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import streamlit as st
import numpy as np
import pandas as pd
import pickle
from statsmodels.tsa.arima.model import ARIMA
from datetime import datetime
st.set_page_config(
page_title = 'Forecasting',
layout = 'wide',
initial_sidebar_state='expanded'
)
with open('mod_train.pkl', 'rb') as file_1:
mod_train = pickle.load(file_1)
# bikin fungsi
def run():
with st.form(key='Forecasting'):
year = st.selectbox('Year', (2023,2024), index=0)
month = st.selectbox('Month', (1,2,3,4,5,6,7,8,9,10,11,12), index=3)
st.markdown('---')
submitted = st.form_submit_button('Predict')
data_inf = {
'year' : year,
'month': month
}
data_inf = pd.DataFrame([data_inf])
data_inf['date'] = data_inf.apply(lambda x: datetime(x[0], x[1], 1), axis=1)
data_inf.drop(['year','month'], axis=1, inplace=True)
data_inf=data_inf.set_index('date')
#st.dataframe(data_inf)
if submitted:
#Predict Inference data
y_pred_test=mod_train.predict(start=data_inf.index[0],end=data_inf.index[-1])
y_pred_test=pd.DataFrame(y_pred_test)
y_pred_test.columns=['quantity_predict']
st.write('# Forcasted quantity for that month is : ', y_pred_test)
if __name__ == '__main__':
run() |