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import gradio as gr |
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import tensorflow as tf |
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import pickle |
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import numpy as np |
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from sklearn.preprocessing import LabelEncoder |
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with open('preprocessing_params.pkl', 'rb') as f: |
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preprocessing_params = pickle.load(f) |
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with open('label_encoder.pkl', 'rb') as f: |
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label_encoder = pickle.load(f) |
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with open('url_tokenizer.pkl', 'rb') as f: |
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url_tokenizer = pickle.load(f) |
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with open('html_tokenizer.pkl', 'rb') as f: |
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html_tokenizer = pickle.load(f) |
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model = tf.keras.models.load_model('new_phishing_detection_model.keras') |
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model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005), |
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loss='binary_crossentropy', |
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metrics=['accuracy', tf.keras.metrics.Precision(), tf.keras.metrics.Recall()]) |
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def preprocess_input(input_text, tokenizer, max_length): |
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sequences = tokenizer.texts_to_sequences([input_text]) |
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padded_sequences = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=max_length, padding='post', truncating='post') |
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return padded_sequences |
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def get_prediction(input_text, input_type): |
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is_url = input_type == "URL" |
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if is_url: |
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input_data = preprocess_input(input_text, url_tokenizer, preprocessing_params['max_url_length']) |
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input_data = [input_data, np.zeros((1, preprocessing_params['max_html_length']))] |
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else: |
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input_data = preprocess_input(input_text, html_tokenizer, preprocessing_params['max_html_length']) |
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input_data = [np.zeros((1, preprocessing_params['max_url_length'])), input_data] |
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prediction = model.predict(input_data)[0][0] |
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return prediction |
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def phishing_detection(input_text, input_type): |
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prediction = get_prediction(input_text, input_type) |
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if prediction > 0.5: |
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return f"Warning: This site is likely a phishing site! ({prediction:.2f})" |
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else: |
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return f"Safe: This site is not likely a phishing site. ({prediction:.2f})" |
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iface = gr.Interface( |
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fn=phishing_detection, |
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inputs=[ |
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gr.components.Textbox(lines=5, placeholder="Enter URL or HTML code"), |
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gr.components.Radio(["URL", "HTML"], type="value", label="Input Type") |
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], |
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outputs=gr.components.Textbox(label="Phishing Detection Result"), |
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title="Phishing Detection Model", |
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description="Check if a URL or HTML is Phishing.", |
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theme="default" |
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) |
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iface.launch() |