| import streamlit as st |
| import pandas as pd |
| import numpy as np |
| from tensorflow.keras.models import load_model |
| from sklearn.preprocessing import StandardScaler |
|
|
| |
| model = load_model('best_dnn_model') |
|
|
| |
| scaler = StandardScaler() |
|
|
| |
| def preprocess_data(data): |
| data = np.array(data).reshape(1, -1) |
| data = scaler.transform(data) |
| return data |
|
|
| |
| st.title('Bank Churn: DNN Model Deployment') |
|
|
| |
| user_input = st.text_area("Enter your input data (comma-separated)") |
|
|
| |
| if st.button('Predict'): |
| try: |
| data = [float(i) for i in user_input.split(',')] |
| data = preprocess_data(data) |
| prediction = model.predict(data) |
| st.write(f"Prediction: {prediction}") |
| except Exception as e: |
| st.write(f"Error: {e}") |
|
|
