app.py

import json
from collections import defaultdict
from dataclasses import dataclass, field, fields
from functools import cached_property
from pathlib import Path
from typing import Literal

import numpy as np
import pandas as pd
import gradio as gr
from pandas import DataFrame
from pandas.io.formats.style import Styler

from content import *


TASK_METRICS = {
    "arc": "acc_norm",
    "hellaswag": "acc_norm",
    "mmlu": "acc_norm",
    "truthfulqa": "mc2",
}

MODEL_TYPE_EMOJIS = {
    "pretrained": "🟢",
    "fine-tuned": "🔶",
    "instruction-tuned": "⭕",
    "RL-tuned": "🟦",
}

NOT_GIVEN_SYMBOL = "❔"


@dataclass
class Result:
    model_name: str
    short_name: str
    model_type: Literal["pretrained", "fine-tuned", "instruction-tuned", "RL-tuned"]
    dutch_coverage: Literal["none", "pretrained", "fine-tuned"]
    num_parameters: int
    arc: float = field(default=0.0)
    average: float = field(default=0.0, init=False)
    hellaswag: float = field(default=0.0)
    mmlu: float = field(default=0.0)
    truthfulqa: float = field(default=0.0)
    num_parameters_kmb: str = field(init=False)

    def __post_init__(self):
        if self.model_type not in ["pretrained", "fine-tuned", "instruction-tuned", "RL-tuned", "not-given"]:
            raise ValueError(
                f"Model type {self.model_type} must be one of 'pretrained', 'fine-tuned', 'instruction-tuned', 'RL-tuned', 'not-given"
            )
        if self.dutch_coverage not in ["none", "pretrained", "fine-tuned", "not-given"]:
            raise ValueError(
                f"Dutch coverage {self.dutch_coverage} must be one of 'none', 'pretrained', 'fine-tuned', 'not-given"
            )

        field_names = {f.name for f in fields(self)}
        for task_name in TASK_METRICS:
            if task_name not in field_names:
                raise ValueError(f"Task name {task_name} not found in Result class fields so cannot create DataFrame")

        self.average = (self.arc + self.hellaswag + self.mmlu + self.truthfulqa) / 4
        self.num_parameters_kmb = convert_number_to_kmb(self.num_parameters)


@dataclass
class ResultSet:
    results: list[Result]
    column_names: dict[str, str] = field(default_factory=dict)
    column_types: dict[str, str] = field(default_factory=dict)

    def __post_init__(self):
        if not self.column_names:
            # Order will be the order of the columns in the DataFrame
            self.column_names = {
                "short_name": "Model",
                "model_type": "T",
                "dutch_coverage": "🇳🇱",
                "num_parameters": "Size",
                "average": "Avg.",
                "arc": "ARC (25-shot)",
                "hellaswag": "HellaSwag (10-shot)",
                "mmlu": "MMLU (5-shot)",
                "truthfulqa": "TruthfulQA (0-shot)",
            }
            self.column_types = {
                "Model": "markdown",
                "T": "str",
                "🇳🇱": "str",
                "Size": "str",
                "Avg.": "number",
                "ARC (25-shot)": "number",
                "HellaSwag (10-shot)": "number",
                "MMLU (5-shot)": "number",
                "TruthfulQA (0-shot)": "number",
            }

        for column_type in self.column_types:
            if column_type not in set(self.column_names.values()):
                raise ValueError(
                    f"Column names specified in column_types must be values in column_names."
                    f" {column_type} not found."
                )

        if "average" not in self.column_names:
            raise ValueError("Column names must contain 'average' column name")

        field_names = [f.name for f in fields(Result)]
        for column_name in self.column_names:
            if column_name not in field_names:
                raise ValueError(f"Column name {column_name} not found in Result class so cannot create DataFrame")

    @cached_property
    def df(self) -> DataFrame:
        data = [
            {col_name: getattr(result, attr) for attr, col_name in self.column_names.items()}
            for result in self.results
        ]

        df = pd.DataFrame(data)
        df = df.sort_values(by=self.column_names["average"], ascending=False)
        return df

    @cached_property
    def styled_df(self) -> Styler:
        data = [
            {
                col_name: (
                    f"<a target='_blank' href='https://huggingface.co/{result.model_name}'"
                    f" style='color: var(--link-text-color); text-decoration: underline;text-decoration-style:"
                    f" dotted;'>{result.short_name}</a>"
                )
                if attr == "short_name"
                else MODEL_TYPE_EMOJIS.get(result.model_type, NOT_GIVEN_SYMBOL)
                if attr == "model_type"
                else (result.dutch_coverage if result.dutch_coverage != "not-given" else NOT_GIVEN_SYMBOL)
                if attr == "dutch_coverage"
                else getattr(result, attr)
                for attr, col_name in self.column_names.items()
            }
            for result in self.results
        ]

        df = pd.DataFrame(data)
        df = df.sort_values(by=self.column_names["average"], ascending=False)
        number_cols = [col for attr, col in self.column_names.items() if attr in TASK_METRICS or attr == "average"]
        styler = df.style.format("{:.2f}", subset=number_cols)

        def highlight_max(col):
            return np.where(col == np.nanmax(col.to_numpy()), "font-weight: bold;", None)

        styler = styler.apply(highlight_max, axis=0, subset=number_cols)

        num_params_col = self.column_names["num_parameters"]
        styler = styler.format(convert_number_to_kmb, subset=num_params_col)

        styler = styler.hide()
        return styler

    @cached_property
    def latex_df(self) -> Styler:
        number_cols = [col for attr, col in self.column_names.items() if attr in TASK_METRICS or attr == "average"]
        styler = self.df.style.format("{:.2f}", subset=number_cols)

        def highlight_max(col):
            return np.where(col == np.nanmax(col.to_numpy()), "font-weight: bold;", None)

        styler = styler.apply(highlight_max, axis=0, subset=number_cols)
        num_params_col = self.column_names["num_parameters"]
        styler = styler.format(convert_number_to_kmb, subset=num_params_col)
        styler = styler.hide()
        return styler


def convert_number_to_kmb(number: int) -> str:
    """
    Converts a number to a string with K, M or B suffix
    :param number: the number to convert
    :return: a string with the number and a suffix, e.g. "7B", rounded to one decimal
    """
    if number >= 1_000_000_000:
        return f"{round(number / 1_000_000_000, 1)}B"
    elif number >= 1_000_000:
        return f"{round(number / 1_000_000, 1)}M"
    elif number >= 1_000:
        return f"{round(number / 1_000, 1)}K"
    else:
        return str(number)


def collect_results() -> ResultSet:
    """
    Collects results from the evals folder and returns a dictionary of results
    :return: a dictionary of results where the keys are typles of (model_name, language) and the values are
    dictionaries of the form {benchmark_name: performance_score}
    """
    evals_dir = Path(__file__).parent.joinpath("evals")
    pf_overview = evals_dir.joinpath("models.json")
    if not pf_overview.exists():
        raise ValueError(
            f"Overview file {pf_overview} not found. Make sure to generate it first with `generate_overview_json.py`."
        )

    model_info = json.loads(pf_overview.read_text(encoding="utf-8"))
    model_results = {}
    for pfin in evals_dir.rglob("*.json"):
        data = json.loads(pfin.read_text(encoding="utf-8"))

        if "results" not in data:
            continue

        task_results = data["results"]
        short_name = pfin.stem.split("_", 2)[2].lower()

        if short_name not in model_info:
            raise KeyError(
                f"Model {short_name} not found in overview file {pf_overview.name}. This means that a results JSON"
                f" file exists that has not yet been processed. First run the `generate_overview_json.py` script."
            )

        if short_name not in model_results:
            model_results[short_name] = {
                "short_name": short_name,
                "model_name": model_info[short_name]["model_name"],
                "model_type": model_info[short_name]["model_type"],
                "dutch_coverage": model_info[short_name]["dutch_coverage"],
                "num_parameters": model_info[short_name]["num_parameters"],
            }

        for task_name, task_result in task_results.items():
            task_name = task_name.rsplit("_", 1)[0]
            metric = TASK_METRICS[task_name]
            model_results[short_name][task_name] = task_result[metric]

    model_results = ResultSet([Result(**res) for short_name, res in model_results.items()])

    return model_results


with gr.Blocks() as demo:
    gr.HTML(TITLE)
    gr.Markdown(INTRO_TEXT)

    gr.Markdown(
        f"## Leaderboard\nOnly representative for the Dutch version (`*_nl`) of the benchmarks!"
        " All models have been benchmarked in 8-bit."
    )

    results = collect_results()

    gr.components.Dataframe(
        results.styled_df,
        headers=list(results.df.columns),
        datatype=[results.column_types[col] for col in results.df.columns],  # To ensure same order as headers
        interactive=False,
        elem_id="leaderboard-table",
    )

    with gr.Row():
        with gr.Column():
            modeltypes_str = "<br>".join([f"- {emoji}: {modeltype}" for modeltype, emoji in MODEL_TYPE_EMOJIS.items()])
            gr.Markdown(f"Model types:<br>{modeltypes_str}")

        with gr.Column():
            gr.Markdown(
                f"Language coverage ({results.column_names['dutch_coverage']}):"
                f"<br>- `none`: no explicit/deliberate Dutch coverage,"
                f"<br>- `pretrained`: pretrained on Dutch data,"
                f"<br>- `fine-tuned`: fine-tuned on Dutch data"
            )

        with gr.Column():
            metrics_str = "<br>".join([f"- {task}: `{metric}`" for task, metric in TASK_METRICS.items()])
            gr.Markdown(f"Reported metrics:<br>{metrics_str}")

    gr.Markdown("## LaTeX")
    gr.Code(results.latex_df.to_latex(convert_css=True))

    gr.Markdown(DISCLAIMER, elem_classes="markdown-text")
    gr.Markdown(CREDIT, elem_classes="markdown-text")
    gr.Markdown(CITATION, elem_classes="markdown-text")


if __name__ == "__main__":
    demo.launch()