Delete app.py

app.py

@@ -1,152 +0,0 @@
-# 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_y =  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_y, 
-            'cluster': cluster
-} 
-
- # Put the input dictionary in a dataset
-    input_data = pd.DataFrame(input_df, index = [0])
-
-
-
-# defining categories and numeric columns
-
-    col = ['city']
-    #columns = list(input_data.columns) 
-    input_encoded_df = pd.DataFrame(Encoder.transform(input_data).toarray(),
-                                    columns=Encoder.get_feature_names_out(col))
-
-    #encoded_cat = Encoder.transform(input_data[col])
-
-    # 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)
-    input_d = pd.concat([train_enc, input_encoded_df], axis=1)
-    prediction = input_d.values
-
-
-
-    # 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(prediction.flatten().reshape(1, -1))
-    
-
-    if st.button('Predict'):
-               st.success('The predicted target is ' + str(round(prediction[0],2)))
-  
-