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Create app.py
12c31ab
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)