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Runtime error
| import numpy as np | |
| import tensorflow as tf | |
| import gradio as gr | |
| from huggingface_hub import from_pretrained_keras | |
| model = from_pretrained_keras("keras-io/conv_mixer_image_classification") | |
| class_names = [ | |
| "Airplane", | |
| "Automobile", | |
| "Bird", | |
| "Cat", | |
| "Deer", | |
| "Dog", | |
| "Frog", | |
| "Horse", | |
| "Ship", | |
| "Truck", | |
| ] | |
| examples = [ | |
| ['./aeroplane.png'], | |
| ['./horse.png'], | |
| ['./ship.png'], | |
| ['./truck.png'] | |
| ] | |
| IMG_SIZE = 32 | |
| def infer(input_image): | |
| image_tensor = tf.convert_to_tensor(input_image) | |
| image_tensor.set_shape([None, None, 3]) | |
| image_tensor = tf.image.resize(image_tensor, (IMG_SIZE, IMG_SIZE)) | |
| predictions = model.predict(np.expand_dims((image_tensor), axis=0)) | |
| predictions = np.squeeze(predictions) | |
| predictions = np.argmax(predictions) | |
| predicted_label = class_names[predictions.item()] | |
| return str(predicted_label) | |
| input = gr.inputs.Image(shape=(IMG_SIZE, IMG_SIZE)) | |
| output = [gr.outputs.Label(label = "Model Output")] | |
| title = "Image Classification using Conv Mixer Model" | |
| description = "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>" | |
| article = "<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>" | |
| gr_interface = gr.Interface( | |
| infer, | |
| input, | |
| output, | |
| examples=examples, | |
| allow_flagging=False, | |
| analytics_enabled=False, | |
| title=title, | |
| description=description, | |
| article=article).launch(enable_queue=True, debug=True) |