app.py

import gradio as gr
import pandas as pd
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import sqlite3

# Load model
model_name = "mrm8488/t5-base-finetuned-wikiSQL"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)  # <-- use slow tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

def nl_to_sql(question, file):
    try:
        df = pd.read_csv(file.name)
    except Exception as e:
        return f"Error reading CSV: {e}", pd.DataFrame()
    
    # Create SQLite DB
    conn = sqlite3.connect(":memory:")
    df.to_sql("data_table", conn, index=False, if_exists="replace")

    # Schema description
    schema = ", ".join(df.columns)
    text = f"translate English to SQL: {question} | table columns: {schema}"

    inputs = tokenizer(text, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=256)
    sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)

    # Execute SQL query
    try:
        result = pd.read_sql_query(sql_query, conn)
    except Exception as e:
        result = pd.DataFrame({"Error": [str(e)]})

    conn.close()
    return sql_query, result.head()

iface = gr.Interface(
    fn=nl_to_sql,
    inputs=[
        gr.Textbox(label="Ask your question (Natural Language)", placeholder="e.g., Show customers older than 30"),
        gr.File(label="Upload your CSV file")
    ],
    outputs=[
        gr.Textbox(label="Generated SQL Query"),
        gr.Dataframe(label="Result Preview")
    ],
    title="🧠 Natural Language to SQL Generator",
    description="Upload a CSV and ask questions in plain English. Generates SQL and shows results instantly."
)

if __name__ == "__main__":
    iface.launch()