This project showcases the capabilities of combining OpenAI's GPT-4 with Streamlit to generate SQL queries based on natural language input. Users can enter a message describing the data they want to query from an SQLite database, and the application will display the generated SQL query as well as the results from the database.

• Features
• Prerequisites
• Installation
• Usage
• How It Works
• Contributing
• License

• Natural Language to SQL: Uses GPT-4 to transform user's natural language input into an SQL query.
• Streamlit Interface: Provides a simple and intuitive interface for users to input their queries.
• SQLite Backend: Uses SQLite as the database backend to store and query the financial data.

• Python 3.6 or above
• Virtual Environment (recommended)

1. Clone the Repository:

   git clone https://github.com/NikhilSehgal123/Azure-OpenAI-SQL.git

2. Set up a Virtual Environment (optional but recommended):

   python -m venv .venv
   source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate

3. Install the Dependencies:

   pip install -r requirements.txt

4. Set up Environment Variables:

   If you're using any external services like Azure or APIs, make sure you have the credentials set up as environment variables or stored safely.

1. Run the Streamlit App:

   streamlit run main_app.py

2. Open the displayed URL in your browser, usually http://localhost:8501.

3. Type in your natural language query into the input box, like "Show me all expenses greater than 1000".

4. View the generated SQL query and the results from the database.

1. SQLite Database:

   The app uses SQLite to create a table representing a company's finances. It holds fields like revenue, expenses, and profit.

2. Schema Retrieval:

   Before generating a query, the system retrieves the schema of the table from SQLite to understand its structure.

3. GPT-4 Model:

   The main functionality relies on the GPT-4 model to convert a user's natural language input into an SQL query. The app sends a formatted message containing the table's schema to GPT-4, which then returns an appropriate SQL query.

4. Query Execution:

   The app then executes the generated SQL query on the SQLite database and retrieves the results.

Feel free to fork this repository, create a feature branch, and submit a pull request if you have improvements or fixes you'd like to share.

This project is open source, under the MIT license.