# CODE BY RAMADHIRRA! DON'T USE WITHOUT PERMISSION!
import gradio as gr
import tensorflow as tf
import numpy as np
import nltk
import pickle
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
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

# Load tokenizers
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)

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()