Blazer007/image_classification_using_conv_mixer

1import numpy as np2import tensorflow as tf3import gradio as gr4from huggingface_hub import from_pretrained_keras5 6model = from_pretrained_keras("keras-io/conv_mixer_image_classification")7 8class_names = [9    "Airplane",10    "Automobile",11    "Bird",12    "Cat",13    "Deer",14    "Dog",15    "Frog",16    "Horse",17    "Ship",18    "Truck",19]20 21examples = [22    ['./aeroplane.png'],23    ['./horse.png'],24    ['./ship.png'],25    ['./truck.png']26] 27 28IMG_SIZE = 3229 30def infer(input_image):31    image_tensor = tf.convert_to_tensor(input_image)32    image_tensor.set_shape([None, None, 3])33    image_tensor = tf.image.resize(image_tensor, (IMG_SIZE, IMG_SIZE))34    predictions = model.predict(np.expand_dims((image_tensor), axis=0))35    predictions = np.squeeze(predictions)36    predictions = np.argmax(predictions)37    predicted_label = class_names[predictions.item()]38    return str(predicted_label)39 40    41input = gr.inputs.Image(shape=(IMG_SIZE, IMG_SIZE))42output = [gr.outputs.Label(label = "Model Output")]43 44title = "Image Classification using Conv Mixer Model"45description = "Upload an image or select from examples to classify it.<br>The allowed classes are - Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck.<br><p><b>Model Repo - https://huggingface.co/keras-io/conv_mixer_image_classification</b> <br><b>Keras Example - https://keras.io/examples/vision/convmixer//</b></p>"46 47 48article = "<div style='text-align: center;'><a href='https://twitter.com/_Blazer_007' target='_blank'>Space by Vivek Rai</a><br><a href='https://twitter.com/RisingSayak' target='_blank'>Keras example by Sayak Paul</a></div>"49 50gr_interface = gr.Interface(51    infer, 52    input, 53    output, 54    examples=examples, 55    allow_flagging=False, 56    analytics_enabled=False, 57    title=title, 58    description=description,59    article=article).launch(enable_queue=True, debug=True)