SkinDx AI

AI-powered skin analysis — know your skin, feed it right.

SkinDx AI uses three machine learning models to analyse your skin type, detect conditions, and generate a personalised skincare routine — all from 3 photos and a 5-question quiz.

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Features

• 📸 Upload 3 skin photos → CNN detects skin type + conditions
• 💬 5-question lifestyle quiz → ANN recommends a routine
• 🤖 Gemini API generates personalised suggestions
• 📄 Download a full PDF skin analysis report

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Tech Stack

Layer	Tech
Frontend	React + Vite + Tailwind CSS + Framer Motion
Backend	Flask + TensorFlow/Keras
AI Suggestions	Google Gemini API
PDF Export	ReportLab

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Models

Three pre-trained Keras models power the predictions:

Model	File	Purpose
Skin Type CNN	skin_type_cnn.keras	Classifies skin as Combination / Dry / Normal / Oily
Skin Disease CNN	skin_disease_cnn.keras	Detects conditions (cellulitis, impetigo, ringworm, etc.)
Skin Quiz ANN	skin_quiz_ann.keras	Recommends routine from lifestyle quiz answers

⚠️ Model files are not included in this repo due to size.
Download them from: https://drive.google.com/drive/folders/13yTbbpTLsoC9QdnTC5REOXsUlfAcrmDO?usp=drive_link
Place them in backend/models/

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Setup

Prerequisites

• Python 3.10+
• Node.js 18+
• A Google Gemini API key → aistudio.google.com

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1. Clone the repo

git clone https://github.com/yourusername/DermaCell.git
cd DermaCell

2. Backend setup

cd backend

# Create and activate virtual environment
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Open .env and add your Gemini API key

3. Add models

Download the 3 model files and place them in:

backend/models/
├── skin_type_cnn.keras
├── skin_disease_cnn.keras
└── skin_quiz_ann.keras

4. Run backend

# Make sure you're in backend/ with .venv active
python app.py
# Runs on http://localhost:8000

5. Frontend setup

# In a new terminal tab
cd frontend
npm install
npm run dev
# Runs on http://localhost:5173

6. Open the app

Visit http://localhost:5173 in your browser.

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Environment Variables

Copy .env.example to .env and fill in your key:

GEMINI_API_KEY=your-gemini-api-key-here

Get your free Gemini API key at aistudio.google.com

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Project Structure

DermaCell/
├── backend/
│   ├── models/              ← .keras model files (not in repo)
│   ├── routes/
│   │   ├── predict.py       ← /api/predict endpoint
│   │   └── pdf_export.py    ← /api/export-pdf endpoint
│   ├── utils/
│   │   ├── model_loader.py  ← loads models + Gemini suggestions
│   │   ├── image_utils.py   ← image preprocessing
│   │   └── pdf_generator.py ← ReportLab PDF builder
│   ├── .env                 ← your keys (not in repo)
│   ├── .env.example         ← template
│   ├── app.py               ← Flask entry point
│   └── requirements.txt
│
├── frontend/
│   └── src/
│       ├── components/      ← Navbar, Quiz, ImageUpload, etc.
│       └── pages/           ← SplashPage, HomePage
│
├── .gitignore
└── README.md

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API Endpoints

Method	Endpoint	Description
GET	/api/health	Health check
POST	/api/predict	Run skin analysis
POST	/api/export-pdf	Generate PDF report

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Disclaimer

Results are AI-generated and are not a substitute for professional dermatological advice. Always consult a qualified dermatologist for medical concerns.

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Author

Parina Bhardwaj · github.com/parinaB