chhunhongteforwork-byte/Self-Service-KIOSK-System: Self Service KIOSK System for Cafe with Analytic Dashboard System
Abstract
Release v1.0.0: Cafe Kiosk & Analytics Suite (AI-Driven Implementation) 🚀 Overview This is the initial stable release of the Self-Service KIOSK System for Cafe, featuring a comprehensive Analytic Dashboard. The system was architected and refined using the Antigravity IDE with high-level assistance from the Gemini 3.1 AI agent to ensure optimized logic and a modular codebase. This project aims to automate the front-of-house ordering process while providing cafe owners with data-driven insights through a centralized management interface. ✨ Key Modules 1. Self-Service Kiosk (Frontend) Intuitive Menu Browsing: Dynamic category filtering for drinks, food, and pastries. Smart Cart System: Real-time price calculation and order customization. AI-Enhanced UI/UX: Built with a focus on high-traffic responsiveness and low-latency interaction. 2. Analytic Dashboard (Backend & Admin) Sales Performance & AI Forecasting: Visualized metrics for historical revenue trends, transaction volume, and machine learning models (XGBoost, ARIMA, Scikit-Learn) that predict future demand quantities. Inventory Intelligence: Real-time tracking of popular items to assist in stock management. Operational Insights: Peak-hour and weekday analysis to help with staff scheduling. 3. Developer Features Modern Architecture: Refactored for clean separation of concerns (Frontend/Backend). AI-Assisted Optimization: Leveraging Gemini 3.1 for efficient state management and database query optimization within the Antigravity IDE environment. 🛠Tech Stack Core Development: Antigravity IDE AI Agent: Gemini 3.1 Frontend: Next.js (React 19, Tailwind CSS v4, Recharts, Zustand) Backend: Python (Django 6.0, Django-Ninja API, Pandas, Scikit-Learn, XGBoost, Statsmodels) Database: SQLite (Expandable to PostgreSQL)
Fields of Study & Subjects
Mobile and Web Applications
Engineering and Information Technology
Decision Support System Applications
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 25 February 2026
- Language
- EN