import os
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,
    CLS_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_leaderboard_df
from src.submission.submit import process_submission


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

### Space initialisation
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,
        ignore_patterns=["*.csv"]
    )
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,
        ignore_patterns=["*.csv"]
    )
except Exception:
    restart_space()

os.makedirs(EVAL_REQUESTS_PATH, exist_ok=True)
os.makedirs(EVAL_RESULTS_PATH, exist_ok=True)

LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)


def init_leaderboard(dataframe):
    if dataframe is None or dataframe.empty:
        print("Initializing empty leaderboard")
        return Leaderboard(
            value=pd.DataFrame(columns=[c.name for c in fields(AutoEvalColumn)]),
            search_columns=['Model Name'],
            interactive=True
        )
    else:
        print("Initializing leaderboard with data")
        return Leaderboard(
            value=dataframe,
            datatype=[c.type for c in fields(AutoEvalColumn)],
            search_columns=['Model Name'],
            hide_columns=['Student ID', 'eval_name'],
            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("🏅 Performance Benchmark", elem_id="benchmark-tab-table", id=0):
            leaderboard = init_leaderboard(LEADERBOARD_DF)

        with gr.TabItem("📝 About", elem_id="benchmark-tab-table", id=2):
            gr.Markdown(CLS_BENCHMARKS_TEXT, elem_classes="markdown-text")

        with gr.TabItem("🚀 Submit here! ", elem_id="benchmark-tab-table", id=3):
            with gr.Column():
                gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
                gr.Markdown("## Submit Your Results")
                
                with gr.Row():
                    student_id = gr.Textbox(label="Student ID")
                    model_name = gr.Textbox(label="Model Name", value='pixelCNN++')
                    csv_upload = gr.UploadButton(
                        label="Upload Predictions CSV",
                        file_types=[".csv"],
                        file_count="single"
                    )
                
                submit_button = gr.Button("Submit Results")
                submission_result = gr.Markdown()
                
                submit_button.click(
                    process_submission,
                    inputs=[student_id, model_name, csv_upload],
                    outputs=submission_result
                )

scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()