import streamlit as st
from typing import List
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("juierror/text-to-sql-with-table-schema")
model = AutoModelForSeq2SeqLM.from_pretrained("juierror/text-to-sql-with-table-schema")

t = st.text_input('enter tables')
q = st.text_input('enter question')

def prepare_input(question: str, table: str):
    table_prefix = "table:"
    question_prefix = "question:"
    inputs = f"{question_prefix} {question} {table_prefix} {table}"
    input_ids = tokenizer(inputs, max_length=700, return_tensors="pt").input_ids
    return input_ids

def inference(question: str, table: str) -> str:
    input_data = prepare_input(question=question, table=table)
    input_data = input_data.to(model.device)
    outputs = model.generate(inputs=input_data, num_beams=10, top_k=10, max_length=700)
    result = tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True)
    return result

st.write(inference(q,t))