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

# load file joblib dengan .pkl
with open('model_rf.pkl', 'rb') as file_1:
  model_rf= joblib.load(file_1)

with open('model_scaler.pkl', 'rb') as file_2:
  model_scaler= joblib.load(file_2)

#loan file txt dengan json.load
with open('list_num_cols.txt', 'r') as file_3:
  num_columns= json.load(file_3)

def run() :

  with st.form(key='from_heart_failure'):
      #name = st.text_input('Full Name', value='')
      age = st.number_input('Age', min_value=25, max_value=100, value=25, step=1, help='Usia Pasien')
      sex = st.selectbox('Sex', ('Man', 'Woman'), index=1)
      smoking = st.selectbox('Smoking', ('No', 'Yes'), index=1)
      st.markdown('---')
      
      phosphokinase = st.slider('Creatinine Phosphokinase', min_value=23, max_value=7861, value=70)
      fraction = st.slider('Ejection Fraction', 10, 90, 50)
      platelets = st.slider('Platelets', 25000, 900000, 125000)
      creatinine = st.number_input('Serum Creatinine', min_value=0.0, max_value=10.0, value=0.0, step=0.1)
      sodium = st.slider('Serum Sodium', 100, 148, 120)
      time = st.slider('Time', 1, 300, 1)
      st.markdown('---')

      anaemia = st.selectbox('Have Anaemia', ('No', 'Yes'), index=1)
      diabetes = st.selectbox('Have Diabetes', ('No', 'Yes'), index=1)
      high_blood_pressure = st.selectbox('Have High Blood Pressure', ('No', 'Yes'), index=1)
      DEATH_EVENT = st.selectbox('Death Event', ('Survived', 'Dead'), index=1)

      submitted = st.form_submit_button('Predict')

  data_inf = {
      'age': age,
      'anaemia': anaemia,
      'creatin_phosphokinase: ': phosphokinase,
      'diabetes': diabetes,
      'ejection_fraction': fraction,
      'high_blood_pressure': high_blood_pressure,
      'platelets': platelets,
      'serum_creatinine': creatinine,
      'serum_sodium': sodium,
      'sex': sex,
      'smoking': smoking,
      'time': time,
      'DEATH_EVENT': DEATH_EVENT,
  }

  data_inf = pd.DataFrame([data_inf])
  data_inf


  if submitted:
    #Numerical Columns
    data_inf_num = data_inf[num_columns]

    # Feature Scaling
    data_inf_final = model_scaler.transform(data_inf_num)
    
    # Predict using Random Forest
    y_pred_inf = model_rf.predict(data_inf_final)
    prediction_label = "Survived" if y_pred_inf == 0 else "Dead"
    st.write('# Rating : ', prediction_label)

if __name__== '__main__':
    run()