import streamlit as st
import pandas as pd
import numpy as np
import pickle 
import json

# load all files

with open('cat_model.pkl', 'rb') as file_1:
    cat_model = pickle.load(file_1)

# Pre-processing
with open('scale_feat.pkl', 'rb') as file_2:
    scale_feat = pickle.load(file_2)

with open('winsoriser.pkl', 'rb') as file_3:
    winsoriser = pickle.load(file_3)
    
# List Numeric & Category
with open('num_cols_sc.txt', 'r') as file_4:
    num_cols_sc = json.load(file_4)
    
with open('num_cols_nsc.txt', 'r') as file_5:
    num_cols_nsc = json.load(file_5)


def run():
    with st.form(key='from_health'):
        age = st.number_input('Usia', min_value=25, max_value=95,
                            value=40, step=1, help='Usia Pasien')
        
        anaemia = st.number_input('Anemia', min_value=0, max_value=1, 
                                value=0, step=1, help='Terindikasi Anemia 0 = Tidak, 1 = Ya')
        
        creatinine_phosphokinase = st.slider('Creatine Value', 0, 10_000, 1500, step=20, help='Kadar Creatinine')

        diabetes = st.number_input('Diabetes', min_value=0, max_value=1, 
                                value=0, step=1, help='Terindikasi Diabetes 0 = Tidak, 1 = Ya')

        ejection_fraction = st.number_input('Ejection Value', min_value=0, max_value=100, 
                                value=50, step=1, help='Kadar Ejection')

        high_blood_pressure = st.number_input('Tekanan Darah Tinggi', min_value=0, max_value=1, 
                                value=0, step=1, help='Terindikasi Tekanan Darah Tinggi 0 = Tidak, 1 = Ya')
        
        platelets = st.slider('Platelets', 0, 850_000, 50_000, step=100, help='Kadar Platelets')

        serum_creatinine = st.number_input('Serum Creatinine', min_value=0, max_value=10, 
                                value=5, step=1, help='Kadar Serum_Creatinine')
        
        serum_sodium = st.number_input('Serum Sodium', min_value=0, max_value=150, 
                                value=50, step=1, help='Kadar Serum_Sodium')
        
        sex = st.number_input('Jenis Kelamin', min_value=0, max_value=1, 
                                value=0, step=1, help='Kode jenis kelamin 0 = wanita , 1 = pria')

        smoking = st.number_input('Merokok', min_value=0, max_value=1, 
                                value=0, step=1, help='Terindikasi Merokok 0 = Tidak, 1 = Ya')

        time = st.number_input('Waktu', min_value=0, max_value=300, 
                                value=10, step=1, help='Waktu berkunjung kembali')

        st.markdown('---')
        submitted = st.form_submit_button('Predict')

        data_inf = {
                    'age': age,
                    'anemia': anaemia,
                    'creatinine_phosphokinase': creatinine_phosphokinase,
                    'diabetes': diabetes,
                    'ejection_fraction': ejection_fraction,
                    'high_blood_pressure': high_blood_pressure,
                    'platelets': platelets,
                    'serum_creatinine': serum_creatinine,
                    'serum_sodium': serum_sodium,
                    'sex': sex,
                    'smoking': smoking,
                    'time': time,
                }
        
        st.markdown('Jika Hasil Prediksi = 1, maka model memprediksi bahwa pasien akan meninggal.')
        st.markdown('Jika Hasil Prediksi = 1, maka model memprediksi bahwa pasien akan meninggal.')
        

        data_inf = pd.DataFrame([data_inf])
        st.dataframe(data_inf)

        if submitted:
            data_inf_sc = data_inf[num_cols_sc]
            data_inf_nsc = data_inf[num_cols_nsc]  

            # scalling
            data_inf_sc = scale_feat.transform(data_inf_sc)
            data_inf_sc = pd.DataFrame(data_inf_sc, columns=num_cols_sc)
            # Reset Index
            data_inf_sc.reset_index(drop= True, inplace= True)
            data_inf_nsc.reset_index(drop = True, inplace = True)
            data_final = pd.concat([data_inf_nsc, data_inf_sc], axis= 1)
            # modeling
            y_pred_inf = cat_model.predict(data_final)

            st.write('Prediction: ', (y_pred_inf))

    if __name__ == '__main__':
        run()