working space

.gitignore

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+venv
+.mypy_cache

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
+
+adamatch_model = from_pretrained_keras("johko/adamatch-keras-io")
+base_model = from_pretrained_keras("johko/wideresnet28-2-mnist")
+
+
+labels = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
+
+def predict_image(image, model):
+    image = tf.constant(image)
+    image = tf.reshape(image, [-1, 32, 32, 3])
+    probs_ada_mnist = model.predict(image)[0,:]
+    top_pred = probs_ada_mnist.tolist()
+    return {labels[i]: top_pred[i] for i in range(10)}
+
+def infer(mnist_img, svhn_img, model):
+    labels_out = []
+    for im in [mnist_img, svhn_img]:
+        labels_out.append(predict_image(im, model))
+    return labels_out
+    
+def infer_ada(mnist_image, svhn_image):
+    return infer(mnist_image, svhn_image, adamatch_model)
+
+def infer_base(mnist_image, svhn_image):
+    return infer(mnist_image, svhn_image, base_model)
+    
+
+def infer_all(mnist_image, svhn_image):
+    base_res = infer_base(mnist_image, svhn_image)
+    ada_res = infer_ada(mnist_image, svhn_image)
+    return base_res.extend(ada_res)
+
+article = """<center>
+
+Authors: <a href='https://twitter.com/johko990' target='_blank'>Johannes Kolbe</a> based on an example by [Sayak Paul](https://twitter.com/RisingSayak) on
+<a href='https://keras.io/examples/vision/adamatch/' target='_blank'>**keras.io**</a>"""
+
+
+
+description = """<center> 
+
+This space lets you compare image classification results of identical architecture (WideResNet-2-28) models. The training of one of the models was improved 
+by using AdaMatch as seen in the example on [keras.io](https://keras.io/examples/vision/adamatch/).
+
+The base model was only trained on the MNIST dataset and shows a low classification accuracy (8.96%) for a different domain dataset like SVHN. The AdaMatch model 
+uses a semi-supervised domain adaption approach to adapt to the SVHN dataset and shows a significantly higher accuracy (26.51%).
+"""
+mnist_image_base = gr.inputs.Image(shape=(32, 32))
+svhn_image_base = gr.inputs.Image(shape=(32, 32))
+mnist_image_ada = gr.inputs.Image(shape=(32, 32))
+svhn_image_ada = gr.inputs.Image(shape=(32, 32))
+
+label_mnist_base = gr.outputs.Label(num_top_classes=3, label="MNIST Prediction Base")
+label_svhn_base = gr.outputs.Label(num_top_classes=3, label="SVHN Prediction Base")
+label_mnist_ada = gr.outputs.Label(num_top_classes=3, label="MNIST Prediction AdaMatch")
+label_svhn_ada = gr.outputs.Label(num_top_classes=3, label="SVHN Prediction AdaMatch")
+    
+
+base_iface = gr.Interface(
+    fn=infer_base,
+    inputs=[mnist_image_base, svhn_image_base],
+    outputs=[label_mnist_base,label_svhn_base]
+)
+
+ada_iface = gr.Interface(
+    fn=infer_ada,
+    inputs=[mnist_image_ada, svhn_image_ada],
+    outputs=[label_mnist_ada,label_svhn_ada]
+)
+
+gr.Parallel(base_iface, 
+            ada_iface, 
+            examples=[
+              ["examples/mnist_3.jpg", "examples/svhn_3.jpeg"], 
+              ["examples/mnist_8.jpg", "examples/svhn_8.jpg"]
+            ],     
+            title="Domain Adaption with AdaMatch",
+            article=article,
+            description=description,
+).launch()

requirements.txt

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+gradio
+tensorflow
+huggingface_hub