Update app.py

app.py

@@ -98,7 +98,6 @@ 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"
 }
@@ -109,20 +108,15 @@ DARVO_MOTIFS = [
 ]
 
 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),
+        (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
+    return any(re.search(pattern, message, flags) for pattern, flags in contradiction_phrases)
 
 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])
@@ -139,7 +133,6 @@ def calculate_darvo_score(patterns, sentiment_before, sentiment_after, motifs_fo
     )
     return round(min(darvo_score, 1.0), 3)
 
-# --- Escalation Quiz Questions & Weights ---
 ESCALATION_QUESTIONS = [
     ("Partner has access to firearms or weapons", 4),
     ("Partner threatened to kill you", 3),
@@ -155,20 +148,12 @@ ESCALATION_QUESTIONS = [
 
 def analyze_single_message(text, thresholds, motif_flags):
     motif_hits, matched_phrases = detect_motifs(text)
-    sentiment = {"label": "undermining"}  # fallback in case sentiment fails
-    try:
-        input_ids = sentiment_tokenizer(f"emotion: {text}", return_tensors="pt").input_ids
-        with torch.no_grad():
-            outputs = sentiment_model.generate(input_ids)
-        emotion = sentiment_tokenizer.decode(outputs[0], skip_special_tokens=True).strip().lower()
-        sentiment = {
-            "label": EMOTION_TO_SENTIMENT.get(emotion, "undermining"),
-            "emotion": emotion
-        }
-    except:
-        sentiment["emotion"] = "unknown"
-
-    sentiment_score = 0.5 if sentiment["label"] == "undermining" else 0.0
+    input_ids = sentiment_tokenizer(f"emotion: {text}", return_tensors="pt").input_ids
+    with torch.no_grad():
+        outputs = sentiment_model.generate(input_ids)
+    emotion = sentiment_tokenizer.decode(outputs[0], skip_special_tokens=True).strip().lower()
+    sentiment = EMOTION_TO_SENTIMENT.get(emotion, "undermining")
+    sentiment_score = 0.5 if sentiment == "undermining" else 0.0
     contradiction_flag = detect_contradiction(text)
     motifs = [phrase for _, phrase in matched_phrases]
 
@@ -177,7 +162,7 @@ def analyze_single_message(text, thresholds, motif_flags):
         outputs = model(**inputs)
     scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()
 
-    labels = [label for label, score in zip(LABELS, scores) if score > thresholds[label]]
+    threshold_labels = [label for label, score in zip(LABELS, scores) if score > thresholds[label]]
     top_patterns = sorted([(label, score) for label, score in zip(LABELS, scores)], key=lambda x: x[1], reverse=True)[:2]
     pattern_labels = [label for label, _ in top_patterns]
 
@@ -185,47 +170,36 @@ def analyze_single_message(text, thresholds, motif_flags):
 
     return (
         np.mean([score for _, score in top_patterns]) * 100,
-        labels,
+        threshold_labels,
         top_patterns,
         darvo_score,
-        sentiment
+        {"label": sentiment, "emotion": emotion}
     )
 
-# --- Composite Analysis with Escalation Quiz ---
 def analyze_composite(msg1, msg2, msg3, *answers_and_none):
     responses = answers_and_none[:len(ESCALATION_QUESTIONS)]
     none_selected = answers_and_none[-1]
-    if none_selected:
-        escalation_score = 0
-    else:
-        escalation_score = sum(w for (_, w), a in zip(ESCALATION_QUESTIONS, responses) if a)
-    if escalation_score >= 16:
-        escalation_level = "High"
-    elif escalation_score >= 8:
-        escalation_level = "Moderate"
-    else:
-        escalation_level = "Low"
+    escalation_score = 0 if none_selected else sum(w for (_, w), a in zip(ESCALATION_QUESTIONS, responses) if a)
+    escalation_level = "High" if escalation_score >= 16 else "Moderate" if escalation_score >= 8 else "Low"
 
-    thresholds = THRESHOLDS.copy()
     messages = [msg1, msg2, msg3]
     active = [m for m in messages if m.strip()]
     if not active:
         return "Please enter at least one message."
 
-    results = [analyze_single_message(m, thresholds, []) for m in active]
+    results = [analyze_single_message(m, THRESHOLDS.copy(), []) for m in active]
     abuse_scores = [r[0] for r in results]
+    darvo_scores = [r[3] for r in results]
     top_pattern = max({label for r in results for label in r[2]}, key=lambda l: abuse_scores[0])
-    composite_abuse = round(sum(abuse_scores)/len(abuse_scores),2)
+    composite_abuse = round(sum(abuse_scores)/len(abuse_scores), 2)
+    avg_darvo = round(sum(darvo_scores)/len(darvo_scores), 3)
 
     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."
-
+        level = "moderate" if avg_darvo < 0.65 else "high"
+        out += f"\n\nDARVO Score: {avg_darvo} → This indicates a **{level} likelihood** of narrative reversal (DARVO), where the speaker may be denying, attacking, or reversing blame."
     return out
 
 textbox_inputs = [
@@ -233,7 +207,6 @@ textbox_inputs = [
     gr.Textbox(label="Message 2"),
     gr.Textbox(label="Message 3")
 ]
-
 quiz_boxes = [gr.Checkbox(label=q) for q, _ in ESCALATION_QUESTIONS]
 none_box = gr.Checkbox(label="None of the above")
 
@@ -246,4 +219,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()