Fashion Recommender System

This project is a Fashion Recommender System that recommends visually similar fashion styles based on a user-uploaded image. It leverages a pre-trained ResNet50 model to extract image features and find the most relevant matches from a dataset.

Table of Contents

• Features
• Core Components
• How It Works
• Setup and Installation
• Usage
• Dependencies
• Future Enhancements

Features

• Pre-trained Model: Uses ResNet50 for feature extraction.
• Efficient Search: Finds the top 5 most similar images using the Nearest Neighbors algorithm.
• Interactive UI: Provides an easy-to-use Streamlit interface for uploading images and viewing recommendations.

Core Components

app.py

• Extracts features from dataset images.
• Saves the precomputed embeddings (embeddings.pkl) and filenames (filenames.pkl).

main.py

• Implements the Streamlit interface.
• Loads precomputed embeddings and filenames for efficient recommendations.
• Provides a recommendation system based on user-uploaded images.

How It Works

1. Feature Extraction:

   • Images are resized to 224x224 and preprocessed using ResNet50.
   • Extracted features are normalized using L2 norm.

2. Recommendation:

   • The Nearest Neighbors algorithm (Euclidean distance) identifies the closest matches.

3. Display:

   • Displays the uploaded image and top 5 similar images in a grid layout.

Setup and Installation

Prerequisites

• Python 3.8.10
• A TensorFlow-compatible environment

Steps

1. Clone the Repository:

   git clone <repository-url>
   cd <repository-folder>

2. Create a Virtual Environment:

   python -m venv env

   Activate the environment:

   • Windows:

     .\env\Scripts\activate

   • Mac/Linux:

     source env/bin/activate

3. Install Dependencies:

   pip install -r requirements.txt

4. Precompute Embeddings (Optional):

   • If embeddings need to be regenerated:

     python app.py

5. Run the Application:

   streamlit run main.py

Usage

1. Upload a fashion image using the file uploader in the Streamlit app.
2. View the uploaded image alongside the top 5 most similar styles from the dataset.

Dependencies

The project dependencies are listed in requirements.txt:

numpy
tensorflow
streamlit
Pillow
scikit-learn
tqdm

Install them with:

pip install -r requirements.txt