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app.py

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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()

config.py

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+class Config():
+
+    def __init__(self):
+        pass
+
+    target = 'demand'
+
+    not_include_features = [
+        target,
+        'date'
+    ]
+
+    cat_features = [
+        'product_id',
+        'product_application',
+        'product_marketing_name',
+        'product_main_family',
+        'planning_method_latest'
+    ]
+
+    # production
+    MAIN_TITLE = 'Infineon Product Demand Forecasting System'
+    SUB_TITLE = 'Data Analytics in Applications'
+    ICON_PATH = 'images/infineon-icon-1.png'
+
+    FORECAST_START_DATE = '01-11-2023'
+    FORECAST_END_DATE = '01-07-2024'
+
+    FORECAST_BUTTON_TEXT = 'Predict'
+
+    LINE_PLOT_SELECTBOX_TEXT = 'Filter at product level'
+    BAR_PLOT_SELECTBOX_TEXT = 'Filter at category level'
+    
+    SAVE_CHECKBOX_TEXT = 'Save predictions'
+    SAVE_BUTTON_TEXT = 'Apply'
+    SAVE_BUTTON_SUCCESS_TEXT = 'File successfully saved'

requirements.txt

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+numpy==1.24.3
+pandas==2.0.0
+streamlit==1.23.1