Update app.py

app.py

@@ -1,23 +1,39 @@
 import streamlit as st
 import numpy as np
+import pandas as pd
 from tensorflow.keras.models import load_model
 from tensorflow.keras.losses import MeanSquaredError
 import joblib
 
 @st.cache_resource
 def load_resources():
+    # Load model and scalers
     custom_objects = {
         'mse': MeanSquaredError(),
         'mean_squared_error': MeanSquaredError(),
     }
     
     try:
+        # Load model resources
         model = load_model('bike_model.h5', custom_objects=custom_objects)
         scaler_x = joblib.load('scaler_x.pkl')
         scaler_y = joblib.load('scaler_y.pkl')
-        # Allow pickle for feature names loading
         features = np.load('feature_names.npy', allow_pickle=True)
-        return model, scaler_x, scaler_y, features
+        
+        # Load original data to get min-max values
+        df = pd.read_csv('train.csv')
+        feature_ranges = {
+            'season': (1, 4, 'Season (1:Spring, 2:Summer, 3:Fall, 4:Winter)'),
+            'holiday': (0, 1, 'Holiday (0:No, 1:Yes)'),
+            'workingday': (0, 1, 'Working Day (0:No, 1:Yes)'),
+            'weather': (1, 4, 'Weather (1:Clear, 2:Mist, 3:Light Rain/Snow, 4:Heavy Rain/Snow)'),
+            'temp': (df['temp'].min(), df['temp'].max(), 'Temperature (Celsius)'),
+            'atemp': (df['atemp'].min(), df['atemp'].max(), 'Feels Like Temperature (Celsius)'),
+            'humidity': (df['humidity'].min(), df['humidity'].max(), 'Humidity (%)'),
+            'windspeed': (df['windspeed'].min(), df['windspeed'].max(), 'Wind Speed')
+        }
+        
+        return model, scaler_x, scaler_y, features, feature_ranges
     except Exception as e:
         st.error(f"Error loading model: {str(e)}")
         raise e
@@ -26,13 +42,33 @@ st.title('🚲 Bike Sharing Demand Predictor')
 st.write('Predict hourly bike rental demand based on weather conditions and time features.')
 
 try:
-    model, scaler_x, scaler_y, feature_names = load_resources()
+    model, scaler_x, scaler_y, feature_names, feature_ranges = load_resources()
 
     col1, col2 = st.columns(2)
     inputs = {}
+    
     for i, feature in enumerate(feature_names):
         with col1 if i % 2 == 0 else col2:
-            inputs[feature] = st.number_input(f'{feature}', value=0.0)
+            min_val, max_val, description = feature_ranges[feature]
+            
+            if feature in ['season', 'holiday', 'workingday', 'weather']:
+                # Integer input for categorical variables
+                inputs[feature] = st.number_input(
+                    f'{description}',
+                    min_value=int(min_val),
+                    max_value=int(max_val),
+                    value=int(min_val),
+                    step=1
+                )
+            else:
+                # Float input for continuous variables
+                inputs[feature] = st.number_input(
+                    f'{description}',
+                    min_value=float(min_val),
+                    max_value=float(max_val),
+                    value=float(min_val),
+                    step=0.1
+                )
 
     if st.button('Predict Demand', use_container_width=True):
         x = scaler_x.transform(np.array(list(inputs.values())).reshape(1, -1))