app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the model and tokenizer
model_name = "premai-io/prem-1B-SQL"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Function to generate SQL from the user's input
def generate_sql_query(question):
    input_text = f"Question: {question} SQL Query:"

    # Tokenize the input
    inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)

    # Generate the SQL query
    outputs = model.generate(inputs["input_ids"], max_length=100)

    # Decode the output
    sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return sql_query

# Streamlit app UI
def main():
    st.title("Text-to-SQL with prem-1B-SQL Model")
    st.write("This app generates SQL queries based on your natural language question.")

    # Input for the user's question
    question_input = st.text_input("Enter your question:")

    if question_input:
        # Generate the SQL query
        sql_query = generate_sql_query(question_input)

        # Display the generated SQL query
        st.write("Generated SQL Query:")
        st.code(sql_query)

if __name__ == "__main__":
    main()