import streamlit as st
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
from PIL import Image


def run():
    #membuat judul
    st.title('Credit Card prediction')
    #membuat sub header
    st.subheader('Tugas GC5')
    #menambahkan deskripsi
    st.write('Page ini dibuat oleh Andryan wijaya')
    #tambahkan gambar
    image = Image.open('credit.png')
    st.image(image, caption = 'credit card')

    #show dataframe
    st.write('# Data')
    data = pd.read_csv('P1G5_Set_1_Andryan_wijaya.csv')
    st.dataframe(data)

    
    st.write('# Boxplot Limit balance')
    fig = plt.figure(figsize=(15,5))
    sns.boxplot(x='limit_balance', data = data)
    st.pyplot(fig)
    st.write('Maximum balance:',data['limit_balance'].max())
    st.write('Minimum balance:',data['limit_balance'].min())
    st.write('Average balance:',data['limit_balance'].mean())

    st.write('# Bar plot sex')
    fig = plt.figure(figsize=(15,5))
    sns.barplot(y=data['sex'].value_counts(),x='sex', data = data,)
    st.pyplot(fig)
    st.write('Male: 1')
    st.write('Female: 2')

    st.write('# Bar plot Marital status')
    fig = plt.figure(figsize=(15,5))
    sns.barplot(y=data['marital_status'].value_counts(),x='marital_status', data = data,)
    st.pyplot(fig)
    st.write('Married: 1')
    st.write('Single: 2')
    st.write('Others: 3')

    st.write('# Boxplot Age')
    fig = plt.figure(figsize=(15,5))
    sns.boxplot(x='age', data = data)
    st.pyplot(fig)
    st.write('Maximum age:',data['age'].max())
    st.write('Minimum age:',data['age'].min())
    st.write('Average age:',data['age'].mean())

    st.write('# Countplot Pay')
    fig = plt.figure(figsize=(15,5))
    sns.countplot(data=data[['pay_0', 'pay_2', 'pay_3', 'pay_4', 'pay_5', 'pay_6']].melt(), x='value', hue='variable')
    plt.xlabel('Nilai Variabel Biner')
    plt.ylabel('Jumlah Data')
    plt.title('Perbandingan Kategori Variabel pay')
    st.pyplot(fig)

    st.write('# Countplot bill')
    fig = plt.figure(figsize=(15,5))
    sns.barplot(data=data[['bill_amt_1', 'bill_amt_2', 'bill_amt_3', 'bill_amt_4', 'bill_amt_5', 'bill_amt_6']].melt(), y='value', hue='variable')
    plt.ylabel('Jumlah Data')
    plt.title('Perbandingan Kategori Variabel bill')
    st.pyplot(fig)

    st.write('# Countplot bill')
    fig = plt.figure(figsize=(15,5))
    sns.barplot(data=data[['pay_amt_1', 'pay_amt_2', 'pay_amt_3', 'pay_amt_4', 'pay_amt_5', 'pay_amt_6']].melt(), y='value', hue='variable')
    plt.ylabel('Jumlah Data')
    plt.title('Perbandingan Kategori Variabel pay amt')
    st.pyplot(fig)

    st.write('# Bar plot default payment')
    fig = plt.figure(figsize=(15,5))
    sns.barplot(y=data['default_payment_next_month'].value_counts(),x='default_payment_next_month', data = data,)
    st.pyplot(fig)
    st.write('Yes: 1')
    st.write('No: 2')


if __name__ == '__main__':
    run()