add login

src/model_utils.py

@@ -1,3 +1,4 @@
+from mlflow import login
 import streamlit as st
 from pathlib import Path
 from catboost import CatBoostClassifier
@@ -72,7 +73,11 @@ def save_model(model, user_model_name, metrics_result_single=None):
     import json
     import pyarrow as pa
     import pyarrow.parquet as pq
-    from huggingface_hub import CommitScheduler
+    from huggingface_hub import login, CommitScheduler
+    import os
+
+    if "HF_TOKEN" in os.environ:
+        login(token=os.environ["HF_TOKEN"])
 
     timestamp = datetime.now().strftime("%y%m%d_%H%M%S")
     filename = f"{timestamp}_{user_model_name}_single.pkl"
@@ -160,7 +165,11 @@ def save_model_ensemble(models, user_model_name, best_iterations=None, fold_scor
     import json
     import pyarrow as pa
     import pyarrow.parquet as pq
-    from huggingface_hub import CommitScheduler
+    from huggingface_hub import login, CommitScheduler
+    import os
+
+    if "HF_TOKEN" in os.environ:
+        login(token=os.environ["HF_TOKEN"])
 
     timestamp = datetime.now().strftime("%y%m%d_%H%M%S")
     filename = f"{timestamp}_{user_model_name}_ensemble.pkl"
@@ -229,9 +238,13 @@ def save_model_ensemble(models, user_model_name, best_iterations=None, fold_scor
 #     return single_model_data
 
 def load_model(model_name):
-    from huggingface_hub import hf_hub_download
+    from huggingface_hub import login, hf_hub_download
     import pyarrow.parquet as pq
     import pickle
+    import os
+
+    if "HF_TOKEN" in os.environ:
+        login(token=os.environ["HF_TOKEN"])
 
     files = hf_hub_download(
         repo_id=st.secrets["HF_REPO_ID"],