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app.py
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@@ -1,24 +1,22 @@
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import requests
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import json
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import pandas as pd
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from tqdm.auto import tqdm
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import gradio as gr
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#import streamlit as st
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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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# Based on Omar Sanseviero work
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# Make model clickable link
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def make_clickable_model(model_name):
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link = "https://huggingface.co/" + model_name
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return f'<a target="_blank" href="{link}">{model_name}</a>'
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# Make user clickable link
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def make_clickable_user(user_id):
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link = "https://huggingface.co/" + user_id
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return f'<a target="_blank" href="{link}">{user_id}</a>'
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def get_model_ids(rl_env):
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api = HfApi()
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@@ -80,14 +78,17 @@ 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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#import pdb; pdb.set_trace()
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if not dataframe.empty:
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# turn the model ids into clickable links
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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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table_html = dataframe.to_html(escape=False, index=False)
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table_html = table_html.replace("<
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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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RL_ENVS = ['
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RL_DETAILS ={'CarRacing-v0':{'title':" The Car Racing
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'MountainCar-v0':{'title':"The Mountain Car
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'LunarLander-v2':{'title':" The Lunar Lander π Leaderboard π",'data':get_data_per_env('LunarLander-v2')}
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}
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with gr.TabItem(rl_env):
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data_html,data_dataframe,is_empty = RL_DETAILS[rl_env]['data']
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We use lower bound result to sort the models: mean_reward - std_reward.
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gr.Markdown(markdown)
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gr.HTML(data_html)
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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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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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# Based on Omar Sanseviero work
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# Make model clickable link
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def make_clickable_model(model_name):
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link = "https://huggingface.co/" + model_name
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return f'<a style="text-decoration: underline; color: #1f3b54 " target="_blank" href="{link}">{model_name}</a>'
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# Make user clickable link
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def make_clickable_user(user_id):
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link = "https://huggingface.co/" + user_id
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return f'<a style="text-decoration: underline; color: #1f3b54 " target="_blank" href="{link}">{user_id}</a>'
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def get_model_ids(rl_env):
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api = HfApi()
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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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# turn the model ids into clickable links
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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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table_html = dataframe.to_html(escape=False, index=False)
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table_html = table_html.replace("<table>", '<table style="width: 100%; margin: auto; border:0.5px solid; border-spacing: 7px 0px">') # center-align the headers
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table_html = table_html.replace("<thead>", '<thead align="center">') # center-align the headers
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table_html = "<div style='text-align: center ; width:100%'>"+table_html+"</div>"
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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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RL_ENVS = ['LunarLander-v2','CarRacing-v0','MountainCar-v0']
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RL_DETAILS ={'CarRacing-v0':{'title':" The Car Racing ποΈ Leaderboard π",'data':get_data_per_env('CarRacing-v0')},
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'MountainCar-v0':{'title':"The Mountain Car β°οΈ π Leaderboard π",'data':get_data_per_env('MountainCar-v0')},
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'LunarLander-v2':{'title':" The Lunar Lander π Leaderboard π",'data':get_data_per_env('LunarLander-v2')}
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}
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with gr.TabItem(rl_env):
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data_html,data_dataframe,is_empty = RL_DETAILS[rl_env]['data']
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if not is_empty:
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markdown = """
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# {name_leaderboard}
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This is a leaderboard of **{len_dataframe}** agents playing {env_name} π©βπ.
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We use lower bound result to sort the models: mean_reward - std_reward.
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You can click on the model's name to be redirected to its model card which includes documentation.
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You want to try your model? Read this [Unit 1](https://github.com/huggingface/deep-rl-class/blob/Unit1/unit1/README.md) of Deep Reinforcement Learning Class.
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""".format(len_dataframe = len(data_dataframe),env_name = rl_env,name_leaderboard = RL_DETAILS[rl_env]['title'])
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else:
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markdown = """
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# {name_leaderboard}
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""".format(name_leaderboard = RL_DETAILS[rl_env]['title'])
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gr.Markdown(markdown)
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gr.HTML(data_html)
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