Update app.py

app.py

@@ -1,10 +1,12 @@
 import streamlit as st
 import pandas as pd
 import pickle
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, Activation
 
 # Load the pre-trained model
 with open('model.pkl', 'rb') as file:
-    model = pickle.load(file, encoding='latin1')
+    model = pickle.load(file)
 
 # Default parameter values
 default_values = [17.99, 10.38, 122.8, 1001, 0.1184, 0.2776, 0.3001, 0.1471, 0.2419, 0.07871,
@@ -36,3 +38,17 @@ if user_input.form_submit_button('Predict'):
     prediction_label = 'Malignant' if prediction[0] == 1 else 'Benign'
     st.subheader('Prediction')
     st.write(f'The lesion is predicted to be: {prediction_label}')
+
+# Implementing ANN
+ann_model = Sequential()
+ann_model.add(Dense(16, input_dim=30, activation='relu'))
+ann_model.add(Dropout(0.2))
+ann_model.add(Dense(1, activation='sigmoid'))
+
+# Compiling the model
+ann_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+# Model summary
+st.subheader('Artificial Neural Network Model Summary')
+with st.echo():
+    ann_model.summary()