REmove model removal name.

app.py

@@ -55,10 +55,10 @@ def evaluate_policy(model_id):
         cfg = OmegaConf.load(hydra_config_file_path)
         cfg.dataset.load_dataset = "skip"
         cfg.testing = True
-        ## load the GRP model from the file doanloaded in the snappshot
+        ## load the GRP model from the file downloaded in the snapshot
         # Dynamically load the module
         import importlib.util, sys
-        sys.path.insert(0, repo_path+"/") ## dangerous for sequrity but ok for now.
+        sys.path.insert(0, repo_path+"/") ## dangerous for security but ok for now.
         from grp_model import GRP
 
         model_ = torch.load(model_file, pickle_module=dill)
@@ -70,7 +70,7 @@ def evaluate_policy(model_id):
 
         tokenizer = None
         text_model = None
-        ## Time the evalaution run
+        ## Time the evaluation run
         start_time = time.time()
         if cfg.dataset.encode_with_t5: ## Load T5 model
             from transformers import T5Tokenizer, T5ForConditionalGeneration
@@ -173,13 +173,13 @@ def run_evaluation_loop():
         )
 
     # Save Requests Updates to Hub
-    requests_df.to_csv("requests.csv", index=False)
-    api.upload_file(
-        path_or_fileobj="requests.csv",
-        path_in_repo="requests.csv",
-        repo_id=REQUESTS_DATASET,
-        repo_type="dataset"
-    )
+    # requests_df.to_csv("requests.csv", index=False)
+    # api.upload_file(
+    #     path_or_fileobj="requests.csv",
+    #     path_in_repo="requests.csv",
+    #     repo_id=REQUESTS_DATASET,
+    #     repo_type="dataset"
+    # )
 
     return f"Processed {model_id}: Score {score}"