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import tensorflow as tf | |
import numpy as np | |
nomanet = tf.keras.models.load_model("Nomanet.h5") | |
import gradio as gr | |
labels = {0: "melanoma", 1: "nevus", 2: "seborrheic_keratosis"} | |
def classify_image(inp): | |
image_array = tf.keras.preprocessing.image.img_to_array( | |
tf.keras.preprocessing.image.load_img(inp, target_size=(256,256))) | |
image_array_with_batchdim = image_array[np.newaxis, :] | |
model_prediction = np.argmax(nomanet.predict(image_array_with_batchdim), axis=-1)[0] | |
return labels[model_prediction] | |
gr.Interface(fn=classify_image, | |
inputs=gr.inputs.Image(type="filepath"), | |
outputs=gr.outputs.Label()).launch() | |