Fashion Recommendation System
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About
Analyzes fashion images using CLIP to extract clothing attributes like style, color, and fabric, then generates personalized recommendations based on detected tags and user behavior.
Details
- Author
- attarmau
- Repository
- attarmau/StyleCLIP
- GitHub stars
- 1
- License
- Apache License 2.0
- Categories
- Design, Media, AI, API, Frontend, Developer Tools, Database
Jump to
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Fashion Recommendation SystemCommand (node, npx, python, etc.)npxArguments-
Argument 1
-y -
Argument 2
@highlight/mcp-server
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
-
Argument 1
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
python -m venv venv
source venv/bin/activate # On macOS or Linux
venv\Scripts\activate # On Windows
pip install -r requirements.txt
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"fashion recommendation system": {
"env": {},
"args": [
"-y",
"@highlight/mcp-server"
],
"command": "npx"
}
}
}Linux
{
"env": [],
"args": [
"-y",
"@highlight/mcp-server"
],
"command": "npx"
}Macos
{
"env": [],
"args": [
"-y",
"@highlight/mcp-server"
],
"command": "npx"
}Windows
{
"env": [],
"args": [
"/c",
"npx",
"-y",
"@highlight/mcp-server"
],
"command": "cmd"
}AWS_RecSys
This is a CLIP-Based Fashion Recommender with AWS.📌 Sample Components for UI
1. Image upload 2. Submit button 3. Display clothing tags + recommendationsMockup
A user uploads a clothing image → YOLO detects clothing → CLIP encodes → Recommend similarFolder Structure
/project-root
│
├── /backend
│ ├── Dockerfile
│ ├── /app
│ ├── /aws
│ │ │ └── rekognition_wrapper.py # AWS Rekognition logic
│ │ ├── /utils
│ │ │ └── image_utils.py # Bounding box crop utils
│ │ ├── /controllers
│ │ │ └── clothing_detector.py # Coordinates Rekognition + cropping
│ │ ├── /tests
│ │ │ ├── test_rekognition_wrapper.py
│ │ │ └── test_clothing_tagging.py
│ │ ├── server.py # FastAPI app code
│ │ ├── /routes
│ │ │ └── clothing_routes.py
│ │ ├── /controllers
│ │ │ ├── clothing_controller.py
│ │ │ ├── clothing_tagging.py
│ │ │ └── tag_extractor.py # Pending: define core CLIP functionality
│ │ ├── schemas/
│ │ │ └── clothing_schemas.py
│ │ ├── config/
│ │ │ ├── tag_list_en.py $ Tool for mapping: https://jsoncrack.com/editor
│ │ │ ├── database.py
│ │ │ ├── settings.py
│ │ │ └── api_keys.py
│ │ └── requirements.txt
│ └── .env
│
├── /frontend
│ ├── Dockerfile
│ ├── package.json
│ ├── package-lock.json
│ ├── /public
│ │ └── index.html
│ ├── /src
│ │ ├── /components
│ │ │ ├── ImageUpload.jsx
│ │ │ ├── DetectedTags.jsx
│ │ │ └── Recommendations.jsx
│ │ ├── /utils
│ │ │ └── api.js
│ │ ├── App.js # Main React component
│ │ ├── index.js
│ │ ├── index.css
│ │ ├── tailwind.config.js
│ │ └── postcss.config.js
│ └── .env
├── docker-compose.yml
└── README.md
Quick Start Guide
Step 1: Clone the GitHub Project
Step 2: Set Up the Python Environment
python -m venv venv
source venv/bin/activate # On macOS or Linux
venv\Scripts\activate # On Windows
Step 3: Install Dependencies
pip install -r requirements.txt
Step 4: Start the FastAPI Server (Backend)
uvicorn backend.app.server:app --reload
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