Hugging Face's logo

Spaces:
rasmodev
/
Sales_Prediction_App
Running

App Files Community
Sales_Prediction_App
File size: 6,592 Bytes 
6ae89f6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
 
import pandas as pd
import streamlit as st
import numpy as np
import pickle

# Load the saved components:
with open("rf_model.pkl", "rb") as f:
    components = pickle.load(f)

# Extract the individual components
num_imputer = components["num_imputer"]
cat_imputer = components["cat_imputer"]
encoder = components["encoder"]
scaler = components["scaler"]
dt_model = components["models"]

st.image("https://pbs.twimg.com/media/DywhyJiXgAIUZej?format=jpg&name=medium")
st.title("Sales Prediction App")

st.caption("This app predicts sales patterns of Corporation Favorita over time in different stores in Ecuador based on the inputs.")

# Sidebar with input field descriptions
st.sidebar.header("Description of The Required Input Fields")
st.sidebar.markdown("**Store Number**: The number of the store.")
st.sidebar.markdown("**Product Family**: Product Family such as 'AUTOMOTIVE', 'BEAUTY', etc. "
                    "Details:\n"
                    " - **AUTOMOTIVE**: Products related to the automotive industry.\n"
                    " - **BEAUTY**: Beauty and personal care products.\n"
                    " - **CELEBRATION**: Products for celebrations and special occasions.\n"
                    " - **CLEANING**: Cleaning and household maintenance products.\n"
                    " - **CLOTHING**: Clothing and apparel items.\n"
                    " - **FOODS**: Food items and groceries.\n"
                    " - **GROCERY**: Grocery products.\n"
                    " - **HARDWARE**: Hardware and tools.\n"
                    " - **HOME**: Home improvement and decor products.\n"
                    " - **LADIESWEAR**: Women's clothing.\n"
                    " - **LAWN AND GARDEN**: Lawn and garden products.\n"
                    " - **LIQUOR,WINE,BEER**: Alcoholic beverages.\n"
                    " - **PET SUPPLIES**: Products for pets and animals.\n"
                    " - **STATIONERY**: Stationery and office supplies.")
st.sidebar.markdown("**Number of Items on Promotion**: Number of items on promotion within a particular shop.")
st.sidebar.markdown("**City**: City where the store is located.")
st.sidebar.markdown("**Cluster**: Cluster number which is a grouping of similar stores.")
st.sidebar.markdown("**Transactions**: Number of transactions.")
st.sidebar.markdown("**Crude Oil Price**: Daily Crude Oil Price.")


 # Create the input fields
input_data = {}
col1,col2,col3 = st.columns(3)
with col1:
    input_data['store_nbr'] = st.slider("Store Number",0,54)
    input_data['family'] = st.selectbox("Product Family", ['AUTOMOTIVE', 'BEAUTY', 'CELEBRATION', 'CLEANING', 'CLOTHING', 'FOODS', 
                                            'GROCERY', 'HARDWARE', 'HOME', 'LADIESWEAR', 'LAWN AND GARDEN', 'LIQUOR,WINE,BEER', 
                                            'PET SUPPLIES', 'STATIONERY'])
    input_data['onpromotion'] =st.number_input("Number of Items on Promotion",step=1)
    input_data['state'] = st.selectbox("State Where The Store Is Located", ['Pichincha', 'Cotopaxi', 'Chimborazo', 'Imbabura',
       'Santo Domingo de los Tsachilas', 'Bolivar', 'Pastaza', 'Tungurahua', 'Guayas', 'Santa Elena', 'Los Rios', 'Azuay', 'Loja',
       'El Oro', 'Esmeraldas', 'Manabi'])
    input_data['transactions'] = st.number_input("Number of Transactions", step=1)

with col2:    
    input_data['store_type'] = st.selectbox("Store Type",['A', 'B', 'C', 'D', 'E'])
    input_data['cluster'] = st.selectbox("Cluster", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17])
    input_data['dcoilwtico'] = st.number_input("Crude Oil Price",step=1)
    input_data['year'] = st.number_input("Year",step=1)
with col3:    
    input_data['month'] = st.slider("Month",1,12)
    input_data['day'] = st.slider("Day",1,31)
    input_data['dayofweek'] = st.number_input("Day of Week (0=Sunday and 6=Satruday)",step=1)


  # Create a button to make a prediction
if st.button("Predict"):

    # Convert the input data to a pandas DataFrame
    input_df = pd.DataFrame([input_data])

    # Product Categorization Based on Families
    food_families = ['BEVERAGES', 'BREAD/BAKERY', 'FROZEN FOODS', 'MEATS', 'PREPARED FOODS', 'DELI', 'PRODUCE', 'DAIRY', 'POULTRY', 'EGGS', 'SEAFOOD']
    home_families = ['HOME AND KITCHEN I', 'HOME AND KITCHEN II', 'HOME APPLIANCES']
    clothing_families = ['LINGERIE', 'LADYSWARE']
    grocery_families = ['GROCERY I', 'GROCERY II']
    stationery_families = ['BOOKS', 'MAGAZINES', 'SCHOOL AND OFFICE SUPPLIES']
    cleaning_families = ['HOME CARE', 'BABY CARE', 'PERSONAL CARE']
    hardware_families = ['PLAYERS AND ELECTRONICS', 'HARDWARE']

    # Apply the same preprocessing steps as done during training
    input_df['family'] = np.where(input_df['family'].isin(food_families), 'FOODS', input_df['family'])
    input_df['family'] = np.where(input_df['family'].isin(home_families), 'HOME', input_df['family'])
    input_df['family'] = np.where(input_df['family'].isin(clothing_families), 'CLOTHING', input_df['family'])
    input_df['family'] = np.where(input_df['family'].isin(grocery_families), 'GROCERY', input_df['family'])
    input_df['family'] = np.where(input_df['family'].isin(stationery_families), 'STATIONERY', input_df['family'])
    input_df['family'] = np.where(input_df['family'].isin(cleaning_families), 'CLEANING', input_df['family'])
    input_df['family'] = np.where(input_df['family'].isin(hardware_families), 'HARDWARE', input_df['family'])

    categorical_columns = ['family', 'store_type', 'state']
    numerical_columns = ['transactions', 'dcoilwtico']

    # Impute missing values
    input_df_cat = input_df[categorical_columns].copy()
    input_df_num = input_df[numerical_columns].copy()
    input_df_cat_imputed = cat_imputer.transform(input_df_cat)
    input_df_num_imputed = num_imputer.transform(input_df_num)

    # Encode categorical features
    input_df_cat_encoded = pd.DataFrame(encoder.transform(input_df_cat_imputed).toarray(),
                                        columns=encoder.get_feature_names_out(categorical_columns))

    # Scale numerical features
    input_df_num_scaled = scaler.transform(input_df_num_imputed)
    input_df_num_sc = pd.DataFrame(input_df_num_scaled, columns=numerical_columns)

    # Combine encoded categorical features and scaled numerical features
    input_df_processed = pd.concat([input_df_num_sc, input_df_cat_encoded], axis=1)

    # Make predictions using the trained model
    predictions = dt_model.predict(input_df_processed)

    # Display the predicted sales value to the user:
    st.write("The predicted sales are:", predictions[0])