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| import gradio as gr | |
| import joblib | |
| from keras.preprocessing import image | |
| import numpy as np | |
| # Load the model using joblib | |
| model = joblib.load("./your_model.pkl") | |
| # Define a function to preprocess an image | |
| def preprocess_image(image_path, target_size=(128, 128)): | |
| img = image.load_img(image_path, target_size=target_size) | |
| img_array = image.img_to_array(img) | |
| img_array = np.expand_dims(img_array, axis=0) | |
| img_array /= 255.0 # Normalize | |
| return img_array | |
| # Define a prediction function for Gradio | |
| def classify_skin_cancer(image): | |
| image_path = "uploaded_image.jpg" | |
| image.save(image_path) | |
| # Preprocess the image | |
| processed_image = preprocess_image(image_path) | |
| # Make a prediction using the model | |
| prediction = model.predict(processed_image) | |
| result = "Malignant" if prediction[0][0] > 0.5 else "Benign" | |
| return result | |
| # Create a Gradio interface | |
| iface = gr.Interface( | |
| fn=classify_skin_cancer, | |
| inputs=gr.inputs.Image(type="pil", label="Upload a skin image"), | |
| outputs="text", | |
| live=True, | |
| title="Skin Cancer Classifier", | |
| description="Upload a skin image and get a classification result (Malignant or Benign)." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |