Update app.py

app.py

@@ -4,7 +4,7 @@ from joblib import load
 
 # Load the trained model and scaler
 model = load('loandefaulter.joblib')
-
+scaler = load('scaler.joblib')
 
 # Define numerical features for scaling
 num_features = [
@@ -12,19 +12,30 @@ num_features = [
 ]
 
 # Create the Streamlit app
-st.title('Loan Default Prediction')
-st.write('Enter the loan details to get a prediction.')
-
-# Input fields for user data
-loan_amnt = st.number_input('Loan Amount', min_value=0.0)
-int_rate = st.number_input('Interest Rate', min_value=0.0)
-installment = st.number_input('Installment', min_value=0.0)
-annual_inc = st.number_input('Annual Income', min_value=0.0)
-dti = st.number_input('Debt-to-Income Ratio', min_value=0.0)
-revol_bal = st.number_input('Revolving Balance', min_value=0.0)
-revol_util = st.number_input('Revolving Utilization', min_value=0.0)
-total_acc = st.number_input('Total Accounts', min_value=0)
-mort_acc = st.number_input('Mortgage Accounts', min_value=0)
+st.set_page_config(page_title='Loan Default Prediction', layout='wide')
+
+# App title and description
+st.markdown("""
+    <style>
+    .title { font-size: 36px; font-weight: bold; color: #2E86C1; }
+    .description { font-size: 20px; color: #34495E; }
+    .input-container { margin-top: 20px; }
+    .slider-container { margin: 10px 0; }
+    </style>
+    <div class="title">Loan Default Prediction</div>
+    <div class="description">Enter the loan details below to get a prediction on whether the loan will be defaulted.</div>
+""", unsafe_allow_html=True)
+
+# Input fields with sliders
+loan_amnt = st.slider('Loan Amount', min_value=0.0, max_value=1000000.0, step=1000.0, value=10000.0)
+int_rate = st.slider('Interest Rate (%)', min_value=0.0, max_value=30.0, step=0.1, value=5.0)
+installment = st.slider('Installment', min_value=0.0, max_value=10000.0, step=10.0, value=200.0)
+annual_inc = st.slider('Annual Income', min_value=0.0, max_value=1000000.0, step=1000.0, value=50000.0)
+dti = st.slider('Debt-to-Income Ratio', min_value=0.0, max_value=100.0, step=0.1, value=15.0)
+revol_bal = st.slider('Revolving Balance', min_value=0.0, max_value=500000.0, step=100.0, value=10000.0)
+revol_util = st.slider('Revolving Utilization (%)', min_value=0.0, max_value=100.0, step=0.1, value=30.0)
+total_acc = st.slider('Total Accounts', min_value=0, max_value=100, step=1, value=10)
+mort_acc = st.slider('Mortgage Accounts', min_value=0, max_value=10, step=1, value=1)
 loan_amnt_by_income = loan_amnt / (annual_inc + 1)
 
 # Create a DataFrame for the input
@@ -41,9 +52,14 @@ input_data = pd.DataFrame({
     'loan_amnt_by_income': [loan_amnt_by_income]
 })
 
-
+# Scale the input data
+input_data[num_features] = scaler.transform(input_data[num_features])
 
 # Predict using the model
 if st.button('Predict'):
     prediction = model.predict(input_data)
-    st.write(f'Prediction: {"Charged Off" if prediction[0] == 1 else "Not Charged Off"}')
+    result = "Defaulted" if prediction[0] == 1 else "Not Defaulted"
+    color = "red" if prediction[0] == 1 else "green"
+    st.markdown(f"""
+        <div style="font-size: 24px; color: {color}; font-weight: bold;">Prediction: {result}</div>
+    """, unsafe_allow_html=True)