keras-io/deit

1import gradio as gr2import tensorflow as tf3from huggingface_hub import from_pretrained_keras4import numpy as np5 6model = from_pretrained_keras("keras-io/deit")7 8classes=['dandelion','daisy','tulip','sunflower','rose']9image_size = 22410 11def classify_images(image):12  image = tf.convert_to_tensor(image)13  image = tf.image.resize(image, (image_size, image_size))14  image = tf.expand_dims(image,axis=0)15  prediction = model.predict(image)16  prediction = tf.squeeze(tf.round(prediction))17  text_output = str(f'{classes[(np.argmax(prediction))]}!')18  return text_output19  20i = gr.inputs.Image()21o = gr.outputs.Textbox()22 23examples = [["./examples/tulip.png"], ["./examples/daisy.jpeg"], ["./examples/dandelion.jpeg"], ["./examples/rose.png"], ["./examples/sunflower.png"]]24title = "Distill ViT Flowers Classification"25description = "Upload an image or select from examples to classify flowers. [Explore model](https://huggingface.co/keras-io/deit) | [Keras Example](https://keras.io/examples/vision/deit/)"26 27article = "<div style='text-align: center;'><a href='https://twitter.com/SatpalPatawat' target='_blank'>Space by Satpal Singh Rathore</a><br><a href='https://twitter.com/RisingSayak' target='_blank'>Keras example by Sayak Paul</a></div>"28gr.Interface(classify_images, i, o, allow_flagging=False, analytics_enabled=False,29  title=title, examples=examples, description=description, article=article).launch(enable_queue=True, debug=True)