app.py

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 *


block = gr.Blocks()
envs = [
    {
        "name": "Snowball-Fight",
        "global": None,
    },
]
matchmaking = Matchmaking()


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


def get_env_data(env_name) -> pd.DataFrame:
    # data = pd.read_csv(f"env_elos/{env_name}.csv")
    data = pd.DataFrame(columns=["user", "model", "elo", "games_played"])
    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.

    Just choose which environment you trained your agent on and with Ctrl+F 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.
    """)

    for i, env in enumerate(envs):
        with gr.TabItem(env["name"]) as tab:
            with gr.Row():
                refresh_data = gr.Button("Refresh")
                val = gr.Variable(value=[env["name"]])
                refresh_data.click(get_env_data, inputs=[val], outputs=env["global"])
            with gr.Row():
                env["global"] = gr.components.DataFrame(
                    get_env_data(env["name"]),
                    headers=["Ranking 🏆", "User 🤗", "Model id 🤖", "ELO 🏆", "Games played 🎮"],
                    datatype=["number", "markdown", "markdown", "number", "number"]
                )

block.launch()