app.py

# some code blocks are taken from https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/tree/main
import json
import os
from datetime import datetime, timezone

import gradio as gr
import pandas as pd
from huggingface_hub import HfApi

from src.css_html import custom_css
from src.text_content import ABOUT_TEXT, SUBMISSION_TEXT, SUBMISSION_TEXT_2
from src.utils import (
    AutoEvalColumn,
    fields,
    is_model_on_hub,
    make_clickable_names,
    plot_throughput,
    styled_error,
    styled_message,
)

TOKEN = os.environ.get("HF_TOKEN", None)
api = HfApi(TOKEN)
df = pd.read_csv("data/code_eval_board.csv")

QUEUE_REPO = "bigcode/evaluation-requests"
EVAL_REQUESTS_PATH = "eval-queue"
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 add_new_eval(
    model: str,
    revision: str,
    precision: str,
    model_type: str,
):
    precision = precision
    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")

    if model_type is None or model_type == "":
        return styled_error("Please select a model type.")

    # check the model actually exists before adding the eval
    if revision == "":
        revision = "main"

    model_on_hub, error = is_model_on_hub(model, revision)
    if not model_on_hub:
        return styled_error(f'Model "{model}" {error}')

    print("adding new eval")

    eval_entry = {
        "model": model,
        "revision": revision,
        "precision": precision,
        "status": "PENDING",
        "submitted_time": current_time,
        "model_type": model_type.split(" ")[1],
    }

    user_name = ""
    model_path = model
    if "/" in model:
        user_name = model.split("/")[0]
        model_path = model.split("/")[1]

    OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
    os.makedirs(OUT_DIR, exist_ok=True)
    out_path = f"{OUT_DIR}/{model_path}_eval_request_{precision}.json"
    print(f"Saving eval request to {out_path}")

    with open(out_path, "w") as f:
        f.write(json.dumps(eval_entry))

    api.upload_file(
        path_or_fileobj=out_path,
        path_in_repo=out_path.split("eval-queue/")[1],
        repo_id=QUEUE_REPO,
        repo_type="dataset",
        commit_message=f"Add {model} to eval queue",
    )

    # remove the local file
    os.remove(out_path)

    return styled_message("Your request has been submitted to the evaluation queue!\n")


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(css=custom_css)
with demo:
    with gr.Row():
        gr.Markdown(
            """<div style="text-align: center;"><h1> ⭐ Big <span style='color: #e6b800;'>Code</span> Models <span style='color: #e6b800;'>Leaderboard</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 open pre-trained multilingual code models, that people can start from as base models for their trainings.</p>""",
            elem_classes="markdown-text",
        )

    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.Accordion("➡️ See All Columns", open=False):
                            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="",
                                elem_id="column-select",
                                interactive=True,
                            )
                        # with gr.Column(min_width=780):
                        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", "🔴 external-evaluation"],
                                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",
                        interactive=False,
                    )

                    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,
                    )
                    filter_columns.change(
                        filter_items,
                        [hidden_leaderboard_df, leaderboard_df, filter_columns],
                        leaderboard_df,
                    )
                    shown_columns.change(
                        select_columns,
                        [hidden_leaderboard_df, shown_columns],
                        leaderboard_df,
                    )
                    gr.Markdown(
                        """
                    **Notes:**
                    - Win Rate represents how often a model outperforms other models in each language, averaged across all languages.
                    - The scores of instruction-tuned models might be significantly higher on humaneval-python than other languages. We use the instruction format of HumanEval. For other languages, we use base MultiPL-E prompts.
                    - For more details check the 📝 About section.
                    - Models with a 🔴 symbol represent external evaluation submission, this means that we didn't verify the results, you can find the author's submission under `Submission PR` field from `See All Columns` tab.
                    """,
                        elem_classes="markdown-text",
                    )

                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,
                        )
                    gr.Markdown(
                        "**Note:** Zero throughput on the right plot refers to OOM, for more details check the 📝 About section.",
                        elem_classes="markdown-text",
                    )
                with gr.TabItem("📝 About", id=2):
                    gr.Markdown(ABOUT_TEXT, elem_classes="markdown-text")
                with gr.TabItem("Submit results 🚀", id=3):
                    gr.Markdown(SUBMISSION_TEXT)
                    gr.Markdown(
                        "## 📤  Submit your model here:", elem_classes="markdown-text"
                    )
                    with gr.Column():
                        with gr.Row():
                            model_name = gr.Textbox(label="Model name")
                            revision_name = gr.Textbox(
                                label="revision", placeholder="main"
                            )
                        with gr.Row():
                            precision = gr.Dropdown(
                                choices=[
                                    "float16",
                                    "bfloat16",
                                    "8bit",
                                    "4bit",
                                ],
                                label="Precision",
                                multiselect=False,
                                value="float16",
                                interactive=True,
                            )
                            model_type = gr.Dropdown(
                                choices=["🟢 base", "🔶 instruction-tuned"],
                                label="Model type",
                                multiselect=False,
                                value=None,
                                interactive=True,
                            )
                        submit_button = gr.Button("Submit Eval")
                        submission_result = gr.Markdown()
                        submit_button.click(
                            add_new_eval,
                            inputs=[model_name, revision_name, precision, model_type],
                            outputs=[submission_result],
                        )
                        gr.Markdown(SUBMISSION_TEXT_2)


demo.launch()