app.py

import os
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

import gradio as gr
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
from apscheduler.schedulers.background import BackgroundScheduler
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
from src.evaluator.evaluate import process_evaluation_queue
import threading
import time


def restart_space():
    try:
        # Restart the space
        API.restart_space(repo_id=REPO_ID)
    except Exception as e:
        print(f"Error restarting space: {str(e)}")
        # If restart fails, try to download the datasets again
        try:
            print("Attempting to download datasets again...")
            snapshot_download(
                repo_id=QUEUE_REPO, 
                local_dir=EVAL_REQUESTS_PATH, 
                repo_type="dataset", 
                tqdm_class=None, 
                etag_timeout=30, 
                token=TOKEN
            )
            snapshot_download(
                repo_id=RESULTS_REPO, 
                local_dir=EVAL_RESULTS_PATH, 
                repo_type="dataset", 
                tqdm_class=None, 
                etag_timeout=30, 
                token=TOKEN
            )
        except Exception as download_error:
            print(f"Error downloading datasets: {str(download_error)}")

### Space initialisation
try:
    print(f"\n=== Starting space initialization ===")
    print(f"EVAL_REQUESTS_PATH: {EVAL_REQUESTS_PATH}")
    print(f"EVAL_RESULTS_PATH: {EVAL_RESULTS_PATH}")
    print(f"QUEUE_REPO: {QUEUE_REPO}")
    print(f"RESULTS_REPO: {RESULTS_REPO}")
    print(f"TOKEN: {bool(TOKEN)}")
    
    print("\n=== Downloading request files ===")
    snapshot_download(
        repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
    )
    
    print("\n=== Downloading results files ===")
    snapshot_download(
        repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
    )
    
    print("\n=== Loading leaderboard data ===")
    LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
    print(f"Leaderboard DataFrame shape: {LEADERBOARD_DF.shape if LEADERBOARD_DF is not None else 'None'}")
    
    print("\n=== Loading evaluation queue data ===")
    (
        finished_eval_queue_df,
        running_eval_queue_df,
        pending_eval_queue_df,
    ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
    print(f"Finished eval queue shape: {finished_eval_queue_df.shape if finished_eval_queue_df is not None else 'None'}")
    print(f"Running eval queue shape: {running_eval_queue_df.shape if running_eval_queue_df is not None else 'None'}")
    print(f"Pending eval queue shape: {pending_eval_queue_df.shape if pending_eval_queue_df is not None else 'None'}")
    
except Exception as e:
    print(f"\n=== Error during space initialization ===")
    print(f"Error: {str(e)}")
    restart_space()


# Start evaluator service in a separate thread
def run_evaluator():
    print("Starting evaluator service...")
    while True:
        try:
            process_evaluation_queue()
            print("Evaluation queue processed. Sleeping for 5 minutes...")
            time.sleep(300)  # Sleep for 5 minutes
        except Exception as e:
            print(f"Error in evaluation process: {e}")
            print("Retrying in 5 minutes...")
            time.sleep(300)

# Start evaluator in a separate thread
evaluator_thread = threading.Thread(target=run_evaluator, daemon=True)
evaluator_thread.start()

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:
        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,
    )

# Add model evaluation functionality
def evaluate_and_update(model_name, revision, precision, weight_type):
    """Add a model evaluation request to the queue"""
    try:
        # Add evaluation request to queue
        add_new_eval(
            model_name=model_name,
            revision=revision,
            precision=precision,
            weight_type=weight_type,
            model_type="LLM",  # Add appropriate model type
        )
        
        # Update leaderboard
        LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
        return "Evaluation request added to queue! Check the leaderboard for updates."
    except Exception as e:
        print(f"Error in evaluate_and_update: {str(e)}")
        print(f"Full traceback: {traceback.format_exc()}")
        return f"Error adding evaluation request: {str(e)}"


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(INTRODUCTION_TEXT)
            gr.Markdown(LLM_BENCHMARKS_TEXT)
            gr.Markdown(EVALUATION_QUEUE_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 ({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,
                            )
            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,
            )

scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()