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# external libraries
import streamlit as st
import pandas as pd
import numpy as np
import os
import datetime
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.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"].loc[st.session_state["predictions_df"]['product_id'] == pid,:].groupby(category)['demand'].mean())
input_save = st.checkbox(config['SAVE_CHECKBOX_TEXT'])
confirm_params = {
'input_save':input_save
}
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"].to_excel(f'{dir}/predictions_{today}.xlsx', index=False)
# forecasting
def predict(input_date):
data = {
'product_id': [f'P{1}' for i in range(5)],
'date':['2022-01-01','2022-02-01','2022-03-01','2022-04-01','2022-05-01'],
'demand': np.random.randint(1, 100, size=5),
'product_application': ['A','A','A','B','B']
}
data2 = {
'product_id': [f'P{2}' for i in range(5)],
'date':['2022-01-01','2022-02-01','2022-03-01','2022-04-01','2022-05-01'],
'demand': np.random.randint(1, 100, size=5),
'product_application': ['A','A','A','B','B']
}
df1 = pd.DataFrame(data)
df2 = pd.DataFrame(data2)
# Concatenate the two DataFrames vertically
combined_df = pd.concat([df1, df2], ignore_index=True)
forecast_start_date = input_date[0].strftime("%Y-%m-%d")
forecast_end_date = input_date[1].strftime("%Y-%m-%d")
print(forecast_start_date, forecast_end_date)
st.session_state["predictions_df"] = combined_df
if __name__ == "__main__":
main() |