Update app.py

app.py

@@ -14,6 +14,24 @@ from tensorflow import keras
 import segmentation_models as sm
 
 
+def dice_metric(y_pred, y_true):
+    intersection = K.sum(K.sum(K.abs(y_true * y_pred), axis=-1))
+    union = K.sum(K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1))
+    # if y_pred.sum() == 0 and y_pred.sum() == 0:
+    #     return 1.0
+
+    return (2*intersection) / union
+
+
+def focal_loss(predict, true):
+    error = tf.keras.losses.binary_crossentropy(predict, true)
+    pt = tf.exp(error)
+    focal_loss = (1 - pt) ** 2 * error
+    return dice_metric(predict, true) + (1*tf.reduce_mean(focal_loss))
+
+def focal_iou(y_true, y_pred):
+    return focal_loss(y_true, y_pred) - K.log(jacard(y_true, y_pred))
+
 def jacard(y_true, y_pred):
     y_true_c = K.flatten(y_true)
     y_pred_c = K.flatten(y_pred)
@@ -203,11 +221,11 @@ def weighted_categorical_crossentropy(weights):
 from tensorflow.python.keras.utils import generic_utils
     
 # Load the model
-model = tf.keras.models.load_model("model.h5", custom_objects={"jacard":jacard, "wcce":weighted_categorical_crossentropy})
+#model = tf.keras.models.load_model("model.h5", custom_objects={"jacard":jacard, "wcce":weighted_categorical_crossentropy})
 #model = tf.keras.models.load_model("model_2.h5", custom_objects={"jacard":jacard, "bce_dice":bce_dice})
 ###model = tf.keras.models.load_model("model_2_A.h5", custom_objects={"jacard":jacard, "bce_dice":bce_dice})
 #model = tf.keras.models.load_model("model_2_A_0.h5", custom_objects={"jacard":jacard, "bce_dice":bce_dice})
-
+model = tf.keras.models.load_model("model_3_A.h5", custom_objects={"jacard":jacard, "bce_dice":bce_dice, "focal_iou":focal_iou})
 
 # Create a user interface for the model
 my_app = gr.Blocks()