Update app.py

app.py

@@ -219,6 +219,38 @@ def generate_risk_snippet(abuse_score, top_label, escalation_score, stage):
     base += "🧠 You can review the pattern in context. This tool highlights possible dynamics—not judgments."
 
     return base
+    def compute_abuse_score(matched_scores, sentiment):
+    """
+    Calculates abuse score based on passed patterns, their weights, and emotional context.
+    """
+    if not matched_scores:
+        return 0
+
+    # Weighted average of passed patterns
+    weighted_total = sum(score * weight for _, score, weight in matched_scores)
+    weight_sum = sum(weight for _, _, weight in matched_scores)
+    base_score = (weighted_total / weight_sum) * 100
+
+    # Boost for pattern count
+    pattern_count = len(matched_scores)
+    scale = 1.0 + 0.25 * max(0, pattern_count - 1)  # 1.25x for 2, 1.5x for 3+
+    scaled_score = base_score * scale
+
+    # Pattern floors
+    FLOORS = {
+        "threat": 70,
+        "control": 40,
+        "gaslighting": 30,
+        "insults": 25
+    }
+    floor = max(FLOORS.get(label, 0) for label, _, _ in matched_scores)
+    adjusted_score = max(scaled_score, floor)
+
+    # Sentiment tweak
+    if sentiment == "undermining" and adjusted_score < 50:
+        adjusted_score += 10
+
+    return min(adjusted_score, 100)
 def analyze_single_message(text, thresholds):
     motif_hits, matched_phrases = detect_motifs(text)
     result = sst_pipeline(text)[0]
@@ -267,12 +299,8 @@ def analyze_single_message(text, thresholds):
         if score > adjusted_thresholds[label]
     ]
 
-    if matched_scores:
-        weighted_total = sum(score * weight for _, score, weight in matched_scores)
-        weight_sum = sum(weight for _, _, weight in matched_scores)
-        abuse_score_raw = (weighted_total / weight_sum) * 100
-    else:
-        abuse_score_raw = 0
+    abuse_score_raw = compute_abuse_score(matched_scores, sentiment)
+    abuse_score = abuse_score_raw
 
     # ✅ Always assign stage
     stage = get_risk_stage(threshold_labels, sentiment) if threshold_labels else 1