SkinIQ

An AI-powered skincare app that analyzes ingredients and builds personalized routines.

Live Demo

🌿 skiniq-plum.vercel.app

📹 Watch the demo on Loom

What it does

SkinIQ helps anyone understand what's in their skincare products and whether those products are safe to use together. Paste in an ingredient list and get a full breakdown of what each ingredient does and how safe it is. Tell SkinIQ your skin type and the products you own and it will build you a safe AM/PM routine, flagging dangerous combinations like retinoids and AHAs.

Features

• Product Analyzer — paste any ingredient list and get safety scores, functions, and conflict warnings powered by a RAG pipeline over 103 curated ingredients
• Routine Builder — input your skin type, concerns, and products to get a personalized AM/PM routine with conflict detection

Tech Stack

• Backend — Python, FastAPI, OpenAI API (GPT-4o-mini), ChromaDB, SQLModel
• Database — PostgreSQL on Supabase (103 ingredients)
• Frontend — Next.js 14, Tailwind CSS
• Deployment — Docker, Railway (backend), Vercel (frontend)

Architecture

User → Next.js frontend (Vercel)
     → FastAPI backend (Railway/Docker)
     → ChromaDB RAG pipeline
     → OpenAI GPT-4o-mini
     → PostgreSQL on Supabase

How to run locally

1. Clone the repo

   git clone https://github.com/Ferduse/skiniq.git
   cd skiniq
   

2. Set up the backend

   cd backend
   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt
   

3. Add your environment variables — create a .env file in /backend

   DATABASE_URL=your_supabase_url
   OPENAI_API_KEY=your_openai_key
   

4. Run the backend

   uvicorn main:app --reload
   

5. Set up the frontend

   cd ../frontend
   npm install
   npm run dev
   

6. Open your browser

   • Backend API: http://localhost:8000/docs
   • Frontend: http://localhost:3000