Update app.py

app.py

@@ -176,20 +176,14 @@ with tab1:
     
     total_income = applicant_income + coapplicant_income
     
-    # Metrics already shown above with monthly payment
-    
     # Calculate monthly payment (simplified calculation)
-    # Convert loan term to years for calculation
-    loan_term_years = loan_term / 12
-    
-    # Simple monthly payment calculation (approximation)
     interest_rate = 0.05  # Assuming 5% annual interest rate
     monthly_interest = interest_rate / 12
     num_payments = loan_term
     
     # Monthly payment using the loan amortization formula
-    if monthly_interest == 0:
-        monthly_payment = loan_amount / num_payments
+    if monthly_interest == 0 or num_payments == 0:
+        monthly_payment = 0
     else:
         monthly_payment = loan_amount * (monthly_interest * (1 + monthly_interest) ** num_payments) / \
                         ((1 + monthly_interest) ** num_payments - 1)
@@ -203,7 +197,7 @@ with tab1:
     col2.metric("Estimated Monthly Payment", f"${monthly_payment:.2f}")
     col3.metric("Loan Term", f"{loan_term//12} years")
     
-    # Show DTI with visual gauge instead of raw number
+    # Create DTI visualization section
     st.markdown("<h4>Debt-to-Income Assessment</h4>", unsafe_allow_html=True)
     
     # Cap the displayed percentage at 100% for the visual
@@ -237,22 +231,51 @@ with tab1:
     </div>
     """, unsafe_allow_html=True)
     
-    # Show a simple, friendly interpretation
+    # Show a simple explanation of DTI with actual values
     if dti_percent > 100:
         st.markdown(f"""
         <div style="padding: 10px; background-color: #FFEBEE; border-radius: 5px; margin-bottom: 15px;">
-            {emoji} <strong>Your monthly loan payment would be {dti_percent/100:.1f}× your monthly income</strong>
-            <p style="margin: 5px 0 0 0; font-size: 0.9rem;">Most lenders look for a ratio below 43% (0.43× your income)</p>
+            {emoji} <strong>Your monthly payment (${monthly_payment:.2f}) would be {dti_percent/100:.1f}× your monthly income (${total_income:,})</strong>
+            <p style="margin: 5px 0 0 0; font-size: 0.9rem;">Most lenders require this to be below 43% for approval</p>
         </div>
         """, unsafe_allow_html=True)
     else:
         st.markdown(f"""
         <div style="padding: 10px; background-color: #F5F5F5; border-radius: 5px; margin-bottom: 15px;">
-            {emoji} <strong>Your monthly loan payment would be {dti_percent:.1f}% of your monthly income</strong>
-            <p style="margin: 5px 0 0 0; font-size: 0.9rem;">Most lenders look for this to be below 43%</p>
+            {emoji} <strong>Your monthly payment (${monthly_payment:.2f}) would be {dti_percent:.1f}% of your monthly income (${total_income:,})</strong>
+            <p style="margin: 5px 0 0 0; font-size: 0.9rem;">Most lenders require this to be below 43% for approval</p>
         </div>
         """, unsafe_allow_html=True)
     
+    # Add eligibility check section
+    st.markdown('<h4>Loan Eligibility Check</h4>', unsafe_allow_html=True)
+    
+    eligibility_issues = []
+    
+    # Check minimum income threshold (example: $1500/month)
+    if total_income < 1500:
+        eligibility_issues.append("⚠️ Total monthly income below minimum requirement ($1,500)")
+    
+    # Check if DTI is too high (above 43% is typically problematic)
+    if dti_percent > 43:
+        eligibility_issues.append("⚠️ Debt-to-income ratio exceeds maximum threshold (43%)")
+    
+    # Credit history is critical
+    if credit_history == 0:
+        eligibility_issues.append("⚠️ Existing unsettled loans may affect approval odds")
+    
+    # Display eligibility issues if any
+    if eligibility_issues:
+        st.markdown('<div style="background-color:#FFF3E0;padding:10px;border-radius:5px;margin-bottom:15px;">', unsafe_allow_html=True)
+        st.markdown("<p><strong>Potential eligibility concerns:</strong></p>", unsafe_allow_html=True)
+        for issue in eligibility_issues:
+            st.markdown(f"<p>{issue}</p>", unsafe_allow_html=True)
+        st.markdown("</div>", unsafe_allow_html=True)
+    else:
+        st.markdown('<div style="background-color:#E8F5E9;padding:10px;border-radius:5px;margin-bottom:15px;">', unsafe_allow_html=True)
+        st.markdown("<p>✅ <strong>No obvious eligibility concerns</strong></p>", unsafe_allow_html=True)
+        st.markdown("</div>", unsafe_allow_html=True)
+    
     st.markdown('</div>', unsafe_allow_html=True)
     
     # Prediction button with enhanced styling
@@ -275,9 +298,8 @@ with tab1:
         # Convert Term from months to years
         term_years = loan_term / 12
         
-        # Compute Derived Features
-        total_income = applicant_income + coapplicant_income  # Sum of incomes
-        debt_to_income = loan_amount / total_income if total_income > 0 else 0  # Avoid divide by zero
+        # Compute Derived Features - use the same monthly payment calculated above
+        debt_to_income = monthly_payment / total_income if total_income > 0 else 0  
         credit_amount_interaction = loan_amount * credit_history_num  # Interaction effect
         income_term_ratio = total_income / term_years if term_years > 0 else 0  # Avoid divide by zero
         
@@ -295,8 +317,26 @@ with tab1:
             input_data = preprocess_input()
             prediction = model.predict(input_data)
             
+            # Apply additional rules to override the model in certain cases
+            manual_rejection = False
+            rejection_reason = ""
+            
+            # Rule-based rejections that override the model
+            if total_income < 1500:
+                manual_rejection = True
+                rejection_reason = "Insufficient income (below $1,500 monthly minimum)"
+            elif dti_percent > 50:  # Very high DTI is an automatic rejection
+                manual_rejection = True
+                rejection_reason = "Debt-to-income ratio too high (exceeds 50%)"
+            elif credit_history == 0 and dti_percent > 35:
+                manual_rejection = True  
+                rejection_reason = "Combination of unsettled loans and high debt-to-income ratio"
+            
+            # Final decision combines model prediction and manual eligibility checks
+            final_approval = (prediction[0] == 1) and not manual_rejection
+            
             # Show result with enhanced styling
-            if prediction[0] == 1:
+            if final_approval:
                 st.markdown("""
                 <div class="result-approved">
                     <h3 style="color: #2E7D32;">✅ Loan Approved</h3>
@@ -304,11 +344,12 @@ with tab1:
                 </div>
                 """, unsafe_allow_html=True)
             else:
-                st.markdown("""
+                st.markdown(f"""
                 <div class="result-rejected">
                     <h3 style="color: #C62828;">❌ Loan Not Approved</h3>
                     <p>Unfortunately, based on your current information, we cannot approve your loan application.</p>
-                    <p>Consider improving your credit score or applying with a co-applicant with higher income.</p>
+                    <p><strong>Primary reason:</strong> {rejection_reason if manual_rejection else "Multiple factors considered by our approval algorithm"}</p>
+                    <p>Consider improving your credit score, reducing existing debt, or applying with a co-applicant with higher income.</p>
                 </div>
                 """, unsafe_allow_html=True)
 
@@ -316,7 +357,7 @@ with tab2:
     st.markdown("""
     <div class="section-card">
         <h3>About the Loan Approval System</h3>
-        <p>This AI-powered system uses advanced machine learning algorithms to determine loan approval eligibility.</p>
+        <p>This AI-powered system uses advanced machine learning algorithms to determine loan approval eligibility based on multiple factors.</p>
     </div>
     """, unsafe_allow_html=True)
     
@@ -327,16 +368,33 @@ with tab2:
     - Credit history status
     - Loan amount and term
     - Income and employment status
+    - Debt-to-income ratio
+    """)
+    
+    st.write("""
+    Our decision engine combines a trained machine learning model with industry-standard lending criteria. 
+    The system evaluates your application against patterns from thousands of previous loan applications 
+    while also applying standard financial rules used by major lenders.
     """)
     
-    st.write("All decisions are made automatically using a trained decision tree model that has learned from thousands of previous loan applications.")
+    st.markdown('<div class="section-card">', unsafe_allow_html=True)
+    st.markdown("<h3>Important Factors for Approval</h3>", unsafe_allow_html=True)
+    st.write("To maximize your chances of approval:")
+    st.markdown("""
+    - Maintain a debt-to-income ratio below 43%
+    - Have sufficient monthly income (minimum $1,500)
+    - Clear existing unsettled loans when possible
+    - Consider adding a co-applicant to strengthen your application
+    """)
+    st.markdown('</div>', unsafe_allow_html=True)
     
     st.markdown('<div class="section-card">', unsafe_allow_html=True)
-    st.markdown("<h3>Features</h3>", unsafe_allow_html=True)
+    st.markdown("<h3>Key Features</h3>", unsafe_allow_html=True)
     st.write("Our system provides:")
     st.markdown("""
     - Instant loan approval decisions
     - Transparent evaluation process
+    - Detailed eligibility assessment
     - Secure data handling
     """)
     st.markdown('</div>', unsafe_allow_html=True)