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| import gradio as gr | |
| import numpy as np | |
| import tensorflow as tf | |
| from tensorflow import keras | |
| from PIL import Image | |
| model = keras.models.load_model("skinCancerClassification.h5") | |
| class_labels = { | |
| 0: 'dermatofibroma', | |
| 1: 'melanoma', | |
| 2: 'nevus', | |
| 3: 'seborrheic keratosis', | |
| 4: 'squamous cell carcinoma', | |
| 5: 'pigmented benign keratosis', | |
| 6: 'basal cell carcinoma', | |
| 7: 'vascular lesion', | |
| 8: 'actinic keratosis' | |
| } | |
| def classify_skin_cancer(image): | |
| # Preprocess the image | |
| image = np.array(image) | |
| image = tf.image.resize(image, (75, 100)) | |
| image = np.expand_dims(image, axis=0) | |
| predictions = model.predict(image) | |
| class_index = np.argmax(predictions) | |
| class_name = class_labels[class_index] | |
| confidence = np.max(predictions) | |
| return f"Predicted Class: {class_name}\nConfidence: {confidence:.2f}" | |
| iface = gr.Interface( | |
| fn=classify_skin_cancer, | |
| inputs="image", | |
| outputs="text", | |
| live=True, | |
| title='<h1 style="text-align: center;">Skin Cancer Classification! 🌻</h1>', | |
| description=( | |
| "<h2><b>Explore Skin Cancer Image Classification!</b></h2>" | |
| "<p>Join me in the world of skin health and medical innovation. " \ | |
| "Be part of a game-changing journey where you can support healthcare, " \ | |
| "make a real difference, and impact lives. 🌍🩺🤝 " \ | |
| "Discover the power of AI in skin cancer diagnosis. Start exploring now!</p>" | |
| ) | |
| ) | |
| iface.launch() | |