats.style import Styler
+
+        if value is None:
+            return self.postprocess(pd.DataFrame({"column 1": []}))
+        if isinstance(value, (str, pd.DataFrame)):
+            if isinstance(value, str):
+                value = pd.read_csv(value)  # type: ignore
+            if len(value) == 0:
+                return DataframeData(
+                    headers=list(value.columns),  # type: ignore
+                    data=[[]],  # type: ignore
+                )
+            return DataframeData(
+                headers=list(value.columns),  # type: ignore
+                data=value.to_dict(orient="split")["data"],  # type: ignore
+            )
+        elif isinstance(value, Styler):
+            if semantic_version.Version(pd.__version__) < semantic_version.Version("1.5.0"):
+                raise ValueError(
+                    "Styler objects are only supported in pandas version 1.5.0 or higher. Please try: `pip install --upgrade pandas` to use this feature."
+                )
+            if self.interactive:
+                warnings.warn(
+                    "Cannot display Styler object in interactive mode. Will display as a regular pandas dataframe instead."
+                )
+            df: pd.DataFrame = value.data  # type: ignore
+            if len(df) == 0:
+                return DataframeData(
+                    headers=list(df.columns),
+                    data=[[]],
+                    metadata=self.__extract_metadata(value),  # type: ignore
+                )
+            return DataframeData(
+                headers=list(df.columns),
+                data=df.to_dict(orient="split")["data"],  # type: ignore
+                metadata=self.__extract_metadata(value),  # type: ignore
+            )
+
+    @staticmethod
+    def __get_cell_style(cell_id: str, cell_styles: list[dict]) -> str:
+        styles_for_cell = []
+        for style in cell_styles:
+            if cell_id in style.get("selectors", []):
+                styles_for_cell.extend(style.get("props", []))
+        styles_str = "; ".join([f"{prop}: {value}" for prop, value in styles_for_cell])
+        return styles_str
+
+    @staticmethod
+    def __extract_metadata(df: Styler) -> dict[str, list[list]]:
+        metadata = {"display_value": [], "styling": []}
+        style_data = df._compute()._translate(None, None)  # type: ignore
+        cell_styles = style_data.get("cellstyle", [])
+        for i in range(len(style_data["body"])):
+            metadata["display_value"].append([])
+            metadata["styling"].append([])
+            for j in range(len(style_data["body"][i])):
+                cell_type = style_data["body"][i][j]["type"]
+                if cell_type != "td":
+                    continue
+                display_value = style_data["body"][i][j]["display_value"]
+                cell_id = style_data["body"][i][j]["id"]
+                styles_str = Leaderboard.__get_cell_style(cell_id, cell_styles)
+                metadata["display_value"][i].append(display_value)
+                metadata["styling"][i].append(styles_str)
+        return metadata
+
+    def process_example(
+        self,
+        value: pd.DataFrame | Styler | str | None,
+    ):
+        import pandas as pd
+
+        if value is None:
+            return ""
+        value_df_data = self.postprocess(value)
+        value_df = pd.DataFrame(value_df_data.data, columns=value_df_data.headers)
+        return value_df.head(n=5).to_dict(orient="split")["data"]
+
+    def example_payload(self) -> Any:
+        return {"headers": ["a", "b"], "data": [["foo", "bar"]]}
+
+    def example_inputs(self) -> Any:
+        return self.example_value()
+
+    def example_value(self) -> Any:
+        return {"headers": ["a", "b"], "data": [["foo", "bar"]]}

src/leaderboard/visualization.py

@@ -0,0 +1,243 @@
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import plotly.graph_objects as go
+
+from utils import get_chart_colors
+
+
+def setup_matplotlib():
+    matplotlib.use("Agg")
+    plt.close("all")
+
+
+def get_performance_chart(df, category_name="Overall"):
+    plt.close("all")
+    colors = get_chart_colors()
+    score_column = "Category Score"
+    df_sorted = df.sort_values(score_column, ascending=True)
+
+    height = max(8, len(df_sorted) * 0.8)
+    fig, ax = plt.subplots(figsize=(16, height))
+    plt.rcParams.update({"font.size": 12})
+
+    fig.patch.set_facecolor(colors["background"])
+    ax.set_facecolor(colors["background"])
+
+    try:
+        bars = ax.barh(
+            np.arange(len(df_sorted)),
+            df_sorted[score_column],
+            height=0.4,
+            capstyle="round",
+            color=[colors[t] for t in df_sorted["Model Type"]],
+        )
+
+        ax.set_title(
+            f"Model Performance - {category_name}",
+            pad=20,
+            fontsize=20,
+            fontweight="bold",
+            color=colors["text"],
+        )
+        ax.set_xlabel(
+            "Average Score (Tool Selection Quality)",
+            fontsize=14,
+            fontweight="bold",
+            labelpad=10,
+            color=colors["text"],
+        )
+        ax.set_xlim(0.0, 1.0)
+
+        ax.set_yticks(np.arange(len(df_sorted)))
+        ax.set_yticklabels(df_sorted["Model"], fontsize=12, fontweight="bold", color=colors["text"])
+
+        plt.subplots_adjust(left=0.35)
+
+        for i, v in enumerate(df_sorted[score_column]):
+            ax.text(
+                v + 0.01,
+                i,
+                f"{v:.3f}",
+                va="center",
+                fontsize=12,
+                fontweight="bold",
+                color=colors["text"],
+            )
+
+        ax.grid(True, axis="x", linestyle="--", alpha=0.2, color=colors["grid"])
+        ax.spines[["top", "right"]].set_visible(False)
+        ax.spines[["bottom", "left"]].set_color(colors["grid"])
+        ax.tick_params(colors=colors["text"])
+
+        legend_elements = [
+            plt.Rectangle((0, 0), 1, 1, facecolor=color, label=label)
+            for label, color in {k: colors[k] for k in ["Private", "Open source"]}.items()
+        ]
+        ax.legend(
+            handles=legend_elements,
+            title="Model Type",
+            loc="lower right",
+            fontsize=12,
+            title_fontsize=14,
+            facecolor=colors["background"],
+            labelcolor=colors["text"],
+        )
+
+        plt.tight_layout()
+        return fig
+    finally:
+        plt.close(fig)
+
+
+def create_radar_plot(df, model_names):
+    datasets = [col for col in df.columns[7:] if col != "IO Cost"]
+    fig = go.Figure()
+
+    colors = ["rgba(99, 102, 241, 0.3)", "rgba(34, 197, 94, 0.3)"]
+    line_colors = ["#4F46E5", "#16A34A"]
+
+    for idx, model_name in enumerate(model_names):
+        model_data = df[df["Model"] == model_name].iloc[0]
+        values = [model_data[m] for m in datasets]
+        values.append(values[0])
+        datasets_plot = datasets + [datasets[0]]
+
+        fig.add_trace(
+            go.Scatterpolar(
+                r=values,
+                theta=datasets_plot,
+                fill="toself",
+                fillcolor=colors[idx % len(colors)],
+                line=dict(color=line_colors[idx % len(line_colors)], width=2),
+                name=model_name,
+                text=[f"{val:.3f}" for val in values],
+                textposition="middle right",
+                mode="lines+markers+text",
+            )
+        )
+
+    fig.update_layout(
+        polar=dict(
+            radialaxis=dict(visible=True, range=[0, 1], showline=False, tickfont=dict(size=12)),
+            angularaxis=dict(
+                tickfont=dict(size=13, family="Arial"),
+                rotation=90,
+                direction="clockwise",
+            ),
+        ),
+        showlegend=True,
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=-0.2,
+            xanchor="center",
+            x=0.5,
+            font=dict(size=14),
+        ),
+        title=dict(
+            text="Model Comparison",
+            x=0.5,
+            y=0.95,
+            font=dict(size=24, family="Arial", color="#1F2937"),
+        ),
+        paper_bgcolor="white",
+        plot_bgcolor="white",
+        height=700,
+        width=900,
+        margin=dict(t=100, b=100, l=80, r=80),
+    )
+
+    return fig
+
+
+def get_performance_cost_chart(df, category_name="Overall"):
+    colors = get_chart_colors()
+    fig, ax = plt.subplots(figsize=(12, 8), dpi=300)
+
+    fig.patch.set_facecolor(colors["background"])
+    ax.set_facecolor(colors["background"])
+    ax.grid(True, linestyle="--", alpha=0.15, which="both", color=colors["grid"])
+
+    score_column = "Category Score"
+
+    for _, row in df.iterrows():
+        color = colors[row["Model Type"]]
+        size = 100 if row[score_column] > 0.85 else 80
+        edge_color = colors["Private"] if row["Model Type"] == "Private" else colors["Open source"]
+
+        ax.scatter(
+            row["IO Cost"],
+            row[score_column] * 100,
+            c=color,
+            s=size,
+            alpha=0.9,
+            edgecolor=edge_color,
+            linewidth=1,
+            zorder=5,
+        )
+
+        bbox_props = dict(boxstyle="round,pad=0.3", fc=colors["background"], ec="none", alpha=0.8)
+
+        ax.annotate(
+            f"{row['Model']}\n(${row['IO Cost']:.2f})",
+            (row["IO Cost"], row[score_column] * 100),
+            xytext=(5, 5),
+            textcoords="offset points",
+            fontsize=8,
+            fontweight="bold",
+            color=colors["text"],
+            bbox=bbox_props,
+            zorder=6,
+        )
+
+    ax.set_xscale("log")
+    ax.set_xlim(0.08, 1000)
+    ax.set_ylim(60, 100)
+
+    ax.set_xlabel(
+        "I/O Cost per Million Tokens ($)",
+        fontsize=10,
+        fontweight="bold",
+        labelpad=10,
+        color=colors["text"],
+    )
+    ax.set_ylabel(
+        "Model Performance Score",
+        fontsize=10,
+        fontweight="bold",
+        labelpad=10,
+        color=colors["text"],
+    )
+
+    legend_elements = [plt.scatter([], [], c=colors[label], label=label, s=80) for label in ["Private", "Open source"]]
+    ax.legend(
+        handles=legend_elements,
+        loc="upper right",
+        frameon=True,
+        facecolor=colors["background"],
+        edgecolor="none",
+        fontsize=9,
+        labelcolor=colors["text"],
+    )
+
+    ax.set_title(
+        f"Performance vs. Cost - {category_name}",
+        fontsize=14,
+        pad=15,
+        fontweight="bold",
+        color=colors["text"],
+    )
+
+    for y1, y2, color in zip([85, 75, 60], [100, 85, 75], colors["performance_bands"]):
+        ax.axhspan(y1, y2, alpha=0.2, color=color, zorder=1)
+
+    ax.tick_params(axis="both", which="major", labelsize=9, colors=colors["text"])
+    ax.tick_params(axis="both", which="minor", labelsize=8, colors=colors["text"])
+    ax.xaxis.set_minor_locator(plt.LogLocator(base=10.0, subs=np.arange(2, 10) * 0.1))
+
+    for spine in ax.spines.values():
+        spine.set_color(colors["grid"])
+
+    plt.tight_layout()
+    return fig

src/populate.py

@@ -40,13 +40,13 @@ def get_tossups_leaderboard_df(repo_dir: str, eval_split: str) -> pd.DataFrame:
 
             row = {
                 "Submission": f"{username}/{model_name}",
-                "Avg Score (Max 10)": metrics["tossup_score"],
-                "Buzzer Accuracy": buzz_accuracy,
-                "Buzzer Position": metrics["buzz_position"],
+                "Avg Score ⬆️": metrics["tossup_score"],
+                "Buzz Accuracy": buzz_accuracy,
+                "Buzz Position": metrics["buzz_position"],
             }
             if "human_win_rate" in metrics:
-                row["Win Rate w/ Human"] = metrics["human_win_rate"]
-                row["Win Rate w/ Human (Aggressive)"] = metrics["human_win_rate_strict"]
+                row["Win Rate w/ Humans"] = metrics["human_win_rate"]
+                row["Win Rate w/ Humans (Aggressive)"] = metrics["human_win_rate_strict"]
             eval_results.append(row)
         except Exception as e:
             logger.error(f"Error processing model result '{username}/{model_name}': {e}")