minor edits

app.py

@@ -33,7 +33,7 @@ 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("/home/aarya/Documents/paper3/model_650M.pth"))
+modelfam.load_state_dict(torch.load("model_650M.pth"))
 modelfam.eval()
 
 x_testfam = ["""MAEVLRTLAGKPKCHALRPMILFLIMLVLVLFGYGVLSPRSLMPGSLERGFCMAVREPDH
@@ -63,7 +63,7 @@ decoded_labelsfam
 #Load donor model from file
 tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t12_35M_UR50D")
 
-with open('donorslabels.pkl', 'rb') as file:
+with open('donor_labels.pkl', 'rb') as file:
     label_encoder = pickle.load(file)
 
 # encoded_labels = label_encoder.fit(y)