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import gradio as gr
import tensorflow as tf
import numpy as np
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import re
# Load the model
model = tf.keras.models.load_model('new_phishing_detection_model.keras')
# Compile the model with standard loss and metrics
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005),
loss='binary_crossentropy',
metrics=['accuracy', tf.keras.metrics.Precision(), tf.keras.metrics.Recall()])
# Preprocessing functions
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
STOPWORDS = set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()
def preprocess_url(url):
url = url.lower()
url = re.sub(r'https?://', '', url)
url = re.sub(r'www\.', '', url)
url = re.sub(r'[^a-zA-Z0-9]', ' ', url)
url = re.sub(r'\s+', ' ', url).strip()
tokens = word_tokenize(url)
tokens = [word for word in tokens if word not in STOPWORDS]
tokens = [lemmatizer.lemmatize(word) for word in tokens]
return ' '.join(tokens)
def preprocess_html(html):
html = re.sub(r'<[^>]+>', ' ', html)
html = html.lower()
html = re.sub(r'https?://', '', html)
html = re.sub(r'[^a-zA-Z0-9]', ' ', html)
html = re.sub(r'\s+', ' ', html).strip()
tokens = word_tokenize(html)
tokens = [word for word in tokens if word not in STOPWORDS]
tokens = [lemmatizer.lemmatize(word) for word in tokens]
return ' '.join(tokens)
max_url_length = 180
max_html_length = 2000
max_words = 10000
url_tokenizer = Tokenizer(num_words=max_words, char_level=True)
html_tokenizer = Tokenizer(num_words=max_words)
# Dummy fit to initialize tokenizers
url_tokenizer.fit_on_texts(["dummy"])
html_tokenizer.fit_on_texts(["dummy"])
def preprocess_input(input_text, tokenizer, max_length):
sequences = tokenizer.texts_to_sequences([input_text])
padded_sequences = pad_sequences(sequences, maxlen=max_length, padding='post', truncating='post')
return padded_sequences
def get_prediction(input_text, input_type):
is_url = input_type == "URL"
if is_url:
cleaned_text = preprocess_url(input_text)
input_data = preprocess_input(cleaned_text, url_tokenizer, max_url_length)
input_data = [input_data, np.zeros((1, max_html_length))] # dummy HTML input
else:
cleaned_text = preprocess_html(input_text)
input_data = preprocess_input(cleaned_text, html_tokenizer, max_html_length)
input_data = [np.zeros((1, max_url_length)), input_data] # dummy URL input
prediction = model.predict(input_data)[0][0]
return prediction
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})"
iface = gr.Interface(
fn=phishing_detection,
inputs=[
gr.components.Textbox(lines=5, placeholder="Enter URL or HTML code"),
gr.components.Radio(["URL", "HTML"], type="value", label="Input Type")
],
outputs=gr.components.Textbox(label="Phishing Detection Result"),
title="Phishing Detection Model",
description="Check if a URL or HTML is Phishing.",
theme="default"
)
iface.launch() |