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| # Importing required Libraries | |
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import os, pickle | |
| from sklearn import preprocessing | |
| # Setting up page configuration and directory path | |
| st.set_page_config(page_title="Sales Forecasting App", page_icon="🐞", layout="centered") | |
| DIRPATH = os.path.dirname(os.path.realpath(__file__)) | |
| # Setting background image | |
| import base64 | |
| def add_bg_from_local(image_file): | |
| with open(image_file, "rb") as image_file: | |
| encoded_string = base64.b64encode(image_file.read()) | |
| st.markdown( | |
| f""" | |
| <style> | |
| .stApp {{ | |
| background-image: url(data:{"jpg"};base64,{encoded_string.decode()}); | |
| background-size: cover | |
| }} | |
| </style> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| add_bg_from_local('background.jpg') | |
| # Setting up logo | |
| left1, mid, right1 = st.columns(3) | |
| with mid: | |
| st.image("logo.jpg", use_column_width=True) | |
| # Setting up Sidebar | |
| social_acc = ['Data Field Description', 'EDA', 'About App'] | |
| social_acc_nav = st.sidebar.radio('**INFORMATION SECTION**', social_acc) | |
| if social_acc_nav == 'Data Field Description': | |
| st.sidebar.markdown("<h2 style='text-align: center;'> Data Field Description </h2> ", unsafe_allow_html=True) | |
| st.sidebar.markdown("**Date:** The date you want to predict sales for") | |
| st.sidebar.markdown("**Family:** identifies the type of product sold") | |
| st.sidebar.markdown("**Onpromotion:** gives the total number of items in a product family that are being promoted at a store at a given date") | |
| st.sidebar.markdown("**Store Number:** identifies the store at which the products are sold") | |
| st.sidebar.markdown("**Holiday Locale:** provide information about the locale where holiday is celebrated") | |
| elif social_acc_nav == 'EDA': | |
| st.sidebar.markdown("<h2 style='text-align: center;'> Exploratory Data Analysis </h2> ", unsafe_allow_html=True) | |
| st.sidebar.markdown('''---''') | |
| st.sidebar.markdown('''The exploratory data analysis of this project can be find in a Jupyter notebook from the linl below''') | |
| st.sidebar.markdown("[Open Notebook](https://github.com/Kyei-frank/Regression-Project-Store-Sales--Time-Series-Forecasting/blob/main/project_workflow.ipynb)") | |
| elif social_acc_nav == 'About App': | |
| st.sidebar.markdown("<h2 style='text-align: center;'> Sales Forecasting App </h2> ", unsafe_allow_html=True) | |
| st.sidebar.markdown('''---''') | |
| st.sidebar.markdown("This App predicts the sales for product families sold at Favorita stores using regression model.") | |
| st.sidebar.markdown("") | |
| st.sidebar.markdown("[ Visit Github Repository for more information](https://github.com/Kyei-frank/Regression-Project-Store-Sales--Time-Series-Forecasting)") | |
| # Loading Machine Learning Objects | |
| def load_saved_objects(file_path = 'ML_items'): | |
| # Function to load saved objects | |
| with open('ML_items', 'rb') as file: | |
| loaded_object = pickle.load(file) | |
| return loaded_object | |
| # Instantiating ML_items | |
| Loaded_object = load_saved_objects(file_path = 'ML_items') | |
| pipeline, train_data, stores, holidays_event = Loaded_object['pipeline'], Loaded_object['train_data'], Loaded_object['stores'], Loaded_object['holidays_event'] | |
| # Setting Function for extracting Calendar features | |
| def getDateFeatures(df, date): | |
| df['date'] = pd.to_datetime(df['date']) | |
| df['month'] = df.date.dt.month | |
| df['day_of_month'] = df.date.dt.day | |
| df['day_of_year'] = df.date.dt.dayofyear | |
| df['week_of_year'] = df.date.dt.isocalendar().week | |
| df['day_of_week'] = df.date.dt.dayofweek | |
| df['year'] = df.date.dt.year | |
| df['is_weekend']= np.where(df['day_of_week'] > 4, 1, 0) | |
| df['is_month_start']= df.date.dt.is_month_start.astype(int) | |
| df['is_month_end']= df.date.dt.is_month_end.astype(int) | |
| df['quarter']= df.date.dt.quarter | |
| df['is_quarter_start']= df.date.dt.is_quarter_start.astype(int) | |
| df['is_quarter_end']= df.date.dt.is_quarter_end.astype(int) | |
| df['is_year_start']= df.date.dt.is_year_start.astype(int) | |
| return df | |
| # Setting up variables for input data | |
| def setup(tmp_df_file): | |
| "Setup the required elements like files, models, global variables, etc" | |
| pd.DataFrame( | |
| dict( | |
| date=[], | |
| store_nbr=[], | |
| family=[], | |
| onpromotion=[], | |
| city=[], | |
| state=[], | |
| store_type=[], | |
| cluster=[], | |
| day_type=[], | |
| locale=[], | |
| locale_name=[], | |
| ) | |
| ).to_csv(tmp_df_file, index=False) | |
| # Setting up a file to save our input data | |
| tmp_df_file = os.path.join(DIRPATH, "tmp", "data.csv") | |
| setup(tmp_df_file) | |
| # setting Title for forms | |
| st.markdown("<h2 style='text-align: center;'> Sales Prediction </h2> ", unsafe_allow_html=True) | |
| st.markdown("<h7 style='text-align: center;'> Fill in the details below and click on SUBMIT button to make a prediction for a specific date and item </h7> ", unsafe_allow_html=True) | |
| # Creating columns for for input data(forms) | |
| left_col, mid_col, right_col = st.columns(3) | |
| # Developing forms to collect input data | |
| with st.form(key="information", clear_on_submit=True): | |
| # Setting up input data for 1st column | |
| left_col.markdown("**PRODUCT DATA**") | |
| date = left_col.date_input("Prediction Date:") | |
| family = left_col.selectbox("Item family:", options= list(train_data["family"].unique())) | |
| onpromotion = left_col.selectbox("Onpromotion code:", options= set(train_data["onpromotion"].unique())) | |
| store_nbr = left_col.selectbox("Store Number:", options= set(stores["store_nbr"].unique())) | |
| # Setting up input data for 2nd column | |
| mid_col.markdown("**STORE DATA**") | |
| city = mid_col.selectbox("City:", options= set(stores["city"].unique())) | |
| state = mid_col.selectbox("State:", options= list(stores["state"].unique())) | |
| cluster = mid_col.selectbox("Store Cluster:", options= list(stores["cluster"].unique())) | |
| store_type = mid_col.radio("Store Type:", options= set(stores["store_type"].unique()), horizontal = True) | |
| # Setting up input data for 3rd column | |
| right_col.markdown("**ADDITIONAL DATA**") | |
| check= right_col.checkbox("Is it a Holiday or weekend?") | |
| if check: | |
| right_col.write('Fill the following information on Day Type') | |
| day_type = right_col.selectbox("Holiday:", options= ('Holiday','Special Day:Transfered/Additional Holiday','No Work/Weekend')) | |
| locale= right_col.selectbox("Holiday Locale:", options= list(holidays_event["locale"].unique())) | |
| locale_name= right_col.selectbox("Locale Name:", options= list(holidays_event["locale_name"].unique())) | |
| else: | |
| day_type = 'Workday' | |
| locale = 'National' | |
| locale_name= 'Ecuador' | |
| submitted = st.form_submit_button(label="Submit") | |
| # Setting up background operations after submitting forms | |
| if submitted: | |
| # Saving input data as csv file after submission | |
| pd.read_csv(tmp_df_file).append( | |
| dict( | |
| date = date, | |
| store_nbr = store_nbr, | |
| family=family, | |
| onpromotion= onpromotion, | |
| city=city, | |
| state=state, | |
| store_type=store_type, | |
| cluster=cluster, | |
| day_type=day_type, | |
| locale=locale, | |
| locale_name=locale_name | |
| ), | |
| ignore_index=True, | |
| ).to_csv(tmp_df_file, index=False) | |
| st.balloons() | |
| # Converting input data to a dataframe for prediction | |
| df = pd.read_csv(tmp_df_file) | |
| df= df.copy() | |
| # Getting date Features | |
| processed_data= getDateFeatures(df, 'date') | |
| processed_data= processed_data.drop(columns=['date']) | |
| # Making predictions | |
| prediction = pipeline.predict(processed_data) | |
| df['Sales']= prediction | |
| # Displaying prediction results | |
| st.markdown('''---''') | |
| st.markdown("<h4 style='text-align: center;'> Prediction Results </h4> ", unsafe_allow_html=True) | |
| st.success(f"Predicted Sales: {prediction[-1]}") | |
| st.markdown('''---''') | |
| # Making expander to view all records | |
| expander = st.expander("See all records") | |
| with expander: | |
| df = pd.read_csv(tmp_df_file) | |
| df['Sales']= prediction | |
| st.dataframe(df) | |