app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codet5-base-multi-summarization-sql-en")
model = AutoModelForSeq2SeqLM.from_pretrained("Salesforce/codet5-base-multi-summarization-sql-en")

def generate_sql(natural_language_query):
    # Tokenize the input query
    input_ids = tokenizer(natural_language_query, return_tensors="pt").input_ids

    # Generate the SQL query
    output_ids = model.generate(input_ids, max_length=512)[0]

    # Decode the generated SQL query
    sql_query = tokenizer.decode(output_ids, skip_special_tokens=True)
    return sql_query

# Example questions for the interface
example_questions = [
    "What is the average salary of employees?",
    "List the names of employees who work in the IT department.",
    "Count the number of employees who joined after 2015."
]

# Create the Gradio interface
interface = gr.Interface(
    fn=generate_sql,
    inputs=gr.Textbox(lines=2, placeholder="Enter your natural language query here..."),
    outputs="text",
    examples=example_questions,
    title="NL to SQL with CodeT5",
    description="This model converts natural language queries into SQL using the WikiSQL dataset. Try one of the example questions or enter your own!"
)

# Launch the interface
interface.launch()