🩺 Skin Disease Detection Web App

A simple web application built with Streamlit that uses a fine-tuned VGG16 model to detect common skin conditions from uploaded images. It provides a prediction and a general suggestion based on the detected condition.

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🚀 Features

• 🖼️ Image Upload: Allows users to upload images of skin conditions (JPG, JPEG, PNG).
• 🧠 AI-Powered Prediction: Uses a pre-trained VGG16 model to classify the image into one of the supported skin conditions.
• 📝 Basic Suggestions: Provides a general prescription or advice for the detected condition.
• Supported conditions:
  • Melanoma
  • Nevus
  • Seborrheic Keratosis

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🛠️ Tech Stack

• Streamlit: For creating the web application interface.
• TensorFlow/Keras: For loading and using the deep learning model.
• Pillow (PIL): For image manipulation.
• OpenCV (cv2): For image preprocessing (resizing).
• NumPy: For numerical operations.

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⚙️ Setup and Installation

1. Clone the Repository (or download the files): If this project were in a repository, you'd clone it. For now, ensure you have derma_web_app.py and the model file in the same directory.

2. Model File: You need the pre-trained model file: derma_disease_detection_VGG16_fine_tuned_colab_finetuned_20241027_202654.h5. Place this file in the same directory as derma_web_app.py.

3. Install Dependencies: Create a virtual environment (recommended) and install the required Python packages. You can create a requirements.txt file with the following content:

   streamlit
   tensorflow
   numpy
   Pillow
   opencv-python

   Then install them using pip:

   pip install -r requirements.txt

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▶️ Running the Application

1. Navigate to the directory containing derma_web_app.py and the model file.
2. Run the Streamlit application from your terminal:

   streamlit run derma_web_app.py

3. The application will open in your web browser.

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📋 How to Use

1. Open the App: Once the app is running, it will display the title "Skin Disease Detection and Prescription."
2. Upload Image: Click on the "Upload an image of the skin condition" button and select an image file (JPG, JPEG, or PNG).
3. View Image: The uploaded image will be displayed.
4. Predict: Click the "Predict" button.
5. Get Results: The application will show the predicted skin condition and a corresponding general prescription or advice.

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⚠️ Disclaimer

This application is for informational purposes only and does not substitute professional medical advice. Always consult with a qualified dermatologist or healthcare provider for any medical concerns or before making any decisions related to your health.