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# learn stremlit, this one for the predictions
#import libs
import streamlit as st
import pandas as pd
import pickle
#open related files/load files
with open('scaler.pkl', 'rb') as file_1:
scaler = pickle.load(file_1)
with open('model.pkl', 'rb') as file_2:
model = pickle.load(file_2)
def run():
#Make the input form for the user to input data?
with st.form('Form_CreditDefaultPredictor'):
#Field limit balance
limit_balance = st.number_input('limit_balance',min_value=10000, max_value=1000000)
#Field age
age = st.number_input('age', min_value= 21, max_value = 70, step = 1, help = 'Age of borrower')
#Field education level
education_level = st.slider('education_level', 1, 4, 2)
st.write('#### - 1 is graduate school')
st.write('#### - 2 is university')
st.write('#### - 3 is high school')
st.write('#### - 4 is others')
#Field marital status
marital_status = st.slider('marital_status', 1, 3, 2)
st.write('#### - 1 is married')
st.write('#### - 2 is single')
st.write('#### - 3 is others')
#Field pay_0
pay_0 = st.slider('pay_0', -2, 9, -1 )
st.write('### latest month payment status')
st.write('#### - -2: pay early')
st.write('#### - -1 = pay on deadline')
st.write('#### - 0 : pay delayed for 0 month')
st.write('#### - 1 = payment delayed for one month')
st.write('#### - 2 = payment delayed for two months')
st.write('#### ...')
st.write('#### - 8 = payment delayed for 8 months')
st.write('#### - 9 = payment delayed for 9 months')
#Field pay_2
pay_2 = st.slider('pay_1', -2, 9, -1, key=2 )
st.write('#### 1 months before latest month payment status, same scale as above')
#Field pay_3
pay_3 = st.slider('pay_2', -2, 9, -1, key=3 )
st.write('#### 2 months before latest month payment status, same scale as above')
#Field pay_4
pay_4 = st.slider('pay_3', -2, 9, -1, key=4 )
st.write('#### 3 months before latest month payment status, same scale as above')
#Field pay_5
pay_5 = st.slider('pay_4', -2, 9, -1, key=5 )
st.write('#### 4 months before latest month payment status, same scale as above')
#Field pay_6
pay_6 = st.slider('pay_5', -2, 9, -1, key=6 )
st.write('#### 5 months before latest month payment status, same scale as above')
# bikin batasan
st.markdown('---------')
#bikin submit button
submitted = st.form_submit_button('Predict!')
#inference/satuin data supaya bisa masuk model
# nama col ('Name',etc) harus sama dengan di model
# keys dari col harus sama dengan nama variable di form streamlit
data_inf = {
'limit_balance' : limit_balance,
'education_level' : education_level,
'marital_status' : marital_status,
'age': age,
'pay_0' : pay_0,
'pay_2' : pay_2,
'pay_3' : pay_3,
'pay_4' : pay_4,
'pay_5' : pay_5,
'pay_6' : pay_6,
}
#turn to dataframe for model
data_inf = pd.DataFrame([data_inf])
#aslo show the input from user
st.dataframe(data_inf)
#what happen when predict button is pushed/clicked:
if submitted: #ketika si submitted itu punya value, maka
#scale
data_inf_scaled = scaler.transform(data_inf)
# predict using linear reg model
y_pred_inf = model.predict(data_inf_scaled)
#kasih tau hasilnya apa
st.write('## Prediction of whether the borrower will default : ',str(int(y_pred_inf)))
st.write('###1 = will default, 0 = will not default')
if __name__ == '__main__':
run() |