app.py

"""A gradio app that renders a static leaderboard. This is used for Hugging Face Space."""

import ast
import argparse
import glob
import pickle

import gradio as gr
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import pandas as pd



MODEL_NAME_COST = {
    "anthropic/claude-2.1": 8,
    "anthropic/claude-3-haiku": 0.25,
    "anthropic/claude-3-opus": 15,
    "anthropic/claude-3-sonnet": 3,
    "cohere/command-r": 0.5,
    "google/gemini-pro": 0.12,
    "google/gemma-7b-it": 0.1,
    "mistralai/mistral-large": 8,
    "mistralai/mistral-medium": 2.7,
    "mistralai/mixtral-8x7b-instruct": 0.7,
    "openai/gpt-3.5-turbo": 0.5,
    "openai/gpt-4-1106-preview": 10,
}


def make_default_md():

    leaderboard_md = f"""
# 🏆 CZ-EVAL Leaderboard
[Developer](https://me.hynky.name/) | [Twitter](https://twitter.com/HKydlicek)

CZ-EVAL is a evaluation leadboard of Tasks in Czech for LLMs.

It's evaluated on following datasets:

- Math Problems Understanding [Klokan-QA](https://huggingface.co/datasets/hynky/klokan-qa)
- Reasoning and General Knowledge [TSP-QA](https://huggingface.co/datasets/hynky/tsp-qa)

💻 Code: The evaluation code can be found at [hynky1999/LLM-Eval](https://github.com/hynky1999/LLM-Eval). Model inference is done using [Open-Router](https://openrouter.ai/) or on cloud using [Modal Labs](https://modal.com/).
"""
    return leaderboard_md


def make_arena_leaderboard_md(arena_df):
    total_models = len(arena_df)

    leaderboard_md = f"""
Total #models: **{total_models}**. Last updated: Mar 17, 2024.
"""
    return leaderboard_md


def make_full_leaderboard_md(elo_results):
    leaderboard_md = f"""
Three benchmarks are displayed: **Arena Elo**, **MT-Bench** and **MMLU**.
- [Klokan-QA](https://huggingface.co/datasets/hynky/klokan-qa) - Mathematical competitions dataset
- [TSP](https://huggingface.co/datasets/hynky/TSP) - Comprehensive dataset of 

"""
    return leaderboard_md


# Combine all category accuracies into a single DataFrame


def plot_spider(df, title):
    categories = df.columns.tolist()[1:]
    categories = [
        *categories,
        categories[0],
    ]  # Ensure the graph is circular by appending the start to the end
    colors = [
        '#1f77b4',  # muted blue
        '#ff7f0e',  # safety orange
        '#2ca02c',  # cooked asparagus green
        '#d62728',  # brick red
        '#9467bd',  # muted purple
        '#8c564b',  # chestnut brown
        '#e377c2',  # raspberry yogurt pink
        '#7f7f7f',  # middle gray
        '#bcbd22',  # curry yellow-green
        '#17becf',   # blue-teal
        '#f7b6d2',  # pastel pink
        '#bcbd22',  # faded green
        '#dbdb8d',  # light olive
        '#17becf',  # soft blue
        '#9edae5',  # light blue
        '#c5b0d5',  # soft purple
        '#c49c94',  # dusty rose
        '#f7b6d2',  # pastel pink
        '#bcbd22',  # faded green
        '#dbdb8d',  # light olive
        '#17becf',  # soft blue
        '#9edae5',  # light blue
        '#c5b0d5',  # soft purple
        '#c49c94',  # dusty rose
    ]

    # Setting for 1000x1000
    fig_1000 = go.Figure()

    for i, (idx, row) in enumerate(df.iterrows()):
        name = row[0]
        row = row.tolist()[1:]
        row = row + [
            row[0]
        ]  # Ensure the graph is circular by appending the start to the end
        color = colors[i]
        fig_1000.add_trace(
            go.Scatterpolar(
                r=row,
                theta=categories,
                opacity=0.4,
                name=name,
                line=dict(
                    color=color, width=4
                ),  # Adjust line width for better visibility
            )
        )

    fig_1000.update_layout(
        width=600,
        height=950,
        polar=dict(
            angularaxis=dict(
                gridwidth=2,  # Increase line width for better visibility
                rotation=90,
                direction="clockwise",
            ),
            radialaxis=dict(
                visible=True,
                range=[0, 100],
                angle=45,
                tickangle=45,
                tickvals=[0, 25, 50, 75, 100],
                ticktext=["0%", "25%", "50%", "75%", "100%"],
            ),
        ),
        title_text=title,
        title_x=0.5,
        title_y=0.97,
        title_xanchor="center",
        title_yanchor="top",
        title_font_size=24,
        title_font_color="#333333",
        font=dict(family="Arial", size=16, color="#333333"),
        legend=dict(
            orientation="h", yanchor="bottom", y=-0.45, xanchor="center", x=0.5
        ),
    )
    return fig_1000


def openrouter_hyperlink(model_name):
    return f'<a target="_blank" href="https://openrouter.ai/models/{model_name}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'


def get_full_table(model_table_df):
    num_cols = ["klokan", "culture", "analytical", "critical", "verbal"]
    # Multiply by 100 and round to 2 decimals
    # Add average
    model_table_df["average"] = model_table_df[num_cols].mean(axis=1)
    model_table_df[num_cols + ["average"]] = model_table_df[
        num_cols + ["average"]
    ].apply(lambda x: round(x * 100, 2))

    # Sort and add rank
    model_table_df.sort_values(by="average", ascending=False, inplace=True)
    model_table_df.insert(0, "rank", np.arange(1, len(model_table_df) + 1))

    # Add cost
    model_table_df["completion_price"] = model_table_df["model_name"].apply(
        lambda x: f"{MODEL_NAME_COST[x]}$"
    )

    # Add link
    model_table_df["model_name"] = model_table_df["model_name"].apply(
        lambda x: openrouter_hyperlink(x)
    )

    # Ensure the dataframe is in the correct order before renaming
    model_table_df = model_table_df[["rank", "model_name", "completion_price", "klokan", "culture", "analytical", "critical", "verbal", "average"]]

    model_table_df.rename(
        columns={
            "model_name": "🤖 Model",
            "completion_price": "💰 Cost (1M-Tokens)",
            "klokan": "🧮 Klokan-QA",
            "culture": "🌍 TSP-Culture",
            "analytical": "🔍 TSP-Analytical",
            "critical": "💡 TSP-Critical",
            "verbal": "📖 TSP-Verbal",
            "average": "📊 Average",
        },
        inplace=True,
    )


    return model_table_df


def build_leaderboard_tab(leaderboard_table_file, klokan_table_file, tsp_table_file):

    results = pd.read_csv(leaderboard_table_file)
    results = get_full_table(results)
    # p1, p2 = get_grafs(pd.read_json(klokan_table_file), pd.read_json(tsp_table_file))
    default_md = make_default_md()

    md_1 = gr.Markdown(default_md, elem_id="leaderboard_markdown")
    with gr.Tabs() as tabs:
        # arena table
        with gr.Tab("CZ-EVAL Leaderboard", id=0):
            md = make_arena_leaderboard_md(results)
            gr.Markdown(md, elem_id="leaderboard_markdown")
            gr.Dataframe(
                datatype=[
                    "str",
                    "markdown",
                    "number",
                    "number",
                    "number",
                    "number",
                    "number",
                    "number",
                    "str",
                    "str",
                    "str",
                ],
                value=results,
                elem_id="arena_leaderboard_dataframe",
                height=700,
                column_widths=[
                    70,
                    200,
                    110,
                    120,
                    120,
                    120,
                    120,
                    100,
                    100,
                ],
                wrap=True,
            )

    p1 = plot_spider(pd.read_csv(klokan_table_file), "Klokan-QA - Acurracy")
    p2 = plot_spider(pd.read_csv(tsp_table_file), "TSP - Accuracy")

    gr.Markdown(
        f"""## More Statistics for CZ-EVAL\n
Below are figures for more statistics.
""",
        elem_id="leaderboard_markdown",
    )
    with gr.Row():
        with gr.Column():
            gr.Markdown(
                "#### Figure 1: Performance of models on Klokan-QA per difficulty"
            )
            plot_1 = gr.Plot(p1, show_label=False)
        with gr.Column():
            gr.Markdown("#### Figure 2: Performance of models on TSP dataset")
            plot_2 = gr.Plot(p2, show_label=False)

    return [md_1, plot_1, plot_2]


block_css = """
#notice_markdown {
    font-size: 104%
}
#notice_markdown th {
    display: none;
}
#notice_markdown td {
    padding-top: 6px;
    padding-bottom: 6px;
}
#leaderboard_markdown {
    font-size: 104%
}
#leaderboard_markdown td {
    padding-top: 6px;
    padding-bottom: 6px;
}
#leaderboard_dataframe td {
    line-height: 0.1em;
}
footer {
    display:none !important
}
.image-container {
    display: flex;
    align-items: center;
    padding: 1px;
}
.image-container img {
    margin: 0 30px;
    height: 20px;
    max-height: 100%;
    width: auto;
    max-width: 20%;
}
"""


def build_demo(leadboard_table, klokan_table, tsp_table):
    text_size = gr.themes.sizes.text_lg

    with gr.Blocks(
        title="CZ-EVAL Leaderboard",
        theme=gr.themes.Base(text_size=text_size),
        css=block_css,
    ) as demo:
        leader_components = build_leaderboard_tab(
            leadboard_table, klokan_table, tsp_table
        )
    return demo


demo = build_demo(
    leadboard_table="./leaderboard/table.csv",
    klokan_table="./leaderboard/klokan.csv",
    tsp_table="./leaderboard/tsp.csv",
)

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