app.py

from sklearn import preprocessing 
import streamlit as st
import pandas as pd
import plotly.figure_factory as ff
import matplotlib.pyplot as plt
import seaborn as sns
import pickle

filename = 'final_model.sav'
loaded_model = pickle.load(open(filename, 'rb'))
df = pd.read_csv("Clustered_Customer_Data.csv")
st.set_option('deprecation.showPyplotGlobalUse', False)

st.markdown('<style>body{background-color: Blue;}</style>',unsafe_allow_html=True)
st.title("Prediction")

with st.form("my_form"):
    balance=st.number_input(label='Balance',step=0.001,format="%.6f")
    balance_frequency=st.number_input(label='Balance Frequency',step=0.001,format="%.6f")
    purchases=st.number_input(label='Purchases',step=0.01,format="%.2f")
    oneoff_purchases=st.number_input(label='OneOff_Purchases',step=0.01,format="%.2f")
    installments_purchases=st.number_input(label='Installments Purchases',step=0.01,format="%.2f")
    cash_advance=st.number_input(label='Cash Advance',step=0.01,format="%.6f")
    purchases_frequency=st.number_input(label='Purchases Frequency',step=0.01,format="%.6f")
    oneoff_purchases_frequency=st.number_input(label='OneOff Purchases Frequency',step=0.1,format="%.6f")
    purchases_installment_frequency=st.number_input(label='Purchases Installments Freqency',step=0.1,format="%.6f")
    cash_advance_frequency=st.number_input(label='Cash Advance Frequency',step=0.1,format="%.6f")
    cash_advance_trx=st.number_input(label='Cash Advance Trx',step=1)
    purchases_trx=st.number_input(label='Purchases TRX',step=1)
    credit_limit=st.number_input(label='Credit Limit',step=0.1,format="%.1f")
    payments=st.number_input(label='Payments',step=0.01,format="%.6f")
    minimum_payments=st.number_input(label='Minimum Payments',step=0.01,format="%.6f")
    prc_full_payment=st.number_input(label='PRC Full Payment',step=0.01,format="%.6f")
    tenure=st.number_input(label='Tenure',step=1)

    data=[[balance,balance_frequency,purchases,oneoff_purchases,installments_purchases,cash_advance,purchases_frequency,oneoff_purchases_frequency,purchases_installment_frequency,cash_advance_frequency,cash_advance_trx,purchases_trx,credit_limit,payments,minimum_payments,prc_full_payment,tenure]]

    submitted = st.form_submit_button("Submit")

if submitted:
    clust=loaded_model.predict(data)[0]
    print('Data Belongs to Cluster',clust)

    cluster_df1=df[df['Cluster']==clust]
    plt.rcParams["figure.figsize"] = (20,3)
    for c in cluster_df1.drop(['Cluster'],axis=1):
        fig, ax = plt.subplots()
        grid= sns.FacetGrid(cluster_df1, col='Cluster')
        grid= grid.map(plt.hist, c)
        plt.show()
        st.pyplot(figsize=(5, 5))