Clean up responses: fix fraud word replacement, reduce repetitive fillers and stuttering

app/agents/persona_engine.py

@@ -182,17 +182,17 @@ class TypingSimulator:
         words = text.split()
         if len(words) < 4: return text
         
-        # 1. Word Repetition (Neurotic stall)
-        if random.random() < 0.3:
+        # 1. Word Repetition (Neurotic stall) - REDUCED probability
+        if random.random() < 0.15:  # Was 0.3, reduced to avoid excessive repetition
             idx = random.randint(0, len(words) - 1)
             words.insert(idx, words[idx])
             
         # 2. Abandoned thoughts (Trailing off)
-        if agitation == "volatile" and random.random() < 0.2:
-            return " ".join(words[:max(2, len(words)//2)]) + "... wait.."
+        if agitation == "volatile" and random.random() < 0.15:  # Reduced from 0.2
+            return " ".join(words[:max(2, len(words)//2)]) + "..."
             
-        # 3. Fragmenting
-        if random.random() < 0.2:
+        # 3. Fragmenting - REDUCED
+        if random.random() < 0.1:  # Was 0.2
              idx = random.randint(1, len(words) - 2)
              words[idx] = words[idx] + ".."
              
@@ -217,14 +217,14 @@ class TypingSimulator:
             
         # 2. 🧱 Add Fillers/Hesitation (Start of sentence)
         # 2. 🧱 Add Fillers/Hesitation (Start of sentence)
-        # Professionals use fewer fillers
-        filler_prob = 0.05 if is_professional else (0.4 if age_group == "elderly" else 0.3)
+        # Professionals use fewer fillers - REDUCED overall probabilities for cleaner responses
+        filler_prob = 0.03 if is_professional else (0.2 if age_group == "elderly" else 0.15)
         if random.random() < filler_prob:
             filler_list = TypingSimulator.FILLERS.get(language, TypingSimulator.FILLERS['english'])
             # Add specific hesitation for high stress - LANGUAGE AWARE
             if stress_level == "high":
                 if language == "english":
-                    filler_list = filler_list + ["oh.. ", "wait.. ", "please.. ", "hold on.. "]
+                    filler_list = filler_list + ["oh.. ", "wait, ", "please, ", "hold on, "]
                 else:
                     filler_list = filler_list + ["oho.. ", "arre.. ", "ruko.. ", "suno.. "]
             text = random.choice(filler_list) + text
@@ -1037,13 +1037,18 @@ class PersonaEngine:
             return None
         
         # 🔥 CRITICAL: Sanitize forbidden words that break honeypot illusion
-        forbidden_words = ['scammer', 'scam', 'fraud', 'honeypot', 'bot', 'ai assistant', 'detection']
-        clean_lower = clean.lower()
-        for word in forbidden_words:
-            if word in clean_lower:
-                # Replace forbidden word with neutral alternative
-                import re
-                clean = re.sub(rf'\b{word}\b', 'sir' if word == 'scammer' else 'this', clean, flags=re.IGNORECASE)
+        # Only replace words that reveal the honeypot's awareness
+        import re
+        forbidden_replacements = [
+            (r'\bscammer\b', 'sir'),
+            (r'\bscam\b(?!\s*(?:team|prevention|department|squad))', 'issue'),  # Don't replace "scam team"
+            (r'\bhoneypot\b', 'system'),
+            (r'\bbot\b', 'person'),
+            (r'\bai assistant\b', 'helper'),
+            (r'\bdetection\b', 'checking'),
+        ]
+        for pattern, replacement in forbidden_replacements:
+            clean = re.sub(pattern, replacement, clean, flags=re.IGNORECASE)
         
         # 🔥 ENGLISH MODE: Replace leaked Hindi words with English equivalents
         import re