added app

app.py

@@ -0,0 +1,73 @@
+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+import tensorflow as tf
+
+
+CLASSES = {
+    0: "airplane",
+    1: "automobile",
+    2: "bird",
+    3: "cat",
+    4: "deer",
+    5: "dog",
+    6: "frog",
+    7: "horse",
+    8: "ship",
+    9: "truck",
+}
+
+IMAGE_SIZE = 32
+
+model = from_pretrained_keras("EdoAbati/cct")
+
+
+def reshape_image(image):
+    image = tf.convert_to_tensor(image)
+    image.set_shape([None, None, 3])
+    image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])
+    image = tf.expand_dims(image, axis=0)
+    return image
+
+
+def classify_image(input_image):
+    input_image = reshape_image(input_image)
+    logits = model.predict(input_image).flatten()
+    predictions = tf.nn.softmax(logits)
+    output_labels = {CLASSES[i]: float(predictions[i]) for i in CLASSES.keys()}
+    return output_labels
+
+
+examples = [["./bird.png"], ["./cat.png"], ["./dog.png"], ["./horse.png"]]
+title = "Image Classification using Compact Convolutional Transformer (CCT)"
+description = """
+Upload an image or select one from the examples and ask the model to label it!
+<br />
+The model was trained on the <a href="https://www.cs.toronto.edu/~kriz/cifar.html" target="_blank">CIFAR-10 dataset</a>. Therefore, it is able to recognise these 10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck.
+<br />
+<br />
+<p>
+    <b>Model: https://huggingface.co/keras-io/cct</b>
+    <br />
+    <b>Keras Example: https://keras.io/examples/vision/cct/</b>
+</p>
+<br />
+"""
+article = """
+<div style="text-align: center;">
+    Space by <a href="https://www.linkedin.com/in/edoardoabati/" target="_blank">Edoardo Abati</a>
+    <br />
+    Keras example by <a href="https://twitter.com/RisingSayak" target="_blank">Sayak Paul</a>
+</div>
+"""
+
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.inputs.Image(),
+    outputs=gr.outputs.Label(),
+    examples=examples,
+    title=title,
+    description=description,
+    article=article,
+    allow_flagging="never",
+)
+interface.launch(enable_queue=True)