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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()
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