new family model

app.py

@@ -20,20 +20,18 @@ import zipfile
 import os
 
 # Load the model from the file
-with open('family_labels.pkl', 'rb') as filefam:
+with open('/home/aarya/Documents/paper3/family_labels.pkl', 'rb') as filefam:
     yfam = pickle.load(filefam)
 
 tokenizerfam = AutoTokenizer.from_pretrained("facebook/esm2_t12_35M_UR50D") #facebook/esm2_t33_650M_UR50D
-label_encoderfam = LabelEncoder()
-encoded_labelsfam = label_encoderfam.fit_transform(yfam)
-labelsfam = torch.tensor(encoded_labelsfam)
-device = 'cpu'
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
 device
 
-modelfam = EsmForSequenceClassification.from_pretrained("facebook/esm2_t12_35M_UR50D", num_labels=len(set(labelsfam.tolist())))
+modelfam = EsmForSequenceClassification.from_pretrained("facebook/esm2_t12_35M_UR50D", num_labels=len(yfam.classes_))
 modelfam = modelfam.to('cpu')
 
-modelfam.load_state_dict(torch.load("family.pth", map_location=torch.device('cpu')))
+modelfam.load_state_dict(torch.load("/home/aarya/Documents/paper3/family.pth"))
 modelfam.eval()
 
 x_testfam = ["""MAEVLRTLAGKPKCHALRPMILFLIMLVLVLFGYGVLSPRSLMPGSLERGFCMAVREPDH
@@ -55,7 +53,7 @@ with torch.no_grad():
     _, predicted_labelsfam = torch.max(logitsfam, dim=1)
 probabilitiesfam[0]
 
-decoded_labelsfam = label_encoderfam.inverse_transform(predicted_labelsfam.tolist())
+decoded_labelsfam = yfam.inverse_transform(predicted_labelsfam.tolist())
 decoded_labelsfam