ECZEMA PROJECT

A simple machine learning-powered web app to detect eczema from skin images.

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🚀 How it Works

• We trained a Support Vector Machine (SVM) model on labeled images of eczema and normal skin.
• Users can upload an image through a Streamlit web interface.
• The app preprocesses the image and predicts whether the skin is eczema-affected or normal.
• It shows the result on screen with a simple message.

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🗂️ Project Structure

eczema_project/
│
├── dataset/              # Training data (eczema / normal images)
│   ├── eczema/
│   └── normal/
│
├── model_train.py        # Trains the machine learning model (SVM)
├── eczema_model.pkl      # Saved trained model
├── app.py                # Streamlit app for user prediction
├── requirements.txt      # Python dependencies
└── .gitignore            # Files & folders to ignore in version control

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⚙️ How to Run the Project

1. Clone the repository

git clone <your-repository-url>
cd eczema_project

2. Set up a virtual environment (optional but recommended)

python -m venv venv
# On Windows
.\venv\Scripts\activate
# On Mac/Linux
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. (Optional) Train the model from scratch

If you want to retrain the model with your own dataset:

python model_train.py

This will create a file called eczema_model.pkl.

5. Run the Streamlit app

streamlit run app.py

6. Open the app in your browser

• After running the above command, Streamlit will give you a local URL (e.g., http://localhost:8501).
• Open it in your browser, upload an image, and see the prediction.

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✅ Features

• Classifies skin images as eczema or normal.
• Easy-to-use web interface.
• Model trained on sample dataset (works best with more data!).

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✅ Future Improvements

• Use a Convolutional Neural Network (CNN) for better accuracy.
• Collect a larger and more diverse dataset.
• Improve the UI/UX of the web app.
• Add real-time camera input support.

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🛠️ Tech Stack

• Python
• Scikit-learn
• Streamlit
• NumPy
• Pillow (PIL)

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📂 Requirements

Make sure you have the following installed (or install them via pip install -r requirements.txt):

• numpy
• pillow
• scikit-learn
• streamlit
• joblib

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