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| # Loading key libraries | |
| import streamlit as st | |
| import os | |
| import pickle | |
| import numpy as np | |
| import pandas as pd | |
| import re | |
| from pathlib import Path | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| # Setting the page configurations | |
| st.set_page_config(page_title= "Prediction Forecasting", layout= "wide", initial_sidebar_state= "auto") | |
| # Setting the page title | |
| st.title("Grocery Store Forecasting Prediction") | |
| # Load the saved data | |
| df = pd.read_csv('Grocery.csv') | |
| toolkit = "toolkit_folder" | |
| def load_toolkit(filepath = toolkit): | |
| with open(toolkit, "rb") as file: | |
| loaded_toolkit = pickle.load(file) | |
| return loaded_toolkit | |
| toolkit = load_toolkit() | |
| Encoder = toolkit["OneHotEncoder"] | |
| model = toolkit["model"] | |
| # main sections of the app | |
| menu = st.sidebar.radio('menu',['Home view','Prediction target']) | |
| if menu == 'Home view': | |
| st.write('Grocery Store Time Series Forecasting') | |
| st.image('images1.jpg',width = 450) | |
| st.write('Graphical representation and Data Overview') | |
| if st.checkbox('Data Set '): | |
| st.table(df.head(15)) | |
| st.title('Charts') | |
| graph = st.selectbox('Varieties of graphs',['scatter plot','Bar chat','Histogram']) | |
| if graph == 'scatter plot': | |
| fig,ax = plt.subplots(figsize=(10,5)) | |
| sns.scatterplot(y = 'target',x = 'onpromotion',data = df.iloc[:1000],palette = 'bright',hue = 'city'); | |
| st.pyplot(fig) | |
| if graph == 'Bar chat': | |
| fig,ax = plt.subplots(figsize=(10,5)) | |
| t = df.groupby("city")["target"].sum().reset_index().sort_values(by="target",ascending=False).iloc[:10] | |
| sns.barplot(data=t[:20] , y="target", x="city", palette='Blues_d') | |
| st.pyplot(fig) | |
| if graph == 'Histogram': | |
| fig,ax = plt.subplots(figsize=(10,5)) | |
| st.write('Target Categories') | |
| sns.distplot(df.target.iloc[:20], kde=True) | |
| st.pyplot(fig) | |
| if menu == 'Prediction target': | |
| st.image('image 2.jpg', width = 460) | |
| st.sidebar.markdown('User Input Details and Information') | |
| store_id= st.sidebar.selectbox('store_id', options = sorted(list(df['store_id'].unique()))) | |
| category_id= st.sidebar.selectbox('categegory_id',options = sorted(list(df['category_id'].unique()))) | |
| onpromotion= st.sidebar.number_input('onpromotion', min_value= df["onpromotion"].min(), value= df["onpromotion"].min()) | |
| year = st.sidebar.selectbox('year', options = sorted(list(df['year'].unique()))) | |
| month = st.sidebar.selectbox('month', options = sorted(list(df['month'].unique()))) | |
| dayofmonth= st.sidebar.number_input('dayofmonth', min_value= df["dayofmonth"].min(), value= df["dayofmonth"].min()) | |
| dayofweek = st.sidebar.number_input('dayofweek', min_value= df["dayofweek"].min(), value= df["dayofweek"].min()) | |
| dayofyear = st.sidebar.number_input('dayofyear', min_value= df["dayofyear"].min(), value= df["dayofyear"].min()) | |
| weekofyear = st.sidebar.number_input('weekofyear', min_value= df["weekofyear"].min(), value= df["weekofyear"].min()) | |
| quarter = st.sidebar.number_input('quarter', min_value= df["quarter"].min(), value= df["quarter"].min()) | |
| is_month_start = st.sidebar.number_input('is_month_start', min_value= df["is_month_start"].min(), value= df["is_month_start"].min()) | |
| is_month_end = st.sidebar.number_input('is_month_end', min_value= df["is_month_end"].min(), value= df["is_month_end"].min()) | |
| is_quarter_start = st.sidebar.number_input('is_quarter_start', min_value= df["is_quarter_start"].min(), value= df["is_quarter_start"].min()) | |
| is_quarter_end = st.sidebar.number_input('is_quarter_end', min_value= df["is_quarter_end"].min(), value= df["is_quarter_end"].min()) | |
| is_year_start = st.sidebar.number_input('is_year_start', min_value= df["is_year_start"].min(), value= df["is_year_start"].min()) | |
| is_year_end = st.sidebar.number_input('is_year_end', min_value= df["is_year_end"].min(), value= df["is_year_end"].min()) | |
| year_weekofyear = st.sidebar.number_input('year_weekofyear', min_value= df["year_weekofyear"].min(), value= df["year_weekofyear"].min()) | |
| city = st.sidebar.selectbox("city:", options= sorted(set(df["city"]))) | |
| store_type= st.sidebar.number_input('type', min_value= df["type"].min(), value= df["type"].min()) | |
| cluster = st.sidebar.selectbox('cluster', options = sorted(list(df['cluster'].unique()))) | |
| input_df = { | |
| 'store_id':[store_id], | |
| 'category_id':[category_id], | |
| 'onpromotion' :[onpromotion], | |
| 'year' : [year], | |
| 'month' :[month], | |
| 'dayofmonth' :[dayofmonth], | |
| 'dayofweek' : [dayofweek], | |
| 'dayofyear' : [dayofyear], | |
| 'weekofyear' : weekofyear, | |
| 'quarter' : [quarter], | |
| 'is_month_start' : [is_month_start], | |
| 'is_month_end' : [is_month_start], | |
| 'is_quarter_start' : [is_quarter_start], | |
| 'is_quarter_end' : [is_quarter_end], | |
| 'is_year_start' : [is_year_start], | |
| 'is_year_end' : [is_year_end], | |
| 'year_weekofyear' : [year_weekofyear], | |
| 'city' : [city], | |
| 'type' : [store_type], | |
| 'cluster': [cluster] | |
| } | |
| # Put the input dictionary in a dataset | |
| input_data = pd.DataFrame(input_df) | |
| # defining categories and numeric columns | |
| col = ['city'] | |
| columns = list(input_data.columns) | |
| encoded_cat = Encoder.transform(input_data[col]) | |
| encoded_cols = Encoder.get_feature_names() | |
| encoded_cat_ = pd.DataFrame(encoded_cat, columns=encoded_cols) | |
| # we dropped the categorical encoder column before we concat | |
| train_enc = input_data.drop(['city'],axis = 1) | |
| input_d = pd.concat([train_enc, encoded_cat_], axis=1) | |
| # convert input_data to a numpy array before flattening to convert it back to a 2D array | |
| input_df= input_d.to_numpy() | |
| prediction = model.predict(input_df.flatten().reshape(1, -1)) | |
| if st.button('Predict'): | |
| st.success('The predicted target is ' + str(round(prediction[0],2))) | |