app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

model_repo_path = 'nxmwxm/answer_generator'
tokenizer_path = 'nxmwxm/answer_generator'

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
model = AutoModelForSeq2SeqLM.from_pretrained(model_repo_path)

# Load the pipeline with your model and tokenizer
qa_pipeline = pipeline(
    'question-answering',
    model=model,
    tokenizer=tokenizer
)

def generate_answer(question, context):
    result = qa_pipeline(question=question, context=context)
    return result.get('answer', 'No answer found')

st.title('Question Answering Model')
question = st.text_input('Enter your question:')
context = st.text_area('Enter context for the question:')

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