app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the pre-trained model and tokenizer from Hugging Face Model Hub
model_id = "HridaAI/Hrida-T2SQL-3B-128k-V0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, trust_remote_code=True)

# Define the function to generate SQL query from natural language input
def generate_sql(query):
    # Tokenize input
    inputs = tokenizer(query, return_tensors="pt")
    
    # Generate the SQL query
    outputs = model.generate(**inputs, max_new_tokens=256)
    
    # Decode the generated output into a string
    sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    return sql_query

# Create the Gradio interface
iface = gr.Interface(
    fn=generate_sql,
    inputs=gr.Textbox(lines=2, placeholder="Enter your natural language question here..."),
    outputs="text",
    title="Text to SQL Converter",
    description="Convert natural language questions into SQL queries using the Hrida-T2SQL-3B model."
)

# Launch the interface
iface.launch()