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='EDA', 
    layout='wide',
    initial_sidebar_state='expanded'
)

def run():
# title
    st.title('Predicting Patient Survival')

    # sub header
    st.subheader('Understanding Mortality Risk Factors and Predictive Modeling')

    # add pic
    image = Image.open('heart.png')
    st.image(image, caption='Health Parameter')
    # add text
    '''
    Page ini dibuat untuk memprediksi **kematian pasien** dengan menggunakan
    dataset yang tersedia. Dengan melakukan explorasi, akan ditinjau beberapa 
    insight yang dapat menjadi faktor-faktor / variabel yang membuat seorang 
    pasien dapat diprediksi meninggal atau tidak.
    '''
    markdown_text = '''
    ## Variable Descriptions
    | Variable                | Description                                                                                   |
    |-------------------------|-----------------------------------------------------------------------------------------------|
    | age                     | Age of the patient in years.                                                                 |
    | anaemia                 | Whether the patient has anemia or not. (1=True, 0=False)                                      |
    | creatinine_phosphokinase| Level of the enzyme creatinine phosphokinase in the patient's blood in units (mcg/L).         |
    | diabetes                | Whether the patient has diabetes or not. (1=True, 0=False)                                    |
    | ejection_fraction       | Percentage of blood volume ejected from the heart during each contraction.                    |
    | high_blood_pressure     | Whether the patient has high blood pressure or not. (1=True, 0=False)                         |
    | platelets               | Level of platelets in the patient's blood in units (kiloplatelets/mL).                         |
    | serum_creatinine        | Level of creatinine in the patient's blood in units (mg/dL).                                  |
    | serum_sodium            | Level of sodium in the patient's blood in units (mEq/L).                                      |
    | sex                     | Gender of the patient. (0=Male, 1=Female)                                                     |
    | smoking                 | Whether the patient smokes or not. (1=True, 0=False)                                          |
    | time                    | Follow-up period in days.                                                                     |
    | DEATH_EVENT             | Indicator of whether the patient experienced a death event or not. (1=True, 0=False)           |
    '''

    st.markdown(markdown_text)
    st.markdown('---')


    st.subheader('Explorasi Data')
    st.markdown('---')

    st.write('### Tabel Data Pasien')
    # show dataframe
    data = pd.read_csv('h8dsft_P1G3_Andikaa_Atmanegara_Putra.csv')
    st.dataframe(data)

    st.markdown('---')

    # visual barplot
    st.write('### Histogram Berdasarkan User Input ')
    choice = st.selectbox('Pilih Columns: ', ('age', 'anaemia', 
                                            'sex', 'DEATH_EVENT',
                                            'creatinine_phosphokinase',
                                            'platelets'))

    fig = plt.figure(figsize=(15,5))
    sns.histplot(data[choice], bins=30, kde=True)
    st.pyplot(fig)

    # visual 2
     ## Categorical Data Plot
    st.write('### Categorical Data Ratio')
    pilihan_kategori = st.selectbox('Pick Categorical Column : ', ('anaemia', 'diabetes', 'high_blood_pressure', 
                                                                   'sex', 'smoking'))

    fig= plt.figure(figsize=(8, 6))
    sns.countplot(data=data, x=pilihan_kategori, hue='DEATH_EVENT', palette=['limegreen', 'firebrick'])

    plt.xlabel(pilihan_kategori.capitalize())
    plt.ylabel('Count')
    plt.title(pilihan_kategori.capitalize()+' Ratio')
    plt.legend(title='DEATH_EVENT')

    st.pyplot(fig)

if __name__ == '__main__':
    run()