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| import streamlit as st | |
| import pandas as pd | |
| import pickle | |
| st.title("Default Risk Prediction for Customer") | |
| # Import model | |
| model = pickle.load(open("model.pkl", "rb")) | |
| st.write('Please choose how to input your information:') | |
| input_option = st.radio('Input Option', ('Manual Input', 'Upload File')) | |
| if input_option == 'Manual Input': | |
| st.write('Please fill in your information:') | |
| # User input | |
| nit = st.selectbox(label='From where is your source of income?', options=['Working', 'State servant', 'Commercial associate', | |
| 'Pensioner', 'Unemployed', 'Student', 'Businessman', | |
| 'Maternity leave']) | |
| org = st.selectbox(label='What is your organization type?', options= | |
| ['Business Entity Type 3', 'School', 'Government', 'Religion', 'XNA', 'Electricity', 'Medicine', | |
| 'Business Entity Type 2', 'Self-employed', 'Transport: type 2', 'Construction', 'Housing', 'Kindergarten', | |
| 'Trade: type 7', 'Industry: type 11', 'Military', 'Services', 'Security Ministries', 'Transport: type 4', | |
| 'Industry: type 1', 'Emergency', 'Security', 'Trade: type 2', 'University', 'Transport: type 3', 'Police', | |
| 'Business Entity Type 1', 'Postal', 'Industry: type 4', 'Agriculture', 'Restaurant', 'Culture', 'Hotel', | |
| 'Industry: type 7', 'Trade: type 3', 'Industry: type 3', 'Bank', 'Industry: type 9', 'Insurance', 'Trade: type 6', | |
| 'Industry: type 2', 'Transport: type 1', 'Industry: type 12', 'Mobile', 'Trade: type 1', 'Industry: type 5', | |
| 'Industry: type 10', 'Legal Services', 'Advertising', 'Trade: type 5', 'Cleaning', 'Industry: type 13', | |
| 'Trade: type 4', 'Telecom', 'Industry: type 8', 'Realtor', 'Industry: type 6', 'Other']) | |
| occ = st.selectbox(label='What is your occupation type?', options= | |
| ['Laborers', 'Core staff', 'Accountants', 'Managers', 'Drivers', 'Sales staff', 'Cleaning staff', 'Cooking staff', | |
| 'Private service staff', 'Medicine staff', 'Security staff', 'High skill tech staff', 'Waiters/barmen staff', | |
| 'Low-skill Laborers', 'Realty agents', 'Secretaries', 'IT staff','HR staff', 'Others']) | |
| xs3 = st.number_input(label='Enter your third external source:', min_value=0.00000, max_value=0.99999, value=0.00000, step=0.00001) | |
| xs2 = st.number_input(label='Enter your second external source:', min_value=0.00000, max_value=0.99999, value=0.00000, step=0.00001) | |
| dbh = st.number_input(label='How old are you in days?:', min_value=-99999, max_value=0, value=-10000, step=1) | |
| acr = st.number_input(label='How much is your amount credit?:', min_value=0, max_value=9999999, value=100000, step=1) | |
| ait = st.number_input(label='How much is your amount income in total?:', min_value=0, max_value=999999999, value=1000000, step=1) | |
| cip = acr / ait | |
| ama = st.number_input(label='How much is your amount annuity?:', min_value=0.0, max_value=999999.9, value=10000.0, step=0.1) | |
| aip = ama / ait | |
| crt = ama / acr | |
| dem = st.number_input(label='How long have you been working in days?:', value=10000, step=1) | |
| dep = dem / dbh | |
| # Convert into dataframe | |
| data = pd.DataFrame({'name_income_type': [nit], | |
| 'organization_type': [org], | |
| 'occupation_type': [occ], | |
| 'ext_source_3': [xs3], | |
| 'ext_source_2': [xs2], | |
| 'days_birth': [dbh], | |
| 'credit_income_percent': [cip], | |
| 'annuity_income_percent': [aip], | |
| 'credit_term': [crt], | |
| 'days_employed_percent': [dep] | |
| }) | |
| # Prediction | |
| if st.button('Predict'): | |
| prediction = model.predict(data).tolist()[0] | |
| if prediction == 0: | |
| prediction = 'Congratulations, this person is most likely to pay!' | |
| else: | |
| prediction = 'Too bad, this person may not be able to pay!' | |
| st.write('Prediction result:') | |
| st.write(prediction) | |
| else: | |
| uploaded_file = st.file_uploader('Upload CSV file', type='csv') | |
| if uploaded_file is not None: | |
| data = pd.read_csv(uploaded_file) | |
| st.write('Data uploaded successfully!') | |
| if st.button('Predict'): | |
| predictions = model.predict(data) | |
| result_data = pd.DataFrame({'SK_ID_CURR': data['sk_id_curr'], 'TARGET': predictions}) | |
| st.write('Prediction result:') | |
| st.dataframe(result_data) |