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| import gradio as gr | |
| import numpy as np | |
| from huggingface_hub import from_pretrained_keras | |
| def loss(margin=1): | |
| """Provides 'constrastive_loss' an enclosing scope with variable 'margin'. | |
| Arguments: | |
| margin: Integer, defines the baseline for distance for which pairs | |
| should be classified as dissimilar. - (default is 1). | |
| Returns: | |
| 'constrastive_loss' function with data ('margin') attached. | |
| """ | |
| # Contrastive loss = mean( (1-true_value) * square(prediction) + | |
| # true_value * square( max(margin-prediction, 0) )) | |
| def contrastive_loss(y_true, y_pred): | |
| """Calculates the constrastive loss. | |
| Arguments: | |
| y_true: List of labels, each label is of type float32. | |
| y_pred: List of predictions of same length as of y_true, | |
| each label is of type float32. | |
| Returns: | |
| A tensor containing constrastive loss as floating point value. | |
| """ | |
| square_pred = tf.math.square(y_pred) | |
| margin_square = tf.math.square(tf.math.maximum(margin - (y_pred), 0)) | |
| return tf.math.reduce_mean( | |
| (1 - y_true) * square_pred + (y_true) * margin_square | |
| ) | |
| return contrastive_loss | |
| siamese = from_pretrained_keras("rushic24/keras-siamese-contrastive", custom_objects={"contrastive_loss": loss}) | |
| def predict_image(img1, img2): | |
| assert img1.shape == (28, 28) | |
| assert img1.shape == img2.shape | |
| print('img 1 shape', img1.shape) | |
| img1 = np.expand_dims(img1, 0) | |
| img2 = np.expand_dims(img2, 0) | |
| lab = str(siamese.predict([img1, img2])[0][0]) | |
| return lab | |
| iface = gr.Interface(predict_image, inputs=["sketchpad", "sketchpad"], outputs="label") | |
| iface.launch(debug='True') |