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
from apscheduler.schedulers.background import BackgroundScheduler


block = gr.Blocks()
env = [
    {
        "name": "Soccer",
        "global": None,
    },
]
matchmaking = Matchmaking()

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()


def get_elo_data() -> pd.DataFrame:
    data = pd.read_csv(f"env_elos/elo.csv")
    return data


with 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.Blocks() as block:
    gr.components.Dataframe(
        value=get_elo_data(),
        headers=["Ranking 🏆", "User 🤗", "Model id 🤖", "ELO 🚀", "Games played 🎮"],
        datatype=["number", "markdown", "markdown", "number", "number"]
    )

block.launch()