Create ineuron_interface.py

ineuron_interface.py

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+import streamlit as st
+import pickle
+import pandas as pd
+from extract_features import ExtractFeatures
+
+@st.cache_resource
+def get_model():
+    """
+    Loads the phishing URL detection model from a pickle file.
+
+    This function reads and loads a pickled file containing the classifier.
+
+    Returns:
+        object: The loaded phishing URL detection model.
+
+    Note:
+        The model should be saved in a file named 'phishing_url_detector.pkl'.
+        XGBoost module must be installed before using the file.
+    """
+    with open('phishing_url_detector.pkl', 'rb') as pickle_model:
+        phishing_url_detector = pickle.load(pickle_model)
+    return phishing_url_detector
+
+st.title("Phishing Website Detector")
+st.header("Are you sure your 'bank' sent that link?")
+
+# Takes in user input
+input_url = st.text_area("Put in your sus site link here: ")
+
+if input_url != "":
+    
+    # Extracts features from the URL and converts it into a dataframe
+    features_url = ExtractFeatures().url_to_features(url=input_url)
+    features_dataframe = pd.DataFrame.from_dict([features_url])
+    features_dataframe = features_dataframe.fillna(-1)
+    features_dataframe = features_dataframe.astype(int)
+
+    st.write("Okay!")
+    st.cache_data.clear()
+    prediction_str = ""
+
+    # Predict outcome using extracted features
+    try: 
+        phishing_url_detector = get_model()
+        prediction = phishing_url_detector.predict(features_dataframe)
+        if prediction == int(True):
+            prediction_str = 'Phishing Website. Do not click!'
+        elif prediction == int(False):
+            prediction_str = 'Not Phishing Website, stay safe!'
+        else:
+            prediction_str = ''
+        st.write(prediction_str)
+        st.write(features_dataframe)
+
+    except Exception as e:
+        print(e)
+        st.error("Not sure, what went wrong. We'll get back to you shortly!")
+
+else:
+    st.write("")