Update app.py

app.py

@@ -109,23 +109,23 @@ def load_models():
         models['xgboost'] = create_dummy_model("xgboost")
     
     # Try to load Linear Regression model
-    try:
-        linear_path = hf_hub_download(
-            repo_id="Lesterchia174/HDB_Price_Predictor",
-            filename="linear_regression.joblib",
-            repo_type="space"
-        )
-        models['linear_regression'] = safe_joblib_load(linear_path)
-        if models['linear_regression'] is None:
-            print("⚠️  Creating dummy model for Linear Regression")
-            models['linear_regression'] = create_dummy_model("linear_regression")
-        else:
-            print("✅ Linear Regression model loaded and validated")
+    #try:
+    #    linear_path = hf_hub_download(
+    #        repo_id="Lesterchia174/HDB_Price_Predictor",
+    #        filename="linear_regression.joblib",
+    #        repo_type="space"
+    #    )
+    #    models['linear_regression'] = safe_joblib_load(linear_path)
+    #    if models['linear_regression'] is None:
+    #        print("⚠️  Creating dummy model for Linear Regression")
+    #        models['linear_regression'] = create_dummy_model("linear_regression")
+    #    else:
+    #        print("✅ Linear Regression model loaded and validated")
             
-    except Exception as e:
-        print(f"❌ Error downloading Linear Regression model: {e}")
-        print("⚠️  Creating dummy model for Linear Regression")
-        models['linear_regression'] = create_dummy_model("linear_regression")
+    #except Exception as e:
+    #    print(f"❌ Error downloading Linear Regression model: {e}")
+    #    print("⚠️  Creating dummy model for Linear Regression")
+    #    models['linear_regression'] = create_dummy_model("linear_regression")
     
     return models
 
@@ -237,11 +237,11 @@ def create_market_insights_chart(data, user_input, predicted_price_xgb, predicte
                                              line=dict(width=2, color='darkred')),
                                  name='XGBoost Prediction'))
 
-        fig.add_trace(go.Scatter(x=[user_input['floor_area_sqm']], y=[predicted_price_lr],
-                                 mode='markers',
-                                 marker=dict(symbol='diamond', size=20, color='blue',
-                                             line=dict(width=2, color='darkblue')),
-                                 name='Linear Regression Prediction'))
+        #fig.add_trace(go.Scatter(x=[user_input['floor_area_sqm']], y=[predicted_price_lr],
+        #                         mode='markers',
+        #                         marker=dict(symbol='diamond', size=20, color='blue',
+        #                                     line=dict(width=2, color='darkblue')),
+        #                         name='Linear Regression Prediction'))
 
         fig.update_layout(template="plotly_white", height=400, showlegend=True)
         return fig
@@ -268,11 +268,11 @@ def predict_hdb_price(town, flat_type, flat_model, floor_area_sqm, storey_level,
             print(f"❌ XGBoost prediction error: {e}")
             predicted_price_xgb = 400000  # Fallback value
         
-        try:
-            predicted_price_lr = max(0, float(models['linear_regression'].predict(processed_input)[0]))
-        except Exception as e:
-            print(f"❌ Linear Regression prediction error: {e}")
-            predicted_price_lr = 380000  # Fallback value
+        #try:
+        #    predicted_price_lr = max(0, float(models['linear_regression'].predict(processed_input)[0]))
+        #except Exception as e:
+        #    print(f"❌ Linear Regression prediction error: {e}")
+        #    predicted_price_lr = 380000  # Fallback value
         
         # Use selected model's prediction
         if model_choice == "XGBoost":
@@ -362,7 +362,7 @@ with gr.Blocks(title="🏠 HDB Price Predictor", theme=gr.themes.Soft()) as demo
             insights = gr.Markdown(label="📋 Property Summary")
 
     with gr.Row():
-        chart_output = gr.Plot(label="📈 Market Insights (Both Models)")
+        chart_output = gr.Plot(label="📈 Market Insights")
 
     # Connect button to function
     predict_btn.click(