import os
import gradio as gr
import pandas as pd
from huggingface_hub import HfApi
from huggingface_hub import Repository
from apscheduler.schedulers.background import BackgroundScheduler

from src.assets.text_content import *
from src.assets.css_html_js import custom_css

OPTIMUM_TOKEN = os.environ.get("OPTIMUM_TOKEN", None)

LLM_PERF_LEADERBOARD_REPO = "optimum/llm-perf-leaderboard"
LLM_PERF_DATASET_REPO = "optimum/llm-perf"

api = HfApi()


def restart_space():
    api.restart_space(
        repo_id=LLM_PERF_LEADERBOARD_REPO, token=OPTIMUM_TOKEN
    )


def load_all_info_from_hub():
    llm_perf_repo = None
    if OPTIMUM_TOKEN:
        print("Loading LLM-Perf-Dataset from Hub...")
        llm_perf_repo = Repository(
            local_dir="./llm-perf/",
            clone_from=LLM_PERF_DATASET_REPO,
            token=OPTIMUM_TOKEN,
            repo_type="dataset",
        )
        llm_perf_repo.git_pull()

    return llm_perf_repo


llm_perf_repo = load_all_info_from_hub()


def has_no_nan_values(df, columns):
    return df[columns].notna().all(axis=1)


def has_nan_values(df, columns):
    return df[columns].isna().any(axis=1)


def get_leaderboard_df():
    if llm_perf_repo:
        llm_perf_repo.git_pull()

    df = pd.read_csv("./llm-perf/reports/cuda_1_100/inference_report.csv")
    print(df.columns)

    return df


original_df = get_leaderboard_df()
leaderboard_df = original_df.copy()


def refresh():
    leaderboard_df = get_leaderboard_df()

    return leaderboard_df


demo = gr.Blocks(css=custom_css)
with demo:
    gr.HTML(TITLE)
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    print("rendering tab...")
    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 LLM-Perf Benchmark", elem_id="llm-perf-benchmark-tab-table", id=0):
            leaderboard_table_lite = gr.components.Dataframe(
                value=leaderboard_df,
                headers=leaderboard_df.columns.tolist(),
                max_rows=None,
                elem_id="leaderboard-table-lite",
            )

scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=3600)
scheduler.start()
demo.queue(concurrency_count=40).launch()