initial files

app.py

@@ -1,24 +1,22 @@
 import requests
-import json
 import pandas as pd
 from tqdm.auto import tqdm
 
 import gradio as gr
-#import streamlit as st
 from huggingface_hub import HfApi, hf_hub_download
 from huggingface_hub.repocard import metadata_load
-#import streamlit.components.v1 as components
+
 
 # Based on Omar Sanseviero work
 # Make model clickable link
 def make_clickable_model(model_name):
     link = "https://huggingface.co/" + model_name
-    return f'<a target="_blank" href="{link}">{model_name}</a>'
+    return f'<a style="text-decoration: underline; color: #1f3b54 " target="_blank" href="{link}">{model_name}</a>'
 
 # Make user clickable link
 def make_clickable_user(user_id):
     link = "https://huggingface.co/" + user_id
-    return f'<a target="_blank" href="{link}">{user_id}</a>'
+    return f'<a style="text-decoration: underline; color: #1f3b54 " target="_blank" href="{link}">{user_id}</a>'
     
 def get_model_ids(rl_env):
     api = HfApi()
@@ -80,14 +78,17 @@ def get_data_per_env(rl_env):
     dataframe = get_data(rl_env)
     dataframe = dataframe.fillna("")
 
-    #import pdb; pdb.set_trace()
     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)
         table_html = dataframe.to_html(escape=False, index=False)
-        table_html = table_html.replace("<th>", '<th align="left">')  # left-align the headers
+        table_html = table_html.replace("<table>", '<table style="width: 100%; margin: auto; border:0.5px solid; border-spacing: 7px 0px">')  # center-align the headers
+      
+        table_html = table_html.replace("<thead>", '<thead align="center">')  # center-align the headers
+
+        table_html = "<div style='text-align: center ; width:100%'>"+table_html+"</div>"
         return table_html,dataframe,dataframe.empty
     else: 
         html = """<div style="color: green">
@@ -98,9 +99,9 @@ def get_data_per_env(rl_env):
 
 
 
-RL_ENVS = ['CarRacing-v0','MountainCar-v0','LunarLander-v2']
-RL_DETAILS ={'CarRacing-v0':{'title':" The Car Racing 🌕 Leaderboard 🚀",'data':get_data_per_env('CarRacing-v0')},
-            'MountainCar-v0':{'title':"The Mountain Car 🌕 Leaderboard 🚀",'data':get_data_per_env('MountainCar-v0')},
+RL_ENVS = ['LunarLander-v2','CarRacing-v0','MountainCar-v0']
+RL_DETAILS ={'CarRacing-v0':{'title':" The Car Racing 🏎️ Leaderboard 🚀",'data':get_data_per_env('CarRacing-v0')},
+            'MountainCar-v0':{'title':"The Mountain Car ⛰️ 🚗 Leaderboard 🚀",'data':get_data_per_env('MountainCar-v0')},
             'LunarLander-v2':{'title':" The Lunar Lander 🌕 Leaderboard 🚀",'data':get_data_per_env('LunarLander-v2')}
             }
 
@@ -113,18 +114,27 @@ with block:
             with gr.TabItem(rl_env):
                 data_html,data_dataframe,is_empty = RL_DETAILS[rl_env]['data'] 
                 
-                markdown = """
-                # {name_leaderboard}
+                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.
 
-                This is a leaderboard of {len_dataframe}** agents playing {env_name} 👩‍🚀.
+                    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.
 
-                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.
+                    """.format(len_dataframe = len(data_dataframe),env_name = rl_env,name_leaderboard =  RL_DETAILS[rl_env]['title'])
 
-                You want to try your model? Read this Unit 1 of Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/blob/Unit1/unit1/README.md.
+                else:
+                    markdown = """
+                    # {name_leaderboard}                   
+                    """.format(name_leaderboard =  RL_DETAILS[rl_env]['title'])
 
-                """.format(len_dataframe = len(data_dataframe),env_name = rl_env,name_leaderboard =  RL_DETAILS[rl_env]['title'])
+                  
                 gr.Markdown(markdown)
                 gr.HTML(data_html)