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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import pickle | |
| import json | |
| #Load All Files | |
| #load model dan files yang sudah di save pada framing | |
| with open('list_num_cols.txt', 'r') as file_1: | |
| list_num_cols = json.load(file_1) | |
| with open('list_cat_cols.txt', 'r') as file_2: | |
| list_cat_cols = json.load(file_2) | |
| with open('model_svm.pkl', 'rb') as file_3: | |
| model_svm = pickle.load(file_3) | |
| def run(): | |
| with st.form('credit_cards_simulation'): | |
| #Field Nama | |
| LimitBalance = st.number_input('Limit Balance', value = 0) | |
| #Field Umur | |
| Sex = st.number_input('Sex', min_value = 1, max_value = 2, value = 1, step = 1, help = '1 = Male, 2 = Female') | |
| #Field Tinggi badan | |
| EducationLevel = st.number_input('Education', min_value = 1, max_value = 6, value = 1, step = 1, help = '1 = Graduate School, 2 = University, 3 = High School, 4 = Other, 5 = Unknown, 6 = Unknown') | |
| #Field Weight | |
| MaritalStatus = st.number_input('Marital Status', min_value = 1, max_value = 3, value = 1, step = 1, help = '1 = Married, 2 = Single, 3 = Other') | |
| #field price | |
| Age = st.slider('Age', 18, 70, 25) | |
| st.markdown('----') | |
| #Field Attacking Work Rate | |
| Pay_0 = st.number_input('Pay 1', min_value = -2, max_value = 9, value = 0, step = 1, help = 'Repayment Status in September, 2025 (-2 = No Transaction, -1 = Pay Duly, 1-9 = Payment Delay 1-9 Month)') | |
| #Field Defensive Work Rate | |
| Pay_2 = st.number_input('Pay 2', min_value = -2, max_value = 9, value = 0, step = 1, help = 'Repayment status in August, 2005 (scale same as above)') | |
| #Field Pace Total | |
| Pay_3 = st.number_input('Pay 3', min_value = -2, max_value = 9, value = 0, step = 1, help = 'Repayment status in July, 2005 (scale same as above)') | |
| #Field Shooting Total | |
| Pay_4 = st.number_input('Pay 4', min_value = -2, max_value = 9, value = 0, step = 1, help = 'Repayment status in June, 2005 (scale same as above)') | |
| #Field Passing Total | |
| Pay_5 = st.number_input('Pay 5', min_value = -2, max_value = 9, value = 0, step = 1, help = 'Repayment status in May, 2005 (scale same as above)') | |
| #Field Dribbling Total | |
| Pay_6 = st.number_input('Pay 6', min_value = -2, max_value = 9, value = 0, step = 1, help = 'Repayment status in April, 2005 (scale same as above)') | |
| #Field Defending Total | |
| Bill_amt_1 = st.number_input('Amount Of Bill 1', value = 0 , help = 'Amount of bill statement in September, 2005 (NT dollar)') | |
| #Field Physicality Total | |
| Bill_amt_2 = st.number_input('Amount Of Bill 2', value = 0, help = 'Amount of bill statement in August, 2005 (NT dollar)') | |
| Bill_amt_3 = st.number_input('Amount Of Bill 3', value = 0, help = 'Amount of bill statement in July, 2005 (NT dollar)') | |
| Bill_amt_4 = st.number_input('Amount Of Bill 4', value = 0, help = 'Amount of bill statement in June, 2005 (NT dollar)') | |
| Bill_amt_5 = st.number_input('Amount Of Bill 5', value = 0, help = 'Amount of bill statement in May, 2005 (NT dollar)') | |
| Bill_amt_6 = st.number_input('Amount Of Bill 6', value = 0, help = 'Amount of bill statement in April, 2005 (NT dollar)') | |
| #Field Pay AMT | |
| Pay_amt_1 = st.number_input('Amount of previous payment 1', value = 0, help = 'Amount of previous payment in September, 2005 (NT dollar)') | |
| Pay_amt_2 = st.number_input('Amount of previous payment 2', value = 0, help = 'Amount of previous payment in August, 2005 (NT dollar)') | |
| Pay_amt_3 = st.number_input('Amount of previous payment 3', value = 0, help = 'Amount of previous payment in July, 2005 (NT dollar)') | |
| Pay_amt_4 = st.number_input('Amount of previous payment 4', value = 0, help = 'Amount of previous payment in June, 2005 (NT dollar)') | |
| Pay_amt_5 = st.number_input('Amount of previous payment 5', value = 0, help = 'Amount of previous payment in May, 2005 (NT dollar)') | |
| Pay_amt_6 = st.number_input('Amount of previous payment 6', value = 0, help = 'Amount of previous payment in April, 2005 (NT dollar)') | |
| #bikin submit button | |
| submitted = st.form_submit_button('Predict') | |
| #Inference | |
| data_inf = { | |
| 'limit_balance': LimitBalance, | |
| 'sex': Sex, | |
| 'education_level': EducationLevel, | |
| 'marital_status': MaritalStatus, | |
| '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, | |
| 'bill_amt_1' :Bill_amt_1, | |
| 'bill_amt_2':Bill_amt_2, | |
| 'bill_amt_3':Bill_amt_3, | |
| 'bill_amt_4':Bill_amt_4, | |
| 'bill_amt_5':Bill_amt_5, | |
| 'bill_amt_6':Bill_amt_6, | |
| 'pay_amt_1':Pay_amt_1, | |
| 'pay_amt_2':Pay_amt_2, | |
| 'pay_amt_3':Pay_amt_3, | |
| 'pay_amt_4':Pay_amt_4, | |
| 'pay_amt_5':Pay_amt_5, | |
| 'pay_amt_6':Pay_amt_6, | |
| } | |
| data_inf = pd.DataFrame([data_inf]) | |
| st.dataframe(data_inf) | |
| #Logic ketika predic button ditekan | |
| if submitted: | |
| #split between numerical and categorical columns | |
| data_inf_num = data_inf[list_num_cols] | |
| data_inf_cat = data_inf[list_cat_cols] | |
| #Scaling & Encoding | |
| data_inf_final = data_inf_num | |
| #predict using linear reg model | |
| y_pred_inf = model_svm.predict(data_inf_final) | |
| st.write('## Payment : ', str(int(y_pred_inf))) | |
| if __name__ == '__main__': | |
| run() |