Upload app.py

app.py

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