initial files

README.md

@@ -4,7 +4,7 @@ emoji: 📚
 colorFrom: green
 colorTo: indigo
 sdk: gradio
-sdk_version: 2.9.4
+sdk_version: 2.9b23
 app_file: app.py
 pinned: false
 ---

app.py

@@ -0,0 +1,132 @@
+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>'
+
+# 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>'
+    
+def get_model_ids(rl_env):
+    api = HfApi()
+    models = api.list_models(filter=rl_env)
+    model_ids = [x.modelId for x in models]
+    return model_ids
+    
+def get_metadata(model_id):
+    try:
+        readme_path = hf_hub_download(model_id, filename="README.md")
+        return metadata_load(readme_path)
+    except requests.exceptions.HTTPError:
+        # 404 README.md not found
+        return None
+        
+def parse_metrics_accuracy(meta):
+    if "model-index" not in meta:
+        return None
+    result = meta["model-index"][0]["results"]
+    metrics = result[0]["metrics"]
+    accuracy = metrics[0]["value"]
+    print("ACCURACY", accuracy)
+    return accuracy
+
+# We keep the worst case episode
+def parse_rewards(accuracy):
+    if accuracy !=  None:
+        parsed = accuracy.split(' +/- ')
+        mean_reward = float(parsed[0])
+        std_reward =  float(parsed[1])
+    else:
+        mean_reward = -1000
+        std_reward = -1000
+    return mean_reward, std_reward
+
+def get_data(rl_env):
+    data = []
+    model_ids = get_model_ids(rl_env)
+    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)
+        print("RETURNED ACCURACY", accuracy)
+        mean_reward, std_reward = parse_rewards(accuracy)
+        print("MEAN REWARD", mean_reward)
+        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("")
+
+    #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
+        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_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')},
+            'LunarLander-v2':{'title':" The Lunar Lander 🌕 Leaderboard 🚀",'data':get_data_per_env('LunarLander-v2')}
+            }
+
+
+block = gr.Blocks()
+with block:
+    
+    with gr.Tabs():
+        for rl_env in RL_ENVS:
+            with gr.TabItem(rl_env):
+                data_html,data_dataframe,is_empty = RL_DETAILS[rl_env]['data'] 
+                
+                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: https://github.com/huggingface/deep-rl-class/blob/Unit1/unit1/README.md.
+
+                """.format(len_dataframe = len(data_dataframe),env_name = rl_env,name_leaderboard =  RL_DETAILS[rl_env]['title'])
+                gr.Markdown(markdown)
+                gr.HTML(data_html)
+            
+
+block.launch()