app.py

import os
import logging
import time
import schedule
import datetime
import gradio as gr
from threading import Thread
import datasets
from huggingface_hub import snapshot_download, WebhooksServer, WebhookPayload, RepoCard
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
from apscheduler.schedulers.background import BackgroundScheduler

# Start ephemeral Spaces on PRs (see config in README.md)
from gradio_space_ci.webhook import IS_EPHEMERAL_SPACE, SPACE_ID, configure_space_ci

from src.display.about import (
    CITATION_BUTTON_LABEL,
    CITATION_BUTTON_TEXT,
    # INTRODUCTION_TEXT,
    TITLE,
    ABOUT_TEXT,
    SUBMISSION_TEXT_3,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
    COLS,
    EVAL_COLS,
    EVAL_TYPES,
    AutoEvalColumn,
    fields,
    EvalQueueColumn
)
from src.envs import (
    API,
    EVAL_REQUESTS_PATH,
    RESULT_REPO,
    DATA_VERSION,
    DATA_REPO,
    HARD_RESULT_REPO,
    ELO_REPO,
    HARD_ELO_REPO,
    SOLVE_REPO,
    HARD_SOLVE_REPO,
    HF_TOKEN,
    QUEUE_REPO,
    REPO_ID,
    VOTES_REPO,
    VOTES_PATH,
    HF_HOME,
)
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.tools.plots import plot_elo_mle, plot_solve_rate
# from src.voting.vote_system import VoteManager, run_scheduler

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

# Start ephemeral Spaces on PRs (see config in README.md)
from gradio_space_ci.webhook import IS_EPHEMERAL_SPACE, SPACE_ID, configure_space_ci

# 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 = True # os.getenv("LEADERBOARD_FULL_INIT", "True") == "True"
NEW_DATA_ON_LEADERBOARD = True
LEADERBOARD_DF = None
HARD_LEADERBOARD_DF = None
ELO_TASK_DF = None
ELO_BENCH_DF = None
HARD_ELO_TASK_DF = None
HARD_ELO_BENCH_DF = None
COMPLETE_SOLVE_DF = None
INSTRUCT_SOLVE_DF = None
HARD_COMPLETE_SOLVE_DF = None
HARD_INSTRUCT_SOLVE_DF = None

DATA = datasets.load_dataset(DATA_REPO, "default", cache_dir=HF_HOME, split=DATA_VERSION,
                             verification_mode="no_checks")


def filter_data(data, keyword):
    if not keyword:
        return data
    filtered_data = [item for item in data if keyword.lower() in item['complete_prompt'].lower()]
    return filtered_data


def update_display(search_keyword, index, show_test):
    filtered_data = filter_data(DATA, search_keyword)
    
    if not filtered_data:
        return ["No data available. Check the search criteria."] + [""] * 4 + [0, gr.update(maximum=0, value=0)]
    
    max_index = len(filtered_data) - 1
    index = min(max(0, index), max_index)
    
    task_id = filtered_data[index]['task_id']
    snippet1 = filtered_data[index]['complete_prompt']
    snippet2 = filtered_data[index]['instruct_prompt']
    # snippet3 = filtered_data[index]['canonical_solution'] if show_solution else ""
    snippet4 = filtered_data[index]['test'] if show_test else ""
    
    return [
        task_id,
        snippet1,
        snippet2,
        # snippet3,
        snippet4,
        len(filtered_data),
        gr.update(maximum=max_index, value=index)
    ]

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 get_latest_data_leaderboard(
    leaderboard_initial_df = None,
    hard_leaderboard_initial_df = None,
    elo_task_df = None,
    elo_bench_df = None,
    hard_elo_task_df = None,
    hard_elo_bench_df = None,
    complete_solve_df = None,
    instruct_solve_df = None,
    hard_complete_solve_df = None,
    hard_instruct_solve_df = None
    ):
    global NEW_DATA_ON_LEADERBOARD
    global LEADERBOARD_DF
    global HARD_LEADERBOARD_DF
    global ELO_TASK_DF
    global ELO_BENCH_DF
    global HARD_ELO_TASK_DF
    global HARD_ELO_BENCH_DF
    global COMPLETE_SOLVE_DF
    global INSTRUCT_SOLVE_DF
    global HARD_COMPLETE_SOLVE_DF
    global HARD_INSTRUCT_SOLVE_DF

    if NEW_DATA_ON_LEADERBOARD:
        print("Leaderboard updated at reload!")
        leaderboard_dataset = datasets.load_dataset(
            RESULT_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,
        )
        hard_leaderboard_dataset = datasets.load_dataset(
            HARD_RESULT_REPO, 
            "default", 
            split="train", 
            cache_dir=HF_HOME, 
            download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS, # Uses the cached dataset 
            verification_mode="no_checks"
        )
        hard_leaderboard_df = get_leaderboard_df(
            leaderboard_dataset=hard_leaderboard_dataset, 
            cols=COLS,
        )
        HARD_LEADERBOARD_DF = hard_leaderboard_df
        
        elo_task_df = datasets.load_dataset(
            ELO_REPO,
            "default", 
            split="task_no_tie", 
            cache_dir=HF_HOME, 
            download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS, # Uses the cached dataset 
            verification_mode="no_checks"
        ).to_pandas()
        elo_bench_df = datasets.load_dataset(
            ELO_REPO,
            "default", 
            split="benchmark_tie", 
            cache_dir=HF_HOME, 
            download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS, # Uses the cached dataset 
            verification_mode="no_checks"
        ).to_pandas()
        ELO_TASK_DF = elo_task_df
        ELO_BENCH_DF = elo_bench_df
        
        hard_elo_task_df = datasets.load_dataset(
            HARD_ELO_REPO,
            "default", 
            split="task_no_tie", 
            cache_dir=HF_HOME, 
            download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS, # Uses the cached dataset 
            verification_mode="no_checks"
        ).to_pandas()
        hard_elo_bench_df = datasets.load_dataset(
            HARD_ELO_REPO,
            "default", 
            split="benchmark_tie", 
            cache_dir=HF_HOME, 
            download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS, # Uses the cached dataset 
            verification_mode="no_checks"
        ).to_pandas()
        HARD_ELO_TASK_DF = hard_elo_task_df
        HARD_ELO_BENCH_DF = hard_elo_bench_df
        
        complete_solve_df = datasets.load_dataset(
            SOLVE_REPO,
            "default",
            split="complete",
            cache_dir=HF_HOME,
            download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS, # Uses the cached dataset
            verification_mode="no_checks"
        ).to_pandas()
        instruct_solve_df = datasets.load_dataset(
            SOLVE_REPO,
            "default",
            split="instruct",
            cache_dir=HF_HOME,
            download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS, # Uses the cached dataset
            verification_mode="no_checks"
        ).to_pandas()
        COMPLETE_SOLVE_DF = complete_solve_df
        INSTRUCT_SOLVE_DF = instruct_solve_df
        
        hard_complete_solve_df = datasets.load_dataset(
            HARD_SOLVE_REPO,
            "default",
            split="complete",
            cache_dir=HF_HOME,
            download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS, # Uses the cached dataset
            verification_mode="no_checks"
        ).to_pandas()
        hard_instruct_solve_df = datasets.load_dataset(
            HARD_SOLVE_REPO,
            "default",
            split="instruct",
            cache_dir=HF_HOME,
            download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS, # Uses the cached dataset
            verification_mode="no_checks"
        ).to_pandas()        
        HARD_COMPLETE_SOLVE_DF = hard_complete_solve_df
        HARD_INSTRUCT_SOLVE_DF = hard_instruct_solve_df
        
        NEW_DATA_ON_LEADERBOARD = False

    else:
        LEADERBOARD_DF = leaderboard_initial_df
        HARD_LEADERBOARD_DF = hard_leaderboard_initial_df
        ELO_TASK_DF = elo_task_df
        ELO_BENCH_DF = elo_bench_df
        HARD_ELO_TASK_DF = hard_elo_task_df
        HARD_ELO_BENCH_DF = hard_elo_bench_df
        COMPLETE_SOLVE_DF = complete_solve_df
        INSTRUCT_SOLVE_DF = instruct_solve_df
        HARD_COMPLETE_SOLVE_DF = hard_complete_solve_df
        HARD_INSTRUCT_SOLVE_DF = hard_instruct_solve_df
        
    return (LEADERBOARD_DF, HARD_LEADERBOARD_DF, ELO_TASK_DF, ELO_BENCH_DF, HARD_ELO_TASK_DF, HARD_ELO_BENCH_DF, COMPLETE_SOLVE_DF, INSTRUCT_SOLVE_DF, HARD_COMPLETE_SOLVE_DF, HARD_INSTRUCT_SOLVE_DF)


def init_space():
    """Initializes the application space, loading only necessary data."""

    # Always redownload the leaderboard DataFrame
    global LEADERBOARD_DF
    global HARD_LEADERBOARD_DF
    global ELO_TASK_DF
    global ELO_BENCH_DF
    global HARD_ELO_TASK_DF
    global HARD_ELO_BENCH_DF
    global COMPLETE_SOLVE_DF
    global INSTRUCT_SOLVE_DF
    global HARD_COMPLETE_SOLVE_DF
    global HARD_INSTRUCT_SOLVE_DF
    
    LEADERBOARD_DF, HARD_LEADERBOARD_DF, ELO_TASK_DF, ELO_BENCH_DF, HARD_ELO_TASK_DF, HARD_ELO_BENCH_DF, COMPLETE_SOLVE_DF, INSTRUCT_SOLVE_DF, HARD_COMPLETE_SOLVE_DF, HARD_INSTRUCT_SOLVE_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, HARD_LEADERBOARD_DF, ELO_TASK_DF, ELO_BENCH_DF, HARD_ELO_TASK_DF, HARD_ELO_BENCH_DF, COMPLETE_SOLVE_DF, INSTRUCT_SOLVE_DF, HARD_COMPLETE_SOLVE_DF, HARD_INSTRUCT_SOLVE_DF)

# Initialize VoteManager
# vote_manager = VoteManager(VOTES_PATH, EVAL_REQUESTS_PATH, VOTES_REPO)


# Schedule the upload_votes method to run every 15 minutes
# schedule.every(15).minutes.do(vote_manager.upload_votes)

# Start the scheduler in a separate thread
# scheduler_thread = Thread(target=run_scheduler, args=(vote_manager,), daemon=True)
# scheduler_thread.start()

# 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, HARD_LEADERBOARD_DF, ELO_TASK_DF, \
ELO_BENCH_DF, HARD_ELO_TASK_DF, HARD_ELO_BENCH_DF, \
COMPLETE_SOLVE_DF, INSTRUCT_SOLVE_DF, HARD_COMPLETE_SOLVE_DF, \
HARD_INSTRUCT_SOLVE_DF = init_space()


# 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

# Function to check if a user is logged in
def check_login(profile: gr.OAuthProfile | None) -> bool:
    if profile is None:
        return False
    return True

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 or c.dummy],
            label="Select Columns to Display:",
        ),
        search_columns=[AutoEvalColumn.model.name],
        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
        filter_columns=[
            ColumnFilter(AutoEvalColumn.type.name, type="checkboxgroup", label="Model Types"),
            ColumnFilter(AutoEvalColumn.openness.name, type="checkboxgroup", label="Openness"),
            ColumnFilter(AutoEvalColumn.size_range.name, type="dropdown", label="Model Size"),
            ColumnFilter(AutoEvalColumn.moe.name, type="checkboxgroup", label="Model Architecture"),
        ],
        bool_checkboxgroup_label="Hide models",
        interactive=False,
        )


def init_others(dataframe):
    if dataframe is None or dataframe.empty:
        raise ValueError("Gradio DataFrame is empty or None.")
    return gr.Dataframe(dataframe, visible=False)

main_block = gr.Blocks(css=custom_css)
with main_block as demo:
    with gr.Row(elem_id="header-row"):
        gr.HTML(TITLE + "<p>Total models: " + str(len(LEADERBOARD_DF))+ "</p>")
    
    # gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")    
    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.Tab("💎 Hard Set") as hard_tabs:
            with gr.TabItem("🏅 Benchmark", elem_id="llm-benchmark-tab-table", id="hard_bench"):
                hard_leaderboard = init_leaderboard(HARD_LEADERBOARD_DF)
                gr.Markdown(
                    """
                **Notes:**
                - _Hard Set_ vs _Full Set_:
                    - <u>Hard Set</u>: A subset of ~150 BigCodeBench tasks which is more user-facing and challenging.
                    - <u>Full Set</u>: The full set of 1140 BigCodeBench tasks.
                - _Complete_ vs _Instruct_:
                    - <u>Complete</u>: Code Completion based on the (verbose) structured docstring. This split tests if the models are good at coding.
                    - <u>Instruct</u> (🔥Vibe Check🔥): Code Generation based on the (less verbose) NL-oriented instructions. This split tests if the models are really capable enough to understand human intents to code.
                - `Complete` and `Instruct` represent the calibrated Pass@1 score on the BigCodeBench benchmark splits.
                - `Average` is the average of `Complete` and `Instruct` when both are available.
                - `Elo Rating` represents the task-level Bootstrap of Maximum Likelihood Elo rating on the BigCodeBench-Complete split. The rating starts from 1000 and is bootstrapped 500 times.
                - `#Act Params (B)` is the number of activated model parameters during inference.
                - Model providers have the responsibility to avoid data contamination. Models trained on close data can be affected by contamination.
                - For more details check the 📝 About section.
                """,
                    elem_classes="markdown-text",
                )
            
            with gr.TabItem("📊 Elo Rating", id="hard_elo"):
                with gr.Column():
                    with gr.Group():
                        gr.Markdown("## (Task-level, No Tie, BigCodeBench-Complete) -- _Recommended_")
                        hard_task_elo_map = gr.Plot()
                        hard_elo_task_gr = init_others(HARD_ELO_TASK_DF)
                        demo.load(plot_elo_mle, [hard_elo_task_gr],
                                    hard_task_elo_map)
                    with gr.Group():
                        gr.Markdown("## (Benchmark-level, BigCodeBench-Complete)")
                        hard_bench_elo_map = gr.Plot()
                        hard_elo_bench_gr = init_others(HARD_ELO_BENCH_DF)
                        demo.load(plot_elo_mle, [hard_elo_bench_gr],
                                    hard_bench_elo_map)
                    
            with gr.TabItem("🧩 Solve Rate", id="hard_solve"):
                with gr.Column():
                    hard_complete_map = gr.Plot()
                    hard_complete_solve_gr = init_others(HARD_COMPLETE_SOLVE_DF)
                    demo.load(plot_solve_rate, [hard_complete_solve_gr,
                                                gr.Textbox("Complete", visible=False),
                                                gr.Number(10, visible=False),
                                                gr.Number(16, visible=False),
                                                ], hard_complete_map)
                    hard_instruct_map = gr.Plot()
                    hard_instruct_solve_gr = init_others(HARD_INSTRUCT_SOLVE_DF)
                    demo.load(plot_solve_rate, [hard_instruct_solve_gr,
                                                gr.Textbox("Instruct", visible=False),
                                                gr.Number(10, visible=False),
                                                gr.Number(16, visible=False),
                                                ], hard_instruct_map)
        with gr.Tab("🎯 Full Set") as full_tabs:
            with gr.TabItem("🏅 Benchmark", elem_id="llm-benchmark-tab-table", id="full_bench"):
                leaderboard = init_leaderboard(LEADERBOARD_DF)
                gr.Markdown(
                    """
                **Notes:**
                - _Complete_ vs _Instruct_:
                    - <u>Complete</u>: Code Completion based on the (verbose) structured docstring. This variant tests if the models are good at coding.
                    - <u>Instruct</u> (🔥Vibe Check🔥): Code Generation based on the (less verbose) NL-oriented instructions. This variant tests if the models are really capable enough to understand human intents to code.
                - `complete` and `instruct` represent the calibrated Pass@1 score on the BigCodeBench benchmark variants.
                - `elo_mle` represents the task-level Bootstrap of Maximum Likelihood Elo rating on the BigCodeBench-Complete split. The rating starts from 1000 and is bootstrapped 500 times.
                - `size` is the amount of activated model weight during inference.
                - Model providers have the responsibility to avoid data contamination. Models trained on close data can be affected by contamination.
                - For more details check the 📝 About section.
                """,
                    elem_classes="markdown-text",
                )
            
            with gr.TabItem("📊 Elo Rating", id="full_elo"):
                with gr.Column():
                    with gr.Group():
                        
                        gr.Markdown("## (Task-level, No Tie, BigCodeBench-Complete) -- _Recommended_")
                        task_elo_map = gr.Plot()
                        elo_task_gr = init_others(ELO_TASK_DF)
                        demo.load(plot_elo_mle, [elo_task_gr], task_elo_map)
                    with gr.Group():
                        gr.Markdown("## (Benchmark-level, BigCodeBench-Complete)")
                        bench_elo_map = gr.Plot()
                        elo_bench_gr = init_others(ELO_BENCH_DF)
                        demo.load(plot_elo_mle, [elo_bench_gr], bench_elo_map)
                    
            with gr.TabItem("🧩 Solve Rate", id="full_solve"):
                with gr.Column():
                    complete_map = gr.Plot()
                    complete_solve_gr = init_others(COMPLETE_SOLVE_DF)
                    demo.load(plot_solve_rate, [complete_solve_gr,
                                                gr.Textbox("Complete", visible=False),
                                                ], complete_map)
                    instruct_map = gr.Plot()
                    instruct_solve_gr = init_others(INSTRUCT_SOLVE_DF)
                    demo.load(plot_solve_rate, [instruct_solve_gr,
                                                gr.Textbox("Instruct", visible=False),
                                                ], instruct_map)
        with gr.TabItem("📝 About", id=3):
            gr.Markdown(ABOUT_TEXT, elem_classes="markdown-text")
        with gr.TabItem("🔎 Data Viewer", id="viewer"):
            search_input = gr.Textbox(label="Search by keyword")
            count_output = gr.Number(label="Number of filtered items")
            index_slider = gr.Slider(minimum=0, maximum=len(DATA)-1, step=1, label="Select Index")
            # show_solution = gr.Checkbox(label="Show Solution")
            show_test = gr.Checkbox(label="Show Test Cases")
            update_button = gr.Button("Update")
            
            task_id_output = gr.Textbox(label="Task ID")
            code_completion = gr.Code(language="python", label="Code Completion")
            nl_instruction = gr.Code(language="python", label="Natural Language Instruction")
            # solution = gr.Code(language="python", label="Solution")
            test_cases = gr.Code(language="python", label="Test Cases")
            
            update_button.click(
                update_display, 
                inputs=[search_input, index_slider, show_test],
                outputs=[task_id_output, code_completion, nl_instruction, test_cases, count_output, index_slider]
            )

            # Initial load
            demo.load(
                update_display, 
                inputs=[search_input, index_slider, show_test],
                outputs=[task_id_output, code_completion, nl_instruction, test_cases, count_output, index_slider]
            )

        with gr.TabItem("🚀 Request", id=4):
            gr.Markdown(SUBMISSION_TEXT_3)
    
    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,
            )
                    
    main_block.load(fn=get_latest_data_leaderboard, inputs=[leaderboard, hard_leaderboard, elo_task_gr, elo_bench_gr, hard_elo_task_gr, hard_elo_bench_gr, complete_solve_gr, instruct_solve_gr, hard_complete_solve_gr, hard_instruct_solve_gr], outputs=[leaderboard, hard_leaderboard, elo_task_gr, elo_bench_gr, hard_elo_task_gr, hard_elo_bench_gr, complete_solve_gr, instruct_solve_gr, hard_complete_solve_gr, hard_instruct_solve_gr])
    # leaderboard.change(fn=get_latest_data_queue, inputs=None, outputs=[finished_eval_table, running_eval_table, pending_eval_table])
    # pending_eval_table.change(fn=vote_manager.create_request_vote_df, inputs=[pending_eval_table], outputs=[pending_eval_table_votes])

main_block.queue(default_concurrency_limit=40)


def enable_space_ci_and_return_server(ui: gr.Blocks) -> WebhooksServer:
    # Taken from https://huggingface.co/spaces/Wauplin/gradio-space-ci/blob/075119aee75ab5e7150bf0814eec91c83482e790/src/gradio_space_ci/webhook.py#L61
    # Compared to original, this one do not monkeypatch Gradio which allows us to define more webhooks.
    # ht to Lucain!
    if SPACE_ID is None:
        print("Not in a Space: Space CI disabled.")
        return WebhooksServer(ui=main_block)

    if IS_EPHEMERAL_SPACE:
        print("In an ephemeral Space: Space CI disabled.")
        return WebhooksServer(ui=main_block)

    card = RepoCard.load(repo_id_or_path=SPACE_ID, repo_type="space")
    config = card.data.get("space_ci", {})
    print(f"Enabling Space CI with config from README: {config}")

    return configure_space_ci(
        blocks=ui,
        trusted_authors=config.get("trusted_authors"),
        private=config.get("private", "auto"),
        variables=config.get("variables", "auto"),
        secrets=config.get("secrets"),
        hardware=config.get("hardware"),
        storage=config.get("storage"),
    )

# Create webhooks server (with CI url if in Space and not ephemeral)
webhooks_server = enable_space_ci_and_return_server(ui=main_block)

# Add webhooks
@webhooks_server.add_webhook
def update_leaderboard(payload: WebhookPayload) -> None:
    """Redownloads the leaderboard dataset each time it updates"""
    if payload.repo.type == "dataset" and payload.event.action == "update":
        global NEW_DATA_ON_LEADERBOARD
        if NEW_DATA_ON_LEADERBOARD:
            return
        NEW_DATA_ON_LEADERBOARD = True

        for repo in [RESULT_REPO, HARD_RESULT_REPO, ELO_REPO, HARD_ELO_REPO, SOLVE_REPO, HARD_SOLVE_REPO]:
            datasets.load_dataset(
                repo, 
                "default", 
                cache_dir=HF_HOME, 
                download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD, 
                verification_mode="no_checks"
            )
        
        

webhooks_server.launch()

scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", hours=3) # restarted every 3h as backup in case automatic updates are not working
scheduler.start()