Update app.py

app.py

@@ -31,7 +31,10 @@ from transformers import AutoProcessor, AutoModelForCausalLM
 
 if not is_spaces:
     from toolkit.job import get_job
-
+else:
+     gr.OAuthProfile = None
+     gr.OAuthToken = None
+    
 MAX_IMAGES = 150
 
 
@@ -152,7 +155,7 @@ def start_training(
     slugged_lora_name = slugify(lora_name)
 
     # Load the default config
-    with open("train_lora_flux_24gb.yaml", "r") as f:
+    with open("train_lora_flux_24gb.yaml" if is_spaces else "config/examples/train_lora_flux_24gb.yaml", "r") as f:
         config = yaml.safe_load(f)
 
     # Update the config with user inputs
@@ -164,7 +167,9 @@ def start_training(
     config["config"]["process"][0]["network"]["linear"] = int(rank)
     config["config"]["process"][0]["network"]["linear_alpha"] = int(rank)
     config["config"]["process"][0]["datasets"][0]["folder_path"] = dataset_folder
+    config["config"]["process"][0]["save"]["push_to_hub"] = True
     config["config"]["process"][0]["save"]["hf_repo_id"] = f"{profile.username}/{slugged_lora_name}"
+    config["config"]["process"][0]["save"]["hf_private"] = True
     if concept_sentence:
         config["config"]["process"][0]["trigger_word"] = concept_sentence
     if sample_1 or sample_2 or sample_2:
@@ -225,7 +230,6 @@ css = """
 .tabitem{border: 0px}
 """
 
-
 def swap_visibilty(profile: gr.OAuthProfile | None):
     print(profile)
     if is_spaces: