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

# Load model and tokenizer from local files
model_path = "./"
tokenizer = T5Tokenizer.from_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()

def generate_sql(user_input):
    inputs = tokenizer(user_input, return_tensors="pt", truncation=True, padding=True).to(device)
    with torch.no_grad():
        outputs = model.generate(**inputs, max_length=512)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

gr.Interface(
    fn=generate_sql,
    inputs=gr.Textbox(lines=15, placeholder="Paste your full SQL prompt here..."),
    outputs=gr.Textbox(label="Generated SQL Query"),
    title="HISAB AI – Text2SQL Generator",
    description="Paste your schema + user query + instructions. Model returns a PostgreSQL query."
).launch()