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| # external libraries | |
| import streamlit as st | |
| import pandas as pd | |
| import os | |
| import datetime | |
| 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.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()) | |
| 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"][['product_id','date','demand']].to_excel(f'{dir}/predictions_{today}.xlsx', index=False) | |
| # forecasting | |
| def predict(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() |