app.py

import json
import requests
from datasets import load_dataset
import gradio as gr
from huggingface_hub import HfApi, hf_hub_download, Repository
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


DATASET_REPO_URL = "https://huggingface.co/datasets/CarlCochet/BotFightData"
ELO_FILENAME = "soccer_elo.csv"
HF_TOKEN = os.environ.get("HF_TOKEN")

repo = Repository(
    local_dir="soccer_elo", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
)

block = gr.Blocks()
matchmaking = Matchmaking()
api = HfApi()

scheduler = BackgroundScheduler()
scheduler.add_job(func=init_matchmaking, trigger="interval", seconds=60)
scheduler.start()


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


def get_elo_data() -> pd.DataFrame:
    repo.git_pull()
    data = pd.read_csv(os.path.join(DATASET_REPO_URL, "resolve", "main", ELO_FILENAME))
    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()