app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

# Initialize the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("defog/sqlcoder-70b-alpha")
model = AutoModelForCausalLM.from_pretrained("defog/sqlcoder-70b-alpha")

def generate_sql(prompt):
    """Generate SQL code based on the provided prompt"""
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    
    # Generate SQL code
    outputs = model.generate(
        inputs.input_ids,
        max_length=1024,
        temperature=0.1,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )
    
    # Decode the generated SQL
    sql_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    return sql_code

# Create Gradio interface
demo = gr.Interface(
    fn=generate_sql,
    inputs=gr.Textbox(lines=5, placeholder="Describe the SQL query you need..."),
    outputs=gr.Textbox(lines=10, label="Generated SQL"),
    title="SQL Code Generator",
    description="Generate SQL code using defog/sqlcoder-70b-alpha. Enter your request in natural language."
)

# Launch the app
if __name__ == "__main__":
    demo.launch()