Update app.py

app.py

@@ -113,6 +113,70 @@ def generate_risk_snippet(abuse_score, top_label, escalation_score):
     base += "🧠 You can review the pattern in context. This tool highlights possible dynamics—not judgments."
 
     return base
+def analyze_single_message(text, thresholds):
+    motif_hits, matched_phrases = detect_motifs(text)
+    result = sst_pipeline(text)[0]
+    sentiment = "supportive" if result['label'] == "POSITIVE" else "undermining"
+    sentiment_score = result['score'] if sentiment == "undermining" else 0.0
+
+    adjusted_thresholds = {
+        k: v + 0.05 if sentiment == "supportive" else v
+        for k, v in thresholds.items()
+    }
+
+    contradiction_flag = detect_contradiction(text)
+    motifs = [phrase for _, phrase in matched_phrases]
+
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()
+
+    threshold_labels = [
+        label for label, score in zip(LABELS, scores)
+        if score > adjusted_thresholds[label]
+    ]
+
+    top_patterns = sorted(
+        [(label, score) for label, score in zip(LABELS, scores)],
+        key=lambda x: x[1],
+        reverse=True
+    )[:2]
+
+    weighted_scores = [(PATTERN_WEIGHTS.get(label, 1.0) * score) for label, score in top_patterns]
+    abuse_score = min(np.mean(weighted_scores) * 100, 100)
+
+    stage = get_risk_stage(threshold_labels, sentiment)
+
+    return abuse_score, threshold_labels, top_patterns, result, stage
+    
+def analyze_composite(msg1, msg2, msg3, *answers_and_none):
+    responses = answers_and_none[:len(ESCALATION_QUESTIONS)]
+    none_selected = answers_and_none[-1]
+    escalation_score = 0 if none_selected else sum(w for (_, w), a in zip(ESCALATION_QUESTIONS, responses) if a)
+    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.copy()) for m in active]
+    abuse_scores = [r[0] for r in results]
+    top_labels = [r[2][0][0] for r in results]
+    top_scores = [r[2][0][1] for r in results]
+    sentiments = [r[3]['label'] for r in results]
+    stages = [r[4] for r in results]
+
+    most_common_stage = max(set(stages), key=stages.count)
+    stage_text = RISK_STAGE_LABELS[most_common_stage]
+
+    top_label = f"{top_labels[0]} – {int(round(top_scores[0] * 100))}%"
+    composite_abuse = int(round(sum(abuse_scores) / len(abuse_scores)))
+
+    out = f"Abuse Intensity: {composite_abuse}%\n"
+    out += f"Escalation Potential: {('High' if escalation_score >= 16 else 'Moderate' if escalation_score >= 8 else 'Low')} ({escalation_score}/{sum(w for _, w in ESCALATION_QUESTIONS)})"
+    out += generate_risk_snippet(composite_abuse, top_label, escalation_score)
+    out += f"\n\n{stage_text}"
+    return out
 textbox_inputs = [gr.Textbox(label=f"Message {i+1}") for i in range(3)]
 quiz_boxes = [gr.Checkbox(label=q) for q, _ in ESCALATION_QUESTIONS]
 none_box = gr.Checkbox(label="None of the above")