Update app.py

app.py

@@ -1,19 +1,20 @@
 import streamlit as st
-from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers import pipeline, T5Tokenizer, T5ForConditionalGeneration
 
 # Replace with your Hugging Face model repository path
 model_repo_path = 'nxmwxm/correct_answer'
 
-# Load the model and tokenizer
-model = BartForConditionalGeneration.from_pretrained(model_repo_path)
-tokenizer = BartTokenizer.from_pretrained(model_repo_path)
+# Load the pipeline with your model and tokenizer
+qa_pipeline = pipeline(
+    'question-answering',
+    model=model_repo_path,
+    tokenizer=model_repo_path
+)
 
 def generate_answer(question, distractors):
     input_text = f"Question: {question} Distractors: {' '.join(distractors)}"
-    inputs = tokenizer(input_text, return_tensors='pt')
-    output = model.generate(inputs['input_ids'], attention_mask=inputs['attention_mask'])
-    decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
-    return decoded_output
+    result = qa_pipeline(question=input_text, context=input_text)
+    return result['answer']
 
 st.title('Question Answering Model')
 question = st.text_input('Enter your question:')