| import gradio as gr | |
| import tensorflow as tf | |
| from keras_tuner import HyperParameters | |
| from src.models import MakeHyperModel | |
| from src.preprocessing import get_data_augmentation | |
| from src.config import IMAGE_SIZE | |
| data_augmentation = get_data_augmentation() | |
| latest = tf.train.latest_checkpoint('./tuner_model/cat-vs-dog/trial_0484d8d758a5ef7b91ca97d334ba7870/checkpoints/epoch_0') | |
| hypermodel = MakeHyperModel(input_shape=IMAGE_SIZE + (3,), num_classes=2, data_augmentation=data_augmentation) | |
| model = hypermodel.build(hp=HyperParameters()) | |
| model.load_weights(latest).expect_partial() | |
| def cat_vs_dog(image): | |
| img_array = tf.constant(image, dtype=tf.float32) | |
| img_array = tf.expand_dims(img_array, 0) | |
| predictions = model.predict(img_array) | |
| score = predictions[0] | |
| return {'cat': float((1 - score)), 'dog': float(score)} | |
| iface = gr.Interface( | |
| cat_vs_dog, | |
| gr.inputs.Image(shape=IMAGE_SIZE), | |
| gr.outputs.Label(num_top_classes=2), | |
| capture_session=True, | |
| interpretation="default", | |
| examples=[ | |
| ["examples/cat1.jpg"], | |
| ["examples/cat2.jpg"], | |
| ["examples/dog1.jpeg"], | |
| ["examples/dog2.jpeg"] | |
| ]) | |
| if __name__ == "__main__": | |
| iface.launch() |