import streamlit as st
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
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'
)

def run():

    # Membuat file
    st.title( 'Time Series Forecasting')

    # Membuat sub header
    st.subheader('Best seller product')

    # Menambahkan gambar
    image = Image.open('ts.jpg')
    st.image(image, caption='Time Series Analysis')

if __name__ == '__main__':
    run()


# Load All Files

with open('mod_train.pkl', 'rb') as file_1:
  mod_train = pickle.load(file_1)



# bikin fungsi  
def run():

  with st.form(key='forecast quantity'):
      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()