Update app.py

app.py

@@ -97,20 +97,21 @@ def analyze_composite(msg1, msg2, msg3, flags):
     abuse_scores = [r[0] for r in results]
 
     base_score = sum(abuse_scores) / len(abuse_scores)
-    danger_weight = 5  # you can tweak this multiplier
-    adjusted_score = min(base_score + len(flags) * danger_weight, 100)
-    composite_score = round(adjusted_score, 2)
-
-    label_sets = [label for result in results for label in set(result[1])]
-    label_counts = {label: label_sets.count(label) for label in set(label_sets)}
+    label_sets = [[label for label, _ in r[2]] for r in results]
+    label_counts = {label: sum(label in s for s in label_sets) for label in set().union(*label_sets)}
     top_labels = sorted(label_counts.items(), key=lambda x: x[1], reverse=True)[:2]
     top_explanations = [EXPLANATIONS.get(label, "") for label, _ in top_labels]
 
+    # Adjust flag-based weight relative to number of messages
+    danger_weight = 5
+    flag_boost = (len(flags) * danger_weight) / len(active_messages)
+    composite_score = min(base_score + flag_boost, 100)
+    composite_score = round(composite_score, 2)
+
     result = f"These messages show patterns of {', '.join(label for label, _ in top_labels)} and are estimated to be {composite_score}% likely abusive."
     for expl in top_explanations:
         if expl:
             result += f"\n• {expl}"
-
     return result
 
 textbox_inputs = [