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import sys
import os
import streamlit as st
import pandas as pd
import pickle
import datetime
from PIL import Image
# Add the root folder to the Python module search path
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
from src.utils import create_dataframe, process_data
# Set Streamlit page configuration
st.set_page_config(
page_title="CAPE TOWN ANALYTICS",
page_icon="📉",
initial_sidebar_state="expanded",
menu_items={
'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, "..", "assets", "ml_components", "ml_components_1.pkl")
ml_components_2 = os.path.join(DIRPATH, "..", "assets", "ml_components", "ml_components_2.pkl")
hist_df = os.path.join(DIRPATH, "..", "assets", "history.csv")
image_path = os.path.join(DIRPATH, "..", "assets", "images", "sales_images.jpg")
# check if csv file exits
def check_csv(csv_file, data):
if os.path.isfile(csv_file):
data.to_csv(csv_file, mode='a', header=False, encoding='utf-8', index=False)
else:
history = data.copy()
history.to_csv(csv_file, encoding='utf-8', index=False)
# Load pickle files
def load_pickle(filename):
with open(filename, 'rb') as file:
data = pickle.load(file)
return data
ml_compos_1 = load_pickle(ml_components_1)
ml_compos_2 = load_pickle(ml_components_2)
# Extract components from ml_compos_2
categorical_pipeline = ml_compos_2['categorical_pipeline']
numerical_pipeliine = ml_compos_2['numerical_pipeline']
model = ml_compos_2['model']
# Extract columns from ml_compos_1
num_cols = ml_compos_1['num_cols']
cat_cols = ml_compos_1['cat_cols']
hol_level_list = ml_compos_1['Holiday_level'].tolist()
hol_city_list = ml_compos_1['Holiday_city'].tolist()
# Remove 'Not Holiday' from lists
hol_city_list.remove('Not Holiday')
hol_level_list.remove('Not Holiday')
# Create a container for expanding content
my_expander = st.container()
holiday_level = 'Not Holiday'
hol_city = 'Not Holiday'
# st.sidebar.selectbox('Menu', ['About', 'Model'])
# Expandable container for displaying content
with my_expander:
image = Image.open(image_path)
st.image(image, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
st.markdown("""
<style>
h1 {
text-align: center;
}
</style>
""", unsafe_allow_html=True)
st.title('Demo Sales Forecasting :red[App]')
st.sidebar.markdown("""
## Demo App
This app predict sales from the parameters on the interface
""")
# 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'])
crude_price = col3.number_input('Price of Crude Oil', min_value=1.0, max_value=500.0, value=1.0)
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=False):
if day_type == 'Additional Holiday' or day_type == 'Holiday' or day_type=='Transferred holiday':
holiday_level = st.radio("level of Holiday?",
hol_level_list)#.tolist().remove('Not Holiday'))
hol_city = st.selectbox("In which city is the holiday?",
hol_city_list)#.tolist().remove('Not Holiday'))
else:
st.markdown('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')
raw_data = [date, store_number, item_family, item_onpromo, crude_price, holiday_level, hol_city, day_type, store_city, store_state, store_type, store_cluster]
data = create_dataframe(raw_data)
processed_data = process_data(data, categorical_pipeline, numerical_pipeliine, cat_cols, num_cols)
if button:
st.balloons()
st.metric('Predicted Sale', value=model.predict(processed_data))
# predictions = model.predict(process_data)
# csv_file = hist_df
check_csv(hist_df, data)
history = pd.read_csv(hist_df)
with st.expander('Download Input History'):
# new_history = history.iloc[1:]
st.dataframe(history)
st.download_button('Download Data',
history.to_csv(index=False),
file_name='input_history.csv')
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