Update app.py

app.py

@@ -98,6 +98,47 @@ def generate_risk_snippet(abuse_score, top_label):
     title, summary, advice = RISK_SNIPPETS[risk_level]
     return f"\n\n{title}\n{summary} (Pattern: **{top_label}**)\n💡 {advice}"
 
+# --- DARVO Detection ---
+DARVO_PATTERNS = {
+    "blame shifting", "projection", "dismissiveness", "guilt tripping", "contradictory statements"
+}
+DARVO_MOTIFS = [
+    "i guess i’m the bad guy", "after everything i’ve done", "you always twist everything",
+    "so now it’s all my fault", "i’m the villain", "i’m always wrong", "you never listen",
+    "you’re attacking me", "i’m done trying", "i’m the only one who cares"
+]
+
+def detect_contradiction(message):
+    contradiction_flag = False
+    contradiction_phrases = [
+        (r"\b(i love you).{0,15}(i hate you|you ruin everything)", re.IGNORECASE),
+        (r"\b(i’m sorry).{0,15}(but you|if you hadn’t)", re.IGNORECASE),
+        (r"\b(i’m trying).{0,15}(you never|why do you)", re.IGNORECASE),
+        (r"\b(do what you want).{0,15}(you’ll regret it|i always give everything)", re.IGNORECASE),
+        (r"\b(i don’t care).{0,15}(you never think of me)", re.IGNORECASE),
+        (r"\b(i guess i’m just).{0,15}(the bad guy|worthless|never enough)", re.IGNORECASE),
+    ]
+    for pattern, flags in contradiction_phrases:
+        if re.search(pattern, message, flags):
+            contradiction_flag = True
+            break
+    return contradiction_flag
+
+def calculate_darvo_score(patterns, sentiment_before, sentiment_after, motifs_found, contradiction_flag=False):
+    pattern_hits = len([p.lower() for p in patterns if p.lower() in DARVO_PATTERNS])
+    pattern_score = pattern_hits / len(DARVO_PATTERNS)
+    sentiment_shift_score = max(0.0, sentiment_after - sentiment_before)
+    motif_hits = len([m.lower() for m in motifs_found if m.lower() in DARVO_MOTIFS])
+    motif_score = motif_hits / len(DARVO_MOTIFS)
+    contradiction_score = 1.0 if contradiction_flag else 0.0
+    darvo_score = (
+        0.3 * pattern_score +
+        0.3 * sentiment_shift_score +
+        0.25 * motif_score +
+        0.15 * contradiction_score
+    )
+    return round(min(darvo_score, 1.0), 3)
+
 # --- Escalation Quiz Questions & Weights ---
 ESCALATION_QUESTIONS = [
     ("Partner has access to firearms or weapons", 4),
@@ -150,6 +191,12 @@ def analyze_composite(msg1, msg2, msg3, *answers_and_none):
     out = f"Abuse Intensity: {composite_abuse}%\n"
     out += f"Escalation Potential: {escalation_level} ({escalation_score}/{sum(w for _,w in ESCALATION_QUESTIONS)})"
     out += generate_risk_snippet(composite_abuse, top_pattern)
+
+    avg_darvo = round(sum([r[3] for r in results]) / len(results), 3)
+    if avg_darvo > 0.25:
+        darvo_descriptor = "moderate" if avg_darvo < 0.65 else "high"
+        out += f"\n\nDARVO Score: {avg_darvo} → This indicates a **{darvo_descriptor} likelihood** of narrative reversal (DARVO), where the speaker may be denying, attacking, or reversing blame."
+
     return out
 
 textbox_inputs = [
@@ -170,4 +217,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()