Update app.py

app.py

@@ -21,8 +21,8 @@ LABELS = [
 ]
 
 THRESHOLDS = {
-    "blame shifting": 0.23,
-    "contradictory statements": 0.25,
+    "blame shifting": 0.3,
+    "contradictory statements": 0.32,
     "control": 0.40,
     "dismissiveness": 0.45,
     "gaslighting": 0.30,
@@ -51,7 +51,8 @@ EXPLANATIONS = {
 }
 
 PATTERN_WEIGHTS = {
-    "gaslighting": 1.3, "mockery": 1.2, "control": 1.2, "dismissiveness": 0.8
+    "gaslighting": 1.3, "mockery": 1.2, "control": 1.2, "dismissiveness": 0.8, "blame_shifting": 0.8,
+    "contradictory_statements": 0.75,
 }
 
 def custom_sentiment(text):
@@ -122,8 +123,13 @@ def analyze_composite(msg1, msg2, msg3, flags):
 }
     flag_boost = sum(flag_weights.get(f, 3) for f in flags) / len(active_messages)
     composite_score = min(base_score + flag_boost, 100)
-    composite_score = round(composite_score, 2)
-    composite_score = round(composite_score)
+  # Apply message count dampening AFTER base and flag boost
+    if len(active_messages) == 1:
+        composite_score *= 0.85  # 15% reduction for 1 message
+    elif len(active_messages) == 2:
+        composite_score *= 0.93  # 7% reduction for 2 messages
+
+composite_score = round(min(composite_score, 100), 2)  # re-cap just in case
 
     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: