app.py

import logging
import gradio as gr
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from apscheduler.executors.pool import ThreadPoolExecutor
from apscheduler.jobstores.memory import MemoryJobStore
from huggingface_hub import snapshot_download

from src.about import (
    CITATION_BUTTON_LABEL,
    CITATION_BUTTON_TEXT,
    EVALUATION_QUEUE_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,
    fields,
    WeightType,
    Precision
)
from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval

# Configure Logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Initialize Scheduler
scheduler = BackgroundScheduler(
    jobstores={'default': MemoryJobStore()},
    executors={'default': ThreadPoolExecutor(10)},
    job_defaults={'coalesce': False, 'max_instances': 1},
)
scheduler.start()

def restart_space():
    API.restart_space(repo_id=REPO_ID)

### Space initialisation
try:
    logger.info(f"Downloading evaluation requests from {QUEUE_REPO} to {EVAL_REQUESTS_PATH}")
    snapshot_download(
        repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
    )
except Exception:
    restart_space()
try:
    logger.info(f"Downloading evaluation results from {RESULTS_REPO} to {EVAL_RESULTS_PATH}")
    snapshot_download(
        repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
    )
except Exception:
    restart_space()


LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)

(
    finished_eval_queue_df,
    running_eval_queue_df,
    pending_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)

def init_leaderboard(dataframe):
    if dataframe is None or dataframe.empty:
        raise ValueError("Leaderboard DataFrame is empty or None.")
    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],
            label="Select Columns to Display:",
        ),
        search_columns=[AutoEvalColumn.model.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="Deleted/incomplete", default=True
            ),
        ],
        bool_checkboxgroup_label="Hide models",
        interactive=False,
    )

def start_evaluation(row):
    logger.info(f"Starting evaluation for row ID {row.get('id')}")
    # Implementation to start evaluation
    pass

def monitor_evaluation(row):
    logger.info(f"Monitoring evaluation for row ID {row.get('id')}")
    # Implementation to monitor evaluation
    pass

def initiate_new_evaluation(row):
    logger.info(f"Initiating new evaluation for row ID {row.get('id')}")
    # Implementation to initiate new evaluation
    pass

def finalize_evaluation(row):
    logger.info(f"Finalizing evaluation for row ID {row.get('id')}")
    # Implementation to finalize evaluation
    pass

def process_evaluation_queue():
    """Process pending evaluation requests."""
    logger.info("Starting processing of evaluation queue")
    try:
        # Retrieve evaluation queues
        finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)

        # Assign statuses to each DataFrame
        finished_eval_queue_df['status'] = 'FINISHED'
        running_eval_queue_df['status'] = 'RUNNING'
        pending_eval_queue_df['status'] = 'PENDING'

        # Handle PENDING_NEW_EVAL
        if 'needs_new_eval' in pending_eval_queue_df.columns:
            pending_new_eval_df = pending_eval_queue_df[pending_eval_queue_df['needs_new_eval']].copy()
            pending_new_eval_df['status'] = 'PENDING_NEW_EVAL'
            pending_eval_queue_df = pending_eval_queue_df[~pending_eval_queue_df['needs_new_eval']]
        else:
            pending_new_eval_df = pd.DataFrame()

        # Combine all queues into a single DataFrame
        full_queue_df = pd.concat([
            finished_eval_queue_df,
            running_eval_queue_df,
            pending_eval_queue_df,
            pending_new_eval_df
        ], ignore_index=True)

        logger.debug(f"Combined queue has {len(full_queue_df)} entries")

        # Process each entry based on status
        for _, row in full_queue_df.iterrows():
            status = row['status']
            logger.debug(f"Processing row ID {row.get('id')} with status {status}")

            if status == 'PENDING':
                start_evaluation(row)
            elif status == 'RUNNING':
                monitor_evaluation(row)
            elif status == 'PENDING_NEW_EVAL':
                initiate_new_evaluation(row)
            elif status == 'FINISHED':
                finalize_evaluation(row)
            else:
                logger.warning(f"Unknown status '{status}' for row ID {row.get('id')}")

        logger.info("Completed processing of evaluation queue")
        return finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df

    except Exception as e:
        logger.error(f"Error processing evaluation queue: {e}", exc_info=True)

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("📝 About", elem_id="llm-benchmark-tab-table", id=2):
            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")

        with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
            with gr.Column():
                with gr.Row():
                    gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")

                with gr.Column():
                    with gr.Accordion(
                        f"✅ Finished Evaluations",
                        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",
                            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",
                        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,
                            )

                # Process the evaluation queue every 2 minutes
                timer = gr.Timer(120, active=True)
                timer.tick(process_evaluation_queue, inputs=[], outputs=[finished_eval_table, running_eval_table, pending_eval_table])

            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=None,
                        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)")

            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).launch()