Update app.py

app.py

@@ -346,34 +346,21 @@ def get_risk_stage(patterns, sentiment):
     elif sentiment == "supportive" and any(p in patterns for p in ["projection", "dismissiveness"]):
         return 4
     return 1
-# Use pattern scores from the top-scoring message for risk snippet
-index_top_msg = abuse_scores.index(max(abuse_scores))
-pattern_scores_for_snippet = {label: score for label, score in results[index_top_msg][0][2]}
-def generate_risk_snippet(abuse_score, top_label, escalation_score, stage, pattern_scores):
-    aggression_score = pattern_scores.get("aggression", 0)
 
-    # Updated risk level logic
+def generate_risk_snippet(abuse_score, top_label, escalation_score, stage):
     if abuse_score >= 85 or escalation_score >= 16:
         risk_level = "high"
-    elif abuse_score >= 60 or escalation_score >= 8 or aggression_score >= 0.75:
+    elif abuse_score >= 60 or escalation_score >= 8:
         risk_level = "moderate"
     elif stage == 2 and abuse_score >= 40:
-        risk_level = "moderate"
+        risk_level = "moderate"  # 🔧 New rule for escalation stage
     else:
         risk_level = "low"
-
-    # Narrative
-    base = f"\n\n🛑 Risk Level: {risk_level.capitalize()}\n"
-    base += f"This message shows strong indicators of **{top_label}**. "
-
-    if risk_level == "high":
-        base += "The language may reflect patterns of emotional control, even when expressed in soft or caring terms.\n"
-    elif risk_level == "moderate":
-        base += "There are signs of emotional pressure or verbal aggression that may escalate if repeated.\n"
+    if isinstance(top_label, str) and " – " in top_label:
+        pattern_label, pattern_score = top_label.split(" – ")
     else:
-        base += "The message does not strongly indicate abuse, but it's important to monitor for patterns.\n"
-
-    return risk_level, base
+        pattern_label = str(top_label) if top_label is not None else "Unknown"
+        pattern_score = ""
 
     WHY_FLAGGED = {
         "control": "This message may reflect efforts to restrict someone’s autonomy, even if it's framed as concern or care.",
@@ -562,50 +549,22 @@ def analyze_composite(msg1, date1, msg2, date2, msg3, date3, *answers_and_none):
     sentiments = [r[0][3]['label'] for r in results]
     stages = [r[0][4] for r in results]
     darvo_scores = [r[0][5] for r in results]
-    tone_tags = [r[0][6] for r in results]
+    tone_tags= [r[0][6] for r in results]
     dates_used = [r[1] or "Undated" for r in results]  # Store dates for future mapping
-
-    # Calculate escalation bump
+    # Calculate escalation bump *after* model results exist
     escalation_bump = 0
     for result, _ in results:
         abuse_score, threshold_labels, top_patterns, sentiment, stage, darvo_score, tone_tag = result
-
         if darvo_score > 0.65:
             escalation_bump += 3
-
-        if tone_tag in ["forced accountability flip", "emotional threat", "aggressive dismissal", "mocking detachment"]:
+        if tone_tag in ["forced accountability flip", "emotional threat"]:
             escalation_bump += 2
-
         if abuse_score > 80:
             escalation_bump += 2
-
         if stage == 2:
             escalation_bump += 3
 
-        if "threat" in threshold_labels or "aggression" in threshold_labels:
-            escalation_bump += 4
-
-    # Helper: score trend of pattern severity
-    def message_severity_index(threshold_labels):
-        weights = {
-            "recovery": 0,
-            "dismissiveness": 1,
-            "deflection": 1,
-            "guilt tripping": 2,
-            "control": 3,
-            "gaslighting": 3,
-            "aggression": 4,
-            "threat": 5,
-        }
-        return max([weights.get(label, 0) for label in threshold_labels], default=0)
-
-    severity_levels = [message_severity_index(r[0][1]) for r in results]
-    if len(severity_levels) >= 2 and severity_levels == sorted(severity_levels):
-        escalation_bump += 2
-
-    escalation_bump = min(escalation_bump, 10)
-
-    # Final escalation score
+    # Now we can safely calculate hybrid_score
     hybrid_score = escalation_score + escalation_bump if escalation_score is not None else 0
     risk_level = (
         "High" if hybrid_score >= 16 else
@@ -613,6 +572,10 @@ def analyze_composite(msg1, date1, msg2, date2, msg3, date3, *answers_and_none):
         "Low"
     )
 
+    # Now compute scores and allow override
+    abuse_scores = [r[0][0] for r in results]
+    stages = [r[0][4] for r in results]
+
     # Post-check override (e.g. stage 2 or high abuse score forces Moderate risk)
     if any(score > 70 for score in abuse_scores) or any(stage == 2 for stage in stages):
         if risk_level == "Low":
@@ -623,18 +586,27 @@ def analyze_composite(msg1, date1, msg2, date2, msg3, date3, *answers_and_none):
 
     # --- Composite Abuse Score with Weighted Patterns ---
     composite_abuse_scores = []
+
     for result, _ in results:
         abuse_score, threshold_labels, top_patterns, _, _, _, _ = result
         weighted_score = 0
         total_weight = 0
+
         for label, score in top_patterns:
             weight = PATTERN_WEIGHTS.get(label, 1.0)
             weighted_score += score * weight
             total_weight += weight
-        final_score = (weighted_score / total_weight) * 100 if total_weight > 0 else 0
+
+        if total_weight > 0:
+            final_score = (weighted_score / total_weight) * 100
+        else:
+            final_score = 0
+
         composite_abuse_scores.append(final_score)
 
     composite_abuse = int(round(sum(composite_abuse_scores) / len(composite_abuse_scores)))
+
+
     most_common_stage = max(set(stages), key=stages.count)
     stage_text = RISK_STAGE_LABELS[most_common_stage]
 
@@ -647,41 +619,34 @@ def analyze_composite(msg1, date1, msg2, date2, msg3, date3, *answers_and_none):
     out = f"Abuse Intensity: {composite_abuse}%\n"
     out += "📊 This reflects the strength and severity of detected abuse patterns in the message(s).\n\n"
 
+# Save this line for later use at the 
     if escalation_score is None:
         escalation_text = "📉 Escalation Potential: Unknown (Checklist not completed)\n"
         escalation_text += "⚠️ *This section was not completed. Escalation potential is unknown.*\n"
+        hybrid_score = 0  # ✅ fallback so it's defined for generate_risk_snippet
     else:
         escalation_text = f"🧨 **Escalation Potential: {risk_level} ({escalation_score}/{sum(w for _, w in ESCALATION_QUESTIONS)})**\n"
         escalation_text += "This score comes directly from the safety checklist and functions as a standalone escalation risk score.\n"
         escalation_text += "It indicates how many serious risk factors are present based on your answers to the safety checklist.\n"
-
-    # Derive top label
+        # Derive top_label from the strongest top_patterns across all messages
     top_label = None
     if results:
         sorted_patterns = sorted(
             [(label, score) for r in results for label, score in r[0][2]],
             key=lambda x: x[1],
             reverse=True
-        )
+    )
         if sorted_patterns:
             top_label = f"{sorted_patterns[0][0]} – {int(round(sorted_patterns[0][1] * 100))}%"
         if top_label is None:
             top_label = "Unknown – 0%"
-
-    out += generate_risk_snippet(
-        composite_abuse,
-        top_label,
-        hybrid_score if escalation_score is not None else 0,
-        most_common_stage,
-        pattern_scores_for_snippet
-)
+    out += generate_risk_snippet(composite_abuse, top_label, hybrid_score if escalation_score is not None else 0, most_common_stage)
     out += f"\n\n{stage_text}"
     out += darvo_blurb
     out += "\n\n🎭 **Emotional Tones Detected:**\n"
     for i, tone in enumerate(tone_tags):
         label = tone if tone else "none"
         out += f"• Message {i+1}: *{label}*\n"
-
     print(f"DEBUG: avg_darvo = {avg_darvo}")
     pattern_labels = [r[0][2][0][0] for r in results]  # top label for each message
     timeline_image = generate_abuse_score_chart(dates_used, abuse_scores, pattern_labels)