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| """" | |
| We are going to deploy our model using Gradio. | |
| """ | |
| import gradio as gr | |
| import numpy as np | |
| import tensorflow as tf | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.preprocessing import image | |
| # Load the model | |
| model = load_model('melanoma_cancer_model.h5') | |
| # Define the function to make predictions | |
| def classify_image(img): | |
| img = np.expand_dims(img, axis=0) | |
| # Resize image | |
| resized_img = tf.image.resize(img, [160, 160]) | |
| # Predict the image | |
| prediction = model.predict(resized_img)[0][0] | |
| # Convert to float value | |
| prediction = float(prediction) | |
| # return dictionary for Gradio | |
| return {"melanoma": prediction, "not melanoma": 1 - prediction} | |
| # Launch the Gradio interface | |
| gr.Interface(fn=classify_image, inputs='image', outputs="label").launch() | |
| # Launch shareble Gradio interface | |
| # gr.Interface(fn=classify_image, inputs='image', outputs="label").launch(share=True) | |