Update app.py

app.py

@@ -23,12 +23,12 @@ model_name = 'distilbert-base-cased'
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 @st.cache(suppress_st_warning=True) 
-def LoadModel(model_path):
+def LoadModel():
     revived = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(terms_list)).to(device)
-    revived.load_state_dict(torch.load(model_path, map_location=torch.device(device)))
     return revived
                                                               
-revived = LoadModel(model_path) 
+revived = LoadModel() 
+revived.load_state_dict(torch.load(model_path, map_location=torch.device(device)))
 
 from torch import nn
 from transformers import Trainer, TrainingArguments