Update app.py

app.py

@@ -155,7 +155,15 @@ def calculate_darvo_score(patterns, sentiment_before, sentiment_after, motifs_fo
         0.25 * motif_score +
         0.15 * contradiction_score, 1.0
     ), 3)
-
+def detect_weapon_language(text):
+    weapon_keywords = [
+        "knife", "knives", "stab", "cut you", "cutting",
+        "gun", "shoot", "rifle", "firearm", "pistol",
+        "bomb", "blow up", "grenade", "explode",
+        "weapon", "armed", "loaded", "kill you", "take you out"
+    ]
+    text_lower = text.lower()
+    return any(word in text_lower for word in weapon_keywords)
 def get_risk_stage(patterns, sentiment):
     if "threat" in patterns or "insults" in patterns:
         return 2
@@ -214,7 +222,7 @@ def analyze_single_message(text, thresholds):
     result = sst_pipeline(text)[0]
     sentiment = "supportive" if result['label'] == "POSITIVE" else "undermining"
     sentiment_score = result['score'] if sentiment == "undermining" else 0.0
-
+    weapon_flag = detect_weapon_language(text)
     adjusted_thresholds = {
         k: v + 0.05 if sentiment == "supportive" else v
         for k, v in thresholds.items()
@@ -256,6 +264,10 @@ def analyze_single_message(text, thresholds):
         weight_sum += weight
 
     abuse_score_raw = (weighted_total / weight_sum) * 100
+    if weapon_flag:
+    abuse_score_raw = min(abuse_score_raw + 25, 100)  # boost intensity
+    if weapon_flag and stage < 2:
+    stage = 2
 
     if "threat" in threshold_labels or "control" in threshold_labels or "insults" in threshold_labels:
         abuse_score = min(abuse_score_raw, 100)