Create app.py

app.py

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+# Importing required Libraries
+import streamlit as st
+import pandas as pd
+import numpy as np
+import os, pickle
+from sklearn.tree import DecisionTreeRegressor
+from sklearn import preprocessing
+
+# Setting up page configuration and directory path
+st.set_page_config(page_title="Sales Forecasting App", page_icon="🐞", layout="centered")
+DIRPATH = os.path.dirname(os.path.realpath(__file__))
+
+# Setting background image
+import base64
+def add_bg_from_local(image_file):
+    with open(image_file, "rb") as image_file:
+        encoded_string = base64.b64encode(image_file.read())
+    st.markdown(
+    f"""
+    <style>
+    .stApp {{
+        background-image: url(data:image/{"jpg"};base64,{encoded_string.decode()});
+        background-size: cover
+    }}
+    </style>
+    """,
+    unsafe_allow_html=True
+    )
+add_bg_from_local('background.jpg')
+
+# Setting up logo
+left1, left2, mid,right1, right2 = st.columns(5)
+with mid:
+    st.image("logo.jpg", use_column_width=True)
+
+# Setting up Sidebar
+social_acc = ['Data Field Description', 'EDA', 'About App']
+social_acc_nav = st.sidebar.radio('**INFORMATION SECTION**', social_acc)
+
+if social_acc_nav == 'Data Field Description':
+    st.sidebar.markdown("<h2 style='text-align: center;'> Data Field Description </h2> ", unsafe_allow_html=True)
+    st.sidebar.markdown("**Date:** The date you want to predict sales  for")
+    st.sidebar.markdown("**Family:** identifies the type of product sold")
+    st.sidebar.markdown("**Onpromotion:** gives the total number of items in a product family that are being promoted at a store at a given date")
+    st.sidebar.markdown("**Store Number:** identifies the store at which the products are sold")
+    st.sidebar.markdown("**Holiday Locale:** provide information about the locale where holiday is celebrated")
+
+elif social_acc_nav == 'EDA':
+    st.sidebar.markdown("<h2 style='text-align: center;'> Exploratory Data Analysis </h2> ", unsafe_allow_html=True)
+    st.sidebar.markdown('''---''')
+    st.sidebar.markdown('''The exploratory data analysis of this project can be find in a Jupyter notebook from the linl below''')
+    st.sidebar.markdown("[Open Notebook](https://github.com/Kyei-frank/Regression-Project-Store-Sales--Time-Series-Forecasting/blob/main/project_workflow.ipynb)")
+
+elif social_acc_nav == 'About App':
+    st.sidebar.markdown("<h2 style='text-align: center;'> Sales Forecasting App </h2> ", unsafe_allow_html=True)
+    st.sidebar.markdown('''---''')
+    st.sidebar.markdown("This App predicts the sales for product families sold at Favorita stores using regression model.")
+    st.sidebar.markdown("")
+    st.sidebar.markdown("[ Visit Github Repository for more information](https://github.com/Kyei-frank/Regression-Project-Store-Sales--Time-Series-Forecasting)")
+
+# Loading Machine Learning Objects
+@st.cache()
+def load_saved_objects(file_path = 'ML_items'):
+    # Function to load saved objects
+    with open('ML_items', 'rb') as file:
+        loaded_object = pickle.load(file)
+        
+    return loaded_object
+
+# Instantiating ML_items
+Loaded_object = load_saved_objects(file_path = 'ML_items')
+model, encoder, train_data, stores, holidays_event = Loaded_object['model'], Loaded_object['encoder'], Loaded_object['train_data'], Loaded_object['stores'], Loaded_object['holidays_event']
+
+# Setting Function for extracting Calendar features
+@st.cache()
+def getDateFeatures(df, date):
+    df['date'] = pd.to_datetime(df['date'])
+    df['month'] = df.date.dt.month
+    df['day_of_month'] = df.date.dt.day
+    df['day_of_year'] = df.date.dt.dayofyear
+    df['week_of_year'] = df.date.dt.isocalendar().week
+    df['day_of_week'] = df.date.dt.dayofweek
+    df['year'] = df.date.dt.year
+    df['is_weekend']= np.where(df['day_of_week'] > 4, 1, 0)
+    df['is_month_start']= df.date.dt.is_month_start.astype(int)
+    df['is_month_end']= df.date.dt.is_month_end.astype(int)
+    df['quarter']= df.date.dt.quarter
+    df['is_quarter_start']= df.date.dt.is_quarter_start.astype(int)
+    df['is_quarter_end']= df.date.dt.is_quarter_end.astype(int)
+    df['is_year_start']= df.date.dt.is_year_start.astype(int)
+    
+    return df
+
+# Setting up variables for input data
+@st.cache()
+def setup(tmp_df_file):
+    "Setup the required elements like files, models, global variables, etc"
+    pd.DataFrame(
+        dict(
+            date=[],
+            store_nbr=[],
+            family=[],
+            onpromotion=[],
+            city=[],
+            state=[],
+            store_type=[],
+            cluster=[],
+            day_type=[],
+            locale=[],
+            locale_name=[],
+        )
+    ).to_csv(tmp_df_file, index=False)
+
+# Setting up a file to save our input data
+tmp_df_file = os.path.join(DIRPATH, "tmp", "data.csv")
+setup(tmp_df_file)
+
+# setting Title for forms
+st.markdown("<h2 style='text-align: center;'> Sales Prediction </h2> ", unsafe_allow_html=True)
+st.markdown("<h7 style='text-align: center;'> Fill in the details below and click on SUBMIT button to make a prediction for a specific date and item </h7> ", unsafe_allow_html=True)
+
+# Creating columns for for input data(forms)
+left_col, mid_col, right_col = st.columns(3)
+
+# Developing forms to collect input data
+with st.form(key="information", clear_on_submit=True):
+    
+    # Setting up input data for 1st column
+    left_col.markdown("**PRODUCT DATA**")
+    date = left_col.date_input("Prediction Date:")
+    family = left_col.selectbox("Item family:", options= list(train_data["family"].unique()))
+    onpromotion = left_col.selectbox("Onpromotion code:", options= set(train_data["onpromotion"].unique()))
+    store_nbr = left_col.selectbox("Store Number:", options= set(stores["store_nbr"].unique()))
+    
+    # Setting up input data for 2nd column
+    mid_col.markdown("**STORE DATA**")
+    city = mid_col.selectbox("City:", options= set(stores["city"].unique()))
+    state = mid_col.selectbox("State:", options= list(stores["state"].unique()))
+    cluster = mid_col.selectbox("Store Cluster:", options= list(stores["cluster"].unique()))
+    store_type = mid_col.radio("Store Type:", options= set(stores["store_type"].unique()), horizontal = True)
+
+    # Setting up input data for 3rd column
+    right_col.markdown("**ADDITIONAL DATA**")
+    check= right_col.checkbox("Is it a Holiday or weekend?")
+    if check:
+        right_col.write('Fill the following information on Day Type')
+        day_type = right_col.selectbox("Holiday:", options= ('Holiday','Special Day:Transfered/Additional Holiday','No Work/Weekend'))
+        locale= right_col.selectbox("Holiday Locale:", options= list(holidays_event["locale"].unique()))
+        locale_name= right_col.selectbox("Locale Name:", options= list(holidays_event["locale_name"].unique()))
+    else:
+        day_type = 'Workday'
+        locale = 'National'
+        locale_name= 'Ecuador'
+ 
+    submitted = st.form_submit_button(label="Submit")
+
+# Setting up background operations after submitting forms
+if submitted:
+    # Saving input data as csv after submission
+    pd.read_csv(tmp_df_file).append(
+        dict(
+                date = date,
+                store_nbr = store_nbr,
+                family=family,
+                onpromotion= onpromotion,
+                city=city,
+                state=state,
+                store_type=store_type,
+                cluster=cluster,
+                day_type=day_type,
+                locale=locale,
+                locale_name=locale_name
+            ),
+            ignore_index=True,
+    ).to_csv(tmp_df_file, index=False)
+    st.balloons()
+
+    # Converting input data to a dataframe for prediction
+    df = pd.read_csv(tmp_df_file)
+    df= df.copy()
+        
+    # Getting date Features
+    processed_data= getDateFeatures(df, 'date')
+    processed_data= processed_data.drop(columns=['date'])
+    
+    # Encoding Categorical Variables
+    encoder = preprocessing.LabelEncoder()
+    cols = ['family', 'city', 'state', 'store_type', 'locale', 'locale_name', 'day_type']
+    for col in cols:
+        processed_data[col] = encoder.fit_transform(processed_data[col])
+    
+    # Making Predictions
+    def predict(X, model):
+        results = model.predict(X)
+        return results
+    
+    prediction = predict(X= processed_data, model= Loaded_object['model'])
+    df['Sales']= prediction 
+    
+    
+    # Displaying prediction results
+    st.markdown('''---''')
+    st.markdown("<h4 style='text-align: center;'> Prediction Results </h4> ", unsafe_allow_html=True)
+    st.success(f"Predicted Sales: {prediction[-1]}")
+    st.markdown('''---''')
+
+    # Making expander to view all records
+    expander = st.expander("See all records")
+    with expander:
+        df = pd.read_csv(tmp_df_file)
+        df['Sales']= prediction
+        st.dataframe(df)