Added the scaler

app.py

@@ -1,13 +1,14 @@
 import streamlit as st
 import tensorflow as tf
 import numpy as np
+import joblib
 from sklearn.preprocessing import MinMaxScaler
 
 # Load the trained model
 model = tf.keras.models.load_model('vitals_model.keras')
 
-# Define scaler
-scaler = MinMaxScaler(feature_range=(0, 1))
+# Load the pre-fitted scaler
+scaler = joblib.load('scaler.save')  # Make sure this file is accessible in your app environment
 
 # Streamlit input fields
 st.title('Vitals Prediction with LSTM')
@@ -20,7 +21,7 @@ steps = st.number_input('Steps', 0, 20000)
 
 # Preprocess input
 input_data = np.array([[systolic_bp, diastolic_bp, glucose_level, heart_rate, steps]])
-scaled_data = scaler.transform(input_data)
+scaled_data = scaler.transform(input_data)  # Now, this will work as the scaler is pre-fitted
 
 # Predict
 if st.button('Predict'):