app.py

import os
import logging
import time
import datetime
import gradio as gr
import datasets
from huggingface_hub import snapshot_download
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
import plotly.graph_objects as go

from src.display.about import (
    CITATION_BUTTON_LABEL,
    CITATION_BUTTON_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,
    AutoEvalColumn,
    fields,
)
from src.envs import (
    EVAL_REQUESTS_PATH,
    AGGREGATED_REPO,
    QUEUE_REPO,
    REPO_ID,
    HF_HOME,
)
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.tools.plots import create_plot_df, create_scores_df

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

# 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"
LAST_UPDATE_LEADERBOARD = datetime.datetime.now()

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 get_latest_data_leaderboard(leaderboard_initial_df = None):
    current_time = datetime.datetime.now()
    global LAST_UPDATE_LEADERBOARD
    if current_time - LAST_UPDATE_LEADERBOARD < datetime.timedelta(minutes=10) and leaderboard_initial_df is not None:
        return leaderboard_initial_df
    LAST_UPDATE_LEADERBOARD = current_time
    leaderboard_dataset = datasets.load_dataset(
        AGGREGATED_REPO, 
        "default", 
        split="train", 
        cache_dir=HF_HOME, 
        download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS, # Uses the cached dataset 
        verification_mode="no_checks"
    )

    leaderboard_df = get_leaderboard_df(
        leaderboard_dataset=leaderboard_dataset, 
        cols=COLS,
        benchmark_cols=BENCHMARK_COLS,
    )

    return leaderboard_df

def get_latest_data_queue():
    eval_queue_dfs = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
    return eval_queue_dfs

def init_space():
    """Initializes the application space, loading only necessary data."""
    if DO_FULL_INIT:
        # These downloads only occur on full initialization
        download_dataset(QUEUE_REPO, EVAL_REQUESTS_PATH)

    # Always redownload the leaderboard DataFrame
    leaderboard_df = get_latest_data_leaderboard()

    # Evaluation queue DataFrame retrieval is independent of initialization detail level
    eval_queue_dfs = get_latest_data_queue()

    return leaderboard_df, eval_queue_dfs

# Initialize the space
leaderboard_df, eval_queue_dfs = init_space()
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

def create_metric_plot_obj(df, metrics, title="Metrics Over Time"):
    """Create plot with Open-Orca models highlighted in purple"""
    fig = go.Figure()
    
    # Add traces for each metric
    for metric in metrics:
        # Get the model names for this metric
        model_names = df[f"{metric}_model"].tolist()
        
        # Create masks for Open-Orca and non-Open-Orca models
        is_open_orca = ["Open-Orca" in str(model) for model in model_names]
        
        # Add trace for non-Open-Orca models
        fig.add_trace(
            go.Scatter(
                x=df[df.index[~is_open_orca]],
                y=df[metric][~is_open_orca],
                name=metric,
                mode='lines+markers',
                line=dict(width=2),
                marker=dict(size=8),
                hovertemplate=(
                    "Date: %{x}<br>"
                    "Score: %{y:.2f}<br>"
                    "Model: %{text}<br>"
                ),
                text=[model_names[i] for i, flag in enumerate(is_open_orca) if not flag]
            )
        )
        
        # Add trace for Open-Orca models with purple color and larger markers
        if any(is_open_orca):
            fig.add_trace(
                go.Scatter(
                    x=df[df.index[is_open_orca]],
                    y=df[metric][is_open_orca],
                    name=f"{metric} (Open-Orca)",
                    mode='lines+markers',
                    line=dict(color='purple', width=3),
                    marker=dict(
                        color='purple',
                        size=12,
                        symbol='star'
                    ),
                    hovertemplate=(
                        "Date: %{x}<br>"
                        "Score: %{y:.2f}<br>"
                        "Model: %{text}<br>"
                    ),
                    text=[model_names[i] for i, flag in enumerate(is_open_orca) if flag]
                )
            )
    
    # Update layout
    fig.update_layout(
        title=title,
        xaxis_title="Date",
        yaxis_title="Score",
        hovermode='x unified',
        showlegend=True,
        legend=dict(
            yanchor="top",
            y=0.99,
            xanchor="left",
            x=0.01
        )
    )
    
    return fig

def init_leaderboard(dataframe):
    return Leaderboard(
        value = dataframe,
        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",
        interactive=False,
    )

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 = init_leaderboard(leaderboard_df)

        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.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.load(fn=get_latest_data_leaderboard, inputs=[leaderboard], outputs=[leaderboard])

demo.queue(default_concurrency_limit=40).launch()