Simplify

A web app that identifies likely skin conditions from a photo and tells you what you can do about it right now — immediate care, home remedies, what to avoid, and the red flags that mean you should stop reading and see a doctor.

Classification runs entirely in the browser via a Teachable Machine model, so the photo never leaves the user's device.

Live: simplify-skin-disease.vercel.app

This is not a medical device. It is an image classifier that can be confidently wrong. Every result is a prompt to get checked — never a reason to delay care or to self-treat something serious.

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Screens

Home

Skin Guide

Reference material on the conditions the model is trained to recognise.

About

Log in

Sign up

Password requirements tick off live as you type.

Prediction

Drag-and-drop or webcam capture, with distinct model-loading and scanning states.

Result and guidance

Shown here with sample data to illustrate the layout.

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Features

• Email/password auth via Firebase, with sessions that persist for 7 days
• In-browser classification — no image upload, no server-side inference
• Upload or webcam — webcam predicts on a frame you capture, not continuously
• Condition guidance for 20 conditions: immediate care, home remedies, what to avoid, and when to seek medical help
• Severity routing — anything cancer-adjacent skips home remedies entirely and directs to urgent care
• Confidence reporting, with an explicit warning below 60%

Model

A Teachable Machine image model with seven classes:

Class	Guidance shown
Acne and Rosacea	Acne & Rosacea
Molluscum contagiosum and warts	Molluscum Contagiosum / Viral Skin Infection
Melanoma	Possible skin cancer — urgent
Vascular Tumor	Vascular Tumour
Eczema	Eczema (Atopic Dermatitis)
Bullous	Bullous Disease — urgent
Psoriasis and lichen	Psoriasis

Labels are matched loosely against static/assets/js/remedies.js, so variants like "Atopic Dermatitis" or "Tinea Corporis" resolve correctly. An unrecognised label falls back to a generic card rather than breaking the page.

Stack

Layer	Choice
Backend	Flask, deployed as a Vercel serverless function
Auth	Firebase Authentication (REST API)
Model	Teachable Machine + TensorFlow.js, client-side
Frontend	Jinja templates, vanilla JS, no build step

Layout

api/index.py              Flask app — all routes, the serverless entry point
templates/                Jinja templates
static/assets/js/
  skin-scan.js            Upload, webcam capture, model loading, result rendering
  remedies.js             Condition guidance and label matching
  auth.js                 Password toggle, live rules, submit state
static/assets/css/
  auth.css                Login and signup
vercel.json               Serverless build and routing

app.py in the repo root is the original single-file version, kept for local reference. Vercel serves api/index.py.

Running locally

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

export SECRET_KEY="$(python3 -c 'import secrets; print(secrets.token_hex(32))')"
python api/index.py

Then open http://127.0.0.1:5000.

Deploying

Pushing to main triggers a Vercel build. vercel.json routes everything to the Python function except /static/*, which is served from the CDN, and includeFiles ensures the templates are bundled into the function.

Environment variables

Name	Required	Purpose
SECRET_KEY	Yes, in production	Signs session cookies. Without it the app falls back to a default that is committed to this repo, which means anyone could forge a session. Generate one with python3 -c "import secrets; print(secrets.token_hex(32))".

The Firebase Web API key in api/index.py is intentionally public — that is how Firebase web keys work. Access is controlled by Firebase security rules, not by hiding the key.

Known limitations

• /map renders map.html, which does not exist in templates/ — the route returns a 500. Nothing links to it.
• Model accuracy is bounded by its training set. Treat low-confidence results as noise.

Licence

MIT — see LICENSE.