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,
    INTRODUCTION_TEXT,
    LLM_BENCHMARKS_TEXT,
    TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
    BENCHMARK_COLS,
    COLS,
    AutoEvalColumn,
    singletable_AutoEvalColumn,
    singlecolumn_AutoEvalColumn,
    ModelType,
    fields,
)
from src.envs import API, EVAL_RESULTS_PATH, REPO_ID, RESULTS_REPO, TOKEN
from src.populate import get_leaderboard_df


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

### Space initialisation
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 as e:
    print(f"Error downloading results: {e}")
    # Create the directory if it doesn't exist
    import os
    os.makedirs(EVAL_RESULTS_PATH, exist_ok=True)


SINGLECOLUMN_LEADERBOARD_DF, SINGLETABLE_LEADERBOARD_DF, MULTITABLE_LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, COLS, BENCHMARK_COLS)

def init_multitable_leaderboard(dataframe):
    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],
        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
        filter_columns=[
            ColumnFilter(AutoEvalColumn.dataset.name, type="checkboxgroup", label="Datasets"),
            ColumnFilter(AutoEvalColumn.model.name, type="checkboxgroup", label="Models"),
        ],
        bool_checkboxgroup_label="Hide models",
        interactive=False,
    )

def init_singletable_leaderboard(dataframe):
    return Leaderboard(
        value=dataframe,
        datatype=[c.type for c in fields(singletable_AutoEvalColumn)],
        select_columns=SelectColumns(
            default_selection=[c.name for c in fields(singletable_AutoEvalColumn) if c.displayed_by_default],
            cant_deselect=[c.name for c in fields(singletable_AutoEvalColumn) if c.never_hidden],
            label="Select Columns to Display:",
        ),
        search_columns=[singletable_AutoEvalColumn.model.name],
        hide_columns=[c.name for c in fields(singletable_AutoEvalColumn) if c.hidden],
        filter_columns=[
            ColumnFilter(singletable_AutoEvalColumn.dataset.name, type="checkboxgroup", label="Datasets"),
            ColumnFilter(singletable_AutoEvalColumn.model.name, type="checkboxgroup", label="Models"),
        ],
        bool_checkboxgroup_label="Hide models",
        interactive=False,
    )

def init_singlecolumn_leaderboard(dataframe):
    return Leaderboard(
        value=dataframe,
        datatype=[c.type for c in fields(singlecolumn_AutoEvalColumn)],
        select_columns=SelectColumns(
            default_selection=[c.name for c in fields(singlecolumn_AutoEvalColumn) if c.displayed_by_default],
            cant_deselect=[c.name for c in fields(singlecolumn_AutoEvalColumn) if c.never_hidden],
            label="Select Columns to Display:",
        ),
        search_columns=[singlecolumn_AutoEvalColumn.model.name],
        hide_columns=[c.name for c in fields(singlecolumn_AutoEvalColumn) if c.hidden],
        filter_columns=[
            ColumnFilter(singlecolumn_AutoEvalColumn.dataset.name, type="checkboxgroup", label="Datasets"),
            ColumnFilter(singlecolumn_AutoEvalColumn.table.name, type="checkboxgroup", label="Tables"),
            ColumnFilter(singlecolumn_AutoEvalColumn.model.name, type="checkboxgroup", label="Models"),
        ],
        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("🏅 MultiTable", elem_id="syntherela-benchmark-tab-table", id=0):
            leaderboard = init_multitable_leaderboard(MULTITABLE_LEADERBOARD_DF)
        
        with gr.TabItem("🏅 SingleTable", elem_id="syntherela-benchmark-tab-table", id=1):
            singletable_leaderboard = init_singletable_leaderboard(SINGLETABLE_LEADERBOARD_DF)

        with gr.TabItem("🏅 SingleColumn", elem_id="syntherela-benchmark-tab-table", id=2):
            singlecolumn_leaderboard = init_singlecolumn_leaderboard(SINGLECOLUMN_LEADERBOARD_DF)

        with gr.TabItem("📝 About", elem_id="syntherela-benchmark-tab-table", id=3):
            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")

    with gr.Row():
        with gr.Accordion("📙 Citation", open=False):
            citation_button = gr.Textbox(
                value=CITATION_BUTTON_TEXT,
                label=CITATION_BUTTON_LABEL,
                lines=8,
                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()