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🤖 Talk to Your Database (Text-to-SQL)

An AI-powered application that allows users to query a SQLite database using natural language. Built with Streamlit, LangChain, and Groq (Llama 3.1).

🚀 Features

• Natural Language Processing: Converts English questions into valid SQL queries.
• Live Execution: Runs generated queries against a local SQLite database and displays results.
• Streamlit UI: A clean, interactive web interface for user input and data visualization.
• Dockerized: Easy to deploy and run in a containerized environment.

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🛠️ Project Structure

├── main.py              # Streamlit application logic
├── database.py          # Script to initialize and seed the SQLite DB
├── requirements.txt     # Python dependencies
├── .env                 # Environment variables (API Keys)
├── .gitignore           # Files excluded from Git
├── Dockerfile           # Docker configuration
└── student.db           # SQLite database (generated locally)

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⚙️ Setup Instructions

1. Clone the Repository

git clone [https://github.com/aniketbhoy/Text-to-SQL.git](https://github.com/aniketbhoy/Text-to-SQL.git)
cd Text-to-SQL

2. Configure Environment Variables

Create a .env file in the root directory and add your Groq API Key:

GROQ_API_KEY=your_lp_api_key_here

3. Local Installation (Python)

It is recommended to use a virtual environment:

# Create virtual environment
python -m venv myenv

# Activate it (Mac/Linux)
source myenv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Initialize the database
python database.py

# Run the app
streamlit run main.py

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🐳 Running with Docker

This project is fully containerized. You can build and run it without needing to install Python locally.

Build the image:

docker build -t text-to-sql:latest .

Run the container:

docker run -p 8501:8501 --env-file .env text-to-sql

Then visit http://localhost:8501 in your browser.

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📊 Database Schema

The app queries a STUDENT table with the following structure:

Column	Type	Description
NAME	VARCHAR	Name of the student
COURSE	VARCHAR	Subject of study
SECTION	VARCHAR	Class section (A, B, etc)
MARKS	INT	Exam score

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📝 Example Queries to Try

• "How many students are there?"
• "Show me all students in the Data Science course."
• "Who got the highest marks in Section A?"
• "List the names of students in DEVOPS."

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🛡️ License

Distributed under the MIT License. See LICENSE for more information.

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