Create app.py

app.py

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+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)