Add base app and comments

app.py

@@ -11,4 +11,49 @@ from matchmaking import *
 
 
 block = gr.Blocks()
+envs = [
+    {
+        "name": "Snowball-Fight",
+        "global": None,
+    },
+]
 
+
+def get_env_data(env_name) -> pd.DataFrame:
+    # data = pd.read_csv(f"env_elos/{env_name}.csv")
+    data = pd.DataFrame(columns=["user", "model", "elo", "games_played"])
+    return data
+
+
+with block:
+    gr.Markdown(f"""
+    # 🏆 The Deep Reinforcement Learning Course Leaderboard 🏆 
+
+    This is the leaderboard of trained agents during the Deep Reinforcement Learning Course. A free course from beginner to expert.
+
+    Just choose which environment you trained your agent on and with Ctrl+F find your rank 🏆
+
+    We use an ELO rating to sort the models.
+    You **can click on the model's name** to be redirected to its model card which includes documentation.
+
+    🤖 You want to try to train your agents? <a href="http://eepurl.com/ic5ZUD" target="_blank">Sign up to the Hugging Face free Deep Reinforcement Learning Course 🤗 </a>.
+
+    You want to compare two agents? <a href="https://huggingface.co/spaces/ThomasSimonini/Compare-Reinforcement-Learning-Agents" target="_blank">It's possible using this Spaces demo 👀 </a>.
+
+    🔧 There is an **environment missing?** Please open an issue.
+    """)
+
+    for i, env in enumerate(envs):
+        with gr.TabItem(env["name"]) as tab:
+            with gr.Row():
+                refresh_data = gr.Button("Refresh")
+                val = gr.Variable(value=[env["name"]])
+                refresh_data.click(get_env_data, inputs=[val], outputs=env["global"])
+            with gr.Row():
+                env["global"] = gr.components.DataFrame(
+                    get_env_data(env["name"]),
+                    headers=["Ranking 🏆", "User 🤗", "Model id 🤖", "ELO 🏆", "Games played 🎮"],
+                    datatype=["number", "markdown", "markdown", "number", "number"]
+                )
+
+block.launch()

matchmaking.py

@@ -4,6 +4,13 @@ import os
 
 
 class Model:
+    """
+    Class containing the info of a model.
+
+    :param name: Name of the model
+    :param elo: Elo rating of the model
+    :param games_played: Number of games played by the model (useful if we implement sigma uncertainty)
+    """
     def __init__(self, name, elo):
         self.name = name
         self.elo = elo
@@ -11,6 +18,15 @@ class Model:
 
 
 class Matchmaking:
+    """
+    Class managing the matchmaking between the models.
+
+    :param models: List of models
+    :param queue: Temporary list of models used for the matching process
+    :param k: Dev coefficient
+    :param max_diff: Maximum difference considered between two models' elo
+    :param matches: Dictionary containing the match history (to later upload as CSV)
+    """
     def __init__(self):
         self.models = []
         self.queue = []
@@ -20,6 +36,7 @@ class Matchmaking:
         self.matches = pd.DataFrame()
 
     def read_history(self):
+        """ Read the match history from the CSV files, concat the Dataframes and sort them by datetime. """
         path = "match_history"
         files = os.listdir(path)
         for file in files:
@@ -32,6 +49,7 @@ class Matchmaking:
         self.models = [Model(name, self.start_elo) for name in model_names]
 
     def compute_elo(self):
+        """ Compute the elo for each model after each match. """
         for i, row in self.matches.iterrows():
             model1 = self.get_model(row["model1"])
             model2 = self.get_model(row["model2"])
@@ -44,6 +62,7 @@ class Matchmaking:
             model2.games_played += 1
 
     def get_model(self, name):
+        """ Return the Model with the given name. """
         for model in self.models:
             if model.name == name:
                 return model