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| 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 | |
| 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') | |
| 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 | |
| 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() | |
| 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 | |
| ) | |
| 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 = 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('Made with ❤️ using Streamlit and TensorFlow') |