Update app.py

app.py

@@ -7,8 +7,6 @@ from sklearn.preprocessing import LabelEncoder
 # Load saved components
 with open('preprocessing_params.pkl', 'rb') as f:
     preprocessing_params = pickle.load(f)
-with open('fisher_information.pkl', 'rb') as f:
-    fisher_information = pickle.load(f)
 with open('label_encoder.pkl', 'rb') as f:
     label_encoder = pickle.load(f)
 with open('url_tokenizer.pkl', 'rb') as f:
@@ -16,47 +14,12 @@ with open('url_tokenizer.pkl', 'rb') as f:
 with open('html_tokenizer.pkl', 'rb') as f:
     html_tokenizer = pickle.load(f)
 
-# Load the model with custom loss
-@tf.keras.utils.register_keras_serializable()
-class EWCLoss(tf.keras.losses.Loss):
-    def __init__(self, model=None, fisher_information=None, importance=1.0, reduction='auto', name=None):
-        super(EWCLoss, self).__init__(reduction=reduction, name=name)
-        self.model = model
-        self.fisher_information = fisher_information
-        self.importance = importance
-        self.prev_weights = [layer.numpy() for layer in model.trainable_weights] if model else None
+# Load the model
+model = tf.keras.models.load_model('new_phishing_detection_model.keras')
 
-    def call(self, y_true, y_pred):
-        standard_loss = tf.keras.losses.binary_crossentropy(y_true, y_pred)
-        ewc_loss = 0.0
-        for layer, fisher_info, prev_weight in zip(self.model.trainable_weights, self.fisher_information, self.prev_weights):
-            ewc_loss += tf.reduce_sum(fisher_info * tf.square(layer - prev_weight))
-        return standard_loss + (self.importance / 2.0) * ewc_loss
-    
-    def get_config(self):
-        config = super().get_config()
-        config.update({
-            'importance': self.importance,
-            'reduction': self.reduction,
-            'name': self.name,
-        })
-        return config
-    
-    @classmethod
-    def from_config(cls, config):
-        with open('fisher_information.pkl', 'rb') as f:
-            fisher_information = pickle.load(f)
-        return cls(model=None, fisher_information=fisher_information, **config)
-
-# Load the model first without the custom loss
-model = tf.keras.models.load_model('new_phishing_detection_model.keras', compile=False)
-
-# Reconstruct the EWC loss
-ewc_loss = EWCLoss(model=model, fisher_information=fisher_information, importance=1000)
-
-# Compile the model with EWC loss and metrics
+# Compile the model with standard loss and metrics
 model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005),
-              loss=ewc_loss,
+              loss='binary_crossentropy',
               metrics=['accuracy', tf.keras.metrics.Precision(), tf.keras.metrics.Recall()])
 
 # Function to preprocess input
@@ -93,7 +56,7 @@ iface = gr.Interface(
         gr.components.Radio(["URL", "HTML"], type="value", label="Input Type")
     ],
     outputs=gr.components.Textbox(label="Phishing Detection Result"),
-    title="Phishing Detection with Enhanced EWC Model",
+    title="Phishing Detection Model",
     description="Check if a URL or HTML is Phishing.",
     theme="default"
 )