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

# Load All Files

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

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

with open('list_num_cols.txt', 'r') as file_3:
  list_num_cols = json.load(file_3)

with open('list_cat_cols.txt', 'r') as file_4:
  list_cat_cols = json.load(file_4)

def run():
    with st.form(key='heart_failure'):
        age = st.number_input('age', min_value=40, max_value=95, value=60, step=1, help='Patient Age')
        plateletes = st.number_input('plateletes', min_value=0, max_value=1000000, value=0)
        creatinine = st.number_input('creatinine', min_value=0, max_value=7861, value=0)
        ejection = st.number_input('ejection', min_value=0, max_value=100, value=0)
        st.markdown('---')

        high_blood_pressure = st.selectbox('high_blood_pressure', (0, 1), index=1)
        anaemia = st.selectbox('anaemia', (0, 1), index=1)
        diabetes = st.selectbox('diabetes', (0, 1), index=1)
        sex = st.selectbox('sex', (0, 1), index=1)
        smoking = st.selectbox('smoking', (0, 1), index=1)

        st.markdown('---')

        serum_creatinine = st.number_input('serum_creatinine', min_value=0, max_value=100, value=0)
        serum_sodium = st.number_input('serum_sodium', min_value=0, max_value=100, value=0)
        time = st.number_input('time', min_value=0, max_value=10000, value=0)
        death = st.selectbox('death', (0, 1), index=1)

        submitted = st.form_submit_button('Predict')

    data_inf = {
        'age': age,
        'anaemia': anaemia,
        'creatinine': creatinine,
        'diabetes': diabetes,
        'ejection': ejection,
        'high_blood_pressure': high_blood_pressure,
        'platelets': plateletes,
        'serum_creatinine': serum_creatinine,
        'serum_sodium': serum_sodium,
        'sex': sex,
        'smoking': smoking,
        'time': time,
        'death': death
    }

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

    if submitted:
        
        # Split between Numerical Columns and Categorical Columns
        data_inf_num = data_inf[list_num_cols]
        data_inf_cat = data_inf[list_cat_cols]
        data_inf_num

        # Feature Scaling and Feature Encoding
        data_inf_num_scaled = model_scaler.transform(data_inf_num)
        data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat], axis=1)

        # Predict using Linear Regression
        y_pred_inf = model_rnd.predict(data_inf_final)
        st.write('# Death Prediction : ', str(int(y_pred_inf)))

if __name__ == '__main__':
    run()