Delete src

src/app.py

@@ -1,172 +0,0 @@
-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)
-