README.md

---
title: Skin Disease Detection Demo
emoji: 🩺
colorFrom: blue
colorTo: pink
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: apache-2.0
---

# 🩺 Skin Disease Detection — Demo

A demo Space where users can:
- **Upload a skin image** (or click a bundled test image) and get an AI classifier's
  prediction across common skin-lesion categories, plus a plain-language explanation
  from an LLM.
- **Chat with an assistant** about skin-health topics in general terms.

> ⚠️ **This is an educational demo, not a medical device.** It does not diagnose
> anyone. Always see a licensed dermatologist or doctor for real concerns.

## How it works

| Component | What it does | Default model |
|---|---|---|
| Image classifier | Predicts a skin-lesion category from a photo | [`Anwarkh1/Skin_Cancer-Image_Classification`](https://huggingface.co/Anwarkh1/Skin_Cancer-Image_Classification) (ViT, trained on a HAM10000-derived dataset) |
| Chat / explanation | Turns predictions into plain language, answers general questions | `HuggingFaceH4/zephyr-7b-beta` via the Hugging Face Inference API |

Both model IDs are configurable via Space variables — see below.

## 🚀 Deploy in 3 steps

1. **Create a new Space**
   Go to [huggingface.co/new-space](https://huggingface.co/new-space) →
   choose **Gradio** as the SDK → CPU basic hardware is fine for the classifier.

2. **Upload these files**
   Upload `app.py`, `requirements.txt`, this `README.md`, and the `examples/`
   folder (if you generated test images — see below) to the Space repo,
   either via the web UI ("Add file") or:
   ```bash
   git clone https://huggingface.co/spaces/<your-username>/<your-space-name>
   cp -r app.py requirements.txt README.md examples <your-space-name>/
   cd <your-space-name>
   git add . && git commit -m "Initial commit" && git push
   ```

3. **Add an `HF_TOKEN` secret (to enable the chatbot)**
   In your Space → **Settings → Variables and secrets** → **New secret**:
   - Name: `HF_TOKEN`
   - Value: a Hugging Face access token (create one at
     [huggingface.co/settings/tokens](https://huggingface.co/settings/tokens),
     "Read" scope is enough)

   Without this secret, image classification still works — only the
   LLM-generated explanations and the chat tab need the token.

That's it — the Space will build and the app will be live.

## 🖼️ Adding test images (optional but recommended)

So users have something to click without needing their own photo, generate a
few sample images once, locally, before you push:

```bash
pip install datasets pillow
python scripts/download_examples.py
```

This saves a handful of JPEGs into `examples/`. Include that folder when you
push to your Space. You can also just drop in your own sample `.jpg`/`.png`
files instead.

## ⚙️ Configuration

Set these as Space **variables** (not secret, unless noted) to customize:

- `IMAGE_MODEL_ID` — any Hugging Face image-classification model compatible
  with `transformers.pipeline("image-classification", ...)`.
- `CHAT_MODEL_ID` — any chat-completion-capable model available via the HF
  Inference API.
- `HF_TOKEN` *(secret)* — required for the chat tab and the LLM explanations.

## 🩹 Limitations & responsible use

- The bundled classifier was trained on a research dataset (HAM10000-derived
  dermatoscopic images) and has real, published accuracy limits — treat its
  output as a talking point, not a result.
- Performance depends heavily on image quality, lighting, and skin tone
  representation in the training data.
- This app must not be used as a substitute for professional medical
  evaluation, and the chatbot is instructed to avoid giving diagnoses,
  treatments, or dosages.

## Local development

```bash
pip install -r requirements.txt
export HF_TOKEN=your_token_here   # optional, for chat
python app.py
```