import requests import pandas as pd from tqdm.auto import tqdm from utils import * import gradio as gr from huggingface_hub import HfApi, hf_hub_download from huggingface_hub.repocard import metadata_load class DeepRL_Leaderboard: def __init__(self) -> None: self.leaderboard= {} def add_leaderboad(self,id=None, title=None): if id is not None and title is not None: id = id.strip() title = title.strip() self.leaderboard.update({id:{'title':title,'data':get_data_per_env(id)}}) def get_data(self): return self.leaderboard def get_ids(self): return list(self.leaderboard.keys()) # CSS file for the with open('app.css','r') as f: BLOCK_CSS = f.read() LOADED_MODEL_IDS = {} def get_data(rl_env): global LOADED_MODEL_IDS data = [] model_ids = get_model_ids(rl_env) LOADED_MODEL_IDS[rl_env]=model_ids for model_id in tqdm(model_ids): meta = get_metadata(model_id) if meta is None: continue user_id = model_id.split('/')[0] row = {} row["User"] = user_id row["Model"] = model_id accuracy = parse_metrics_accuracy(meta) mean_reward, std_reward = parse_rewards(accuracy) row["Results"] = mean_reward - std_reward row["Mean Reward"] = mean_reward row["Std Reward"] = std_reward data.append(row) return pd.DataFrame.from_records(data) def get_data_per_env(rl_env): dataframe = get_data(rl_env) dataframe = dataframe.fillna("") if not dataframe.empty: # turn the model ids into clickable links dataframe["User"] = dataframe["User"].apply(make_clickable_user) dataframe["Model"] = dataframe["Model"].apply(make_clickable_model) dataframe = dataframe.sort_values(by=['Results'], ascending=False) if not 'Ranking' in dataframe.columns: dataframe.insert(0, 'Ranking', [i for i in range(1,len(dataframe)+1)]) else: dataframe['Ranking'] = [i for i in range(1,len(dataframe)+1)] table_html = dataframe.to_html(escape=False, index=False,justify = 'left') return table_html,dataframe,dataframe.empty else: html = """

⌛ Please wait. Results will be out soon...

""" return html,dataframe,dataframe.empty rl_leaderboard = DeepRL_Leaderboard() rl_leaderboard.add_leaderboad('CarRacing-v0'," The Car Racing 🏎️ Leaderboard 🚀") rl_leaderboard.add_leaderboad('MountainCar-v0',"The Mountain Car ⛰️ 🚗 Leaderboard 🚀") rl_leaderboard.add_leaderboad('LunarLander-v2',"The Lunar Lander 🌕 Leaderboard 🚀") rl_leaderboard.add_leaderboad('BipedalWalker-v3',"The BipedalWalker Leaderboard 🚀") rl_leaderboard.add_leaderboad('Taxi-v3','The Taxi-v3🚖 Leaderboard 🚀') rl_leaderboard.add_leaderboad('FrozenLake-v1-4x4-no_slippery','The FrozenLake-v1-4x4-no_slippery Leaderboard 🚀') rl_leaderboard.add_leaderboad('FrozenLake-v1-8x8-no_slippery','The FrozenLake-v1-8x8-no_slippery Leaderboard 🚀') rl_leaderboard.add_leaderboad('FrozenLake-v1-4x4','The FrozenLake-v1-4x4 Leaderboard 🚀') rl_leaderboard.add_leaderboad('FrozenLake-v1-8x8','The FrozenLake-v1-8x8 Leaderboard 🚀') RL_ENVS = rl_leaderboard.get_ids() RL_DETAILS = rl_leaderboard.get_data() def update_data(rl_env): global LOADED_MODEL_IDS data = [] model_ids = [x for x in get_model_ids(rl_env) if x not in LOADED_MODEL_IDS[rl_env]] LOADED_MODEL_IDS[rl_env]+=model_ids for model_id in tqdm(model_ids): meta = get_metadata(model_id) if meta is None: continue user_id = model_id.split('/')[0] row = {} row["User"] = user_id row["Model"] = model_id accuracy = parse_metrics_accuracy(meta) mean_reward, std_reward = parse_rewards(accuracy) row["Results"] = mean_reward - std_reward row["Mean Reward"] = mean_reward row["Std Reward"] = std_reward data.append(row) return pd.DataFrame.from_records(data) def update_data_per_env(rl_env): global RL_DETAILS _,old_dataframe,_ = RL_DETAILS[rl_env]['data'] new_dataframe = update_data(rl_env) new_dataframe = new_dataframe.fillna("") if not new_dataframe.empty: new_dataframe["User"] = new_dataframe["User"].apply(make_clickable_user) new_dataframe["Model"] = new_dataframe["Model"].apply(make_clickable_model) dataframe = pd.concat([old_dataframe,new_dataframe]) if not dataframe.empty: dataframe = dataframe.sort_values(by=['Results'], ascending=False) if not 'Ranking' in dataframe.columns: dataframe.insert(0, 'Ranking', [i for i in range(1,len(dataframe)+1)]) else: dataframe['Ranking'] = [i for i in range(1,len(dataframe)+1)] table_html = dataframe.to_html(escape=False, index=False,justify = 'left') return table_html,dataframe,dataframe.empty else: html = """

⌛ Please wait. Results will be out soon...

""" return html,dataframe,dataframe.empty def get_info_display(len_dataframe,env_name,name_leaderboard,is_empty): if not is_empty: markdown = """

{name_leaderboard}


This is a leaderboard of {len_dataframe} agents playing {env_name} 👩‍🚀.


We use lower bound result to sort the models: mean_reward - std_reward.


You can click on the model's name to be redirected to its model card which includes documentation.


You want to try your model? Read this Unit 1 of Deep Reinforcement Learning Class.

""".format(len_dataframe = len_dataframe,env_name = env_name,name_leaderboard = name_leaderboard) else: markdown = """

{name_leaderboard}


""".format(name_leaderboard = name_leaderboard) return markdown def reload_all_data(): global RL_DETAILS,RL_ENVS for rl_env in RL_ENVS: RL_DETAILS[rl_env]['data'] = update_data_per_env(rl_env) html = """

✅ Leaderboard updated! Click `Reload Leaderboard` to see the current leaderboard.

""" return html def reload_leaderboard(rl_env): global RL_DETAILS data_html,data_dataframe,is_empty = RL_DETAILS[rl_env]['data'] markdown = get_info_display(len(data_dataframe),rl_env,RL_DETAILS[rl_env]['title'],is_empty) return markdown,data_html block = gr.Blocks(css=BLOCK_CSS) with block: notification = gr.HTML("""

⌛ Updating leaderboard...

""") block.load(reload_all_data,[],[notification]) with gr.Tabs(): for rl_env in RL_ENVS: with gr.TabItem(rl_env) as rl_tab: data_html,data_dataframe,is_empty = RL_DETAILS[rl_env]['data'] markdown = get_info_display(len(data_dataframe),rl_env,RL_DETAILS[rl_env]['title'],is_empty) env_state =gr.Variable(default_value=rl_env) output_markdown = gr.HTML(markdown) reload = gr.Button('Reload Leaderboard') output_html = gr.HTML(data_html) reload.click(reload_leaderboard,inputs=[env_state],outputs=[output_markdown,output_html]) rl_tab.select(reload_leaderboard,inputs=[env_state],outputs=[output_markdown,output_html]) block.launch()