Update app.py

app.py

@@ -84,13 +84,20 @@ class IrisPredictor:
                     labels={'x': 'Features', 'y': 'Importance'})
         return fig
     
-    def predict_single_sample(self, model, features):
-        # Scale features
-        scaled_features = self.scaler.transform([features])
-        # Make prediction
-        prediction = model.predict(scaled_features)
-        probabilities = model.predict_proba(scaled_features)
-        return prediction[0], probabilities[0]
+ def predict_single_sample(self, model, features):
+    # Ensure scaler is fitted
+    if not hasattr(self.scaler, "mean_"):
+        raise RuntimeError("The scaler is not fitted. Please train the model first.")
+    
+    # Scale features
+    scaled_features = self.scaler.transform([features])
+    
+    # Make prediction
+    prediction = model.predict(scaled_features)
+    probabilities = model.predict_proba(scaled_features)
+    
+    return prediction[0], probabilities[0]
+
 
 def main():
     st.title("🌸 Iris Flower Prediction App")