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Update app.py
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app.py
CHANGED
@@ -2,7 +2,41 @@ import gradio as gr
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import numpy as np
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from huggingface_hub import from_pretrained_keras
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def predict_image(img1, img2):
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assert img1.shape == (28, 28)
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@@ -14,4 +48,4 @@ def predict_image(img1, img2):
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return lab
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iface = gr.Interface(predict_image, inputs=["sketchpad", "sketchpad"], outputs="label")
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iface.launch(debug='True')
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import numpy as np
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from huggingface_hub import from_pretrained_keras
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def loss(margin=1):
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"""Provides 'constrastive_loss' an enclosing scope with variable 'margin'.
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Arguments:
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margin: Integer, defines the baseline for distance for which pairs
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should be classified as dissimilar. - (default is 1).
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Returns:
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'constrastive_loss' function with data ('margin') attached.
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"""
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# Contrastive loss = mean( (1-true_value) * square(prediction) +
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# true_value * square( max(margin-prediction, 0) ))
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def contrastive_loss(y_true, y_pred):
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"""Calculates the constrastive loss.
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Arguments:
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y_true: List of labels, each label is of type float32.
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y_pred: List of predictions of same length as of y_true,
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each label is of type float32.
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Returns:
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A tensor containing constrastive loss as floating point value.
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"""
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square_pred = tf.math.square(y_pred)
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margin_square = tf.math.square(tf.math.maximum(margin - (y_pred), 0))
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return tf.math.reduce_mean(
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(1 - y_true) * square_pred + (y_true) * margin_square
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)
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return contrastive_loss
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siamese = from_pretrained_keras("rushic24/keras-siamese-contrastive", custom_objects={"contrastive_loss": loss})
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def predict_image(img1, img2):
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assert img1.shape == (28, 28)
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return lab
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iface = gr.Interface(predict_image, inputs=["sketchpad", "sketchpad"], outputs="label")
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iface.launch(debug='True')
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