import json
import requests
from datasets import load_dataset
import gradio as gr
from huggingface_hub import HfApi, hf_hub_download
from huggingface_hub.repocard import metadata_load
import pandas as pd
from matchmaking import *
from background_task import init_matchmaking, get_elo_data
from apscheduler.schedulers.background import BackgroundScheduler
import asyncio

matchmaking = Matchmaking()
api = HfApi()

# launch
scheduler = BackgroundScheduler()
scheduler.add_job(func=init_matchmaking, trigger="interval", seconds=15000)
scheduler.start()


def update_elos():
    matchmaking.read_history()
    matchmaking.compute_elo()
    matchmaking.save_elo_data()


with gr.Blocks() as block:
    gr.Markdown(f"""
        # 🏆 The Deep Reinforcement Learning Course Leaderboard 🏆 

        This is the leaderboard of trained agents during the Deep Reinforcement Learning Course. A free course from beginner to expert.

        This is the Soccer environment leaderboard, use Ctrl+F to find your rank 🏆

        We use an ELO rating to sort the models.
        You **can click on the model's name** to be redirected to its model card which includes documentation.

        🤖 You want to try to train your agents? <a href="http://eepurl.com/ic5ZUD" target="_blank">Sign up to the Hugging Face free Deep Reinforcement Learning Course 🤗 </a>.

        You want to compare two agents? <a href="https://huggingface.co/spaces/ThomasSimonini/Compare-Reinforcement-Learning-Agents" target="_blank">It's possible using this Spaces demo 👀 </a>.

        🔧 There is an **environment missing?** Please open an issue.
        """)
    with gr.Row():
        output = gr.components.Dataframe(
            value=get_elo_data(),
            headers=["Ranking 🏆", "User 🤗", "Model id 🤖", "ELO 🚀", "Games played 🎮"],
            datatype=["number", "markdown", "markdown", "number", "number"]
        )
    with gr.Row():
        refresh = gr.Button("Refresh")
        refresh.click(get_elo_data, inputs=[], outputs=output)

block.launch()