Explainable-Fashion-Design-AI

This project is an explainable AI-based fashion design decision-support system that combines deep learning, machine learning, and explainable AI to generate fashion concepts, estimate pre-production market potential, model customer preference, explain model decisions, and refine designs before final selection. It investigates a pre-production AI decision-support workflow where fashion concepts can be generated using deep learning, represented using visual and product features, evaluated for expected demand and customer preference, explained using interpretable AI methods, refined based on identified strengths and weaknesses, and re-evaluated before human approval. The system is intended to provide data-driven support during early design decision-making, not to replace fashion designers or guarantee sales. The project addresses cold-start evaluation and time sensitivity by using visual, product, seasonal, and similar-product information, and by employing chronological evaluation and time-aware re-evaluation. The final application is planned as a separated frontend and AI backend architecture, with a Flutter web dashboard and a Python backend providing REST API services for generation, demand prediction, preference modeling, explainability, and refinement.