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import requests |
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import pandas as pd |
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from tqdm.auto import tqdm |
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from utils import * |
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import gradio as gr |
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from huggingface_hub import HfApi, hf_hub_download |
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from huggingface_hub.repocard import metadata_load |
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class DeepRL_Leaderboard: |
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def __init__(self) -> None: |
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self.leaderboard= {} |
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def add_leaderboard(self,id=None, title=None): |
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if id is not None and title is not None: |
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id = id.strip() |
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title = title.strip() |
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self.leaderboard.update({id:{'title':title,'data':get_data_per_env(id)}}) |
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def get_data(self): |
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return self.leaderboard |
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def get_ids(self): |
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return list(self.leaderboard.keys()) |
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with open('app.css','r') as f: |
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BLOCK_CSS = f.read() |
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LOADED_MODEL_IDS = {} |
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LOADED_MODEL_METADATA = {} |
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def get_data(rl_env): |
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global LOADED_MODEL_IDS ,LOADED_MODEL_METADATA |
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data = [] |
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model_ids = get_model_ids(rl_env) |
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LOADED_MODEL_IDS[rl_env]=model_ids |
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for model_id in tqdm(model_ids): |
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meta = get_metadata(model_id) |
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LOADED_MODEL_METADATA[model_id] = meta if meta is not None else '' |
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if meta is None: |
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continue |
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user_id = model_id.split('/')[0] |
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row = {} |
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row["User"] = user_id |
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row["Model"] = model_id |
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accuracy = parse_metrics_accuracy(meta) |
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mean_reward, std_reward = parse_rewards(accuracy) |
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mean_reward = mean_reward if not pd.isna(mean_reward) else 0 |
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std_reward = std_reward if not pd.isna(std_reward) else 0 |
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row["Results"] = mean_reward - std_reward |
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row["Mean Reward"] = mean_reward |
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row["Std Reward"] = std_reward |
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data.append(row) |
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return pd.DataFrame.from_records(data) |
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def get_data_per_env(rl_env): |
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dataframe = get_data(rl_env) |
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dataframe = dataframe.fillna("") |
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if not dataframe.empty: |
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dataframe["User"] = dataframe["User"].apply(make_clickable_user) |
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dataframe["Model"] = dataframe["Model"].apply(make_clickable_model) |
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dataframe = dataframe.sort_values(by=['Results'], ascending=False) |
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if not 'Ranking' in dataframe.columns: |
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dataframe.insert(0, 'Ranking', [i for i in range(1,len(dataframe)+1)]) |
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else: |
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dataframe['Ranking'] = [i for i in range(1,len(dataframe)+1)] |
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table_html = dataframe.to_html(escape=False, index=False,justify = 'left') |
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return table_html,dataframe,dataframe.empty |
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else: |
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html = """<div style="color: green"> |
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<p> ⌛ Please wait. Results will be out soon... </p> |
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</div> |
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""" |
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return html,dataframe,dataframe.empty |
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rl_leaderboard = DeepRL_Leaderboard() |
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rl_leaderboard.add_leaderboard('CartPole-v1','The Cartpole-v1 Leaderboard') |
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rl_leaderboard.add_leaderboard('LunarLander-v2',"The Lunar Lander 🌕 Leaderboard") |
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rl_leaderboard.add_leaderboard('FrozenLake-v1-4x4-no_slippery','The FrozenLake-v1-4x4-no_slippery Leaderboard') |
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rl_leaderboard.add_leaderboard('FrozenLake-v1-8x8-no_slippery','The FrozenLake-v1-8x8-no_slippery Leaderboard') |
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rl_leaderboard.add_leaderboard('FrozenLake-v1-4x4','The FrozenLake-v1-4x4 Leaderboard') |
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rl_leaderboard.add_leaderboard('FrozenLake-v1-8x8','The FrozenLake-v1-8x8 Leaderboard') |
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rl_leaderboard.add_leaderboard('Taxi-v3','The Taxi-v3🚖 Leaderboard') |
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rl_leaderboard.add_leaderboard('CarRacing-v0'," The Car Racing 🏎️ Leaderboard") |
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rl_leaderboard.add_leaderboard('MountainCar-v0',"The Mountain Car ⛰️ 🚗 Leaderboard") |
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rl_leaderboard.add_leaderboard('BipedalWalker-v3',"The BipedalWalker Leaderboard") |
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rl_leaderboard.add_leaderboard('SpaceInvadersNoFrameskip-v4','The SpaceInvadersNoFrameskip-v4 Leaderboard') |
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rl_leaderboard.add_leaderboard('Pong-PLE-v0','The Pong-PLE-v0 🎾 Leaderboard') |
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rl_leaderboard.add_leaderboard('Walker2DBulletEnv-v0','The Walker2DBulletEnv-v0 🤖 Leaderboard') |
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rl_leaderboard.add_leaderboard('AntBulletEnv-v0','The AntBulletEnv-v0 🕸️ Leaderboard') |
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rl_leaderboard.add_leaderboard('HalfCheetahBulletEnv-v0','The HalfCheetahBulletEnv-v0 🤖 Leaderboard') |
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RL_ENVS = rl_leaderboard.get_ids() |
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RL_DETAILS = rl_leaderboard.get_data() |
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def update_data(rl_env): |
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global LOADED_MODEL_IDS,LOADED_MODEL_METADATA |
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data = [] |
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model_ids = [x for x in get_model_ids(rl_env)] |
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LOADED_MODEL_IDS[rl_env]+=model_ids |
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for model_id in tqdm(model_ids): |
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meta = get_metadata(model_id) |
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LOADED_MODEL_METADATA[model_id] = meta if meta is not None else '' |
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if meta is None: |
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continue |
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user_id = model_id.split('/')[0] |
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row = {} |
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row["User"] = user_id |
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row["Model"] = model_id |
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accuracy = parse_metrics_accuracy(meta) |
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mean_reward, std_reward = parse_rewards(accuracy) |
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mean_reward = mean_reward if not pd.isna(mean_reward) else 0 |
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std_reward = std_reward if not pd.isna(std_reward) else 0 |
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row["Results"] = mean_reward - std_reward |
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row["Mean Reward"] = mean_reward |
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row["Std Reward"] = std_reward |
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data.append(row) |
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return pd.DataFrame.from_records(data) |
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def update_data_per_env(rl_env): |
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global RL_DETAILS |
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_,old_dataframe,_ = RL_DETAILS[rl_env]['data'] |
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new_dataframe = update_data(rl_env) |
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new_dataframe = new_dataframe.fillna("") |
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if not new_dataframe.empty: |
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new_dataframe["User"] = new_dataframe["User"].apply(make_clickable_user) |
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new_dataframe["Model"] = new_dataframe["Model"].apply(make_clickable_model) |
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dataframe = pd.concat([old_dataframe,new_dataframe]) |
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if not dataframe.empty: |
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dataframe = dataframe.sort_values(by=['Results'], ascending=False) |
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if not 'Ranking' in dataframe.columns: |
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dataframe.insert(0, 'Ranking', [i for i in range(1,len(dataframe)+1)]) |
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else: |
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dataframe['Ranking'] = [i for i in range(1,len(dataframe)+1)] |
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table_html = dataframe.to_html(escape=False, index=False,justify = 'left') |
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return table_html,dataframe,dataframe.empty |
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else: |
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html = """<div style="color: green"> |
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<p> ⌛ Please wait. Results will be out soon... </p> |
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</div> |
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""" |
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return html,dataframe,dataframe.empty |
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def get_info_display(dataframe,env_name,name_leaderboard,is_empty): |
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if not is_empty: |
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markdown = """ |
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<div class='infoPoint'> |
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<h1> {name_leaderboard} </h1> |
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<br> |
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<p> This is a leaderboard of <b>{len_dataframe}</b> agents, from <b>{num_unique_users}</b> unique users, playing {env_name} 👩🚀. </p> |
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<br> |
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<p> We use <b>lower bound result to sort the models: mean_reward - std_reward.</b> </p> |
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<br> |
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<p> You can click on the model's name to be redirected to its model card which includes documentation. </p> |
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<br> |
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<p> You want to try to train your agents? <a href="http://eepurl.com/h1pElX" target="_blank">Sign up to the Hugging Face free Deep Reinforcement Learning Class 🤗 </a>. |
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</p> |
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<br> |
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<p> 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>. |
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</p> |
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</div> |
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""".format(len_dataframe = len(dataframe),env_name = env_name,name_leaderboard = name_leaderboard,num_unique_users = len(set(dataframe['User'].values))) |
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else: |
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markdown = """ |
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<div class='infoPoint'> |
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<h1> {name_leaderboard} </h1> |
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<br> |
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</div> |
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""".format(name_leaderboard = name_leaderboard) |
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return markdown |
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def reload_all_data(): |
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global RL_DETAILS,RL_ENVS |
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for rl_env in RL_ENVS: |
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RL_DETAILS[rl_env]['data'] = update_data_per_env(rl_env) |
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html = """<div style="color: green"> |
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<p> ✅ Leaderboard updated! </p> |
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</div> |
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""" |
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return html |
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def reload_leaderboard(rl_env): |
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global RL_DETAILS |
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data_html,data_dataframe,is_empty = RL_DETAILS[rl_env]['data'] |
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markdown = get_info_display(data_dataframe,rl_env,RL_DETAILS[rl_env]['title'],is_empty) |
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return markdown,data_html |
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block = gr.Blocks(css=BLOCK_CSS) |
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with block: |
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notification = gr.HTML("""<div style="color: green"> |
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<p> ⌛ Updating leaderboard... </p> |
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</div> |
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""") |
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block.load(reload_all_data,[],[notification]) |
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with gr.Tabs(): |
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for rl_env in RL_ENVS: |
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with gr.TabItem(rl_env) as rl_tab: |
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data_html,data_dataframe,is_empty = RL_DETAILS[rl_env]['data'] |
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markdown = get_info_display(data_dataframe,rl_env,RL_DETAILS[rl_env]['title'],is_empty) |
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env_state =gr.Variable(value=f'\"{rl_env}\"') |
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output_markdown = gr.HTML(markdown) |
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output_html = gr.HTML(data_html) |
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rl_tab.select(reload_leaderboard,inputs=[env_state],outputs=[output_markdown,output_html]) |
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block.launch() |
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