Update app.py

app.py

@@ -1,37 +1,46 @@
 import streamlit as st
 import numpy as np
 from tensorflow.keras.models import load_model
-from tensorflow.keras.losses import MeanSquaredError
 import joblib
 
 @st.cache_resource
 def load_resources():
-    # Load model with custom objects
-    custom_objects = {'loss': MeanSquaredError()}
-    model = load_model('bike_model.h5', custom_objects=custom_objects)
+    model = load_model('bike_model.h5')
     scaler_x = joblib.load('scaler_x.pkl')
     scaler_y = joblib.load('scaler_y.pkl')
     features = np.load('feature_names.npy')
     return model, scaler_x, scaler_y, features
 
 st.title('🚲 Bike Sharing Demand Predictor')
+st.write('Predict hourly bike rental demand based on weather conditions and time features.')
 
-model, scaler_x, scaler_y, feature_names = load_resources()
+try:
+    model, scaler_x, scaler_y, feature_names = load_resources()
 
-inputs = {}
-for feature in feature_names:
-    inputs[feature] = st.number_input(f'{feature}', value=0.0)
+    # Create columns for better layout
+    col1, col2 = st.columns(2)
+    
+    inputs = {}
+    for i, feature in enumerate(feature_names):
+        # Alternate between columns
+        with col1 if i % 2 == 0 else col2:
+            inputs[feature] = st.number_input(
+                f'{feature}',
+                value=0.0,
+                help=f'Enter value for {feature}'
+            )
 
-if st.button('Predict'):
-    try:
-        # Scale and reshape input
+    if st.button('Predict Demand', use_container_width=True):
         x = scaler_x.transform(np.array(list(inputs.values())).reshape(1, -1))
         x = x.reshape(1, 1, x.shape[1])
         
-        # Predict and inverse transform
         pred_scaled = model.predict(x)
         pred = scaler_y.inverse_transform(pred_scaled)[0][0]
         
         st.success(f'Predicted demand: {max(0, int(pred))} bikes')
-    except Exception as e:
-        st.error(f'Error: {str(e)}')
+
+except Exception as e:
+    st.error(f'Error: {str(e)}')
+
+st.markdown('---')
+st.markdown('Made with ❤️ using Streamlit and TensorFlow')