https://github.com/atxtechbro/fashion-recommender
Automate Your Wardrobe. Fashion Intelligence at Your Fingertips. Snazzy Closet by Algofit.io digitizes your wardrobe, analyzes over 20,000 data points, and recommends outfits based on your unique traits—like eye color, hair color, and skin tone. Welcome to the future of fashion.
https://github.com/atxtechbro/fashion-recommender
digital-fashion fashion-intelligence fastapi machine-learning mongodb personalized-style python react wardrobe-automation web-application
Last synced: 7 months ago
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Automate Your Wardrobe. Fashion Intelligence at Your Fingertips. Snazzy Closet by Algofit.io digitizes your wardrobe, analyzes over 20,000 data points, and recommends outfits based on your unique traits—like eye color, hair color, and skin tone. Welcome to the future of fashion.
- Host: GitHub
- URL: https://github.com/atxtechbro/fashion-recommender
- Owner: atxtechbro
- Created: 2024-08-09T14:34:50.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-21T02:25:59.000Z (about 1 year ago)
- Last Synced: 2025-01-25T21:28:01.380Z (9 months ago)
- Topics: digital-fashion, fashion-intelligence, fastapi, machine-learning, mongodb, personalized-style, python, react, wardrobe-automation, web-application
- Language: Python
- Homepage: https://algofit.io/
- Size: 320 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Snazzy Closet
Snazzy Closet is a fashion recommender app that helps users manage their wardrobe and receive personalized outfit recommendations. The project leverages FastAPI for the backend, MongoDB for data storage, React for the frontend, and machine learning for analyzing user data to improve recommendations.
## Table of Contents
- [Project Overview](#project-overview)
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Backend](#backend)
- [API](#api)
- [Database](#database)
- [Machine Learning](#machine-learning)
- [Frontend](#frontend)
- [Testing](#testing)
- [Collaboration](#collaboration)
- [License](#license)
## Project Overview
Snazzy Closet aims to provide a seamless and personalized fashion recommendation experience. Users can upload information about their wardrobe, and the system will suggest outfits based on their preferences, previous selections, and other criteria. The core of this functionality is powered by machine learning models that classify clothing items and detect colors, enhancing the personalization of recommendations.
## Getting Started
### Prerequisites
- **Python 3.10+**
- **MongoDB**
- **Node.js and npm/yarn**
### Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/snazzy-closet.git
cd snazzy-closet
```
2. Install backend dependencies:
```bash
pip install -r requirements.txt
```
3. Install frontend dependencies:
```bash
cd snazzy-closet
npm install
```
4. Run the backend:
```bash
uvicorn backend.api.main:app --reload
```
5. Run the frontend:
```bash
npm start
```
## Backend
The backend is responsible for handling API requests, interacting with the database, and running machine learning models.
### API
- **`main.py`**: The entry point for the FastAPI application.
- **`user_routes.py`**: Manages user-related API endpoints (create, read, update, delete).
- **`clothing_item_routes.py`**: Handles CRUD operations for clothing items.
### Database
- **`db_config.py`**: Configures the MongoDB connection.
- **`models.py`**: Defines the data schemas for users and clothing items.
### Machine Learning
The machine learning components are the heart of Snazzy Closet, providing intelligent recommendations based on image classification and color detection.
- **`model.py`**: Defines the structure of the machine learning model.
- **`preprocess.py`**: Handles data preprocessing, including image resizing, normalization, and feature extraction.
- **`train.py`**: Manages the training process for the model, including loading and preprocessing images, training the model, and saving the trained model to disk.
### Model Versioning and Storage
When saving the trained model with `model.save('final_model.h5')`, ensure that a versioning strategy is in place to avoid overwriting models, especially in a CI/CD pipeline or production environment.
## Frontend
The frontend is built using React and includes all the user interface components.
- **`App.js`**: Main entry point for the React application.
- **`index.js`**: Renders the React application to the DOM.
- **`App.css`** and **`index.css`**: Styles for the application.
## Testing
We use `pytest` for backend testing, with tests located in the `tests` directory.
- **`test_api.py`**: Contains unit tests for the API routes, covering user creation, retrieval, updating, and deletion.
To run the tests:
```bash
export PYTHONPATH=$(pwd)
pytest snazzy-closet/backend/tests
```
## Collaboration
All changes should be made through pull requests. Follow branch naming conventions tied to issues for clarity (e.g., `issue-1-add-tests`).
### Creating a Pull Request
1. Create a new branch:
```bash
git checkout -b issue-1-add-tests
```
2. Push your branch to GitHub:
```bash
git push origin issue-1-add-tests
```
3. Create a PR:
You can use the GitHub website or the CLI:
```bash
gh pr create --title "Add update and delete test coverage" --body "This PR adds tests for updating and deleting users via the API. Closes #1."
```
## License
This project is licensed under the MIT License. See the LICENSE file for details.