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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)
ml_compos_2 = load_pickle(ml_components_2)
# 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(image_path)
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)
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