predictED

Overview

predictED is a Gen AI-powered data analysis application designed for EdTech founders. It allows users to perform SQL queries, generate visualizations, and receive AI-driven insights from student data to enhance educational outcomes. The application is built using Python and Streamlit, leveraging various libraries for data manipulation and visualization.

Features

• Interactive SQL Queries: Users can input SQL queries to analyze student data.
• Data Visualization: Generate visualizations based on the queried data.
• AI-Driven Insights: Utilize OpenAI's language model to provide insights and answer user queries.
• User-Friendly Interface: Built with Streamlit for an intuitive user experience.

Technologies Used

• Python: The primary programming language.
• Streamlit: For building the web application interface.
• LangChain: For integrating language models and tools.
• OpenAI: For AI-driven insights and responses.
• Matplotlib: For data visualization.
• SQLAlchemy: For database interactions.
• dotenv: For managing environment variables.

Installation

1. Clone the Repository
2. Create a Virtual Environment
3. Install Dependencies
4. Set Up Environment Variables
   • Create a .env file in the root directory and add the following:

Usage

• Run the Application
• Access the Application
  • Open your web browser and go to http://localhost:8501.

Code Structure

• src/
  • app/
    • main.py: The main application file that initializes the Streamlit app and handles user interactions.
    • streamlit_config.py: Contains the configuration settings for the Streamlit app.
    • db_agent.py: Contains functions to create a database agent for handling SQL queries.
    • file_management.py: Provides tools for file management operations.
  • config/
    • config.py: Loads environment variables and contains configuration settings.
  • requirements.txt: Lists all the dependencies required for the project.

Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeature).
3. Make your changes and commit them (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Open a pull request.