Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import pickle
+import numpy as np
+import requests
+from ProGPT import Conversation
+
+# 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:
+    url_tokenizer = pickle.load(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, fisher_information, 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]
+
+    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):
+        # Load fisher information from external file
+        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
+model = tf.keras.models.load_model('new_phishing_detection_model.keras',
+                                   custom_objects={'EWCLoss': EWCLoss})
+
+# Recompile the model
+ewc_loss = EWCLoss(model=model, fisher_information=fisher_information, importance=1000)
+model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005),
+              loss=ewc_loss,
+              metrics=['accuracy', tf.keras.metrics.Precision(), tf.keras.metrics.Recall()])
+
+# Chatbot setup
+access_token = 'your_pro_gpt_access_token'
+chatbot = Conversation(access_token)
+
+# Function to preprocess input
+def preprocess_input(input_text, tokenizer, max_length):
+    sequences = tokenizer.texts_to_sequences([input_text])
+    padded_sequences = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=max_length, padding='post', truncating='post')
+    return padded_sequences
+
+# Function to get prediction
+def get_prediction(input_text, input_type):
+    is_url = input_type == "URL"
+    if is_url:
+        input_data = preprocess_input(input_text, url_tokenizer, preprocessing_params['max_url_length'])
+    else:
+        input_data = preprocess_input(input_text, html_tokenizer, preprocessing_params['max_html_length'])
+    
+    prediction = model.predict([input_data, input_data])[0][0]
+    return prediction
+
+# Function to fetch latest phishing sites from PhishTank
+def fetch_latest_phishing_sites():
+    try:
+        response = requests.get('https://data.phishtank.com/data/online-valid.json')
+        data = response.json()
+        return data[:5]
+    except Exception as e:
+        return []
+
+# Gradio UI
+def phishing_detection(input_text, input_type):
+    prediction = get_prediction(input_text, input_type)
+    if prediction > 0.5:
+        return f"Warning: This site is likely a phishing site! ({prediction:.2f})"
+    else:
+        return f"Safe: This site is not likely a phishing site. ({prediction:.2f})"
+
+def latest_phishing_sites():
+    sites = fetch_latest_phishing_sites()
+    return [f"{site['url']}" for site in sites]
+
+def chatbot_response(user_input):
+    response = chatbot.prompt(user_input)
+    return response
+
+iface = gr.Interface(
+    fn=phishing_detection,
+    inputs=[gr.inputs.Textbox(lines=5, placeholder="Enter URL or HTML code"), gr.inputs.Radio(["URL", "HTML"], type="value", label="Input Type")],
+    outputs="text",
+    title="Phishing Detection with Enhanced EWC Model",
+    description="Check if a URL or HTML is Phishing",
+    theme="default"
+)
+
+iface.launch()