Update app.py

app.py

@@ -1,80 +1,75 @@
 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 tensorflow.keras.losses
 import joblib
 
+tensorflow.keras.losses.mse = tensorflow.keras.losses.mean_squared_error
+
 @st.cache_resource
 def load_resources():
-    # Load model and scalers
     custom_objects = {
-        'mse': MeanSquaredError(),
-        'mean_squared_error': MeanSquaredError(),
+        'mse': tensorflow.keras.losses.mean_squared_error
     }
-    
-    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')
-        features = np.load('feature_names.npy', allow_pickle=True)
-        
-        # 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
+    model = load_model('bike_model.h5', custom_objects=custom_objects)
+    scaler_x = joblib.load('scaler_x.pkl')
+    scaler_y = joblib.load('scaler_y.pkl')
+    features = np.load('feature_names.npy', allow_pickle=True)
+    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.')
 
 try:
-    model, scaler_x, scaler_y, feature_names, feature_ranges = load_resources()
+    model, scaler_x, scaler_y, feature_names = load_resources()
 
     col1, col2 = st.columns(2)
-    inputs = {}
     
-    for i, feature in enumerate(feature_names):
-        with col1 if i % 2 == 0 else col2:
-            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
-                )
+    with col1:
+        # Categorical inputs
+        season = st.selectbox('season', options=[1, 2, 3, 4], 
+                            help='1:Spring, 2:Summer, 3:Fall, 4:Winter')
+        
+        holiday = st.selectbox('holiday', options=[0, 1],
+                             help='0:No, 1:Yes')
+        
+        workingday = st.selectbox('workingday', options=[0, 1],
+                                 help='0:No, 1:Yes')
+        
+        weather = st.selectbox('weather', options=[1, 2, 3, 4],
+                             help='1:Clear, 2:Mist, 3:Light Rain/Snow, 4:Heavy Rain')
+
+    with col2:
+        # Continuous inputs with sliders
+        temp = st.slider('temp (°C)', min_value=0.82, max_value=41.0, 
+                        value=20.0, step=0.1)
+        
+        atemp = st.slider('feels like temp', min_value=0.76, max_value=45.5, 
+                         value=23.7, step=0.1)
+        
+        humidity = st.slider('humidity (%)', min_value=0, max_value=100, 
+                           value=62)
+        
+        windspeed = st.slider('windspeed', min_value=0.0, max_value=57.0, 
+                            value=13.0, step=0.1)
+
+    # Create input dictionary
+    inputs = {
+        'season': season,
+        'holiday': holiday,
+        'workingday': workingday,
+        'weather': weather,
+        'temp': temp,
+        'atemp': atemp,
+        'humidity': humidity,
+        'windspeed': windspeed
+    }
 
     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])
         
-        pred_scaled = model.predict(x)
+        pred_scaled = model.predict(x, verbose=0)
         pred = scaler_y.inverse_transform(pred_scaled)[0][0]
         
         st.success(f'Predicted demand: {max(0, int(pred))} bikes')
@@ -83,4 +78,15 @@ except Exception as e:
     st.error(f'Error: {str(e)}')
 
 st.markdown('---')
+st.markdown("""
+### Feature Information:
+- **season**: 1=Spring, 2=Summer, 3=Fall, 4=Winter
+- **holiday**: 0=No, 1=Yes
+- **workingday**: 0=No, 1=Yes
+- **weather**: 1=Clear, 2=Mist, 3=Light Rain/Snow, 4=Heavy Rain
+- **temp**: Temperature in Celsius (0.82°C to 41°C)
+- **atemp**: "Feels like" temperature (0.76°C to 45.5°C)
+- **humidity**: Relative humidity (0% to 100%)
+- **windspeed**: Wind speed (0 to 57)
+""")
 st.markdown('Made with ❤️ using Streamlit and TensorFlow')