Create app.py

examples/app.py

@@ -0,0 +1,29 @@
+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/mobile-vit-xxs")
+
+classes=['dandelion','daisy','tulip','sunflower','rose']
+image_size = 256
+
+def classify_images(image):
+  image = tf.convert_to_tensor(image)
+  image = tf.image.resize(image, (image_size, image_size))
+  image = tf.expand_dims(image,axis=0)
+  prediction = model.predict(image)
+  prediction = tf.squeeze(tf.round(prediction))
+  text_output = str(f'{classes[(np.argmax(prediction))]}!')
+  return text_output
+  
+i = gr.inputs.Image()
+o = gr.outputs.Textbox()
+
+examples = [["./examples/tulip.png"], ["./examples/daisy.jpeg"], ["./examples/dandelion.jpeg"], ["./examples/rose.png"], ["./examples/sunflower.png"]]
+title = "Flowers Classification MobileViT"
+description = "Upload an image or select from examples to classify flowers"
+
+article = "<div style='text-align: center;'><a href='https://twitter.com/SatpalPatawat' target='_blank'>Space by Satpal Singh Rathore</a><br><a href='https://keras.io/examples/vision/mobilevit/' target='_blank'>Keras example by Sayak Paul</a></div>"
+gr.Interface(classify_images, i, o, allow_flagging=False, analytics_enabled=False,
+  title=title, examples=examples, description=description, article=article).launch(enable_queue=True)