Update app.py

app.py

@@ -97,7 +97,8 @@ if (runModel=='1'):
     test_dataset = IntentDataset(test_encodings, list(test_df['label']))
 
  
-    
+    token = os.getenv("hf_token")
+    login(token=token)
     
     
     # Create an instance of the custom loss function
@@ -180,14 +181,14 @@ if (runModel=='1'):
     
 else:
     print('Load Pre-trained')
-    #model_save_path = "./" + modelNameToUse + "_model"
-    #tokenizer_save_path = "./" + modelNameToUse + "_tokenizer"
+    model_save_path = "./" + modelNameToUse + "_model"
+    tokenizer_save_path = "./" + modelNameToUse + "_tokenizer"
 
-    model_name = "Reyad-Ahmmed/hf-data-timeframe"
+    #model_name = "Reyad-Ahmmed/hf-data-timeframe"
 
     # RobertaTokenizer.from_pretrained(model_save_path)
-    model = AutoModelForSequenceClassification.from_pretrained(model_name, subfolder="data-timeframe_model").to('cpu')
-    tokenizer = AutoTokenizer.from_pretrained(model_name, subfolder="data-timeframe_tokenizer")
+    #model = AutoModelForSequenceClassification.from_pretrained(model_name, subfolder="data-timeframe_model").to('cpu')
+    #tokenizer = AutoTokenizer.from_pretrained(model_name, subfolder="data-timeframe_tokenizer")
 
 #Define the label mappings (this must match the mapping used during training)
 label_mapping = {