severity_msg

app.py

@@ -315,13 +315,36 @@ def predict(
     # Evaluate severity using count_ones
     severity_base = predictor.evaluate_severity(count_ones)
 
+  #  # -------------------------------
+  #  # Sum of predicted probabilities
+  #  # -------------------------------
+  #  # 'probs' is a list of arrays; each array is the prob for class=2 from each model.
+  #  sum_prob_2 = sum(prob[0] for prob in probs if not np.isnan(prob[0]))
+  #  sum_prob_1 = sum((1 - prob[0]) for prob in probs if not np.isnan(prob[0]))
+  #  severity_msg = f"{severity_base} (Sum of Prob (Bad Mental Status)={sum_prob_1:.2f}, Prob (Ok Mental Status)={sum_prob_2:.2f})"
+
     # -------------------------------
-    # Sum of predicted probabilities
+    # Sum, average, and standard deviation of predicted probabilities
     # -------------------------------
-    # 'probs' is a list of arrays; each array is the prob for class=2 from each model.
-    sum_prob_2 = sum(prob[0] for prob in probs if not np.isnan(prob[0]))
-    sum_prob_1 = sum((1 - prob[0]) for prob in probs if not np.isnan(prob[0]))
-    severity_msg = f"{severity_base} (Sum of Prob (Bad Mental Status)={sum_prob_1:.2f}, Prob (Ok Mental Status)={sum_prob_2:.2f})"
+    
+    # Filter probabilities and exclude NaN values
+    filtered_probs_2 = [prob[0] for prob in probs if not np.isnan(prob[0])]
+    filtered_probs_1 = [1 - prob[0] for prob in probs if not np.isnan(prob[0])]
+    
+    sum_prob_2 = sum(filtered_probs_2)
+    sum_prob_1 = sum(filtered_probs_1)
+    
+    avg_prob_2 = np.mean(filtered_probs_2)
+    avg_prob_1 = np.mean(filtered_probs_1)
+    
+    std_dev_prob_2 = np.std(filtered_probs_2)
+    std_dev_prob_1 = np.std(filtered_probs_1)
+
+    severity_msg = (
+        f"{severity_base} "
+        f"(Avg Prob (Bad Mental Status)={avg_prob_1:.2f} ± {std_dev_prob_1:.2f}, "
+        f"Avg Prob (Ok Mental Status)={avg_prob_2:.2f} ± {std_dev_prob_2:.2f})"
+    )
 
     # 4) Summarize predictions (with probabilities)
     label_prediction_info = {}