Update app.py

app.py

@@ -37,7 +37,7 @@ LABELS = [
 THRESHOLDS = {
     "blame shifting": 0.3,
     "contradictory statements": 0.32,
-    "control": 0.40,
+    "control": 0.45,
     "dismissiveness": 0.45,
     "gaslighting": 0.30,
     "guilt tripping": 0.20,
@@ -188,19 +188,36 @@ def analyze_single_message(text, thresholds, motif_flags):
 
 # --- Composite Message Analysis ---
 def analyze_composite(msg1, msg2, msg3, flags):
-    thresholds = THRESHOLDS
+    thresholds = THRESHOLDS.copy()
     messages = [msg1, msg2, msg3]
     active_messages = [m for m in messages if m.strip()]
     if not active_messages:
         return "Please enter at least one message."
 
     results = []
+    sentiment_labels = []
+    sentiment_score_total = 0.0
+
     for m in active_messages:
         result = analyze_single_message(m, thresholds, flags)
         print(f"Message: {m}")
         print(f"Sentiment result: {result[4]}")
         results.append(result)
+        sentiment_labels.append(result[4]["label"])
+        if result[4]["label"] == "undermining":
+            sentiment_score_total += 0.5  # fixed value from `analyze_single_message`
+
+    # Sentiment adjustment based on average and balance
+    undermining_count = sentiment_labels.count("undermining")
+    supportive_count = sentiment_labels.count("supportive")
+
+    if undermining_count > supportive_count:
+        thresholds = {k: v * 0.9 for k, v in thresholds.items()}
+    elif undermining_count and supportive_count:
+        thresholds = {k: v * 0.95 for k, v in thresholds.items()}  # very subtle if mixed
+        print("⚖️ Detected conflicting sentiment across messages.")
 
+    # Abuse scoring
     abuse_scores = [r[0] for r in results]
     darvo_scores = [r[3] for r in results]
     average_darvo = round(sum(darvo_scores) / len(darvo_scores), 3)
@@ -218,7 +235,6 @@ def analyze_composite(msg1, msg2, msg3, flags):
         "They monitor/follow me": 4,
         "I feel unsafe when alone with them": 6
     }
-
     flag_boost = sum(flag_weights.get(f, 3) for f in flags) / len(active_messages)
     composite_score = min(base_score + flag_boost, 100)
     if len(active_messages) == 1:
@@ -234,6 +250,10 @@ def analyze_composite(msg1, msg2, msg3, flags):
         darvo_descriptor = "moderate" if average_darvo < 0.65 else "high"
         result += f"\n\nDARVO Score: {average_darvo} → This indicates a **{darvo_descriptor} likelihood** of narrative reversal (DARVO), where the speaker may be denying, attacking, or reversing blame."
     result += generate_risk_snippet(composite_score, top_label[0])
+    
+    if undermining_count and supportive_count:
+        result += "\n\n⚖️ These messages contain **conflicting emotional tones** — this may indicate mixed signals, ambivalence, or a push-pull dynamic. Use caution interpreting any one message alone."
+
     return result
 
 # --- Gradio Interface ---