Added app files

src/app/.streamlit/config.toml

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+[theme]
+primaryColor = "#1E8449"
+backgroundColor = "#F9E79F"
+secondaryBackgroundColor = "#F5B041"
+textColor = "#17202A"
+font = "sans serif"

src/app/app.py

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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", "justin-lim-JKjBsuKpatU-unsplash.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(csv_file, data)
+    history = pd.read_csv(csv_file)
+    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')
+