app.py

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)
        mean_reward = mean_reward if not pd.isna(mean_reward) else 0
        std_reward = std_reward if not pd.isna(std_reward) else 0
        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 = """<div style="color: green">
                <p> ⌛ Please wait. Results will be out soon... </p>
                </div>
               """
        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)
        mean_reward = mean_reward if not pd.isna(mean_reward) else 0
        std_reward = std_reward if not pd.isna(std_reward) else 0

        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 = """<div style="color: green">
                <p> ⌛ Please wait. Results will be out soon... </p>
                </div>
               """
        return html,dataframe,dataframe.empty   






def get_info_display(len_dataframe,env_name,name_leaderboard,is_empty):
    if not is_empty:
        markdown = """
        <div class='infoPoint'>
        <h1> {name_leaderboard} </h1>
        <br>
        <p> This is a leaderboard of <b>{len_dataframe}</b> agents playing {env_name} 👩‍🚀. </p>
        <br>
        <p> We use lower bound result to sort the models: mean_reward - std_reward. </p>
        <br>    
        <p> You can click on the model's name to be redirected to its model card which includes documentation. </p>
        <br>
        <p> You want to try your model? Read this <a href="https://github.com/huggingface/deep-rl-class/blob/Unit1/unit1/README.md" target="_blank">Unit 1</a> of Deep Reinforcement Learning Class.
        </p>
        </div>
        """.format(len_dataframe = len_dataframe,env_name = env_name,name_leaderboard = name_leaderboard)

    else:
        markdown = """
        <div class='infoPoint'>
        <h1> {name_leaderboard} </h1>
        <br>
        </div>                  
        """.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 = """<div style="color: green">
                <p> ✅ Leaderboard updated! Click `Reload Leaderboard` to see the current leaderboard.</p>
                </div>
               """    
    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("""<div style="color: green">
                <p> ⌛ Updating leaderboard... </p>
                </div>
               """)
    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()