Update app.py

app.py

@@ -1,4 +1,3 @@
-# app.py
 import streamlit as st
 import joblib
 import numpy as np
@@ -8,18 +7,16 @@ 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.")
 
-# load model once
 @st.cache_resource
 def load_model(path="model.pkl"):
     return joblib.load(path)
 
 try:
-    model = load_model("model.pkl")  # update path if different
+    model = load_model("model.pkl")  
 except Exception as e:
     st.error(f"Unable to load model.pkl — check file path. Error: {e}")
     st.stop()
 
-# --- Input widgets (use your defaults) ---
 st.subheader("Applicant features")
 
 col1, col2 = st.columns(2)
@@ -62,8 +59,6 @@ with col4:
     HighDebtRatio = st.selectbox("HighDebtRatio", options=[0, 1], index=0)
     HighUtilization = st.selectbox("HighUtilization", options=[0, 1], index=0)
 
-# Build feature vector in the same order used during training
-# IMPORTANT: ensure this order matches exactly the order your model expects
 feature_order = [
     RevolvingUtilizationOfUnsecuredLines,
     age,
@@ -86,7 +81,6 @@ feature_order = [
 
 X_input = np.array(feature_order).reshape(1, -1)
 
-# Predict button
 if st.button("Predict"):
     try:
         pred = model.predict(X_input)[0]
@@ -94,15 +88,12 @@ if st.button("Predict"):
         st.error(f"Prediction failed. Check feature order/types. Error: {e}")
         st.stop()
 
-    # mapping
     label_map = {0: "No Default (Good Credit)", 1: "Default (High Risk)"}
     meaning = label_map.get(int(pred), str(pred))
 
-    # probability if available
     proba_str = ""
     try:
         proba = model.predict_proba(X_input)[0]
-        # If binary, proba[1] is probability of class 1
         if len(proba) == 2:
             proba_str = f" — P(Default) = {proba[1]:.4f}"
         else:
@@ -115,11 +106,10 @@ if st.button("Predict"):
     else:
         st.success(f"Prediction: {pred} → {meaning}{proba_str}")
 
-    # show raw output for debugging
     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.")
+st.caption("Model expects features in a specific order. If predictions seem off, verify the feature order and preprocessing used during training.")