Spaces:
Runtime error
Runtime error
| 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=60) | |
| scheduler.start() | |
| def update_elos(): | |
| matchmaking.read_history() | |
| matchmaking.compute_elo() | |
| matchmaking.save_elo_data() | |
| def get_env_data() -> pd.DataFrame: | |
| data = pd.read_csv(f"env_elos/elo.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. | |
| 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(): | |
| 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(), | |
| headers=["Ranking 🏆", "User 🤗", "Model id 🤖", "ELO 🚀", "Games played 🎮"], | |
| datatype=["number", "markdown", "markdown", "number", "number"] | |
| ) | |
| block.launch() | |