Update app.py

app.py

@@ -2,7 +2,41 @@ import gradio as gr
 import numpy as np
 from huggingface_hub import from_pretrained_keras
 
-siamese = from_pretrained_keras("rushic24/keras-siamese-contrastive")
+
+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)
@@ -14,4 +48,4 @@ def predict_image(img1, img2):
   return lab
 
 iface = gr.Interface(predict_image, inputs=["sketchpad", "sketchpad"], outputs="label")
-iface.launch(debug='True')
+iface.launch(debug='True')