CartIQ is a Python-based web application designed to assist users in making informed purchasing decisions. The system analyzes structured product data and generates recommendation insights using a modular architecture built with Flask. It focuses on providing a scalable backend structure for product evaluation, comparison, and decision support.
The objective of this project is to simulate an intelligent assistant capable of analyzing shopping data and assisting users in identifying suitable products based on defined criteria.
The application integrates backend processing modules with a frontend interface to deliver structured outputs in a user-friendly format.
- Web-based interface using Flask
- Modular backend design
- Data-driven recommendation logic
- Structured output generation
- Extensible architecture for additional features
Smart-Shopping-Assistant/
app.py – Main Flask application
main.py – Core execution logic
modules/ – Supporting functional modules
data/ – Input datasets
results/ – Generated outputs
templates/ – HTML templates
requirements.txt
README.md
- Clone the repository:
git clone https://github.com/your-username/Smart-Shopping-Assistant.git
cd Smart-Shopping-Assistant
- Create a virtual environment:
python -m venv venv
source venv/bin/activate
- Install dependencies:
pip install -r requirements.txt
- Run the application:
python app.py
Access the application at:
The project follows a modular design to separate concerns between data handling, processing logic, and presentation. This structure improves maintainability and allows further expansion such as integrating APIs, recommendation algorithms, or database storage.
- Integration with real-time product APIs
- Database support
- User authentication
- Advanced recommendation algorithms
- Deployment on cloud platforms
Krish Yadav
B.Tech Computer Science Engineering