Spaces:
Runtime error
Runtime error
| import streamlit as st | |
| import numpy as np | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.losses import MeanSquaredError | |
| import joblib | |
| def load_resources(): | |
| custom_objects = { | |
| 'mse': MeanSquaredError() | |
| } | |
| 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 = load_resources() | |
| col1, col2 = st.columns(2) | |
| 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, verbose=0) | |
| 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)}') | |
| 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') |