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Shubham-Mohite7 / repository
SkinAI is an intelligent, real-time facial skin analysis web application that transforms a simple selfie into a comprehensive dermatology report using computer vision and color science — no doctor visit needed. Built for everyone — from skincare enthusiasts to students, developers, and dermatology researchers — SkinAI instantly detects acne
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Live Demo: https://acnemodel.streamlit.app
SkinAI is a real-time facial skin analysis tool that detects:
It uses computer vision + color science to give instant dermatology-level insights from a selfie.
| Feature | Description |
|---|---|
| Face Detection | Auto-detects face using OpenCV Haar Cascade |
| Acne Detection | Finds red, white, blackheads with numbered red circles |
| Location Mapping | Labels acne as: Forehead, Left/Right Cheek, Chin, Jaw |
| Skin Tone Analysis | Classifies: Very Fair → Deep Brown |
| Undertone | Warm (Golden) vs Cool (Pink) |
| Genetic Origin (Approx) | Europe, Mediterranean, South Asia, Africa, East Asia |
| Skin Type | Oily / Combination / Dry based on brightness variance |
| Smoothness Score | Texture analysis (0–100) |
| Zoomable Acne Crops | Click each pimple to see close-up |
| Web App (Streamlit) | Upload selfie → Instant report |
| Layer | Technology | Version / Notes |
|---|---|---|
| Frontend / UI | Streamlit | 1.30+ – Interactive web app |
| Backend / Logic |
Selected from shared topics, language and repository description—not editorial ratings.
Ermi21 /
SkinAI is a Python-based web application that uses an AI model to help screen common skin lesions from images. It provides a simple and responsive interface where users can upload a skin photo, run inference, and see the most likely skin condition along with risk information and educational guidance.
49/100 health| Python |
3.11 |
| Computer Vision | OpenCV | opencv-python-headless (cloud-safe) |
| Image Processing | NumPy, PIL (Pillow) | Core array & image handling |
| Color Analysis | HSV & Lab color spaces | For tone, acne, texture |
| Face Detection | Haar Cascade (haarcascade_frontalface_default.xml) | Auto-downloaded |
| Deployment | Streamlit Community Cloud | Free, auto-deploy from GitHub |
| Version Control | Git + GitHub | git |
| Package Management | requirements.txt | Cloud dependency install |
| Local Dev | Conda (Miniconda) | skinai environment |
SkinAI/
│
├── app.py # Main Streamlit app (all logic)
├── requirements.txt # Cloud dependencies
├── haarcascade_frontalface_default.xml # Auto-downloaded
├── README.md # This file
└── .git/ # Git repo
# 1. Clone repo
git clone https://github.com/yourusername/SkinAI.git
cd SkinAI
# 2. Create conda env
conda create -n skinai python=3.11 -y
conda activate skinai
# 3. Install deps
pip install -r requirements.txt
# 4. Run locally
streamlit run app.py
Open http://localhost:8501 → Upload selfie → See results!
app.py → Deploy!| Screenshot | Description |
|---|---|
| Upload + Face + Acne Circles | |
| Color, Genes, Type, Smoothness |
| Current Limit | Future Upgrade |
|---|---|
| Lighting-sensitive | Add auto-light correction |
| Single face only | Multi-face support |
| No age/gender | Add CNN models (MediaPipe) |
| No history | Add user login + save reports |
| No PDF export | Add "Download Report" button |
Shubham
"Turning selfies into skin science — one pimple at a time."
If you like it, give it a star on GitHub!
Help others find this AI dermatology tool.
SkinAI — Because your skin deserves AI.