https://github.com/mohammadreza-mohammadi94/text2sql-gemini
This project is a web application that utilizes the power of Google's Gemini model to convert natural language questions into SQL queries
https://github.com/mohammadreza-mohammadi94/text2sql-gemini
gemini gemini-api llm python
Last synced: 3 months ago
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This project is a web application that utilizes the power of Google's Gemini model to convert natural language questions into SQL queries
- Host: GitHub
- URL: https://github.com/mohammadreza-mohammadi94/text2sql-gemini
- Owner: mohammadreza-mohammadi94
- License: mit
- Created: 2025-01-03T20:01:53.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-01-04T19:58:43.000Z (4 months ago)
- Last Synced: 2025-01-15T19:39:01.541Z (4 months ago)
- Topics: gemini, gemini-api, llm, python
- Language: Python
- Homepage:
- Size: 18.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Text-to-SQL Query Generator with Gemini
This project is a web application that utilizes the power of Google's Gemini model to convert natural language questions into SQL queries. The application retrieves data from a SQLite database based on user queries. It's built using **Streamlit** for the frontend, **Google Gemini** for natural language processing, and **SQLite** as the database.
## Features
- Convert English natural language questions into SQL queries.
- Execute the SQL queries on a SQLite database and display the results.
- Display results as a DataFrame or a single value (e.g., AVG, COUNT).
- Easy-to-use web interface for querying the database.
## Technologies Used
- **Streamlit**: For building the web application interface.
- **Google Gemini API**: For converting natural language questions into SQL queries.
- **SQLite**: For storing and retrieving student records.
- **Pandas**: For displaying SQL query results in a user-friendly format.
## Setup Instructions
To run this project locally, follow these steps:
### 1. Clone the repository
```bash
git clone https://github.com/yourusername/Text-to-SQL-Query-Generator.git
cd Text-to-SQL-Query-Generator
```
### 2. Install required dependencies
Create a virtual environment and activate it, then install the required libraries using the following commands:
```bash
# Create and activate a virtual environment (optional but recommended)
python -m venv venv
# For Windows
venv\Scripts\activate
# For Mac/Linux
source venv/bin/activate
# Install the required libraries
pip install -r requirements.txt
```
### 3. Set up environment variables
Create a `.env` file in the root directory of the project and add the following environment variables:
```bash
DATABASE_URL=student.db
GENAI_API_KEY=your_google_gemini_api_key
```
Replace `your_google_gemini_api_key` with your actual Google Gemini API key.
### 4. Running the App
After setting up the environment, run the Streamlit application using the following command:
```bash
streamlit run app.py
```
This will start the Streamlit app locally, and you can access it by opening a browser and going to:
```
http://localhost:8501
```
### 5. Database Setup
Ensure you have the SQLite database file `student.db` with the appropriate data in the same directory as `app.py` or set up the database according to your needs. You can use the provided `sql.py` script to create the database and insert sample data.
## Sample Queries
Here are some examples of questions you can ask in the app:
- "How many students are there in the database?"
- "What are the names of students studying Data Science?"
- "What is the average marks of students?"
## Project Structure
```
Text-to-SQL-Query-Generator/
│
├── app.py # Streamlit app that interacts with the user and runs the query
├── sql.py # Script for setting up the SQLite database and inserting data
├── requirements.txt # Python dependencies for the project
├── .env # Environment variables (GENAI_API_KEY, DATABASE_URL)
└── README.md # Project documentation
```
## Contributing
Feel free to fork this repository and submit pull requests for improvements or bug fixes. Ensure you follow the project structure and add appropriate tests for new features.
## License
This project is licensed under the MIT License.