🗃️ Gemini SQL Query App (Streamlit + Google Generative AI)

This is a Streamlit-based application that converts natural language questions into SQL queries and retrieves data from a SQLite database. It uses:

• 🤖 Google Generative AI (Gemini 2.0 Flash) for natural language to SQL conversion
• 🗄️ SQLite for database operations
• 🖥️ Streamlit for the web interface
• 🎯 Smart prompt engineering for accurate SQL generation

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🚀 Setup Instructions

1. Clone the repository (if you haven't yet)

git clone https://github.com/yourusername/your-repo-name.git
cd your-repo-name

2. Install dependencies:

pip install -r requirements.txt

3. Create a .env file in the project root and add your API key:

GOOGLE_API_KEY=your_google_api_key

4. Prepare your SQLite database:
   • Ensure you have a student.db file in your project root
   • The database should contain a STUDENT table with columns: NAME, CLASS, SECTION, MARKS

📊 Database Schema

The application expects a SQLite database with the following structure:

CREATE TABLE STUDENT (
    NAME TEXT,
    CLASS TEXT,
    SECTION TEXT,
    MARKS INTEGER
);

▶️ How to Run

Start the Streamlit app:

streamlit run app.py

🧠 How It Works

1. Enter your question in natural language (e.g., "How many students are in Data Science class?")
2. The app uses Google Gemini to convert your question into a SQL query
3. The generated SQL query is executed against the SQLite database
4. Results are displayed in the Streamlit interface

💡 Example Queries

• "How many entries of records are present?"

  • Generates: SELECT COUNT(*) FROM STUDENT

• "Tell me all the students studying in Data Science class?"

  • Generates: SELECT * FROM STUDENT WHERE CLASS = 'Data Science'

• "Show me students with marks greater than 80"

  • Generates: SELECT * FROM STUDENT WHERE MARKS > 80

📌 Features

• ✅ Natural language to SQL conversion
• ✅ Real-time query execution
• ✅ Clean and intuitive web interface
• ✅ Error handling for database operations
• ✅ Smart prompt engineering for accurate results

📌 Notes

• The database file student.db must be present in the project root
• Queries are limited to the STUDENT table structure
• The app automatically handles SQL formatting and execution
• No SQL injection protection - use only for trusted environments

🔧 Requirements

streamlit
google-generativeai
python-dotenv
sqlite3