{"repo":"micpana/AI-Powered-Skin-Facial-Condition-Diagnosis-Mobile-Application","free":true,"listed":false,"github":"https://github.com/micpana/AI-Powered-Skin-Facial-Condition-Diagnosis-Mobile-Application","clone":"git clone https://github.com/micpana/AI-Powered-Skin-Facial-Condition-Diagnosis-Mobile-Application.git","description":"Cross-platform mobile app that detects facial skin type and conditions using AI. Recommends skincare products based on skin profile and allergies. React Native frontend, Flask backend, MongoDB database, OpenCV face detection, and CNN-based diagnosis. Includes lifestyle and product suggestions.","language":"JavaScript","stars":21,"topics":["ai","computer-vision","deep-learning","dermatology","expo","face-detection","flask","fullstack","health-tech","image-classification"],"license":null,"category":"machine-learning","readme_excerpt":"AI Powered Skin Facial Condition Diagnosis Mobile Application An intelligent cross-platform mobile app that detects facial skin types and conditions using AI, then suggests skincare products and lifestyle adjustments. It helps users find the most compatible skincare products based on their skin profile and allergies. 🧠 Features - Detects facial skin type (e.g. Oily, Dry, Combination). - Detects facial skin conditions (e.g. Acne, Eczema, Rosacea). - Suggests skincare products based on: - Detected skin type/condition - Product ingredients - User-uploaded allergens - Provides lifestyle advice per skin type - Face detection and cropping using OpenCV before AI diagnosis - RESTful API backend in Flask, AI in Python, frontend in React Native (Expo) - MongoDB database for data storage --- 🚀 Getting Started Prerequisites - Python 3.8+ - Node.js & npm/yarn - Expo CLI ( npm install -g expo-cli ) - MongoDB (local or cloud instance) 1. Dataset Setup Create the following folder structure inside the server/ folder: Each subfolder inside train/ and test/ should be named after a skin type or skin disease . Example names: - Normal Skin - Oily Skin - Eczema - Rosacea Train and test folders must contain the same categories for accurate evaluation. --- 2. Model Training In server/train.py , update the number of classes: To train the model: Improving Training Accuracy If you experience low accuracy, here are adjustments you can make: - Increase the number of experiments (epochs): Set number of e","default_branch":null,"files":null,"tree":[],"storefront":"/r/micpana","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/micpana/AI-Powered-Skin-Facial-Condition-Diagnosis-Mobile-Application/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}