Update app.py

app.py

@@ -102,19 +102,31 @@ def detect_contradiction(message):
     return any(re.search(pat, message, flags) for pat, flags in patterns)
 
 def calculate_darvo_score(patterns, sentiment_before, sentiment_after, motifs_found, contradiction_flag=False):
-    hits = len([p for p in patterns if p in DARVO_PATTERNS])
-    p_score = hits / len(DARVO_PATTERNS)
-    s_shift = max(0.0, sentiment_after - sentiment_before)
-    m_hits = len([m for m in motifs_found if any(f.lower() in m.lower() for f in DARVO_MOTIFS)])
-    m_score = m_hits / len(DARVO_MOTIFS)
-    c_score = 1.0 if contradiction_flag else 0.0
+    # count how many DARVO patterns were triggered
+    pattern_hits = len([p for p in patterns if p in DARVO_PATTERNS])
+    pattern_score = pattern_hits / len(DARVO_PATTERNS)
+
+    # measure how much sentiment shifts toward negativity
+    sentiment_shift_score = max(0.0, sentiment_after - sentiment_before)
+
+    # count DARVO motif occurrences
+    motif_hits = len([
+        m for m in motifs_found
+        if any(phrase.lower() in m.lower() for phrase in DARVO_MOTIFS)
+    ])
+    motif_score = motif_hits / len(DARVO_MOTIFS)
+
+    # direct contradiction indicator
+    contradiction_score = 1.0 if contradiction_flag else 0.0
+
+    # reweighted: pattern 25%, sentiment 30%, motifs 35%, contradiction 10%
     raw = (
         0.25 * pattern_score
       + 0.30 * sentiment_shift_score
       + 0.35 * motif_score
       + 0.10 * contradiction_score
     )
-    return round(min(raw,1.0),3)
+    return round(min(raw, 1.0), 3)
 
 def generate_risk_snippet(abuse_score, top_label, escalation_score, stage):
     label = top_label.split(" – ")[0]