app.py

import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load the GaussAlgo model
model_path = "gaussalgo/T5-LM-Large-text2sql-spider"
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

def generate_sql(payload):
    # Extract components from payload
    question = payload.get("question", "")
    schema = payload.get("schema", "")
    
    # Build model input
    full_prompt = f"Question: {question} Schema: {schema}"
    
    inputs = tokenizer(full_prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=512)
    generated_sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    return generated_sql

# Define expected input as a JSON object (dict)
demo = gr.Interface(
    fn=generate_sql,
    inputs=gr.JSON(label="Input JSON (with 'question' and 'schema')"),
    outputs="text",
    title="Text-to-SQL Generator",
    description="Input a JSON with your natural language question and database schema. Output is SQL."
)

demo.launch()