AUTOMATED SYSTEM FOR GENERATING OPTIMAL CLOTHING COMBINATIONS BASED ON COMPATIBILITY ASSESSMENT AND WEATHER CONDITIONS USING LARGE LANGUAGE MODELS

Abstract

 This article presents the development and implementation of a web-based intelligent assistant for generating optimal clothing combinations based on real-time weather conditions and individual style preferences. The core innovation lies in the integration of large language models (LLMs), specifically OpenAI’s GPT-based API, which enables the automated classification, description, and compatibility assessment of clothing items. The system operates as a personalized wardrobe assistant that leverages cloud-based data storage, modern front-end technologies, weather APIs, and semantic analysis to deliver stylistically coherent and weather-appropriate outfit recommendations. 
 The proposed solution addresses the growing need for intelligent wardrobe management and outfit planning tools by automating decision-making processes that traditionally require personal judgment or domain-specific expertise. Users can upload images of their clothing items, which are processed using the LLM to extract relevant attributes including type, subtype, style, color, weather suitability, and gender. The processed data is stored in Firebase Firestore and serves as the basis for generating personalized outfit recommendations. 
 A key component of the recommendation algorithm is the compatibility matrix, which evaluates the stylistic and color harmony between various clothing elements. Compatibility scores are pre-defined based on common fashion principles and empirical observations. The algorithm calculates a weighted score for each possible outfit by considering both structural (e.g., shirt with jeans) and visual (e.g., black with white) compatibility. It also incorporates current weather data obtained via the Open-Meteo API, which includes parameters such as temperature, wind speed, and precipitation. These values are transformed into descriptive weather categories (e.g., cold, hot, windy) to refine the selection of suitable clothing items. 
 The system’s user interface is built with HTML, CSS, Bootstrap 5, and JavaScript, providing a responsive and intuitive environment. Users interact with the system through several dedicated pages: uploading and classifying clothes, browsing the wardrobe, viewing local weather forecasts, and generating optimal outfit suggestions. The architecture supports drag-and-drop functionality for image uploads, automatic geolocation, real-time API interactions, and dynamic filtering based on weather and style input. 
 The recommendation algorithm performs multi-stage processing: initial weather and style filtering, grouping clothes by type (top, bottom, footwear, outerwear), calculating all feasible combinations, and scoring them using compatibility formulas. The five best-scoring combinations are presented to the user. This approach ensures that the generated outfits are not only weather-compliant but also visually and stylistically balanced. 
 Testing of the web application demonstrated high accuracy in clothing classification and relevance of generated outfits. The results validate the feasibility of using LLMs for semantic garment analysis and highlight the potential of such systems in fashion consulting, virtual wardrobes, and e-commerce personalization. Furthermore, the modular design and cloud-based architecture facilitate future expansion, including social features and brand-based recommendations.