result

app.py

@@ -188,7 +188,6 @@ def classify(tabular_data):
 
     Args:
         tabular_data (list or array-like): Input data points (e.g., a single row of features)
-        model (object): Pre-trained classification model from PyCaret
 
     Returns:
         str: Classification result and probabilities
@@ -211,8 +210,18 @@ def classify(tabular_data):
         predicted_class = prediction.loc[0, "prediction_label"]
         class_probability = prediction.loc[0, "prediction_score"]
 
-        # Format the result
-        result = f"Predicted Class: {predicted_class}, Probability: {class_probability:.2f}"
+        # Generate appropriate output based on the prediction
+        if predicted_class == 1:
+            result = (
+                f"Based on the provided data, this tool estimates a {class_probability:.2f} probability "
+                "of a common bile duct stone. Further medical review is necessary."
+            )
+        else:
+            result = (
+                f"This analysis estimates a {class_probability:.2f} probability that no common bile duct stone is present. "
+                "Please consult a medical professional for final diagnosis."
+            )
+        
         return result
 
     except Exception as e: