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# external libraries
import streamlit as st
import pandas as pd
import os
import datetime
from src.utils.helper_functions import send_mail
import pipeline
from config import Config
config = vars(Config)
def main():
# variables
if "state" not in st.session_state:
st.session_state["state"] = True
st.session_state["predictions_df"] = pd.DataFrame()
st.session_state["send_mail"] = False
st.session_state["text_input"] = ''
st.session_state["input_date"] = None
st.set_page_config(
layout="centered", # Can be "centered" or "wide". In the future also "dashboard", etc.
initial_sidebar_state="auto", # Can be "auto", "expanded", "collapsed"
page_title=config['MAIN_TITLE'], # String or None. Strings get appended with "• Streamlit".
page_icon=config['ICON_PATH'], # String, anything supported by st.image, or None.
)
col1, col2, col3 = st.columns(3)
col1.write(' ')
col2.image(config['ICON_PATH'])
col3.write(' ')
st.markdown(f"<h1 style='text-align: center;'>{config['MAIN_TITLE']}</h1>", unsafe_allow_html=True)
st.markdown(f"<h3 style='text-align: center;'>{config['SUB_TITLE']}</h3>", unsafe_allow_html=True)
if st.session_state["state"]:
dates = get_forecasting_horizon(config['FORECAST_START_DATE'],config['FORECAST_END_DATE'])
col1, col2= st.columns([100,1])
input_date = col1.slider(
"Forecast Interval", dates[0].date(), dates[-1].date(),
value=(dates[0].date(),dates[0].date() + datetime.timedelta(weeks=12)),
step=datetime.timedelta(weeks=4))
col1, col2, col3 , col4, col5 = st.columns(5)
with col3 :
center_button = st.button(config['FORECAST_BUTTON_TEXT'], on_click=predict, args=(input_date,))
if not st.session_state["predictions_df"].empty:
pid = st.selectbox(config['LINE_PLOT_SELECTBOX_TEXT'],st.session_state["predictions_df"]['product_id'].unique())
st.table(st.session_state["predictions_df"].loc[st.session_state["predictions_df"]['product_id'] == pid,['product_id','date','demand']][:10])
st.write(f"Monthly demand for the product {pid}")
st.line_chart(st.session_state["predictions_df"].loc[st.session_state["predictions_df"]['product_id'] == pid,:],
x="date", y="demand", use_container_width=True)
category = st.selectbox(config['BAR_PLOT_SELECTBOX_TEXT'],['product_application',
'product_marketing_name',
'product_main_family',
'planning_method_latest'])
st.write(f'Average demand by category "{category}"')
st.bar_chart(st.session_state["predictions_df"].groupby(category)['demand'].mean())
do_mail_sending = st.checkbox('Send mail')
if do_mail_sending:
# Display a text area if the checkbox is clicked
st.session_state["send_mail"] = do_mail_sending
st.session_state["text_input"] = st.text_input('Mail address:')
input_save = st.checkbox(config['SAVE_CHECKBOX_TEXT'])
confirm_params = {
'input_save':input_save,
'send_mail':do_mail_sending,
'email_adress': st.session_state["text_input"],
'forecast_start_date':st.session_state["input_date"][0].strftime("%Y/%m/%d"),
'forecast_end_date':st.session_state["input_date"][1].strftime("%Y/%m/%d")
}
confirm = st.button(config['SAVE_BUTTON_TEXT'], on_click=save, args=(confirm_params,))
if confirm:
st.success(config['SAVE_BUTTON_SUCCESS_TEXT'])
def get_forecasting_horizon(start_date, end_date):
horizon = pd.date_range(start_date,
end_date,
freq='MS')
return horizon
# saving results
def save(params):
dir = 'demand_predictions'
if not os.path.exists(f'{dir}'):
os.makedirs(f'{dir}')
if params['input_save']:
today = datetime.datetime.today().strftime("%d-%m-%Y")
st.session_state["predictions_df"][['product_id','date','demand']].to_excel(f'{dir}/predictions_{today}.xlsx', index=False)
if params['send_mail']:
status = send_mail(
name='Alper Temel',
email=params['email_adress'],
subject='Forecast Results',
message='Hi Captain',
dataframe=st.session_state["predictions_df"].loc[:, ['product_id','date','demand']],
forecast_start_date=params['forecast_start_date'],
forecast_end_date=params['forecast_end_date'],
toMail=params['email_adress']
)
if not status: # not successfull
return False
# forecasting
def predict(input_date):
st.session_state["input_date"] = input_date
forecast_start_date = input_date[0].strftime("%Y-%m-%d")
forecast_end_date = input_date[1].strftime("%Y-%m-%d")
st.session_state["predictions_df"] = pipeline.run(forecast_start_date, forecast_end_date)
if __name__ == "__main__":
main() |