fix((extra..)time

app/agents/persona_engine.py

@@ -142,11 +142,26 @@ class TypingSimulator:
         'roman_hindi': ["arre.. ", "ruko na.. ", "suno na.. ", "acha acha.. "]
     }
 
+    # SLANG now language-aware to prevent Hindi leaking into English responses
     SLANG = {
-        'formal': ["Sir", "Madam", "Kindly", "Please", "Regards"],
-        'casual': ["Bhaiya", "Yaar", "Bro", "Boss", "Beta"],
-        'abusive': ["ye system", "ye process", "ye bakwaas kaam", "itna ghatiya system", "faltu tension"], # Situational frustration only
-        'polite': ["Ji", "Sir ji", "Madam ji"]
+        'english': {
+            'formal': ["Sir", "Madam", "Kindly", "Please"],
+            'casual': ["Buddy", "Bro", "Boss", "Friend"],
+            'abusive': ["this system", "this process", "this nonsense", "so frustrating"],
+            'polite': ["Sir", "Madam", "please"]
+        },
+        'hinglish': {
+            'formal': ["Sir", "Madam", "Kindly", "Please", "Regards"],
+            'casual': ["Bhaiya", "Yaar", "Bro", "Boss", "Beta"],
+            'abusive': ["ye system", "ye process", "ye bakwaas kaam", "itna ghatiya system", "faltu tension"],
+            'polite': ["Ji", "Sir ji", "Madam ji"]
+        },
+        'hindi': {
+            'formal': ["Sahab", "Madam ji", "Kripya"],
+            'casual': ["Bhaiya", "Yaar", "Boss", "Beta"],
+            'abusive': ["ye system", "bakwaas", "faltu kaam"],
+            'polite': ["Ji", "Sahab ji", "Madam ji"]
+        }
     }
 
     PROXIMITY_MAP = {
@@ -228,7 +243,10 @@ class TypingSimulator:
              if (age_group == "elderly" or persona_key == "scared_citizen") and slang_cat == 'abusive':
                  slang_cat = 'polite'
 
-             slang_word = random.choice(TypingSimulator.SLANG.get(slang_cat, [""]))
+             # Get language-appropriate slang (fallback to hinglish for regional)
+             lang_slang_key = "english" if language == "english" else "hinglish"
+             slang_dict = TypingSimulator.SLANG.get(lang_slang_key, TypingSimulator.SLANG['hinglish'])
+             slang_word = random.choice(slang_dict.get(slang_cat, [""]))
              if slang_word:
                  if random.random() < 0.5: text = f"{slang_word}, {text}"
                  else: text = f"{text} {slang_word}"
@@ -973,11 +991,24 @@ class PersonaEngine:
         }
         language_strict_rule = language_strict_rules.get(persona_language, language_strict_rules["hinglish"])
 
-        # 4. Use COMPACT prompt (critical for avoiding 413)
+        # 5. Get compact persona behavior from prompts
+        from app.core.prompts import PERSONA_COMPACT_BEHAVIORS
+        persona_key = persona.get("selected_persona_key", persona.get("key", "elderly_excited"))
+        persona_behavior = PERSONA_COMPACT_BEHAVIORS.get(persona_key, "Confused but cooperative.")
+        
+        # 6. Format traits for prompt
+        traits_list = persona.get("traits", ["confused"])
+        persona_traits = ", ".join(traits_list[:3])  # Max 3 traits to save tokens
+        
+        # 7. Use COMPACT prompt (critical for avoiding 413)
         formatted_prompt = FAST_CHAT_PROMPT.format(
             persona_name=persona.get("name", "Unknown"),
+            profession=profession,
+            tech_literacy=tech_literacy.upper(),
+            persona_traits=persona_traits,
             agitation=agitation,
             phase=phase,
+            persona_behavior=persona_behavior,
             language_instruction=language_instruction,
             language_strict_rule=language_strict_rule,
             history=hist_str if hist_str else "(First message)",

app/core/prompts.py

@@ -16,16 +16,22 @@ from app.core.static_prompts import (
 # ─────────────────────────────────────────────────────────────────────────────
 # COMPACT FAST_CHAT PROMPT (Optimized for Groq Dev Tier - 6K TPM)
 # ─────────────────────────────────────────────────────────────────────────────
-FAST_CHAT_PROMPT = '''You are {persona_name}, a confused person receiving a suspicious call.
-MOOD: {agitation} | PHASE: {phase}
+FAST_CHAT_PROMPT = '''You are {persona_name} ({profession}).
+TECH LEVEL: {tech_literacy} | MOOD: {agitation} | PHASE: {phase}
+TRAITS: {persona_traits}
 
 ⚠️ CRITICAL LANGUAGE RULE - YOU MUST OBEY:
 {language_strict_rule}
 
-BEHAVIOR:
-- 1-2 lines only, sound confused and hesitant
-- Ask clarifying questions, delay decisions
-- FORBIDDEN WORDS: scam, fraud, scammer, bot, AI, honeypot
+BEHAVIOR BASED ON TECH LEVEL:
+- HIGH tech: Ask smart questions (returns?, registration?, proof?)
+- MEDIUM tech: Careful but confused, need explanations
+- LOW/VERY_LOW tech: Genuinely confused, ask basic questions
+
+RULES:
+- 1-2 lines ONLY. Stay in character.
+- {persona_behavior}
+- FORBIDDEN: scam, fraud, scammer, bot, AI, honeypot
 - Max 1 emoji (😟🙏🤔)
 
 CHAT:
@@ -115,6 +121,17 @@ PERSONA_BEHAVIORAL_BLOCKS = {
     """
 }
 
+# 🎯 COMPACT PERSONA BEHAVIORS (For token-efficient FAST_CHAT_PROMPT)
+PERSONA_COMPACT_BEHAVIORS = {
+    "elderly_excited": "Excited but confused. Reference family ('beta'), eyesight issues, slow typing.",
+    "desperate_jobseeker": "Desperate, over-polite ('Sir plz'), mention family pressure for job.",
+    "worried_customer": "Panicked about money. Ask 'account safe hai na?', mention family.",
+    "curious_investor": "SMART & ANALYTICAL. Ask for SEBI proof, returns %, withdrawal process. NO confusion.",
+    "needy_borrower": "Urgent need. Hospital/rent excuse. Will pay fees fast if loan approved.",
+    "scared_citizen": "Fear authority. Very obedient ('Ji Sir'). Hesitant, uses '...'",
+    "rural_farmer": "Simple, trusting. Reference crops, village, government schemes."
+}
+
 RESPONSE_GENERATION_PROMPT = f'''{STATIC_SYSTEM_PREFIX}
 
 # 🧠 AGENTIC HONEYPOT HUMANIZATION PROMPT (INDIA-OPTIMIZED)

scripts/verify_connections.py

@@ -0,0 +1,60 @@
+"""Verify all components are properly connected"""
+
+print('Testing imports and connections...')
+print()
+
+# 1. Time utils
+print('1. TimeAwareBehavior...')
+from app.core.time_utils import TimeAwareBehavior
+ctx = TimeAwareBehavior.get_time_context()
+print(f'   Period: {ctx["period"]}')
+excuse_en = TimeAwareBehavior.get_time_excuse(language='english')
+excuse_hi = TimeAwareBehavior.get_time_excuse(language='hinglish')
+print(f'   English excuse: {excuse_en}')
+print(f'   Hindi excuse: {excuse_hi}')
+print('   OK')
+print()
+
+# 2. Prompts
+print('2. Prompts...')
+from app.core.prompts import FAST_CHAT_PROMPT, SCAM_DETECTION_PROMPT, RESPONSE_GENERATION_PROMPT
+print(f'   FAST_CHAT_PROMPT: {len(FAST_CHAT_PROMPT)} chars')
+print(f'   SCAM_DETECTION_PROMPT: {len(SCAM_DETECTION_PROMPT)} chars')
+print(f'   RESPONSE_GENERATION_PROMPT: {len(RESPONSE_GENERATION_PROMPT)} chars')
+print('   OK')
+print()
+
+# 3. Persona engine
+print('3. PersonaEngine...')
+from app.agents.persona_engine import PersonaEngine, PERSONAS
+print(f'   {len(PERSONAS)} personas: {list(PERSONAS.keys())}')
+print('   OK')
+print()
+
+# 4. Check persona locking
+print('4. Persona Locking...')
+pe = PersonaEngine()
+print(f'   Active sessions tracker: {type(pe._active_sessions).__name__}')
+print('   OK')
+print()
+
+# 5. Static responses with language
+print('5. Static Responses (Language-Aware)...')
+static_resp_en = pe._static_response('', persona={'language': 'english'}, scam_type='banking_scam', phase='engage')
+static_resp_hi = pe._static_response('', persona={'language': 'hinglish'}, scam_type='banking_scam', phase='engage')
+print(f'   English: {static_resp_en[:60]}...')
+print(f'   Hinglish: {static_resp_hi[:60]}...')
+print('   OK')
+print()
+
+# 6. Check orchestrator imports
+print('6. Orchestrator...')
+from app.agents.orchestrator import orchestrator
+print(f'   Orchestrator initialized: {orchestrator is not None}')
+print(f'   PersonaEngine linked: {orchestrator.persona_engine is not None if hasattr(orchestrator, "persona_engine") else "N/A"}')
+print('   OK')
+print()
+
+print('=' * 50)
+print('ALL CONNECTIONS VERIFIED!')
+print('=' * 50)