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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()
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