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import gradio as gr
import tensorflow as tf
from huggingface_hub import from_pretrained_keras
import numpy as np
model = from_pretrained_keras("keras-io/semi-supervised-classification-simclr")
labels_gradio = ["Avi贸n", "Pajaro", "Coche", "Gato", "Ciervo", "Perro", "Caballo", "Mono", "Barco", "Cami贸n"]
def predict(imput_image):
image = tf.constant(imput_image)
image = tf.reshape(image, [-1, 96, 96, 3])
pred = model.predict(image)
pred_list = pred[0, :]
pred_softmax = np.exp(pred_list)/np.sum(np.exp(pred_list))
softmax_list = pred_softmax.tolist()
return {labels_gradio[i]: softmax_list[i] for i in range(10)}
image = gr.inputs.Image(shape=(96, 96))
label = gr.outputs.Label(num_top_classes=4)
pie_pag = """<center>
Modelo: <a href='https://huggingface.co/keras-io/semi-supervised-classification-simclr' target='_blank'>keras.io</a>
Basado en el Space: <a href='https://huggingface.co/spaces/keras-io/semi-supervised-classification' target='_blank'>Andr谩s B茅res</a>
Autor: <a href='https://huggingface.co/machde' target='_blank'>Manuel Chac贸n De Dios</a>"""
titulo = "Mini clasificador"
descripcion = """<center>Clasificador capaz de etiquetar si es un Avi贸n, Pajaro, Coche,
Gato, Ciervo, Perro, Caballo, Mono, Barco, Cami贸n</center>"""
Iface = gr.Interface(
fn=predict,
inputs=image,
outputs=label,
layout="vertical",
theme="seafoam",
examples=[["test_img/pajaro-test.jpeg"], ["test_img/coche-test.jpg"], ["test_img/perro-test.jpg"]],
title=titulo,
article=pie_pag,
description=descripcion,
).launch()
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