V1

app.py

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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 = ["Avión", "Pajaro", "Coche", "Gato", "Ciervo", "Perro", "Caballo", "Mono", "Barco", "Camión"]
+
+
+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'>András Béres</a>
+Autor: <a href='https://huggingface.co/machde' target='_blank'>Manuel Chacón De Dios</a>"""
+
+titulo = "Mini clasificador"
+descripcion = """<center>Clasificador capaz de etiquetar si es un Avión, Pajaro, Coche, 
+Gato, Ciervo, Perro, Caballo, Mono, Barco, Camión</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()

requirements.txt

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+tensorflow >=2.6.0
+gradio
+jinja2