International Research Journal of Education and Technology

This project focuses on developing a Plagiarism Detector Web App using Flask for the backend and SQLite for local storage. It allows users to upload PDF assignments, which are analyzed for plagiarism by comparing them against previous submissions and external online sources via an integrated plagiarism detection API. If plagiarism exceeds a set threshold, users are prompted to re-submit; otherwise, the document is stored for future reference.Key features include file uploads, plagiarism scoring, submission tracking, and CSV report generation. The front-end, built with Bootstrap 5, ensures a responsive user experience, while PyMuPDF handles PDF text extraction. The system efficiently detects both internal and external duplication, assisting educators in maintaining academic integrity. Its modular architecture allows for future upgrades, such as expanded document formats and additional comparison sources, making it a scalable and user-friendly solution.

Key Words: . plagiarism detection , python , flask , PyMuPDF (fitz)