🧠 AI-Based OOTD (Outfit of the Day) for Online Shoppers

An intelligent fashion recommendation system designed to enhance the online shopping experience by integrating AI-driven outfit recommendations, virtual try-on (VTON) visualization, and an interactive chatbot assistant.
This project aims to help users discover, visualize, and interact with fashion choices through a seamless and engaging AI interface.

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🎯 Project Objectives

1. To develop an AI recommendation module that suggests clothing combinations based on user preferences and outfit attributes.
2. To implement a virtual try-on system (VTON) that enables users to visualize recommended outfits on their own images.
3. To design a fashion chatbot assistant that allows natural interaction with the system for outfit recommendations, virtual try-ons, and fashion tips.
4. To improve the online shopping experience by providing intelligent, interactive, and personalized fashion suggestions.

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📘 Project Scope

The AI-Based OOTD for Online Shoppers system focuses on:

• Recommendation Intelligence — generating suitable outfit combinations using rule-based knowledge.
• Visualization Experience — providing realistic outfit try-on simulation through VTON techniques using GenAI API call.
• Conversational Assistance — enabling users to communicate with the system using a chatbot interface.

The project does not include real-time e-commerce transactions but focuses on the intelligent fashion recommendation pipeline.

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📚 Literature Review Summary

Fashion recommendation systems have evolved from simple attribute-based filtering to advanced AI-driven personalization.
Research indicates that computer vision and deep learning can effectively analyze fashion features like color, texture, and compatibility.
Key developments in the field include:

Study	Focus	Key Findings
Han et al. (2017) – FashionNet	Outfit compatibility learning	Deep neural networks can model clothing relationships and aesthetics effectively.
Jetchev & Bergmann (2018) – Conditional VTON	Virtual try-on using GANs	Image-based try-on models improve user visualization accuracy.
Wang et al. (2020) – FashionBERT	Multimodal embeddings	Combining text and image features enhances outfit understanding.
Recent Trends (2023–2024)	Chat-based retail assistants	NLP-based chatbots improve personalization and user engagement.

The integration of recommendation, visualization, and conversational AI forms the backbone of modern intelligent shopping systems — which this project aims to replicate in a simplified, modular structure.

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🧩 System Modules Overview

🧠 Module 1: AI-Based Recommendation System

• Suggests outfit combinations based on color harmony, texture, and style.
• Uses image feature extraction and similarity-based matching to generate recommendations.

Output: Recommended outfit images.

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🧥 Module 2: Virtual Try-On (VTON)

• Allows users to visualize how recommended outfits appear on their uploaded image.
• Uses image segmentation, warping, and blending techniques.
• Using the GenAI API call to implement the functions.

Output: Realistic visualization of user wearing the selected outfit.

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💬 Module 3: Fashion Chatbot (AI Shopping Assistant)

• Provides a conversational interface for user interaction.
• Detects user intents (recommend outfit) and triggers corresponding modules.
• Offers styling tips and answers basic fashion-related questions.

Output: Chat-based recommendations and interactive responses.

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🏗️ System Architecture

+--------------------------------------------------------------+
|                      AI-Based OOTD System                    |
+--------------------------------------------------------------+
|                     USER INTERFACE LAYER                     |
|  - Web UI                                                    |
|  - Login and Register Page                                   |
|  - Upload Image Interface                                    |
|  - Images Generation Recommendation UI                       |
|  - Virtual Try-On Results UI                                 |
|  - Chatbot UI                                                |
+--------------------------------------------------------------+
|                   APPLICATION / LOGIC LAYER                  |
|  [Module 1] Recommendation Engine    --> Suggests outfits    |
|  [Module 2] Virtual Try-On System    --> Visualizes results  |
|  [Module 3] Fashion Chatbot          --> Interacts with user |
+--------------------------------------------------------------+
|                       DATA LAYER                             |
|  - User preferences / profile data                           |
|  - Pre-trained models (GenAI, Segmentation Warping)          |
+--------------------------------------------------------------+

Project Flow

  ┌──────────────────────────────────────────────────────┐
  │                   User Interaction                   │
  │                    (Image Upload)                    │
  └──────────────────────────────────────────────────────┘
                        │
                        ▼
        ┌──────────────────────────────────────┐
        │  Module 1: Recommendation Engine     │
        │  → Generates top outfit matches      │
        └──────────────────────────────────────┘
                        │
                        ▼
        ┌──────────────────────────────────────┐
        │  Module 2: Virtual Try-On (VTON)     │
        │  → Visualizes outfit on user image   │
        └──────────────────────────────────────┘
                        │
                        ▼
        ┌──────────────────────────────────────┐
        │  Module 3: Fashion Chatbot           │
        │  → Handles user chat & feedback      │
        └──────────────────────────────────────┘
                        │
                        ▼
           ┌────────────────────────────────┐
           │      Display Final Output      │
           │ (Recommended & Try-On Results) │
           └────────────────────────────────┘

Run Locally

Prerequisites: Node.js

1. Install dependencies: npm install
2. Set the GEMINI_API_KEY in .env.local to your Gemini API key
3. Run the app: npm run dev