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

st.set_page_config(
    page_title = 'Customer Churn Predictor'
)

def run():

    # Membuat Title 
    st.title('Customer Churn Predictor')

    #Sub header 
    st.subheader('Description for Customer Churn Predicto') 

    # Insert Gambar 
    image = Image.open('music.jpg')
    st.image(image, caption ='Dengar')

    #description 
    st.write('The goals of this churn estimator')
    st.write('Dengar adalah sebuah platform streaming music yang ingin memprediksi customer yang akan churn sehingga meminta bantuan data scientist untuk membuat model dalam melakukan prediksi.')
    st.write('Dengan model ini diharapkan dengar dapat memprediksi customer churn atau tidak sehingga bisa lebih berfokus pada tujuannya.')
    st.markdown('---')
    
    st.write('This page is created to show the visualization of the dataset')

    st.markdown('---')




    #show dataframe
    st.write('Dataset') 
    dup = pd.read_csv('https://raw.githubusercontent.com/FerdiErs/SQL/main/churn.csv')
    st.dataframe(dup)

    #visualization Function 

    def plot_hist(data, title, x_label):
        #create hist plot
        fig = plt.figure(figsize=(7, 5))
        sns.histplot(data, kde=True, bins=20, edgecolor='black')

        #Title and Labels
        st.title(title)

        st.pyplot(fig)

    def plot_countplot_with_numbers(data, x, hue, title, palette, figsize=(7, 5)):
        # Create CountPlot
        fig = plt.figure(figsize=figsize)
        g = sns.countplot(x=x, hue=hue, data=data, palette=palette)

        # Rotate x labels and move legend outside of the plot
        g.set_xticklabels(g.get_xticklabels(), rotation=45, ha="right")
        sns.move_legend(g, "upper left", bbox_to_anchor=(1, 1))

        # Number in visualization
        for p in g.patches:
            height = p.get_height()
            g.annotate(f'{height}', (p.get_x() + p.get_width() / 2., height), ha='center', va='bottom', fontsize=10)

        # Title and labels
        st.title(title)

        st.pyplot(fig)

    #Age Distribution
    plot_hist(data=dup['age'], title='Age distribution', x_label='age')

    #Time Spent
    plot_hist(data=dup['avg_time_spent'], title='Time Spent', x_label='avg_time_spent')

    #pie chart customer region 
    st.write('### Customer Region Distribution')
    reg = dup.region_category.value_counts()
    def make_autopct(values):
        def my_autopct(pct):
            total = sum(values)
            val = int(round(pct*total/100.0))
            return '{p:.2f}%  ({v:d})'.format(p=pct,v=val)
        return my_autopct
    # Define a custom color palette
    colors = plt.cm.tab20c.colors
    fig = plt.figure(figsize=(5,5))
    reg.plot.pie(autopct=make_autopct(reg), startangle=90, colors=colors)
    plt.title('Customer Region')
    plt.axis('equal')
    st.pyplot(fig)

     #Memberhsip based on Region
    plot_countplot_with_numbers(x='membership_category',hue='region_category', title='Memberhsip based on Region', data=dup, palette='flare', figsize=(7, 5))

    #membuat pie chart churn risk
    #count churn
    ch = dup.churn_risk_score.value_counts()

    # Define a custom color palette
    colors = plt.cm.Set3.colors

    # plot the data
    fig = plt.figure(figsize=(5,5))
    ch.plot.pie(autopct=make_autopct(ch), startangle=90, colors=colors)
    plt.title('Churn Risk')
    plt.axis('equal')
    st.pyplot(fig)
    
    

    #churn risk based on gender
    plot_countplot_with_numbers(data=dup, x='gender', hue='churn_risk_score', title='Churn Risk based on gender', palette='crest', figsize=(7, 5))
    
    #churn risk based on membership
    plot_countplot_with_numbers(data=dup, x='membership_category', hue='churn_risk_score', title='Churn Risk based on Membership', palette='flare', figsize=(7, 5))

   


if __name__== '__main__':
    run()