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Natural Language to SQL Query Generator

https://sql-query-generator2.vercel.app/

Project Overview

The Natural Language to SQL Query Generator is a Python-based web application that allows users to query a database using plain English instead of writing SQL queries manually.

The system converts natural language input into SQL queries, executes them on a SQLite database, and displays the results through a web interface.

This project demonstrates the integration of Natural Language Processing concepts, SQL databases, and web application development using Flask.

Features

  • Convert English queries into SQL statements.
  • Execute SQL queries on a SQLite database
  • Display query results in a web interface
  • Support filtering, sorting, and analytical queries
  • Simple and clean **Flask-based web application
  • Beginner-friendly rule-based NLP system

Example Queries

Users can ask questions like:

show all students
show cse students
show students marks greater than 80
show students marks less than 70
show top students
show average marks
show total students

Generated SQL example:

SELECT * FROM students WHERE marks > 80;

Tech Stack

  • Python
  • SQL
  • SQLite
  • Flask
  • HTML / CSS

Project Architecture

User Input (Browser)
        ↓
Flask Web Application
        ↓
Natural Language Processing Logic
(sql_generator.py)
        ↓
Generated SQL Query
        ↓
Database Execution
(SQLite)
        ↓
Results Displayed in Browser

Project Structure

sql-query-generator
│
├── app.py
├── sql_generator.py
├── execute_query.py
├── database_setup.py
│
├── templates
│     └── index.html
│
├── college.db
└── README.md

How to Run the Project

Step 1: Clone the Repository

git clone https://github.com/your-username/sql-query-generator.git

Step 2: Navigate to the Project Folder

cd sql-query-generator

Step 3: Install Required Libraries

pip install flask pandas nltk

Step 4: Create the Database

python database_setup.py

Step 5: Run the Flask Application

python app.py

Step 6: Open in Browser

http://127.0.0.1:5000

Challenges Faced

  • Understanding different natural language query formats
  • Extracting numeric values from user input
  • Handling invalid queries gracefully

Limitations

  • Uses rule-based NLP, so it cannot understand complex sentences
  • Currently supports only a single database table
  • Limited query patterns

Future Improvements

  • Integrate AI-based Text-to-SQL models
  • Support multiple database tables
  • Add voice-based queries
  • Improve UI with modern frontend frameworks
  • Deploy the application online

Learning Outcomes

Through this project, I learned:

  • How to integrate Python with SQL databases
  • How to build web applications using Flask
  • Basic concepts of Natural Language Processing
  • How backend logic interacts with databases

Author

Bharath Kumar

Computer Science Student Interested in AI, Web Development, and Software Engineering