Update app.py

app.py

@@ -1,29 +1,44 @@
+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
-import pandas as pd
-import numpy as np
 
-import warnings
-warnings.filterwarnings('ignore')
+# Load the pre-trained model
+fitted_model = joblib.load('modelling_all123.sav')
 
-import streamlit as st
-import joblib
+# Streamlit App
+st.title("Time Series Prediction Dashboard")
 
+# Date Range Input
+start_date = st.date_input("Select Start Date", pd.to_datetime('2024-01-01'))
+end_date = st.date_input("Select End Date", pd.to_datetime('2024-01-30'))
 
-fitted_model = joblib.load('modelling_all123.sav')
-predict=fitted_model.get_prediction(start=1401,end=1430, dynamic=True)
+# 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=predict.predicted_mean
+# Display Predictions Table
+st.subheader("Predictions for the Selected Time Period")
+st.write(predictions)
 
-predictions
+# Plotting Predictions
+fig, ax = plt.subplots(figsize=(10, 6))
 
+for column in predictions.columns:
+    ax.plot(predictions.index, predictions[column], label=column)
 
-predictions['Harga Bawang']=predictions['Harga Bawang'].round()
-predictions.columns=['Prediction Harga Bawang',
-    'Prediction T2M',
-    'Prediction WS10M_RANGE',
-     'Prediction PRECTOTCORR']
-predictions=predictions.abs()
+ax.set_xlabel("Time Period")
+ax.set_ylabel("Predicted Values")
+ax.set_title("Time Series Predictions")
 
-predictions
+# Show plot in Streamlit
+st.pyplot(fig)