| import streamlit as st | |
| import tensorflow as tf | |
| # Load your model | |
| model = tf.keras.models.load_model('best_dnn_model.h5') | |
| # Define a function to make predictions | |
| def predict(input_data): | |
| prediction = model.predict(input_data) | |
| return prediction | |
| # Streamlit app | |
| st.title('Bank Churn Prediction') | |
| user_input = st.text_input('Enter your input data') | |
| if st.button('Predict'): | |
| input_data = preprocess_user_input(user_input) # Implementing the function based on preprocessing logic | |
| prediction = predict(input_data) | |
| st.write('Prediction:', prediction) | |