Update app.py

app.py

@@ -1,154 +1,164 @@
-import base64
-import pickle
-import numpy as np
-import streamlit as st
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.ensemble import RandomForestClassifier  # Example model
-from sklearn.preprocessing import StandardScaler
-# Streamlit app title
-st.title('ITI105 Team Project')
-st.subheader('Machine Learning Project for Phishing web site prediction App')
-
-if 'clear_output' not in st.session_state:
-    st.session_state.clear_output = False
-
-# Function to clear specific elements
-def clear_previous_output():
-    st.session_state.clear_output = True
-
-# Load the pre-uploaded dataset
-default_file_path = 'https://raw.githubusercontent.com/JimmyYehtut/ITI105Files/main/test_dataset.csv'
-df_new = pd.read_csv(default_file_path)
-
-
-# Upload the CSV file
-uploaded_file = st.file_uploader("Choose a CSV file with website data", type="csv")
-row_index = None
-if uploaded_file is not None: 
-    # Read the CSV file
-    df = pd.read_csv(uploaded_file)
-    # st.write("Original Dataframe:", df)
-
-    # Extract the URL column to display in the dropdown
-    url_list = df['url'].tolist()
-
-    # Display the dropdown with URL options
-    selected_url = st.selectbox("Select URL for Prediction", url_list)
-
-    # Display the list fo model
-    selected_model = st.selectbox("Select Model for Prediction", ['Random Forest', 'Logistic Regression', 'SVM', 'KNN', 'Decision Tree'])
-
-
-    # Remove the first (non-numeric) and last (target) columns
-    if df.shape[1] > 2:  # Ensure there are enough columns to remove
-        features_df = df.iloc[:, 1:-1]  # Drop first and last columns
-       
-
-        # Select a row for prediction
-        # row_index = st.number_input("Select a row index for prediction", min_value=0, max_value=len(features_df)-1, step=1)
-        row_index = df[df['url'] == selected_url].index[0]
-        # Display the selected row's features in a table
-        selected_row = df.iloc[row_index, :]
-        st.subheader("List of selected website features:")
-        st.table(selected_row.to_frame().T)
-        
-    else:
-        st.write("The dataset does not have enough columns after removing the first and last columns.")
-else:
-    # st.error("ERROR!!! Please upload a CSV file to continue.")
-    st.write("Using pre-uploaded sample data:")
-    df = df_new
-    # Extract the URL column to display in the dropdown
-    url_list = df['url'].tolist()
-
-    # Display the dropdown with URL options
-    selected_url = st.selectbox("Select URL for Prediction", url_list)
-
-    # Display the list fo model
-    selected_model = st.selectbox("Select Model for Prediction", ['Random Forest', 'Logistic Regression', 'SVM', 'KNN', 'Decision Tree'])
-
-
-    # Remove the first (non-numeric) and last (target) columns
-    if df.shape[1] > 2:  # Ensure there are enough columns to remove
-        features_df = df.iloc[:, 1:-1]  # Drop first and last columns
-       
-
-        # Select a row for prediction
-        # row_index = st.number_input("Select a row index for prediction", min_value=0, max_value=len(features_df)-1, step=1)
-        row_index = df[df['url'] == selected_url].index[0]
-        # Display the selected row's features in a table
-        selected_row = df.iloc[row_index, :]
-        st.subheader("List of selected website features:")
-        st.table(selected_row.to_frame().T)
-        
-    else:
-        st.write("The dataset does not have enough columns after removing the first and last columns.")
-
-if st.button("Predict"):
-
-    # Clear previous st.success, st.error, and st.markdown elements
-    clear_previous_output()
-    file_ = open("It'ok.webp", "rb")
-    contents = file_.read()
-    data_url_ok = base64.b64encode(contents).decode("utf-8")
-    file_.close()
-
-    file = open("Warning.gif", "rb")
-    contents = file.read()
-    data_url_warning = base64.b64encode(contents).decode("utf-8")
-    file.close()
-    if row_index is not None:
-            input_values = features_df.iloc[row_index].values  # Get selected row data
-            # st.write("Selected Features Dataframe for predicton:", input_values)
-            # st.write("Selected Row Data (Features Only):", input_values)
-            single_sample = np.array(input_values)
-            # Dummy model for the purpose of this example
-            # Normally you would load a pre-trained model or train one
-            # X = features_df  # Using the processed features data
-            # y = [0]*len(df)  # Dummy target variable for training the model (since we don't have a real target)
-
-            # # Train/test split
-            # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-            # model = RandomForestClassifier()
-            # model.fit(X_train, y_train)
-
-            # Show progress spinner while making predictions
-            with st.spinner('Making prediction...'):
-                # Predict based on selected row
-                # prediction = model.predict([input_values])
-                # Load the pre-trained scaler and model
-                with open('scaler.pkl', 'rb') as f:
-                    scalar = pickle.load(f)
-                
-                with open('rf_clf.pkl', 'rb') as f:
-                    rf_clf = pickle.load(f)
-
-                            # Scale the new data using the pre-trained scaler
-                X_new_scaled = scalar.transform(single_sample.reshape(1, -1))
-
-                # Make predictions using the pre-trained model
-                prediction = rf_clf.predict(X_new_scaled)
-
-                # loaded_model = pickle.load(open('Random_Forest.sav', 'rb'))
-                # prediction = loaded_model.predict(np.array(single_sample))
-
-
-            # st.write(f"Prediction : {prediction[0]}")
-            if prediction[0] == 0:
-                st.success("The website is not a phishing website.")
-                st.markdown(f'<img src="data:image/gif;base64,{data_url_ok}" alt="cat gif">', unsafe_allow_html=True,)
-            else:
-                st.error("The website is a phishing website.")
-                st.markdown(f'<img src="data:image/gif;base64,{data_url_warning}" alt="cat gif">', unsafe_allow_html=True,)
-
-            # Note: Since we don't have a real target, accuracy calculation is skipped.
-    else:
-        st.error("ERROR!!! Please provide web site information for prediction !!!")
-
-# This block clears the elements only if the prediction button is pressed
-if st.session_state.clear_output:
-    st.session_state.clear_output = False
-    # st.success("")  # Clear any previous success messages
-    # st.error("")    # Clear any previous error messages
-    # st.markdown("") # Clear any previous markdown content
+import base64
+import pickle
+import numpy as np
+import streamlit as st
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier  # Example model
+from sklearn.preprocessing import StandardScaler
+# Streamlit app title
+st.title('ITI105 Team Project')
+st.subheader('Machine Learning Project for Phishing web site prediction App')
+
+if 'clear_output' not in st.session_state:
+    st.session_state.clear_output = False
+
+# Function to clear specific elements
+def clear_previous_output():
+    st.session_state.clear_output = True
+
+# Load the pre-uploaded dataset
+default_file_path = 'https://raw.githubusercontent.com/JimmyYehtut/ITI105Files/main/test_dataset.csv'
+df_new = pd.read_csv(default_file_path)
+
+
+# Upload the CSV file
+uploaded_file = st.file_uploader("Choose a CSV file with website data", type="csv")
+row_index = None
+if uploaded_file is not None: 
+    # Read the CSV file
+    df = pd.read_csv(uploaded_file)
+    # st.write("Original Dataframe:", df)
+
+    # Extract the URL column to display in the dropdown
+    url_list = df['url'].tolist()
+
+    # Display the dropdown with URL options
+    selected_url = st.selectbox("Select URL for Prediction", url_list)
+
+    # Display the list fo model
+    selected_model = st.selectbox("Select Model for Prediction", ['Random Forest', 'Logistic Regression', 'SVM', 'KNN', 'Decision Tree'])
+
+
+    # Remove the first (non-numeric) and last (target) columns
+    if df.shape[1] > 2:  # Ensure there are enough columns to remove
+        features_df = df.iloc[:, 1:-1]  # Drop first and last columns
+       
+
+        # Select a row for prediction
+        # row_index = st.number_input("Select a row index for prediction", min_value=0, max_value=len(features_df)-1, step=1)
+        row_index = df[df['url'] == selected_url].index[0]
+        # Display the selected row's features in a table
+        selected_row = df.iloc[row_index, :]
+        st.subheader("List of selected website features:")
+        st.table(selected_row.to_frame().T)
+        
+    else:
+        st.write("The dataset does not have enough columns after removing the first and last columns.")
+else:
+    # st.error("ERROR!!! Please upload a CSV file to continue.")
+    st.write("Using pre-uploaded sample data:")
+    df = df_new
+    # Extract the URL column to display in the dropdown
+    url_list = df['url'].tolist()
+
+    # Display the dropdown with URL options
+    selected_url = st.selectbox("Select URL for Prediction", url_list)
+
+    # Display the list fo model
+    selected_model = st.selectbox("Select Model for Prediction", ['Random Forest', 'Logistic Regression', 'SVM', 'KNN', 'Decision Tree'])
+
+
+    # Remove the first (non-numeric) and last (target) columns
+    if df.shape[1] > 2:  # Ensure there are enough columns to remove
+        features_df = df.iloc[:, 1:-1]  # Drop first and last columns
+       
+
+        # Select a row for prediction
+        # row_index = st.number_input("Select a row index for prediction", min_value=0, max_value=len(features_df)-1, step=1)
+        row_index = df[df['url'] == selected_url].index[0]
+        # Display the selected row's features in a table
+        selected_row = df.iloc[row_index, :]
+        st.subheader("List of selected website features:")
+        st.table(selected_row.to_frame().T)
+        
+    else:
+        st.write("The dataset does not have enough columns after removing the first and last columns.")
+
+if st.button("Predict"):
+
+    # Clear previous st.success, st.error, and st.markdown elements
+    clear_previous_output()
+    file_ = open("It'ok.webp", "rb")
+    contents = file_.read()
+    data_url_ok = base64.b64encode(contents).decode("utf-8")
+    file_.close()
+
+    file = open("Warning.gif", "rb")
+    contents = file.read()
+    data_url_warning = base64.b64encode(contents).decode("utf-8")
+    file.close()
+    if row_index is not None:
+            input_values = features_df.iloc[row_index].values  # Get selected row data
+            # st.write("Selected Features Dataframe for predicton:", input_values)
+            # st.write("Selected Row Data (Features Only):", input_values)
+            single_sample = np.array(input_values)
+            # Dummy model for the purpose of this example
+            # Normally you would load a pre-trained model or train one
+            # X = features_df  # Using the processed features data
+            # y = [0]*len(df)  # Dummy target variable for training the model (since we don't have a real target)
+
+            # # Train/test split
+            # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+            # model = RandomForestClassifier()
+            # model.fit(X_train, y_train)
+
+            # Show progress spinner while making predictions
+            with st.spinner('Making prediction...'):
+                # Predict based on selected row
+                # prediction = model.predict([input_values])
+                # Load the pre-trained scaler and model
+                with open('scaler.pkl', 'rb') as f:
+                    scalar = pickle.load(f)
+                
+                with open('rf_clf.pkl', 'rb') as f:
+                    rf_clf = pickle.load(f)
+
+                            # Scale the new data using the pre-trained scaler
+                X_new_scaled = scalar.transform(single_sample.reshape(1, -1))
+
+                # Make predictions using the pre-trained model
+                prediction = rf_clf.predict(X_new_scaled)
+
+                # loaded_model = pickle.load(open('Random_Forest.sav', 'rb'))
+                # prediction = loaded_model.predict(np.array(single_sample))
+
+
+            # st.write(f"Prediction : {prediction[0]}")
+            if prediction[0] == 0:
+                st.success("The website is not a phishing website.")
+                st.markdown(f'<img src="data:image/gif;base64,{data_url_ok}" alt="cat gif">', unsafe_allow_html=True,)
+            else:
+                st.error("The website is a phishing website.")
+                st.markdown(f'<img src="data:image/gif;base64,{data_url_warning}" alt="cat gif">', unsafe_allow_html=True,)
+
+            
+             # Visualize prediction confidence scores as a bar chart
+            st.write("Prediction Confidence Scores:")
+            class_names = rf_clf.classes_
+            plt.figure(figsize=(8, 4))
+            sns.barplot(x=class_names, y=y_pred_proba[0])
+            plt.title("Prediction Confidence Scores")
+            plt.xlabel("Class")
+            plt.ylabel("Probability")
+            st.pyplot(plt)
+           
+    else:
+        st.error("ERROR!!! Please provide web site information for prediction !!!")
+
+# This block clears the elements only if the prediction button is pressed
+if st.session_state.clear_output:
+    st.session_state.clear_output = False
+    # st.success("")  # Clear any previous success messages
+    # st.error("")    # Clear any previous error messages
+    # st.markdown("") # Clear any previous markdown content