I Built an AI Skin Disease Detector with Flask, TensorFlow Lite, and Groq — Here's How

What if you could upload a photo of a skin lesion and get an AI-powered prediction in under 2 seconds — no signup, no data stored, completely free? That's exactly what I built for my capstone thesis. SKIN is a web app that runs two CNN models: a 7-class skin lesion classifier trained on the dataset, and a binary monkeypox detector. It also uses Groq's Llama 4 Scout to generate plain-language medical explanations for each prediction. Here's how I built it. Live Demo | GitHub Repo The Problem Skin diseases are one of the most common reasons people visit a doctor, but access to dermatologists is limited in many parts of the world. Early detection of conditions like melanoma can be life-saving, yet most people don't know what to look for. I wanted to build something that could give people a starting point — not a diagnosis, but an informed nudge to see a doctor. Important disclaimer: This is an educational tool. It's not a medical device and should never replace a real dermatologist. The S