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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Grocery.csv filter=lfs diff=lfs merge=lfs -text

Grocery.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c2026ebae35dd7285f1ac84c4dd08c760f672e68aee2aefa128bab8ee0aedad8
+size 162805137

app.py

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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"
+@st.cache_resource
+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"])))
+    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' : type, 
+            'cluster': cluster
+} 
+
+ # Put the input dictionary in a dataset
+    input_data = pd.DataFrame(input_df, index = [0])
+
+
+
+# defining categories and numeric columns
+
+    categoric_column = ['city']
+    columns = list(input_data.columns) 
+    encoded_cat = Encoder.transform(input_data[categoric_column])
+
+    # 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)))
+  
+  

requirements.txt

@@ -0,0 +1,4 @@
+streamlit
+pandas
+numpy
+xgboost