import streamlit as st
from transformers import pipeline, T5Tokenizer, T5ForConditionalGeneration

# Replace with your Hugging Face model repository path
model_repo_path = 'nxmwxm/correct_answer'

# 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)}"
    result = qa_pipeline(question=input_text, context=input_text)
    return result['answer']

st.title('Question Answering Model')
question = st.text_input('Enter your question:')
distractors = st.text_input('Enter distractors (comma separated):').split(',')

if st.button('Generate Answer'):
    answer = generate_answer(question, distractors)
    st.write('Answer:', answer)