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import streamlit as st
import pandas as pd
import joblib
def run():
st.markdown("<h1 style='text-align: center;'>Welcome to the Credit Default Prediction Model</h1>", unsafe_allow_html=True)
st.markdown("========================================================================================")
st.markdown("<h2 style='text-align: left;'>User Input Features</h2>", unsafe_allow_html=True)
def user_input():
limit_balance = st.number_input('limit balance', min_value=10000, max_value=100000000, step=10000)
col1,col2= st.columns(2)
pay_1 = col1.slider('pay in september', min_value=-12, max_value=12, format="paid %d month")
pay_2 = col2.slider('pay in august', min_value=-12, max_value=12, format="paid %d month")
pay_3 = col1.slider('pay in july', min_value=-12, max_value=12, format="paid %d month")
pay_4 = col2.slider('pay in june', min_value=-12, max_value=12, format="paid %d month")
pay_5 = col1.slider('pay in may', min_value=-12, max_value=12, format="paid %d month")
pay_6 = col2.slider('pay in april', min_value=-12, max_value=12, format="paid %d month")
data = {
'limit_balance': limit_balance,
'pay_0': pay_1,
'pay_2': pay_2,
'pay_3': pay_3,
'pay_4': pay_4,
'pay_5': pay_5,
'pay_6': pay_6
}
features = pd.DataFrame(data, index=[0])
return features
input = user_input()
st.markdown("<h2 style='text-align: left;'>User Input Result</h2>", unsafe_allow_html=True)
st.table(input)
load_model = joblib.load("my_model.pkl")
if st.button("Predict", help='Click me!' ):
prediction = load_model.predict(input)
if prediction == 1:
prediction = 'Defaulted Payment'
else:
prediction = 'Not Defaulted'
st.markdown("<h4 style='text-align: center;'>Based on user input, the default model is predicted:</h4>", unsafe_allow_html=True)
st.markdown(f"<h1 style='text-align: center;'>{prediction}</h1>", unsafe_allow_html=True)
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