minor edits

app.py

@@ -27,13 +27,13 @@ tokenizerfam = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D") #fa
 label_encoderfam = LabelEncoder()
 encoded_labelsfam = label_encoderfam.fit_transform(yfam)
 labelsfam = torch.tensor(encoded_labelsfam)
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
+device = 'cpu'
 device
 
 modelfam = EsmForSequenceClassification.from_pretrained("facebook/esm2_t33_650M_UR50D", num_labels=len(set(labelsfam.tolist())))
 modelfam = modelfam.to('cpu')
 
-modelfam.load_state_dict(torch.load("model_650M.pth", map_location=torch.device('cpu')))
+modelfam.load_state_dict(torch.load("model_650M.pth"))
 modelfam.eval()
 
 x_testfam = ["""MAEVLRTLAGKPKCHALRPMILFLIMLVLVLFGYGVLSPRSLMPGSLERGFCMAVREPDH
@@ -74,7 +74,7 @@ device
 model = EsmForSequenceClassification.from_pretrained("facebook/esm2_t12_35M_UR50D", num_labels=len(label_encoder.classes_))
 model = model.to('cpu')
 
-model.load_state_dict(torch.load("best_model_35M_t12_5v5.pth", map_location=torch.device('cpu'))) #model_best_35v2M.pth
+model.load_state_dict(torch.load("best_model_35M_t12_5v5.pth")) #model_best_35v2M.pth
 model.eval()
 
 x_test = ["""MAEVLRTLAGKPKCHALRPMILFLIMLVLVLFGYGVLSPRSLMPGSLERGFCMAVREPDH