import gradio as gr
import tensorflow as tf
import pickle
import numpy as np
from sklearn.preprocessing import LabelEncoder

# Load saved components
with open('preprocessing_params.pkl', 'rb') as f:
    preprocessing_params = 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
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()])

# 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'])
        input_data = [input_data, np.zeros((1, preprocessing_params['max_html_length']))]  # dummy HTML input
    else:
        input_data = preprocess_input(input_text, html_tokenizer, preprocessing_params['max_html_length'])
        input_data = [np.zeros((1, preprocessing_params['max_url_length'])), input_data]  # dummy URL input
    
    prediction = model.predict(input_data)[0][0]
    return prediction

# 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})"

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()