import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import numpy as np
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    PreTrainedModel,
    PreTrainedTokenizer
)

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("declare-lab/flan-alpaca-base")

model = AutoModelForSeq2SeqLM.from_pretrained("declare-lab/flan-alpaca-base")
def main():
    # Set app title
    st.title("Flan Alpaca Large Model")

    # Create input for user's question
    question = st.text_input("Enter your question here:")

    # Create button to submit question
    if st.button("Submit"):
        # Generate answer using Flan Alpaca Large model
        answer = qa_pipeline(question=question, context="")["answer"]
        # Display answer in output box
        st.write("Answer: ", answer)

if __name__ == "__main__":
    main()