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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)
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