Add onnx filter

background_task.py

@@ -191,7 +191,17 @@ def match(model1, model2):
     print(f"Match {model1_id} against {model2_id} ended.")
 
 
-def get_models_list(filter_bad_models) -> list:
+def check_for_onnx_file(model_info: ModelInfo) -> bool:
+    """
+    Checks if the model contains a `.onnx` file.
+    """
+    for repo_file in model_info.siblings:
+        if repo_file.rfilename.endswith(".onnx"):
+            return True
+    return False
+
+
+def get_models_list(filter_bad_models):
     """
     Get the list of models from the hub and the ELO file.
 
@@ -200,14 +210,15 @@ def get_models_list(filter_bad_models) -> list:
     models = []
     models_ids = []
     data = pd.read_csv(os.path.join(DATASET_REPO_URL, "resolve", "main", ELO_FILENAME))
-    models_on_hub = api.list_models(filter=["reinforcement-learning", "ml-agents", "ML-Agents-SoccerTwos"])
+    models_with_onnx_on_hub = filter(check_for_onnx_file, api.list_models(filter=["reinforcement-learning", "ml-agents", "ML-Agents-SoccerTwos"]))
+    model_ids_with_onnx = {model.modelId for model in models_with_onnx_on_hub}
     for i, row in data.iterrows():
         model_id = row["author"] + "/" + row["model"]
-        if model_id in filter_bad_models:
+        if model_id in filter_bad_models or model_id not in model_ids_with_onnx:
             continue
         models.append(Model(row["author"], row["model"], row["elo"], row["games_played"]))
         models_ids.append(model_id)
-    for model in models_on_hub:
+    for model in models_with_onnx_on_hub:
         if model.modelId in filter_bad_models:
             continue
         author, name = model.modelId.split("/")[0], model.modelId.split("/")[1]