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| # Import library yang dibutuhkan | |
| import joblib | |
| import pandas as pd | |
| import streamlit as st | |
| from huggingface_hub import hf_hub_download | |
| # Nama repo | |
| REPO_ID = "chanyaphas/creditc" | |
| # Personal Access Token untuk koneksi pada model | |
| access_token = st.secrets["HF_TOKEN"] | |
| # Inisialisasi model yang lebih dari satu | |
| model = joblib.load( | |
| hf_hub_download(repo_id=REPO_ID, filename='model.joblib', token=access_token, repo_type="space") | |
| ) | |
| unique_values = joblib.load( | |
| hf_hub_download(repo_id=REPO_ID, filename='unique_values.joblib', token=access_token, repo_type="space") | |
| ) | |
| # Dictionary pada model | |
| EDU_DICT = {'Lower secondary': 1, | |
| 'Secondary / Secondary special' :2 , | |
| 'Academic degree': 3, | |
| 'Incomplete higher': 4, | |
| 'Higher education': 5 | |
| } | |
| # Beri judul pada apps | |
| st.title("Credit Card Approval Prediction") | |
| # Buat form input user, hasil input akan di prediksi | |
| with st.form("questionare"): | |
| Gender = st.selectbox('Gender', unique_values['CODE_GENDER']) | |
| Own_car = st.selectbox('Own_Car', unique_values['FLAG_OWN_CAR']) | |
| Property = st.selectbox('Property', unique_values['FLAG_OWN_REALTY']) | |
| Income_type = st.selectbox('Income_type', unique_values['NAME_INCOME_TYPE']) | |
| Marital_status = st.selectbox('Marital_status', unique_values['NAME_FAMILY_STATUS']) | |
| Housing_type = st.selectbox('Housing_type', unique_values['NAME_HOUSING_TYPE']) | |
| Education = st.selectbox('Education', unique_values['NAME_EDUCATION_TYPE']) | |
| Income = st.slider('Income', min_value = 27000, max_value=157500) | |
| Children = st.number_input('Children', min_value=0, max_value=19) | |
| Day_Employed = st.number_input('Day_Employed', min_value=0, max_value=3) | |
| Flag_Mobile = st.number_input('Flag_Mobile', min_value=0, max_value=1) | |
| Flag_work_phone = st.number_input('Flag_work_phone', min_value=0, max_value=1) | |
| Flag_Phone = st.number_input('Flag_Phone', min_value=0, max_value=1) | |
| Flag_Email = st.number_input('Flag_Email', min_value=0, max_value=1) | |
| Family_mem = st.number_input('Family_mem', min_value=1, max_value=20) | |
| clicked = st.form_submit_button("Result") | |
| if clicked: | |
| result = model.predict(pd.DataFrame({ | |
| 'CODE_GENDER': [Gender], | |
| 'FLAG_OWN_CAR': [Own_car], | |
| 'FLAG_OWN_REALTY':[Property], | |
| 'CNT_CHILDREN': [Children], | |
| 'AMT_INCOME_TOTAL': [Income], | |
| 'NAME_INCOME_TYPE': [Income_type], | |
| 'NAME_EDUCATION_TYPE': [EDU_DICT[Education]], | |
| 'NAME_FAMILY_STATUS': [Marital_status], | |
| 'NAME_HOUSING_TYPE': [Housing_type], | |
| 'DAYS_EMPLOYED': [Day_Employed], | |
| 'FLAG_MOBIL': [Flag_Mobile], | |
| 'FLAG_WORK_PHONE': [Flag_work_phone], | |
| 'FLAG_PHONE': [Flag_Phone], | |
| 'FLAG_EMAIL':[Flag_Email], | |
| 'CNT_FAM_MEMBERS': [Family_mem]})) | |
| result = 'Pass' if result[0] == 1 else 'Did not pass' | |
| st.success(f'Credit card approval prediction result is {result}') |