Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from huggingface_hub import from_pretrained_keras
+
+model = from_pretrained_keras('SimSiam')
+
+index_to_name = {0:'Airplane', 1:'Car', 2:'Bird',
+                 3:'Cat', 4:'Deer', 5:'Dog',
+                 6:'Frog', 7:'Horse', 8:'Ship',
+                 9:'Truck'}
+                 
+def predict_with_simsiam(original_image): 
+    image = asarray(original_image)
+    image = np.expand_dims(image, axis=0)
+    pred_prob = m.predict(image).flatten().tolist()
+    return {index_to_name[i]: pred_prob[i] for i in range(10)}
+    
+                     
+title = "Self-supervised contrastive learning with SimSiam"
+description = "This space implements a SimSiam network to the task of image classification of the Cifar 10 dataset."
+examples = ['horse1.png', 'airplane4.png', 'dog6.png']
+article = """<p style='text-align: center'>
+        <a href='https://keras.io/examples/vision/simsiam/#evaluating-our-ssl-method' target='_blank'>Keras Example given by Sayak Paul</a>
+        <br>
+        Space by @Jezia
+    </p>
+    """
+
+iface = gr.Interface(predict_with_simsiam, inputs=[gr.inputs.Image(label="image", type="pil")], outputs="label", title=title, description=description, article=article, examples=examples)
+iface.launch(debug='True')