Update app.py

app.py

@@ -26,8 +26,8 @@ model_file = "./seg_unet_model.h5"
 #Custom objects for model
 
 def Combo_loss(y_true, y_pred, eps=1e-9, smooth=1):
-    targets = tf.dtypes.cast(K.flatten(y_true), tf.float32)
-    inputs = tf.dtypes.cast(K.flatten(y_pred), tf.float32)
+    targets = keras.ops.cast(K.flatten(y_true), dtype="float32")
+    inputs = keras.ops.cast(K.flatten(y_pred), dtype="float32")
     intersection = K.sum(targets * inputs)
     dice = (2. * intersection + smooth) / (K.sum(targets) + K.sum(inputs) + smooth)
     inputs = K.clip(inputs, eps, 1.0 - eps)
@@ -37,15 +37,15 @@ def Combo_loss(y_true, y_pred, eps=1e-9, smooth=1):
     return combo
 
 def dice_coef(y_true, y_pred, smooth=1):
-    y_pred = tf.dtypes.cast(y_pred, tf.int32)
-    y_true = tf.dtypes.cast(y_true, tf.int32)
+    y_pred = keras.ops.cast(y_pred, dtype="int32")
+    y_true = keras.ops.cast(y_true, tdtype="int32")
     intersection = K.sum(y_true * y_pred, axis=[1,2,3])                     
     union = K.sum(y_true, axis=[1,2,3]) + K.sum(y_pred, axis=[1,2,3])           
     return K.mean((2 * intersection + smooth) / (union + smooth), axis=0)
 
 def focal_loss_fixed(y_true, y_pred, gamma=2.0, alpha=0.25):
-    pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
-    pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
+    pt_1 = keras.ops.where(keras.ops.equal(y_true, 1), y_pred, keras.ops.ones_like(y_pred))
+    pt_0 = keras.ops.where(keras.ops.equal(y_true, 0), y_pred, keras.ops.ones_like(y_pred))
     focal_loss_fixed = -K.mean(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1+K.epsilon())) - K.mean((1 - alpha) * K.pow(pt_0, gamma) * K.log(1. - pt_0 + K.epsilon()))
     return focal_loss_fixed