Cleanup and run in headless mode

app.py

@@ -1,14 +1,8 @@
-import json
-import requests
-from datasets import load_dataset
 import gradio as gr
-from huggingface_hub import HfApi, hf_hub_download
-from huggingface_hub.repocard import metadata_load
-import pandas as pd
+from huggingface_hub import HfApi
 from matchmaking import *
 from background_task import init_matchmaking, get_elo_data
 from apscheduler.schedulers.background import BackgroundScheduler
-import asyncio
 
 matchmaking = Matchmaking()
 api = HfApi()

background_task.py

@@ -1,7 +1,5 @@
 import os
-import time
 import random
-import asyncio
 import subprocess
 import pandas as pd
 from datetime import datetime
@@ -155,13 +153,15 @@ class Matchmaking:
 
 def match(model1, model2):
     """
+    Simulate a match between two models using the Unity environment.
+
     :param model1: First Model object
     :param model2: Second Model object
     :return: match result (0: model1 lost, 0.5: draw, 1: model1 won)
     """
     model1_id = model1.author + "/" + model1.name
     model2_id = model2.author + "/" + model2.name
-    subprocess.run(["./SoccerTows.x86_64", "-model1", model1_id, "-model2", model2_id])
+    subprocess.run(["./SoccerTows.x86_64", "-model1", model1_id, "-model2", model2_id, "-nographics", "-batchmode"])
     print(f"Match {model1_id} against {model2_id} ended.")
     model1.games_played += 1
     model2.games_played += 1
@@ -169,6 +169,8 @@ def match(model1, model2):
 
 def get_models_list() -> list:
     """
+    Get the list of models from the hub and the ELO file.
+
     :return: list of Model objects
     """
     models = []
@@ -187,12 +189,26 @@ def get_models_list() -> list:
 
 
 def get_elo_data() -> pd.DataFrame:
+    """
+    Get the ELO data from the hub for all the models that have played at least one game.
+
+    :return: ELO data as a pandas DataFrame
+    """
     repo.git_pull()
     data = pd.read_csv(os.path.join(DATASET_REPO_URL, "resolve", "main", ELO_FILENAME))
     return data
 
 
 def init_matchmaking():
+    """
+    Run the matchmaking algorithm and save the results to the hub.
+
+    1. Get the list of models from the hub and the ELO data
+    2. Match models together based on their ELO rating
+    3. Simulate the matches using Unity to get the match result
+    4. Compute the new ELO rating for each model
+    5. Save the results to the hub
+    """
     models = get_models_list()
     matchmaking = Matchmaking(models)
     matchmaking.run()