Smart Pharmacy AI: A Full-Stack Web Application for Medicine Inventory Management with an Extensible Computer-Vision and AI-Chatbot Architecture

Abstract: Community pharmacies and small dispensaries in India largely continue to track stock, batch numbers, and expiry dates on paper registers or in disconnected spreadsheets, a practice that leaves them exposed to stock-outs, undetected expired inventory, and slow reconciliation. This paper presents Smart Pharmacy AI, a full-stack web application that digitizes medicine inventory management through a Flask and SQLAlchemy REST API backed by a five-table relational data model, and a React (Vite, Tailwind CSS) single-page front end. The system exposes CRUD endpoints for medicine records and a dashboard endpoint that aggregates total stock, low-stock counts, and expired-item counts directly from the database using SQL aggregate functions. Beyond the operational CRUD and analytics layer, the system is architected with dedicated data models and route modules (Scan History, OCR Record, Inventory History, Chat History) that are designed to host a computer-vision medicine-package detector, an OCR-based batch/expiry-date extractor, and a conversational assistant as they are progressively integrated. We describe the system architecture, the relational schema, the implemented REST endpoints, the front-end design, the testing procedure followed, security and data-integrity considerations, a comparison against manual and generic point-of-sale alternatives, and the current implementation status of every module, before outlining the machine-learning components planned for the computer- vision and OCR subsystems and the corresponding development roadmap.

Keywords: Pharmacy Inventory Management, Flask REST API, SQLAlchemy ORM, React.js, Single-Page Application, Computer Vision, Optical Character Recognition, Expiry Tracking, AI Chatbot, Full-Stack Web Development.