app.py

import os
import logging
import time
import gradio as gr
import datasets
from huggingface_hub import snapshot_download, WebhooksServer, WebhookPayload
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
from gradio_space_ci import enable_space_ci

from src.display.about import (
    CITATION_BUTTON_LABEL,
    CITATION_BUTTON_TEXT,
    EVALUATION_QUEUE_TEXT,
    FAQ_TEXT,
    INTRODUCTION_TEXT,
    LLM_BENCHMARKS_TEXT,
    TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
    BENCHMARK_COLS,
    COLS,
    EVAL_COLS,
    EVAL_TYPES,
    AutoEvalColumn,
    ModelType,
    Precision,
    WeightType,
    fields,
)
from src.envs import (
    API,
    EVAL_REQUESTS_PATH,
    AGGREGATED_REPO,
    HF_TOKEN,
    QUEUE_REPO,
    REPO_ID,
    HF_HOME,
)
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval
from src.tools.plots import create_metric_plot_obj, create_plot_df, create_scores_df

# Configure logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")

# Start ephemeral Spaces on PRs (see config in README.md)
enable_space_ci()


def restart_space():
    API.restart_space(repo_id=REPO_ID, token=HF_TOKEN)


def time_diff_wrapper(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        diff = end_time - start_time
        logging.info(f"Time taken for {func.__name__}: {diff} seconds")
        return result

    return wrapper


@time_diff_wrapper
def download_dataset(repo_id, local_dir, repo_type="dataset", max_attempts=3, backoff_factor=1.5):
    """Download dataset with exponential backoff retries."""
    attempt = 0
    while attempt < max_attempts:
        try:
            logging.info(f"Downloading {repo_id} to {local_dir}")
            snapshot_download(
                repo_id=repo_id,
                local_dir=local_dir,
                repo_type=repo_type,
                tqdm_class=None,
                etag_timeout=30,
                max_workers=8,
            )
            logging.info("Download successful")
            return
        except Exception as e:
            wait_time = backoff_factor**attempt
            logging.error(f"Error downloading {repo_id}: {e}, retrying in {wait_time}s")
            time.sleep(wait_time)
            attempt += 1
    raise Exception(f"Failed to download {repo_id} after {max_attempts} attempts")


def init_space(full_init: bool = True):
    """Initializes the application space, loading only necessary data."""
    if full_init:
        # These downloads only occur on full initialization
        try:
            download_dataset(QUEUE_REPO, EVAL_REQUESTS_PATH)
        except Exception:
            restart_space()

    # Always retrieve the leaderboard DataFrame
    leaderboard_dataset = datasets.load_dataset(AGGREGATED_REPO, "default", split="train", cache_dir=HF_HOME)
    leaderboard_df = get_leaderboard_df(
        leaderboard_dataset=leaderboard_dataset, 
        cols=COLS,
        benchmark_cols=BENCHMARK_COLS,
    )

    # Evaluation queue DataFrame retrieval is independent of initialization detail level
    eval_queue_dfs = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)

    return leaderboard_df, eval_queue_dfs


# Convert the environment variable "LEADERBOARD_FULL_INIT" to a boolean value, defaulting to True if the variable is not set.
# This controls whether a full initialization should be performed.
do_full_init = os.getenv("LEADERBOARD_FULL_INIT", "True") == "True"

# Calls the init_space function with the `full_init` parameter determined by the `do_full_init` variable.
# This initializes various DataFrames used throughout the application, with the level of initialization detail controlled by the `do_full_init` flag.
leaderboard_df, eval_queue_dfs = init_space(full_init=do_full_init)
finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = eval_queue_dfs


# Data processing for plots now only on demand in the respective Gradio tab
#def load_and_create_plots():
#    plot_df = create_plot_df(create_scores_df(leaderboard_df))
#    return plot_df


demo = gr.Blocks(css=custom_css)
with demo:
    gr.HTML(TITLE)
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
            leaderboard = Leaderboard(
                value=leaderboard_df,
                datatype=[c.type for c in fields(AutoEvalColumn)],
                select_columns=SelectColumns(
                    default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
                    cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden or c.dummy],
                    label="Select Columns to Display:",
                ),
                search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.fullname.name, AutoEvalColumn.license.name],
                hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
                filter_columns=[
                    ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
                    ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
                    ColumnFilter(
                        AutoEvalColumn.params.name,
                        type="slider",
                        min=0.01,
                        max=150,
                        label="Select the number of parameters (B)",
                    ),
                    ColumnFilter(
                        AutoEvalColumn.still_on_hub.name, type="boolean", label="Private or deleted", default=True
                    ),
                    ColumnFilter(
                        AutoEvalColumn.merged.name, type="boolean", label="Contains a merge/moerge", default=True
                    ),
                    ColumnFilter(AutoEvalColumn.moe.name, type="boolean", label="MoE", default=False),
                    ColumnFilter(AutoEvalColumn.not_flagged.name, type="boolean", label="Flagged", default=True),
                ],
                bool_checkboxgroup_label="Hide models",
            )

        #with gr.TabItem("📈 Metrics through time", elem_id="llm-benchmark-tab-table", id=2):
        #    with gr.Row():
        #        with gr.Column():
        #            plot_df = load_and_create_plots()
        #            chart = create_metric_plot_obj(
        #                plot_df,
        #                [AutoEvalColumn.average.name],
        #                title="Average of Top Scores and Human Baseline Over Time (from last update)",
        #            )
        #            gr.Plot(value=chart, min_width=500)
        #        with gr.Column():
        #            plot_df = load_and_create_plots()
        #            chart = create_metric_plot_obj(
        #                plot_df,
        #                BENCHMARK_COLS,
        #                title="Top Scores and Human Baseline Over Time (from last update)",
        #            )
        #            gr.Plot(value=chart, min_width=500)

        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=3):
            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")

        with gr.TabItem("❗FAQ", elem_id="llm-benchmark-tab-table", id=4):
            gr.Markdown(FAQ_TEXT, elem_classes="markdown-text")

        with gr.TabItem("🚀 Submit ", elem_id="llm-benchmark-tab-table", id=5):
            with gr.Column():
                with gr.Row():
                    gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")

            with gr.Row():
                gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")

            with gr.Row():
                with gr.Column():
                    model_name_textbox = gr.Textbox(label="Model name")
                    revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
                    model_type = gr.Dropdown(
                        choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
                        label="Model type",
                        multiselect=False,
                        value=ModelType.FT.to_str(" : "),
                        interactive=True,
                    )

                with gr.Column():
                    precision = gr.Dropdown(
                        choices=[i.value.name for i in Precision if i != Precision.Unknown],
                        label="Precision",
                        multiselect=False,
                        value="float16",
                        interactive=True,
                    )
                    weight_type = gr.Dropdown(
                        choices=[i.value.name for i in WeightType],
                        label="Weights type",
                        multiselect=False,
                        value="Original",
                        interactive=True,
                    )
                    base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")

            with gr.Column():
                with gr.Accordion(
                    f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
                    open=False,
                ):
                    with gr.Row():
                        finished_eval_table = gr.components.Dataframe(
                            value=finished_eval_queue_df,
                            headers=EVAL_COLS,
                            datatype=EVAL_TYPES,
                            row_count=5,
                        )
                with gr.Accordion(
                    f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
                    open=False,
                ):
                    with gr.Row():
                        running_eval_table = gr.components.Dataframe(
                            value=running_eval_queue_df,
                            headers=EVAL_COLS,
                            datatype=EVAL_TYPES,
                            row_count=5,
                        )

                with gr.Accordion(
                    f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
                    open=False,
                ):
                    with gr.Row():
                        pending_eval_table = gr.components.Dataframe(
                            value=pending_eval_queue_df,
                            headers=EVAL_COLS,
                            datatype=EVAL_TYPES,
                            row_count=5,
                        )

            submit_button = gr.Button("Submit Eval")
            submission_result = gr.Markdown()
            submit_button.click(
                add_new_eval,
                [
                    model_name_textbox,
                    base_model_name_textbox,
                    revision_name_textbox,
                    precision,
                    weight_type,
                    model_type,
                ],
                submission_result,
            )

    with gr.Row():
        with gr.Accordion("📙 Citation", open=False):
            citation_button = gr.Textbox(
                value=CITATION_BUTTON_TEXT,
                label=CITATION_BUTTON_LABEL,
                lines=20,
                elem_id="citation-button",
                show_copy_button=True,
            )

demo.queue(default_concurrency_limit=40)

# Add webhooks
app = WebhooksServer(ui=demo)

@app.add_webhook
async def update_leaderboard(payload: WebhookPayload) -> None:
    if payload.repo.type == "dataset" and payload.event.action == "update":
        leaderboard_dataset = datasets.load_dataset(AGGREGATED_REPO, "default", split="train", cache_dir=HF_HOME)
        leaderboard_df = get_leaderboard_df(
            leaderboard_dataset=leaderboard_dataset, 
            cols=COLS,
            benchmark_cols=BENCHMARK_COLS,
        )
        leaderboard.value = leaderboard_df

@app.add_webhook    
async def update_queue(payload: WebhookPayload) -> None:
    if payload.repo.type == "dataset" and payload.event.action == "update":
        download_dataset(QUEUE_REPO, EVAL_REQUESTS_PATH)
        eval_queue_dfs = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
        finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = eval_queue_dfs

        finished_eval_table.value = finished_eval_queue_df
        running_eval_table.value = running_eval_queue_df
        pending_eval_table.value = pending_eval_queue_df


app.launch(share=True)