README.md

metadata

title: NL to SQL Chatbot Demo
emoji: 🧠
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 5.29.0
app_file: app.py
pinned: false

NL → SQL Chatbot Demo

This Hugging Face Space demonstrates a simple natural-language-to-SQL chatbot built in Python with Gradio and SQLite.

What it does

• Creates a small demo database automatically on first startup
• Accepts business questions in plain English
• Converts the question into a SQL query using a lightweight rule-based parser
• Executes the SQL against the SQLite database
• Returns both the generated SQL and the result preview

Demo schema

• customers(id, name, country, segment)
• products(id, name, category, price)
• orders(id, customer_id, order_date, status, shipping_days)
• order_items(id, order_id, product_id, quantity, unit_price)

Example questions

• Show all customers
• Show orders from March 2026
• What is the total revenue by country?
• Top 5 products by revenue
• Average order value by customer
• How many orders are delayed?
• Show revenue by month
• List products in the Electronics category

Local run

pip install -r requirements.txt
python app.py

Files

• app.py — Gradio UI and query execution
• init_db.py — creates and seeds the example SQLite database
• nl_to_sql.py — converts natural language into SQL
• demo_store.db — created automatically at runtime

Next steps

A natural upgrade path is to replace the rule-based parse_question_to_sql() function with an LLM prompt that:

1. Receives the schema
2. Generates SQL only
3. Applies guardrails (read-only, table allowlist)
4. Validates the result before execution

That lets you keep the same UI while making the translator more flexible.