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import streamlit as st
import joblib
import numpy as np
st.set_page_config(page_title="Loan Default Predictor", layout="centered")
st.title("Loan Default Predictor — Give Me Some Credit")
st.write("Enter applicant features below and click **Predict** to get `SeriousDlqin2yrs` prediction.")
@st.cache_resource
def load_model(path="model.pkl"):
return joblib.load(path)
try:
model = load_model("model.pkl")
except Exception as e:
st.error(f"Unable to load model.pkl — check file path. Error: {e}")
st.stop()
st.subheader("Applicant features")
col1, col2 = st.columns(2)
with col1:
RevolvingUtilizationOfUnsecuredLines = st.number_input(
"Revolving Utilization Of Unsecured Lines", value=0.769413, format="%.6f"
)
age = st.number_input("age", value=32, min_value=18, max_value=120, step=1)
NumberOfTime30_59DaysPastDueNotWorse = st.number_input(
"Number Of Time 30-59 Days Past Due Not Worse", value=2, step=1
)
DebtRatio = st.number_input("DebtRatio", value=0.161342, format="%.6f")
MonthlyIncome = st.number_input("MonthlyIncome", value=45000.0, format="%.2f")
with col2:
NumberOfOpenCreditLinesAndLoans = st.number_input(
"Number Of Open Credit Lines And Loans", value=6.0, step=1.0
)
NumberOfTimes90DaysLate = st.number_input(
"Number Of Times 90 Days Late", value=1.0, step=1.0
)
NumberRealEstateLoansOrLines = st.number_input(
"Number Real Estate Loans Or Lines", value=6.0, step=1.0
)
NumberOfTime60_89DaysPastDueNotWorse = st.number_input(
"Number Of Time 60-89 Days Past Due Not Worse", value=0.0, step=1.0
)
NumberOfDependents = st.number_input(
"Number Of Dependents", value=0.999883, format="%.6f"
)
col3, col4 = st.columns(2)
with col3:
TotalPastDue = st.number_input("Total Past Due", value=2.0, step=1.0)
DebtRatioPerDependent = st.number_input("DebtRatio Per Dependent", value=25.1344)
UtilizationPerLine = st.number_input("Utilization Per Line", value=0.217371)
with col4:
IncomeDebtRatio = st.number_input("IncomeDebtRatio", value=3003.393701)
HasDependents = st.selectbox("HasDependents", options=[0, 1], index=1)
HighDebtRatio = st.selectbox("HighDebtRatio", options=[0, 1], index=0)
HighUtilization = st.selectbox("HighUtilization", options=[0, 1], index=0)
feature_order = [
RevolvingUtilizationOfUnsecuredLines,
age,
NumberOfTime30_59DaysPastDueNotWorse,
DebtRatio,
MonthlyIncome,
NumberOfOpenCreditLinesAndLoans,
NumberOfTimes90DaysLate,
NumberRealEstateLoansOrLines,
NumberOfTime60_89DaysPastDueNotWorse,
NumberOfDependents,
TotalPastDue,
DebtRatioPerDependent,
UtilizationPerLine,
IncomeDebtRatio,
HasDependents,
HighDebtRatio,
HighUtilization,
]
X_input = np.array(feature_order).reshape(1, -1)
if st.button("Predict"):
try:
pred = model.predict(X_input)[0]
except Exception as e:
st.error(f"Prediction failed. Check feature order/types. Error: {e}")
st.stop()
label_map = {0: "No Default (Good Credit)", 1: "Default (High Risk)"}
meaning = label_map.get(int(pred), str(pred))
proba_str = ""
try:
proba = model.predict_proba(X_input)[0]
if len(proba) == 2:
proba_str = f" — P(Default) = {proba[1]:.4f}"
else:
proba_str = " — probabilities: " + ", ".join(f"{p:.4f}" for p in proba)
except Exception:
proba_str = ""
if int(pred) == 1:
st.error(f"Prediction: {pred} → {meaning}{proba_str}")
else:
st.success(f"Prediction: {pred} → {meaning}{proba_str}")
with st.expander("Show raw inputs and model output"):
st.write("Input vector (order):", feature_order)
st.write("Raw prediction:", int(pred))
if proba_str:
st.write("Raw probabilities:", proba if 'proba' in locals() else None)
st.caption("Model expects features in a specific order. If predictions seem off, verify the feature order and preprocessing used during training.") |