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| import streamlit as st | |
| import joblib | |
| import pandas as pd | |
| import numpy as np | |
| from statsmodels.tsa.statespace.varmax import VARMAX | |
| import matplotlib.pyplot as plt | |
| import statsmodels.api as sm | |
| # Load the pre-trained model | |
| fitted_model = joblib.load('modelling_all123.sav') | |
| # Streamlit App | |
| st.title("Aplikasi Prediksi Harga Bawang") | |
| # Date Range Input | |
| start_date = st.date_input("Pilih Tanggal Mulai", pd.to_datetime('2023-12-01')) | |
| end_date = st.date_input("Select End Date", pd.to_datetime('2023-12-30')) | |
| # Make predictions based on user input | |
| predict = fitted_model.get_prediction(start=start_date, end=end_date, dynamic=True) | |
| predictions = predict.predicted_mean | |
| predictions['Harga Bawang'] = predictions['Harga Bawang'].round() | |
| predictions.columns = ['Prediction Harga Bawang', | |
| 'Prediction T2M', | |
| 'Prediction WS10M_RANGE', | |
| 'Prediction PRECTOTCORR'] | |
| predictions = predictions.abs() | |
| predictions = predictions.set_index(pd.date_range(start=start_date, periods=len(predictions))) | |
| # Display Predictions Table | |
| st.subheader("Hasil Prediksi") | |
| st.write(predictions) | |
| # Plotting Predictions | |
| fig, ax = plt.subplots(figsize=(10, 6)) | |
| for column in predictions.columns: | |
| ax.plot(predictions.index, predictions[column], label=column) | |
| ax.set_xlabel("Tanggal") | |
| ax.set_ylabel("Hasil Prediksi") | |
| ax.set_title("Prediksi") | |
| # Show plot in Streamlit | |
| st.pyplot(fig) | |