app.py

import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
import gradio as gr

# Load model from local folder
model_dir = "./"

tokenizer = T5Tokenizer.from_pretrained(model_dir)
model = T5ForConditionalGeneration.from_pretrained(model_dir)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

# Inference function
def generate_sql(schema, instructions, user_query):
    combined_input = f"{instructions.strip()}\n\n{schema.strip()}\n\nUser Query: \"{user_query.strip()}\"\n\nSQL Query:"
    inputs = tokenizer(combined_input, padding=True, truncation=True, return_tensors="pt").to(device)
    
    with torch.no_grad():
        outputs = model.generate(**inputs, max_length=512)
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# UI layout
with gr.Blocks() as demo:
    gr.Markdown("# 🧠 Text-to-SQL Generator")
    gr.Markdown("Enter the **schema**, **prompt/instructions**, and a **user query** to get the SQL output.")

    schema = gr.Textbox(label="Database Schema", lines=10, placeholder="CREATE TABLE students (...) ...")
    instructions = gr.Textbox(label="SQL Instructions / Prompt", lines=15, placeholder="Explain how to generate SQL queries...")
    user_query = gr.Textbox(label="User Query", placeholder="e.g., Show me students who never attended class")

    output = gr.Textbox(label="Generated SQL Query")

    submit = gr.Button("Generate SQL")
    submit.click(fn=generate_sql, inputs=[schema, instructions, user_query], outputs=output)

demo.launch()