app.py

# some code blocks are taken from https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/tree/main
import gradio as gr
import pandas as pd
from src.utils import AutoEvalColumn, fields, make_clickable_names, plot_throughput


df = pd.read_csv("data/code_eval_board.csv")

COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
COLS_LITE = [
    c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden
]
TYPES_LITE = [
    c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden
]


def select_columns(df, columns):
    always_here_cols = [
        AutoEvalColumn.model_type_symbol.name,
        AutoEvalColumn.model.name,
    ]
    # We use COLS to maintain sorting
    filtered_df = df[
        always_here_cols
        + [c for c in COLS if c in df.columns and c in columns]
    ]
    return filtered_df


def filter_items(df, leaderboard_table, query):
    if query == "all":
        return df[leaderboard_table.columns]
    else:
        query = query[0]  # take only the emoji character
    filtered_df = df[(df["T"] == query)]
    return filtered_df[leaderboard_table.columns]


def search_table(df, leaderboard_table, query):
    filtered_df = df[(df["Models"].str.contains(query, case=False))]
    return filtered_df[leaderboard_table.columns]


df = make_clickable_names(df)


demo = gr.Blocks()
with demo:
    with gr.Row():
        gr.Markdown(
            """<div style="text-align: center;"><h1> ⭐ Multilingual <span style='color: #e6b800;'>Code</span> Models <span style='color: #e6b800;'>Evaluation</span></h1></div>\
            <br>\
            <p>Inspired from the <a href="https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard">🤗 Open LLM Leaderboard</a> and <a href="https://huggingface.co/spaces/optimum/llm-perf-leaderboard">🤗 Open LLM-Perf Leaderboard 🏋️</a>, we compare performance of base multilingual code generation models on <a href="https://huggingface.co/datasets/openai_humaneval">HumanEval</a> benchmark and <a href="https://huggingface.co/datasets/nuprl/MultiPL-E">MultiPL-E</a>. We also measure throughput and provide\
            information about the models. We only compare pre-trained multilingual code models, that people can start from as base models for their trainings.</p>"""
        )

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.Column():
            with gr.Tabs(elem_classes="A100-tabs") as A100_tabs:
                with gr.TabItem("🔍 Evaluation table", id=0):
                    with gr.Column():
                        # with gr.Column(min_width=780):
                        shown_columns = gr.CheckboxGroup(
                            choices=[
                                c
                                for c in COLS
                                if c
                                not in [
                                    AutoEvalColumn.dummy.name,
                                    AutoEvalColumn.model.name,
                                    AutoEvalColumn.model_type_symbol.name,
                                ]
                            ],
                            value=[
                                c
                                for c in COLS_LITE
                                if c
                                not in [
                                    AutoEvalColumn.dummy.name,
                                    AutoEvalColumn.model.name,
                                    AutoEvalColumn.model_type_symbol.name,
                                ]
                            ],
                            label="Select columns to show",
                            elem_id="column-select",
                            interactive=True,
                        )
                        with gr.Row():
                            search_bar = gr.Textbox(
                                placeholder="🔍 Search for your model and press ENTER...",
                                show_label=False,
                                elem_id="search-bar",
                            )
                            filter_columns = gr.Radio(
                                label="⏚ Filter model types",
                                choices=["all", "🟢 base", "🔶 instruction-tuned"],
                                value="all",
                                elem_id="filter-columns",
                            )

                    leaderboard_df = gr.components.Dataframe(
                        value=df[
                            [
                                AutoEvalColumn.model_type_symbol.name,
                                AutoEvalColumn.model.name,
                            ]
                            + shown_columns.value
                        ],
                        headers=[
                            AutoEvalColumn.model_type_symbol.name,
                            AutoEvalColumn.model.name,
                        ]
                        + shown_columns.value,
                        datatype=TYPES,
                        elem_id="leaderboard-table",
                    )

                    hidden_leaderboard_df = gr.components.Dataframe(
                        value=df,
                        headers=COLS,
                        datatype=["str" for _ in range(len(COLS))],
                        visible=False,
                    )
                    search_bar.submit(
                        search_table,
                        [hidden_leaderboard_df, leaderboard_df, search_bar],
                        leaderboard_df,
                    )
                    shown_columns.change(
                        select_columns,
                        [hidden_leaderboard_df, shown_columns],
                        leaderboard_df,
                    )
                    filter_columns.change(
                        filter_items,
                        [hidden_leaderboard_df, leaderboard_df, filter_columns],
                        leaderboard_df,
                    )

                with gr.TabItem("📊 Performance Plot", id=1):
                    with gr.Row():
                        bs_1_plot = gr.components.Plot(
                            value=plot_throughput(df, bs=1),
                            elem_id="bs1-plot",
                            show_label=False,
                        )
                        bs_50_plt = gr.components.Plot(
                            value=plot_throughput(df, bs=50),
                            elem_id="bs50-plot",
                            show_label=False,
                        )
    with gr.Row():
        gr.Markdown(
            """Notes:
            <ul>
            <li> Throughputs and peak memory usage are measured using <a href="https://github.com/huggingface/optimum-benchmark/tree/main">Optimum-Benchmark</a> which powers <a href="https://huggingface.co/spaces/optimum/llm-perf-leaderboard">Open LLM-Perf Leaderboard</a>. (0 throughput corresponds to OOM).</li>
            <li> All models were evaluated with the <a href="https://github.com/bigcode-project/bigcode-evaluation-harness/tree/main">bigcode-evaluation-harness</a> with top-p=0.95, temperature=0.2 and n_samples=50.</li>
            <li> HumanEval-Python, reports the pass@1 on HumanEval, the rest is from MultiPL-E benchmark.</li>
            <li> Average score is the average pass@1 over all languages. For Win Rate, we compute model rank for each language as <code style="white-space: nowrap; display: inline;">num_models - (rank -1)</code> and average their rankings.</li>
            <li> #Languages column represents the number of programming languages included during the pretraining.
            </ul>"""
        )
demo.launch()