app.py

from dotenv import 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
import threading

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.evaluators.run_evaluator import evaluator_runner

load_dotenv()
def restart_space():
    try:
        print("Restarting space...")
        space_runtime = API.restart_space(repo_id=REPO_ID,token=TOKEN)
        print(f"Space restarted successfully: {space_runtime}")
    except Exception as e:
        print(f"Error restarting space: {str(e)}")
        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, force_download=True
            )       
            snapshot_download(
                repo_id=RESULTS_REPO,  local_dir=EVAL_RESULTS_PATH,  repo_type="dataset",  tqdm_class=None,  etag_timeout=30,  token=TOKEN, force_download=True
            )
        except Exception as download_error:
            print(f"Error downloading datasets: {str(download_error)}")



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,
    )



# API.delete_files(repo_id=QUEUE_REPO, token=TOKEN,delete_patterns=["*"],commit_message="Clearing queue",repo_type="dataset")
# API.delete_files(repo_id=RESULTS_REPO, token=TOKEN,delete_patterns=["*"],commit_message="Clearing results",repo_type="dataset")
# sys.exit(0)

### 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,force_download=True
    )
    
    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,force_download=True
    )
    
    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()


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)




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():
                            print(running_eval_queue_df)
                            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=120)
thread = threading.Thread(target=evaluator_runner)
# scheduler.start()
thread.start()
demo.queue(default_concurrency_limit=40).launch()