satpalsr commited on
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Create app.py

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  1. examples/app.py +29 -0
examples/app.py ADDED
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+ import gradio as gr
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+ import tensorflow as tf
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+ from huggingface_hub import from_pretrained_keras
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+ import numpy as np
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+
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+ model = from_pretrained_keras("keras-io/mobile-vit-xxs")
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+
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+ classes=['dandelion','daisy','tulip','sunflower','rose']
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+ image_size = 256
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+
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+ def classify_images(image):
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+ image = tf.convert_to_tensor(image)
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+ image = tf.image.resize(image, (image_size, image_size))
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+ image = tf.expand_dims(image,axis=0)
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+ prediction = model.predict(image)
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+ prediction = tf.squeeze(tf.round(prediction))
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+ text_output = str(f'{classes[(np.argmax(prediction))]}!')
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+ return text_output
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+
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+ i = gr.inputs.Image()
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+ o = gr.outputs.Textbox()
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+
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+ examples = [["./examples/tulip.png"], ["./examples/daisy.jpeg"], ["./examples/dandelion.jpeg"], ["./examples/rose.png"], ["./examples/sunflower.png"]]
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+ title = "Flowers Classification MobileViT"
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+ description = "Upload an image or select from examples to classify flowers"
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+
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+ 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>"
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+ gr.Interface(classify_images, i, o, allow_flagging=False, analytics_enabled=False,
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+ title=title, examples=examples, description=description, article=article).launch(enable_queue=True)