app.py

from functools import partial

import gradio as gr
# from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
import pandas as pd
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.datamodel.data import F1Data

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, REPO_ID, TOKEN, CODE_PROBLEMS_REPO, SUBMISSIONS_REPO, RESULTS_REPO
from src.logger import get_logger
# from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_solutions

logger = get_logger(__name__)

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

lbdb = F1Data(cp_ds_name=CODE_PROBLEMS_REPO, sub_ds_name=SUBMISSIONS_REPO, res_ds_name=RESULTS_REPO)

logger.info("Initialized LBDB")

# (
#     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,
#     )


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):
            logger.info("Tab about")
            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")

        with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
            logger.info("Tab submission")
            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 sulutions here!", elem_classes="markdown-text")

            with gr.Row():
                with gr.Column():
                    submitter_textbox = gr.Textbox(label="Submitter")
                    # 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():
                #     submission_file = gr.File(label="JSONL solutions file", file_types=[".jsonl"])
                    # 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)")

            logger.info("Submut button")
            submit_button = gr.Button("Submit")
            submission_result = gr.Markdown()
            def add_solution_cbk(submitter):
                return add_new_solutions(lbdb, submitter, submission_path=None)
            submit_button.click(
                add_solution_cbk,
#                partial(add_new_solutions, lbdb=lbdb, submission_path=None),
                [
                    submitter_textbox,
                    # submission_file,
                ],
                submission_result,
            )

    with gr.Row():
        logger.info("Citation")
        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,
            )

logger.info("Scheduler")
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
logger.info("Launch")
demo.queue(default_concurrency_limit=40).launch()
logger.info("Done")