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

def run():
    st.header("Model Prediction")
    with open('scaler.pkl', 'rb') as file_1:
        scaler = pickle.load(file_1)

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

    limit_balance = st.number_input(label='Limit balance nasabah')
    pay_1 = st.selectbox(label='Delay Payment on September 2015',options=[-2.0,-1.0,0.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0])
    pay_2 = st.selectbox(label='Delay Payment on Agustus 2015',options=[-2.0,-1.0,0.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0])
    pay_3 = st.selectbox(label='Delay Payment on Juli 2015',options=[-2.0,-1.0,0.0,2.0,3.0,4.0,5.0,6.0,7.0])
    pay_4 = st.selectbox(label='Delay Payment on Juni 2015',options=[-2.0,-1.0,0.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0])
    pay_5 = st.selectbox(label='Delay Payment on May 2015',options=[-2.0,-1.0,0.0,2.0,3.0,4.0,5.0,6.0,7.0])
    pay_6 = st.selectbox(label='Delay Payment on April 2015',options=[-2.0,-1.0,0.0,2.0,3.0,4.0,6.0,7.0])

    df_inf = pd.DataFrame({

        'limit_balance': limit_balance,
        'pay_1': pay_1,
        'pay_2': pay_2,
        'pay_3': pay_3,
        'pay_4': pay_4,
        'pay_5': pay_5,
        'pay_6': pay_6,
        
    },index=[0])

    st.table(df_inf)



    if st.button(label='predict'):
        # define data bedasarkan numerik dan kategori
        df_inf_num = df_inf[['limit_balance']]
        df_inf_cat= df_inf[['pay_1', 'pay_2', 'pay_3', 'pay_4','pay_5','pay_6']]

        df_inf_num_scaled = scaler.transform(df_inf_num)
        df_inf_num_scaled=pd.DataFrame(df_inf_num_scaled)

        df_inf_final = np.concatenate([df_inf_num_scaled,df_inf_cat],axis = 1)

        y_pred_inf = model_knn.predict(df_inf_final)
        

        st.write(y_pred_inf[0])
        if y_pred_inf == 0:
            st.write('Nasabah Terprediksi bisa membayar')
        else:
            st.write('Nasabah Terprediksi tidak bisa membayar')