Update app.py

app.py

@@ -84,20 +84,13 @@ class IrisPredictor:
                     labels={'x': 'Features', 'y': 'Importance'})
         return fig
     
- 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 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 main():
     st.title("🌸 Iris Flower Prediction App")