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import tensorflow as tf | |
nomanet = tf.keras.models.load_model("Nomanet.h5") | |
import gradio as gr | |
labels = {0: "melanoma", 1: "nevus", 2: "seborrheic_keratosis"} | |
def classify_image(inp): | |
img = tf.keras.utils.load_img( | |
inp, target_size=(256,256) | |
) | |
img_array = tf.keras.utils.img_to_array(img) | |
img_array = tf.expand_dims(img_array, 0) | |
predictions = nomanet.predict(img_array)[0] | |
return labels[model_prediction] | |
gr.Interface(fn=classify_image, | |
inputs=gr.inputs.Image(type="filepath"), | |
outputs=gr.outputs.Label()).launch() | |