Update app.py

app.py

@@ -10,20 +10,20 @@ model = timm.create_model('hf_hub:pseudolab/AI_Tutor_BERT', pretrained=True)
 #model = BertForQuestionAnswering.from_pretrained("bert-base-uncased")
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-def get_prediction(context, question):
-  inputs = tokenizer.encode_plus(question, context, return_tensors='pt').to(device)
-  outputs = model(**inputs)
+# def get_prediction(context, question):
+#   inputs = tokenizer.encode_plus(question, context, return_tensors='pt').to(device)
+#   outputs = model(**inputs)
   
-  answer_start = torch.argmax(outputs[0])  
-  answer_end = torch.argmax(outputs[1]) + 1 
+#   answer_start = torch.argmax(outputs[0])  
+#   answer_end = torch.argmax(outputs[1]) + 1 
   
-  answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs['input_ids'][0][answer_start:answer_end]))
+#   answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs['input_ids'][0][answer_start:answer_end]))
   
-  return answer
+#   return answer
   
-def question_answer(context, question):
-  prediction = get_prediction(context,question)
-  return prediction
+# def question_answer(context, question):
+#   prediction = get_prediction(context,question)
+#   return prediction
 
 def split(text):
     context, question = '', ''