Supplementary Materials: Code, Figures, and Prototype Data for Intelligent Inventory Management using Computer Vision

This repository contains the supplementary materials, source code, and figures supporting the research paper: "Design, Implementation, and Experimental Evaluation of a Prototype for Intelligent Inventory Management". The project integrates YOLOv8 for object detection and Google Gemini 3.0 Pro for multimodal analysis to automate inventory counting in retail environments .<b>Files included in this dataset:</b><b>Software-Tienda-main.zip:</b> Complete source code of the prototype implemented in Python using Streamlit, OpenCV, and the YOLOv8 + Gemini API integration .<b>Articulo Inventario Joseph.docx:</b> Full manuscript detailing the experimental protocol and results.<b>Figure 1.1 (Performance):</b> Graphical comparison showing a 58.3% reduction in operational time and 91.7% error reduction .<b>Figure 1.2 (Architecture):</b> System flow diagram illustrating the interaction between the mobile interface, AI models, and Firebase Firestore .<b>System Requirements:</b> Python 3.9.18, Streamlit 1.28.0 .