Integration of Artificial Intelligence Models into a Flask-Based Web Application for Classification and Generation of Batik Cual Motifs from Bangka Belitung - Syifa Nandini Girfita; Muhammad Ressi Rabsanjani; Zikri Wahyuzi; Rike Pradila (Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret, 2026)
Artificial Intelligence (AI) has been increasingly adopted to support digital preservation and creative exploration of cultural heritage. However, many existing studies primarily focus on model development, while limited attention is given to the practical integration and deployment of AI models in real-world applications. This study presents the integration of AI models into a web-based application using the Flask framework for Batik Cual motif classification and generation. The proposed system enables users to interact with AI functionalities through a browser-based interface, supporting motif classification from uploaded images and motif generation based on textual input and reference images. The integration is implemented using a RESTful backend architecture, where Flask manages request handling, preprocessing, AI inference workflows, postprocessing, and result visualization. Functional testing results indicate that the system operates according to its intended design, providing structured outputs with acceptable response times. The findings demonstrate that Flask is an effective and practical framework for deploying AI-driven web applications, particularly for prototype-scale systems aimed at supporting digital preservation and dissemination of traditional cultural motifs.