👗 outfits.ai

Overview

outfits.ai is a sophisticated mobile application designed to revolutionize wardrobe management. Easily catalog, organize, and style your clothing collection with advanced digital tools.

Check out the video demonstration below:

🌟 Features

• Wardrobe Digitization: Catalog entire clothing collection
• Outfit Composition: Create and save outfit combinations
• Smart Recommendations: AI-powered outfit suggestions
• Cross-Device Sync: Seamless experience across platforms
• Inventory Tracking: Monitor clothing items and usage
• [TO DO] Auto-Cart Addition: Add your shopping cart items into the app without having to upload

🚀 Technologies

Frontend:

Backend

Infrastructure

🔧 Installation

Prerequisites

• Node.js (v16+)
• Go (v1.18+)
• Docker
• AWS CLI
• Python 3.9.21
• Pipenv
• Yarn
• Terraform
• Ansible

Make sure to configure ".env" and "variables.tf"

Clone repository

git clone https://github.com/ironnicko/outfits.ai

Install App dependencies

cd ./App
yarn install

Install backend dependencies

cd ./Go-Backend
go mod download

cd ../Segment
pipenv --python 3.9.21
pipenv shell
pipenv sync

Start AWS

Make sure to set up your ~/.aws/credentials file with your AWS SECRET KEY and AWS ACCESS KEY ID.

cd ./Terraform
terraform init
terraform apply --auto-approve

Build and run entire stack

docker-compose up --build

To run locally:

• Backend:

  cd ./Go-Backend
  go run .
  

  Make sure to set VITE_PUBLIC_IP=http://localhost in .env

• Segment:

  cd ./Segment
  python3 main.py
  

• App:

  cd ./App
  yarn install
  yarn run start
  

🌐 Architecture

outfits.ai/
│
├── App/ # React-Native TypeScript Application
│
├── Go-Backend/ # Golang Fiber Backend
│
├── Terraform/ # Terraform & Ansible Configs
│ ├── terraform/
│ └── ansible/
│
├── Segment/ # Python FastAPI Service
 ├── Model/U2Net
 ├── Model/SAM(Segment Anything Model)
 ├── Model/MiniLM-L6-v2(Embedding Model)
 ├── Model/GPT-4o-mini(Recommendation)
 └── s3_upload

🔒 Environment Variables

.env:

DB_USERNAME=
DB_PORT=
DB_NAME=
DB_PASSWORD=
DB_HOST=
PORT=8000
JWT_SECRET=<JWT secret>
TIMEZONE=UTC
BUCKET_PREFIX=<S3 bucket prefix>
VITE_PORT=3000
BUCKET_NAME=<bucket name>
MODEL=sam
SEGMENT_URL=http://segment
REM_HOST=rembg
URL=<supabase project URL>
ANON=<supabase API KEY>

VITE_PUBLIC_IP will be necessary when running locally

variables.tf:

variable "ssh_user" {
  type        = string
  description = "SSH user"
  default     = "ec2-user"
}

variable "private_key_path" {
  type        = string
  description = "Private Key Path"
  default     = "<Path to AWS Key Pair>"
}

variable "bucket_name"{
  type = string
  description = "S3 Bucket Name"
  default = "<Your AWS S3 Bucket's Name>"
}