# app.py
import subprocess

# Install dependencies from requirements.txt
subprocess.run(["pip", "install", "-r", "requirements.txt"])

import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer

def main():
    st.title("Hugging Face SQL Generator")

    # Get user input
    prompt = st.text_area("Enter your SQL prompt:")

    if st.button("Generate SQL"):
        # Call a function to generate SQL using the Hugging Face model
        sql_result = generate_sql(prompt)

        # Display the SQL result
        st.write("Generated SQL:")
        st.code(sql_result, language="sql")

def generate_sql(prompt):
    # Load the "NumbersStation/nsql-350M" model
    model_name = "NumbersStation/nsql-350M"
    model = AutoModelForCausalLM.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)

    # Tokenize and generate SQL
    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
    generated_ids = model.generate(input_ids, max_length=500)
    sql_query = tokenizer.decode(generated_ids[0], skip_special_tokens=True)

    return sql_query

if __name__ == "__main__":
    main()