app.py

import os
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

# Load the model
filename = 'gb_model.sav'
loaded_model = pickle.load(open(filename, 'rb'))

# Construct the path to the CSV file dynamically
current_dir = os.path.dirname(__file__)
file_path = os.path.join(current_dir, 'Clustered_Customer_Data.csv')

# Load the CSV file
try:
    df = pd.read_csv(file_path)
except FileNotFoundError:
    st.error(f"File not found: {file_path}")
    st.stop()

# Set Streamlit options
st.set_option('deprecation.showPyplotGlobalUse', False)

# CSS for background color
st.markdown(
    """
    <style>
    .stApp {
        background-color: lightblue !important;
    }
    </style>
    """,
    unsafe_allow_html=True
)

st.title("Market Segment 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 Frequency', 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]
    st.write(f'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()
        sns.histplot(cluster_df1, x=c, ax=ax)
        st.pyplot(fig)