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 datetime import datetime
import pytz

from src.about import (
    CITATION_BUTTON_LABEL,
    CITATION_BUTTON_TEXT,
    EVALUATION_QUEUE_TEXT,
    get_INTRODUCTION_TEXT,
    LLM_BENCHMARKS_TEXT,
    TITLE,
    INTRODUCE_BENCHMARK
)
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_open_model_eval



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

### Space initialisation
# load the evaluation requests and results locally
try:
    print(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:
    print(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.")
    dataframe.insert(0, '', range(1, len(dataframe) + 1))
    return Leaderboard(
        value=dataframe,
        datatype=[int]+[c.type for c in fields(AutoEvalColumn)],
        search_columns=[AutoEvalColumn.model.name],
        hide_columns=["Available on the hub"],
        filter_columns=[
            ColumnFilter(
                AutoEvalColumn.still_on_hub.name, type="boolean", label="🔑 Show Open Models Only", default=False
            ),
        ],
        bool_checkboxgroup_label="Hide models",
        interactive=False
    )






demo = gr.Blocks(css=custom_css)
with demo:
    gr.HTML(TITLE)
    gr.HTML(get_INTRODUCTION_TEXT(LEADERBOARD_DF.shape[0] , datetime.now(pytz.timezone('US/Pacific')).strftime("%Y-%m-%d %H:%M:%S"), paper_link= "https://arxiv.org/abs/2503.12329"), 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=1):
            gr.HTML(INTRODUCE_BENCHMARK)  #TODO
            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 ({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 Open model here!", elem_classes="markdown-text")

        #     with gr.Row():
        #         with gr.Column():
        #             model_name = gr.Textbox(label="Model name")
        #     submit_button = gr.Button("Submit Eval")
        #     submission_result = gr.Markdown()
        #     submit_button.click(
        #         add_new_open_model_eval,
        #         [
        #             model_name
        #         ],
        #         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()