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b4fbdbc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import streamlit as st
from predictor import make_prediction
st.set_page_config(page_title='Webfala', page_icon='💰')
st.title('Webfala Loan Eligibility Predictor')
st.markdown("Provide your details to check if you're eligible for a loan")
#collect user inputs
full_name = st.text_input('Full name', placeholder='Enter your full name')
gender = st.selectbox('Gender', ['Female', 'Male'])
married = st.selectbox('Married', ['Yes', 'No'])
dependents = st.selectbox('Number of Dependents', ['0', '1', '2', '3', '3+'])
education = st.selectbox('Education', ['Graduate', 'Not Graduate'])
self_employed = st.selectbox('Self Employed', ['Yes', 'No'])
applicant_income = st.number_input('Applicant Income(₦)', min_value=0)
coapplicant_income = st.number_input('Co-applicant Income(₦)', min_value=0)
loan_amount = st.number_input('Loan Amount (in thousand ₦)', min_value=0)
loan_term = st.number_input('Loan term (in days: 0 - 365)', min_value=0)
credit_history = st.selectbox('Credit History', [1, 0])
property_area = st.selectbox('Property Area', ['Urban', 'Semiurban', 'Rural'])
#Bundle inputs
user_input = {
'Gender': gender,
'Married': married,
'Dependents': dependents,
'Education': education,
'Self_Employed': self_employed,
'ApplicantIncome': applicant_income,
'CoapplicantIncome': coapplicant_income,
'LoanAmount': loan_amount,
'Loan_Amount_Term': loan_term,
'Credit_History': credit_history,
'Property_Area': property_area
}
if st.button('Check Eligibility'):
with st.spinner("Analyzing..."):
pred, prob = make_prediction(user_input)
if pred == 1:
st.success(f"Dear {full_name}, you're eligible for this loan ")
else:
st.error(f"Dear {full_name}, sorry, you're not eligible for this loan")
st.info(f'Confidence: **{prob:.2f}** %')
st.markdown("---")
st.markdown("Copyright 2025") |