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


# Load All Files
with open('rf.pkl', 'rb') as file_1:
  rf= pickle.load(file_1)

with open('GB.pkl', 'rb') as file_2:
  GB=pickle.load(file_2)

with open('scaler.pkl', 'rb') as file_3:
    scaler=pickle.load(file_3)

def run():

    with st.form(key='form_prediksi'):
        name = st.text_input('Nama', value='')
        anaemia= st.radio('Apakah memiliki anaemia?', ('Ya', 'Tidak'))
        if anaemia=='Ya':
            anaemianum=1
        else: anaemianum=0
                
        age= st.number_input('Umur', min_value=16, max_value=80, value=50, step=1)
        creatinine_phosphokinase = st.number_input('Creatinine Phosphokinase', min_value=0, max_value=2000, value=1000)
        diabetes = st.radio('Apakah memiliki Diabetes?', ('Ya', 'Tidak'))
        if diabetes=='Ya':
            diabetesnum=1
        else: diabetesnum=0
                
        ejection_fraction = st.number_input('Ejection Fraction', min_value=0, max_value=100, value=50)
        high_blood_pressure = st.radio('Apakah memiliki darah tinggi?', ('Ya', 'Tidak'))
        if high_blood_pressure=='Ya':
            high_blood_pressurenum=1
        else: high_blood_pressurenum=0
        
        platelets = st.number_input('Jumlah trombosit', min_value=0, max_value=300000, value=200000)
        serum_creatinine = st.number_input('Serum creatinine', min_value=0, max_value=100, value=50)
        serum_sodium = st.number_input('Serum sodium', min_value=0.0, max_value=2.0, value=0.4, format='%.2f', step=0.1)
        sex = st.radio('Kelamin', ('Perempuan', 'Laki-Laki'))
        if sex=='Laki-Laki':
            sexnum=1
        else: sexnum=0
        
        smoking = st.radio('Apakah merokok?', ('Ya', 'Tidak'))
        if smoking=='Ya':
            smokingnum=1
        else: smokingnum=0
        
        time = st.number_input('Waktu', min_value=0, max_value=240, value=120)

        submitted = st.form_submit_button('Predict')

    data_inf = {
        'age': age,
        'anaemia': anaemianum,
        'creatinine_phosphokinase':creatinine_phosphokinase,
        'diabetes':diabetesnum,
        'ejection_fraction':ejection_fraction,
        'high_blood_pressure':high_blood_pressurenum,
        'platelets': platelets,
        'serum_creatinine': serum_creatinine,
        'serum_sodium':serum_sodium,
        'sex':sexnum,
        'smoking':smokingnum,
        'time':time
    }

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

    if submitted:

        data_inf_scaled=scaler.transform(data_inf)
        y_pred_inf_rf = rf.predict(data_inf_scaled)
        y_pred_inf_GB= GB.predict(data_inf_scaled)
        st.write('# Prediksi Gradient Boosting: ', str(int(y_pred_inf_GB)))
        st.write('# Prediksi Randomforest: ', str(int(y_pred_inf_rf)))

if __name__== '__main__':
    run()