LibraFlow AI

The LibraFlow AI was developed to address the main technical problem was creating a reliable library management system that could handle concurrent borrow/return actions, fuzzy book discovery, and personalized recommendations without data inconsistency or poor performance. the codebase shows this was solved in a real production-style setup using async database access, rule-heavy business logic, and a full-stack architecture This project showcases my skills in Python, FastAPI, SQLModel, and other technologies.

During development, I focused on creating a solution that would be both effective and user-friendly. The approach involved the solution was a fastapi + sqlmodel + postgresql backend with transaction-safe services, including row-level locking in the circulation logic to prevent race conditions on available copies and overdue status updates. search uses a hybrid db filter + rapidfuzz levenshtein scoring for typo-tolerant results, and recommendations combine collaborative filtering with category popularity; the frontend is a next.js app that surfaces admin/student flows in a clean, responsive interface.

The application features a clean, intuitive interface that allows users to easily navigate and utilize all functionality. Key features include Intelligent fuzzy search for books, Real-time book availability tracking, Personalized recommendations based on reading history, and more.

This project demonstrates my ability to understand complex problems and develop effective solutions using modern technologies and best practices in software development.