A smart fashion web app built with Python (Flask) that recommends personalized outfits based on user profiling and AI analysis. It includes features like real-time virtual try-on with OOTDiffusion, skin tone & demographic detection using DeepFace, and outfit recommendations powered by TensorFlow, Gemini API, and PyTorch.

A deep learning-based fashion recommendation model

AI-powered fashion recommendation engine combining CLIP (visual) and SBERT (text) embeddings with FAISS HNSW search. Features a FastAPI backend, multimodal fusion for 44k+ products.

AI-powered fashion recommendation engine built with TensorFlow (CNN) and spaCy (NLP). It analyzes user preferences (age, gender, event, size, climate, budget, etc.) and recommends matching clothing items (top & bottom) with personalized recommendations. Exposes REST APIs via Flask for predictions, entity extraction, and image serving.

Fashion Product Recommendation System : This project is a Fashion Product Recommendation System that utilizes ResNet50 for feature extraction. The system takes a query image and suggests the most visually similar fashion products from a dataset of 44,400 images.

A MERN-based web app that recommends outfits based on weather, occasion, and user preferences.