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,
    EVAL_COLS,
    EVAL_TYPES,
    AutoEvalColumn,
    ModelType,
    fields,
    WeightType,
    Precision,
)
from src.envs import (
    API,
    EVAL_RESULTS_PATH_CDM,
    EVAL_RESULTS_PATH_CDM_FI,
    REPO_ID,
    RESULTS_REPO_CDM,
    RESULTS_REPO_CDM_FI,
    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_CDM)
    snapshot_download(
        repo_id=RESULTS_REPO_CDM,
        local_dir=EVAL_RESULTS_PATH_CDM,
        repo_type="dataset",
        tqdm_class=None,
        etag_timeout=30,
        token=TOKEN,
    )
except Exception:
    restart_space()

try:
    print(EVAL_RESULTS_PATH_CDM_FI)
    snapshot_download(
        repo_id=RESULTS_REPO_CDM_FI,
        local_dir=EVAL_RESULTS_PATH_CDM_FI,
        repo_type="dataset",
        tqdm_class=None,
        etag_timeout=30,
        token=TOKEN,
    )
except Exception:
    restart_space()


LEADERBOARD_DF_CDM = get_leaderboard_df(EVAL_RESULTS_PATH_CDM, COLS, BENCHMARK_COLS)
LEADERBOARD_DF_CDM_FI = get_leaderboard_df(EVAL_RESULTS_PATH_CDM_FI, COLS, BENCHMARK_COLS)


def init_leaderboard(dataframe):
    if dataframe is None or dataframe.empty:
        print("Warning: Empty dataframe provided to leaderboard")
        return gr.Dataframe(
            headers=COLS, datatype=[c.type for c in fields(AutoEvalColumn)], label="No results available"
        )

    print(f"Initializing leaderboard with {len(dataframe)} rows")
    print(f"Columns: {dataframe.columns.tolist()}")

    # Convert the dataframe to ensure proper types
    for col in dataframe.columns:
        if col == AutoEvalColumn.model.name:
            # Keep model column as is since it contains HTML
            continue
        elif col == AutoEvalColumn.still_on_hub.name:
            dataframe[col] = dataframe[col].astype(bool)
        elif col in [AutoEvalColumn.seq_length.name, AutoEvalColumn.model_quantization_bits.name]:
            dataframe[col] = dataframe[col].astype(int)
        else:
            # Convert other numeric columns to float
            try:
                dataframe[col] = dataframe[col].astype(float)
            except:
                pass

    try:
        return Leaderboard(
            value=dataframe,
            headers=COLS,
            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],
            interactive=False,
        )
    except Exception as e:
        print(f"Error initializing leaderboard: {e}")
        # Instead of showing error message, try simpler table display
        return gr.Dataframe(
            value=dataframe, headers=COLS, datatype=[c.type for c in fields(AutoEvalColumn)], 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("MIMIC CDM", elem_id="llm-benchmark-tab-table", id=0):
            leaderboard_cdm = init_leaderboard(LEADERBOARD_DF_CDM)

        with gr.TabItem("MIMIC CDM FI", elem_id="llm-benchmark-tab-table", id=1):
            leaderboard_cdm_fi = init_leaderboard(LEADERBOARD_DF_CDM_FI)

        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
            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=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(share=True)