updated app.py

app.py

@@ -6,17 +6,14 @@ from transformers import BertTokenizer, BertForSequenceClassification
 import os
 
 tokenizer = BertTokenizer.from_pretrained("dmis-lab/biobert-base-cased-v1.1")
-model = BertForSequenceClassification.from_pretrained("dmis-lab/biobert-base-cased-v1.1", num_labels=2, return_dict=True)
+model = BertForSequenceClassification.from_pretrained("dmis-lab/biobert-base-cased-v1.1", num_labels=2)
 
-# freezing the layers
-for param in model.parameters():
-    param.requires_grad = False
-    
 # loading the pretrained weights into the model
 state_dict = torch.load('Bio_BERT_model.pth', map_location=torch.device('cpu'), weights_only=True)
 model.load_state_dict(state_dict)
 print("Weights initialized!")
 
+model.eval()
 device = "cpu"
 
 def predict_drug_target_interaction(sentence):