1_😘_Home.py

# import streamlit as st
# import machine_learning as ml
# import feature_extraction as fe
# from bs4 import BeautifulSoup
# import requests as re
# import matplotlib.pyplot as plt
#
# # col1, col2 = st.columns([1, 3])
#
# st.title('Phishing Website Detection using Machine Learning')
# st.write('This ML-based app is developed for educational purposes. Objective of the app is detecting phishing websites only using content data. Not URL!'
#          ' You can see the details of approach, data set, and feature set if you click on _"See The Details"_. ')
#
#
# with st.expander("PROJECT DETAILS"):
#     st.subheader('Approach')
#     st.write('We used _supervised learning_ to classify phishing and legitimate websites. '
#              'We benefit from content-based approach and focus on html of the websites. '
#              'Also, We used scikit-learn for the ML models.'
#              )
#     st.write('For this educational project, '
#              'We created my own data set and defined features, some from the literature and some based on manual analysis. '
#              'We used requests library to collect data, BeautifulSoup module to parse and extract features. ')
#     st.write('The source code and data sets are available in the below Github link:')
#     st.write('_https://github.com/AdarshVajpayee19/Phishing-Website-Detection-ML_')
#
#     st.subheader('Data set')
#     st.write('We used _"phishtank.org"_ & _"tranco-list.eu"_ as data sources.')
#     st.write('Totally 26584 websites ==> **_16060_ legitimate** websites | **_10524_ phishing** websites')
#
#     # ----- FOR THE PIE CHART ----- #
#     labels = 'phishing', 'legitimate'
#     phishing_rate = int(ml.phishing_df.shape[0] / (ml.phishing_df.shape[0] + ml.legitimate_df.shape[0]) * 100)
#     legitimate_rate = 100 - phishing_rate
#     sizes = [phishing_rate, legitimate_rate]
#     explode = (0.1, 0)
#     fig, ax = plt.subplots()
#     ax.pie(sizes, explode=explode, labels=labels, shadow=True, startangle=90, autopct='%1.1f%%')
#     ax.axis('equal')
#     st.pyplot(fig)
#     # ----- !!!!! ----- #
#
#     st.write('Features + URL + Label ==> Dataframe')
#     st.markdown('label is 1 for phishing, 0 for legitimate')
#     number = st.slider("Select row number to display", 0, 100)
#     st.dataframe(ml.legitimate_df.head(number))
#
#
#     @st.cache
#     def convert_df(df):
#         # IMPORTANT: Cache the conversion to prevent computation on every rerun
#         return df.to_csv().encode('utf-8')
#
#     csv = convert_df(ml.df)
#
#     st.download_button(
#         label="Download data as CSV",
#         data=csv,
#         file_name='phishing_legitimate_structured_data.csv',
#         mime='text/csv',
#     )
#
#     st.subheader('Features')
#     st.write('We used only content-based features. I didn\'t use url-based faetures like length of url etc.'
#              'Most of the features extracted using find_all() method of BeautifulSoup module after parsing html.')
#
#     st.subheader('Results')
#     st.write('We used 7 different ML classifiers of scikit-learn and tested them implementing k-fold cross validation.'
#              'Firstly obtained their confusion matrices, then calculated their accuracy, precision and recall scores.'
#              'Comparison table is below:')
#     st.table(ml.df_results)
#     st.write('NB --> Gaussian Naive Bayes')
#     st.write('SVM --> Support Vector Machine')
#     st.write('DT --> Decision Tree')
#     st.write('RF --> Random Forest')
#     st.write('AB --> AdaBoost')
#     st.write('NN --> Neural Network')
#     st.write('KN --> K-Neighbours')
#
# with st.expander('EXAMPLE PHISHING URLs:'):
#     st.write('_https://rtyu38.godaddysites.com/_')
#     st.write('_https://karafuru.invite-mint.com/_')
#     st.write('_https://defi-ned.top/h5/#/_')
#     st.caption('REMEMBER, PHISHING WEB PAGES HAVE SHORT LIFECYCLE! SO, THE EXAMPLES SHOULD BE UPDATED!')
#
# choice = st.selectbox("Please select your machine learning model",
#                  [
#                      'Gaussian Naive Bayes', 'Support Vector Machine', 'Decision Tree', 'Random Forest',
#                      'AdaBoost', 'Neural Network', 'K-Neighbours'
#                  ]
#                 )
#
# model = ml.nb_model
#
# if choice == 'Gaussian Naive Bayes':
#     model = ml.nb_model
#     st.write('GNB model is selected!')
# elif choice == 'Support Vector Machine':
#     model = ml.svm_model
#     st.write('SVM model is selected!')
# elif choice == 'Decision Tree':
#     model = ml.dt_model
#     st.write('DT model is selected!')
# elif choice == 'Random Forest':
#     model = ml.rf_model
#     st.write('RF model is selected!')
# elif choice == 'AdaBoost':
#     model = ml.ab_model
#     st.write('AB model is selected!')
# elif choice == 'Neural Network':
#     model = ml.nn_model
#     st.write('NN model is selected!')
# else:
#     model = ml.kn_model
#     st.write('KN model is selected!')
#
#
# url = st.text_input('Enter the URL')
# # check the url is valid or not
# if st.button('Check!'):
#     try:
#         response = re.get(url, verify=False, timeout=4)
#         if response.status_code != 200:
#             print(". HTTP connection was not successful for the URL: ", url)
#         else:
#             soup = BeautifulSoup(response.content, "html.parser")
#             vector = [fe.create_vector(soup)]  # it should be 2d array, so I added []
#             result = model.predict(vector)
#             if result[0] == 0:
#                 st.success("This web page seems a legitimate!")
#                 st.balloons()
#             else:
#                 st.warning("Attention! This web page is a potential PHISHING!")
#                 st.snow()
#
#     except re.exceptions.RequestException as e:
#         print("--> ", e)
#
#
#
#
#








import streamlit as st
import machine_learning as ml
import feature_extraction as fe
from bs4 import BeautifulSoup
import requests as re
import matplotlib.pyplot as plt
from pathlib import Path
from PIL import Image
#
# # File names with Bootstrap icons
# file_names = {
#     "1_😘_Home.py": "home",  # Replace ❤️ with "house-fill" icon
#     "2_❤️_Project_Description.py": "file-earmark-text",  # Replace ⭐ with "file-earmark-text" icon
#     "3_😊_Contact_Us.py": "telephone-fill"  # Replace 📞 with "telephone-fill" icon
# }
#
#
# # Display file names with icons
# for file_name, icon_name in file_names.items():
#     st.write(f'<i class="bi bi-{icon_name}"></i> {file_name}', unsafe_allow_html=True)


st.set_page_config(page_title='Phishing Website Detection Using Machine Learning', page_icon='./static/favicon.png')

# --- PATH SETTINGS ---
current_dir = Path(__file__).parent if "__file__" in locals() else Path.cwd()
css_file = current_dir / "styles" / "main.css"
phishing_account_pic = current_dir / "static" / "Phishing-account.gif"



# Add content for the Home page here
# Set page title and description
st.title("PHISHr")
st.write("Phish the Phisher before they phish you!!!", unsafe_allow_html=True)
# Add a horizontal line
st.markdown("<hr>", unsafe_allow_html=True)
st.subheader("Understanding Phishing Attacks")
st.write('Phishing attacks are a common type of cyber attack where malicious actors attempt to deceive individuals or organizations into revealing '
         'sensitive information such as usernames, passwords, credit card numbers, or other personal or financial data. These attacks typically '
         'involve impersonating a trusted entity, such as a bank, a government agency, a company, or even a colleague or friend.')




# st.image("static\Phishing-account.gif", use_column_width=True)
# st.markdown(
#     '<img src="phishing_account_pic">',
#     unsafe_allow_html=True,
# )
#
# # --- LOAD CSS, PDF & PROFIL PIC ---
# with open(css_file) as f:
#     st.markdown("<style>{}</style>".format(f.read()), unsafe_allow_html=True)
# phishing_account_pic = Image.open(phishing_account_pic)


# # --- HERO SECTION ---
# col1, col2 = st.columns(2, gap="small")
# with col1:
#     st.image(phishing_account_pic)


# Load the GIF
phishing_acc = "static/Phishing-account.gif"

# Display the GIF
st.image(phishing_acc, caption='PHISHr', use_column_width=True)

st.markdown("<hr>", unsafe_allow_html=True)
with st.expander('EXAMPLE PHISHING URLs:'):
    st.write('_https://rtyu38.godaddysites.com/_')
    st.write('_https://karafuru.invite-mint.com/_')
    st.write('_https://defi-ned.top/h5/#/_')
    st.caption('REMEMBER, PHISHING WEB PAGES HAVE SHORT LIFECYCLE! SO, THE EXAMPLES SHOULD BE UPDATED!')

# Add a horizontal line
st.markdown("<hr>", unsafe_allow_html=True)

choice = st.selectbox("Please select your machine learning model",
                 [
                     'Gaussian Naive Bayes', 'Support Vector Machine', 'Decision Tree', 'Random Forest',
                     'AdaBoost', 'Neural Network', 'K-Neighbours'
                 ]
                )

model = ml.nb_model

if choice == 'Gaussian Naive Bayes':
    model = ml.nb_model
    st.write('GNB model is selected!')
elif choice == 'Support Vector Machine':
    model = ml.svm_model
    st.write('SVM model is selected!')
elif choice == 'Decision Tree':
    model = ml.dt_model
    st.write('DT model is selected!')
elif choice == 'Random Forest':
    model = ml.rf_model
    st.write('RF model is selected!')
elif choice == 'AdaBoost':
    model = ml.ab_model
    st.write('AB model is selected!')
elif choice == 'Neural Network':
    model = ml.nn_model
    st.write('NN model is selected!')
else:
    model = ml.kn_model
    st.write('KN model is selected!')


url = st.text_input('Enter the URL')
# check the url is valid or not
if st.button('Check!'):
    try:
        response = re.get(url, verify=False, timeout=4)
        if response.status_code != 200:
            print(". HTTP connection was not successful for the URL: ", url)
        else:
            soup = BeautifulSoup(response.content, "html.parser")
            vector = [fe.create_vector(soup)]  # it should be 2d array, so I added []
            result = model.predict(vector)
            if result[0] == 0:
                st.success("This web page seems legitimate!")
                # st.image("static\Safe.gif", use_column_width=True)
                # st.markdown(
                #     '<img src="./app/static/Safe.gif">',
                #     unsafe_allow_html=True,
                # )
                # Load the GIF
                Safe = "static/Safe.gif"

                # Display the GIF
                st.image(Safe, caption='Safe', use_column_width=True)
                st.balloons()
            else:
                st.warning("Attention! This web page is a potential phishing!")
                # st.image("static\Warning.gif", use_column_width=True)
                # st.markdown(
                #     '<img src="./app/static/Warning.gif">',
                #     unsafe_allow_html=True,
                # )
                # Load the GIF
                warning = "static/Warning.gif"

                # Display the GIF
                st.image(warning, caption='Warning', use_column_width=True)
                st.snow()

    except re.exceptions.RequestException as e:
        print("--> ", e)

st.markdown("<hr>", unsafe_allow_html=True)
st.subheader("Mitigating Phishing Risks")
st.write(
    'Phishing attacks pose significant risks to individuals, businesses, and organizations. They can lead to identity theft, financial loss, data breaches, '
    'and reputational damage. To protect against phishing attacks, it\'s essential to stay vigilant, be cautious of unsolicited emails or messages, '
    'verify the authenticity of websites and communications, and regularly update security measures such as antivirus software and firewalls. '
    'Additionally, education and awareness training for employees and users are crucial in preventing successful phishing attacks.')
# Add a horizontal line
st.markdown("<hr>", unsafe_allow_html=True)

# Add Navigation Bar Styling
st.markdown(
    """
    <style>
    .sidebar .sidebar-content {
        background-color: #f0f2f6;
    }
    .css-1aumxhk {
        color: #000000;
        background-color: #4CAF50;
    }
    </style>
    """,
    unsafe_allow_html=True
)


# Initialize session state
if 'page' not in st.session_state:
    st.session_state.page = "Home"
#
# # Navigation bar
# page = st.sidebar.radio("Navigation", ("Home", "Project Details", "About Us"))
# #
# if page == "Home":
#     home()
# elif page == "Project Details":
#     project_details()
# elif page == "About Us":
#     about_us()

# # Handle navigation from the button
# if st.session_state.page == "Project Details":
#     project_details()