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import pickle
import json
import pandas as pd
import numpy as np
import streamlit as st
# Load All Files
with open('best_param.pkl', 'rb') as file_1:
best_params = pickle.load(file_1)
with open('preprocessing_pipeline.pkl', 'rb') as file_2:
preprocessing_pipeline= pickle.load(file_2)
def run ():
with st.form(key ='Credit FORM'): #Nulis nama sendiri menggunakan name= st.text_input('')
education_level= st.radio('Select Education_level', options=['1','2','3','4','5','6'])
sex = st.radio(
'Select gender',
options=['male','female'])
limit_balance= st.text_input('limit_balance', value= 'None')
st.markdown('---')
pay_0= st.slider('pay_0', min_value=-2,max_value=2,)
pay_2= st.slider('pay_2', min_value=-2,max_value=2,)
pay_3= st.slider('pay_3', min_value=-2,max_value=2,)
pay_4= st.slider('pay_4', min_value=-2,max_value=2,)
pay_5= st.slider('pay_5', min_value=-2,max_value=2,)
pay_6= st.slider('pay_6', min_value=-2,max_value=2,)
submitted = st.form_submit_button('Predict')
# Create New Data
df_inf={
'limit_balance': limit_balance,
'sex': sex,
'education_level': education_level ,
'pay_0': pay_0 ,
'pay_2': pay_2,
'pay_3': pay_3,
'pay_4': pay_4,
'pay_5': pay_5,
'pay_6': pay_6,
}
df_inf = pd.DataFrame([df_inf])
if submitted:
df_inf_best_params = df_inf[best_params]
df_inf_classifier= df_inf[preprocessing_pipeline]
df_inf_final = np.concatenate([preprocessing_pipeline], axis=1)
y_pred_inf = best_params.predict(df_inf_final)
st.write(f'# Rating {best_params}:', int(y_pred_inf))
if best_params == '__main__':
run() |