app.py

import streamlit as st
from PIL import Image
import pandas as pd
import numpy as np
import pickle
import datetime
# import os
# import sys
# from utils import payday, date_extracts
# sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))

from utils import payday, date_extracts


st.set_page_config(
    page_title="Ex-stream-ly Cool App",
    page_icon="🧊",
    initial_sidebar_state="expanded",
    menu_items={
        'Get Help': 'https://www.extremelycoolapp.com/help',
        'Report a bug': "https://www.extremelycoolapp.com/bug",
        'About': "# This is a header. This is an *extremely* cool app!"
    }
)

# # Define directory paths
# DIRPATH = os.path.dirname(os.path.realpath(__file__))
# ml_components_1 = os.path.join(DIRPATH, "..", "src", "assets", "ml_components", "ml_components_1.pkl")
# ml_components_2 = os.path.join(DIRPATH, "..", "src", "assets", "ml_components", "ml_components_2.pkl")
# image_path = os.path.join(DIRPATH, "..", "src", "assets", "images", "sales.png")


# create a functions to load pickle file.
def load_pickle(filename):
    with open(filename, 'rb') as file:
        data = pickle.load(file)
        return data


#load all pickle files
ml_compos_1 = load_pickle('ml_components_1.pkl')
ml_compos_2 = load_pickle('ml_components_2.pkl')

# components in ml_compos_2  
categorical_pipeline = ml_compos_2['categorical_pipeline']
numerical_pipeliine = ml_compos_2['numerical_pipeline']
model = ml_compos_2['model']

num_cols = ml_compos_1['num_cols']
cat_cols = ml_compos_1['cat_cols'] 
     

# the title for the app
st.title('✨SALES FORECASTING APP✨')

# adding image
image=Image.open('sales.png')
st.image(image, width=600)

     
st.subheader("Hi there! 👋 Let's start predicting sales 🙂")

    


# create an  expander to contain the app
my_expander = st.container()


holiday_level = 'No Holiday'
hol_city = 'No Holiday'
st.sidebar.selectbox('Menu', ['About', 'Model'])
with my_expander:
# create a three column layout
    col1, col2, col3 = st.columns(3)

# create a date input to receive date
    date = col1.date_input(
            "Enter the Date",
            datetime.date(2019, 7, 6))
        
# create a select box to select a family
item_family = col2.selectbox('What is the category of item?',
                                    ml_compos_1['family'])

# create a select box for store city
store_city = col3.selectbox("Which city is the store located?",
                                    ml_compos_1['Store_city'])

store_state = col1.selectbox("What state is the store located?",
                                    ml_compos_1['Store_state'])
        # hol_city = col2.selectbox("In which city is the holiday?",
        #                           ml_compos_1['Holiday_city'])

crude_price = col3.number_input('Price of Crude Oil', min_value=0.0, max_value=500.0, value=0.01)

day_type = col2.selectbox("Type of Day?",
                                ml_compos_1['Type_of_day'], index=2)
        # holiday_level = col3.radio("level of Holiday?",
        #                            ml_compos_1['Holiday_level'])
colZ, colY = st.columns(2)
store_type = colZ.radio("Type of store?",
                                ml_compos_1['Store_type'][::-1])
st.write('<style>div.row-widget.stRadio > div{flex-direction:row;}</style>', unsafe_allow_html=True)

holi = colY.empty()
with holi.expander(label='Holiday', expanded=True):
        if day_type == 'Additional Holiday' or day_type == 'Holiday' or day_type=='Transferred holiday':
        
                holiday_level = st.radio("level of Holiday?",
                                    ml_compos_1['Holiday_level'])#.tolist().remove('Not Holiday'))
                hol_city = st.selectbox("In which city is the holiday?",
                                    ml_compos_1['Holiday_city'])#.tolist().remove('Not Holiday'))
        else:
                st.markdown('Not Holiday')
                holiday_level = 'Not Holiday'
                hol_city = 'Not Holiday'


        colA, colB, colC = st.columns(3)

        store_number = colA.slider("Select the Store number ",
                                min_value=1,
                                max_value=54,
                                value=1)
        store_cluster = colB.slider("Select the Store Cluster ",
                                    min_value=1,
                                    max_value=17,
                                    value=1)
        item_onpromo = colC.slider("Number of items onpromo ",
                                min_value=0,
                                max_value=800,
                                value=1)
        button = st.button(label='Predict', use_container_width=True, type='primary')
        




X = np.array([[date, store_number, item_family, item_onpromo, crude_price, holiday_level, hol_city, day_type,
                    store_city, store_state, store_type, store_cluster]])
df = pd.DataFrame(X, columns=['date', 'Store_number', 'Family', 'Item_onpromo', 'Oil_prices', 'Holiday_level', 'Holiday_city',
                                    'TypeOfDay', 'Store_city', 'Store_state', 'Store_type', 'Cluster'])

df_raw = df.copy()

df[['Store_number', 'Item_onpromo', 'Cluster']] = df[['Store_number', 'Item_onpromo', 'Cluster']].apply(lambda x: x.astype(int))
df['date'] = pd.to_datetime(df['date'])
df = df.set_index('date')

date_extracts(df)
df['Is_payday']= df[['DayOfMonth', 'Is_month_end']].apply(payday, axis=1)

    



if button:
        st.balloons()
        df[cat_cols] = categorical_pipeline.transform(df[cat_cols])
        df[num_cols] = numerical_pipeliine.transform(df[num_cols])
        # predicted_sale = model.predict(df)
        st.metric('Predicted Sale', value=model.predict(df))

        st.write(df_raw)
        
        st.download_button('Download Data', 
                    df.to_csv(index=False), 
                    file_name='data.csv')

        print(df.shape)