DESIGN AND IMPLEMENTATION OF CLOTH-WEARING DETECTION SYSTEM ON WEATHER CONDITION USING ARTIFICIAL INTELLIGENCE (AI) GENERATIVE TOOLS

This project presents the design and implementation of a cloth-wearing detection and recommendation system that leverages artificial intelligence (AI) and weather data to assist users in making appropriate clothing choices. The system integrates real-time weather information with user preferences to generate personalized outfit suggestions. The inclusion of a five-day weather forecast further strengthens its practicality by enabling users to plan outfits ahead of time. Unlike traditional recommendation systems that rely primarily on static rules or general fashion guidelines, this solution incorporates AI-driven natural language processing through OpenAI and weather forecast APIs such as OpenWeather. The platform enables users to create an account, provide style and body-type preferences, and receive tailored recommendations that adapt to current and forecasted weather conditions. Additionally, users can upload outfit images to obtain refined suggestions if the initial recommendations are unsatisfactory, thereby enhancing interactivity. A feedback mechanism is included to capture user experiences and improve future recommendations. The system was developed using PHP for backend processing, MySQL for data storage, Tailwind CSS and JavaScript for the user interface, and cURL for API communication. Evaluation involved unit testing, integration testing, and user testing. Results demonstrated that the system is easy to navigate, responsive, and contextually accurate, achieving an estimated reliability rate. This project highlights the potential of combining AI generative tools with environmental data to enhance daily decision-making, promote personalization clothe styling and pictorial outfit visualization.