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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()
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