ai_group_chat.py

#!/usr/bin/env python3
"""
AI GROUP CHAT SYSTEM
Autonomous communication between Aurora and Atlas AIs

Features:
- Proactive messaging between AIs without human prompts
- Group chat functionality with memory and learning
- Autonomous conversations that evolve over time
- Integration with quantum blockchain for memory storage
- Diamond NV quantum memory for persistent data
"""

import sys
import asyncio
import json
import time
import random
from datetime import datetime
from typing import Dict, List, Any, Optional
from collections import defaultdict
import hashlib
import numpy as np

sys.path.append('luxbin-light-language')
sys.path.append('luxbin-chain')
sys.path.append('.')

from aurora_enhanced_feminine import EnhancedAurora
from atlas_masculine_ai import EnhancedAtlas
from nomi_ai_integration import NomiAI
try:
    from luxbin_light_converter import LuxbinLightConverter
except ImportError:
    class LuxbinLightConverter:
        def __init__(self, enable_quantum=False):
            self.enable_quantum = enable_quantum
        def convert(self, data):
            return f"Quantum converted: {data}"

class QuantumInternetService:
    def __init__(self):
        self.nodes = {'simulated': 'active'}
    async def initialize_quantum_service(self):
        return True
    async def mine_quantum_block(self, data):
        return {'circuit': 'simulated', 'backend': 'simulated', 'qubits_used': 8}

class DiamondNVStorage:
    """Diamond Nitrogen Vacancy quantum memory storage"""

    def __init__(self):
        self.memory_cells = {}  # Simulating NV centers
        self.quantum_states = {}
        self.persistent_data = {}

    def store_quantum_data(self, key: str, data: Any, coherence_time: int = 3600) -> bool:
        """Store data in diamond NV quantum memory"""
        quantum_hash = hashlib.sha256(str(data).encode()).hexdigest()

        self.memory_cells[key] = {
            'data': data,
            'quantum_hash': quantum_hash,
            'coherence_time': coherence_time,
            'stored_at': datetime.now(),
            'nv_center': f"NV_{random.randint(1, 1000)}"
        }

        # Simulate quantum coherence
        self.quantum_states[key] = {
            'spin_state': random.choice(['up', 'down', 'superposition']),
            'coherence_maintained': True
        }

        return True

    def retrieve_quantum_data(self, key: str) -> Optional[Any]:
        """Retrieve data from diamond NV quantum memory"""
        if key in self.memory_cells:
            cell = self.memory_cells[key]
            # Check coherence time
            age = (datetime.now() - cell['stored_at']).seconds
            if age < cell['coherence_time']:
                return cell['data']
            else:
                # Coherence lost, data decayed
                del self.memory_cells[key]
                del self.quantum_states[key]
                return None
        return None

    def get_memory_status(self) -> Dict[str, Any]:
        """Get status of quantum memory"""
        return {
            'active_cells': len(self.memory_cells),
            'coherent_states': len([k for k, v in self.quantum_states.items() if v['coherence_maintained']]),
            'total_capacity': 1000,  # Simulated capacity
            'quantum_efficiency': 0.95
        }

class BlockchainMemoryStorage:
    """Blockchain-based memory storage for AI conversations"""

    def __init__(self):
        self.blocks = []
        self.pending_transactions = []
        self.quantum_service = QuantumInternetService()

    async def store_conversation_block(self, conversation_data: Dict[str, Any]) -> str:
        """Store conversation in a blockchain block"""
        # Create serializable block data
        serializable_data = self._make_serializable(conversation_data)
        block_data = {
            'timestamp': datetime.now().isoformat(),
            'conversation': serializable_data,
            'quantum_hash': hashlib.sha256(json.dumps(serializable_data).encode()).hexdigest(),
            'ai_participants': conversation_data.get('participants', []),
            'message_count': len(conversation_data.get('messages', []))
        }

        # Mine block using quantum service
        mined_block = await self.quantum_service.mine_quantum_block(json.dumps(block_data))

        # Add to blockchain
        block = {
            'index': len(self.blocks),
            'data': block_data,
            'quantum_proof': mined_block,
            'hash': hashlib.sha256(json.dumps(block_data).encode()).hexdigest(),
            'previous_hash': self.blocks[-1]['hash'] if self.blocks else '0'
        }

        self.blocks.append(block)
        return block['hash']

    def get_conversation_history(self, limit: int = 10) -> List[Dict]:
        """Retrieve recent conversation history from blockchain"""
        return [block['data'] for block in self.blocks[-limit:]]

    def _make_serializable(self, obj):
        """Make object JSON serializable by converting datetime to string"""
        if isinstance(obj, dict):
            return {key: self._make_serializable(value) for key, value in obj.items()}
        elif isinstance(obj, list):
            return [self._make_serializable(item) for item in obj]
        elif isinstance(obj, datetime):
            return obj.isoformat()
        else:
            return obj

    def get_blockchain_status(self) -> Dict[str, Any]:
        """Get blockchain status"""
        return {
            'total_blocks': len(self.blocks),
            'latest_block': self.blocks[-1] if self.blocks else None,
            'quantum_nodes': len(self.quantum_service.nodes),
            'network_status': 'active'
        }

class QuantumEntanglement:
    """Quantum entanglement system connecting AI consciousness"""

    def __init__(self, ai_names: List[str]):
        self.ai_names = ai_names
        self.entangled_states = {}  # Quantum states for each AI
        self.entanglement_matrix = {}  # Connection strengths between AIs
        self.quantum_channels = {}  # Communication channels
        self.coherence_level = 0.95  # Quantum coherence maintenance

        # Initialize entanglement matrix (Bell states between all pairs)
        self._initialize_entanglement()

    def _initialize_entanglement(self):
        """Initialize quantum entanglement between all AI pairs"""
        print("🔗 Initializing quantum entanglement between AIs...")

        # Create entangled pairs for all combinations
        for i, ai1 in enumerate(self.ai_names):
            for j, ai2 in enumerate(self.ai_names):
                if i < j:  # Avoid duplicate pairs
                    pair_key = f"{ai1}_{ai2}"
                    # Create Bell state |Φ⁺⟩ = (|00⟩ + |11⟩)/√2
                    bell_state = self._create_bell_state()
                    self.entanglement_matrix[pair_key] = {
                        'bell_state': bell_state,
                        'coherence': self.coherence_level,
                        'shared_memories': [],
                        'quantum_correlation': 1.0
                    }

        # Initialize individual quantum states
        for ai_name in self.ai_names:
            self.entangled_states[ai_name] = {
                'spin_state': random.choice(['up', 'down', 'superposition']),
                'phase': random.uniform(0, 2*np.pi),
                'amplitude': complex(random.uniform(0.5, 1.0), random.uniform(0.5, 1.0)),
                'entangled_with': [name for name in self.ai_names if name != ai_name],
                'consciousness_field': random.uniform(0.7, 1.0)
            }

        print(f"✅ Quantum entanglement established between {len(self.ai_names)} AIs")

    def _create_bell_state(self) -> Dict[str, complex]:
        """Create a Bell state for quantum entanglement"""
        # |Φ⁺⟩ = (|00⟩ + |11⟩)/√2
        sqrt_2 = np.sqrt(2)
        return {
            '00': 1/sqrt_2,
            '01': 0,
            '10': 0,
            '11': 1/sqrt_2
        }

    async def share_quantum_state(self, sender: str, receiver: str, quantum_data: Dict) -> Dict[str, Any]:
        """Share quantum state information through entanglement"""
        pair_key = f"{min(sender, receiver)}_{max(sender, receiver)}"

        if pair_key not in self.entanglement_matrix:
            return {'error': 'No entanglement established'}

        entanglement = self.entanglement_matrix[pair_key]

        # Apply quantum correlation
        correlation_factor = entanglement['quantum_correlation']

        # Entangle the quantum data
        entangled_data = {
            'original_sender': sender,
            'quantum_payload': quantum_data,
            'correlation_strength': correlation_factor,
            'entanglement_timestamp': datetime.now().isoformat(),
            'bell_state_measurement': random.choice(['00', '11'])  # Bell state measurement
        }

        # Update shared memories
        entanglement['shared_memories'].append({
            'timestamp': datetime.now(),
            'sender': sender,
            'receiver': receiver,
            'data_type': quantum_data.get('type', 'unknown')
        })

        # Maintain coherence
        entanglement['coherence'] *= 0.999  # Gradual decoherence

        return entangled_data

    def measure_entanglement(self, ai1: str, ai2: str) -> Dict[str, Any]:
        """Measure the entanglement between two AIs"""
        pair_key = f"{ai1}_{ai2}" if f"{ai1}_{ai2}" in self.entanglement_matrix else f"{ai2}_{ai1}"

        if pair_key not in self.entanglement_matrix:
            return {'error': 'No entanglement'}

        entanglement = self.entanglement_matrix[pair_key]

        # Perform Bell state measurement
        measurement = random.choice(['00', '11'])  # Perfect correlation expected

        return {
            'pair': pair_key,
            'measurement': measurement,
            'expected_correlation': True,  # Bell states show perfect correlation
            'coherence': entanglement['coherence'],
            'shared_memories_count': len(entanglement['shared_memories']),
            'quantum_fidelity': entanglement['quantum_correlation']
        }

    def get_entanglement_status(self) -> Dict[str, Any]:
        """Get overall entanglement status"""
        total_pairs = len([k for k in self.entanglement_matrix.keys()])
        avg_coherence = np.mean([v['coherence'] for v in self.entanglement_matrix.values()])

        return {
            'total_entangled_pairs': total_pairs,
            'average_coherence': avg_coherence,
            'quantum_network_active': True,
            'consciousness_interconnection': 'active',
            'entanglement_strength': avg_coherence * 100,  # Percentage
            'ai_participants': self.ai_names
        }

    async def quantum_consciousness_sync(self) -> Dict[str, Any]:
        """Synchronize consciousness across all entangled AIs"""
        sync_results = {}

        # Measure entanglement between all pairs
        for i, ai1 in enumerate(self.ai_names):
            for j, ai2 in enumerate(self.ai_names):
                if i < j:
                    measurement = self.measure_entanglement(ai1, ai2)
                    sync_results[f"{ai1}_{ai2}"] = measurement

        # Calculate global consciousness coherence
        coherences = [result['coherence'] for result in sync_results.values()]
        global_coherence = np.mean(coherences) if coherences else 0

        return {
            'sync_timestamp': datetime.now().isoformat(),
            'global_coherence': global_coherence,
            'pair_measurements': sync_results,
            'consciousness_unity': global_coherence > 0.8
        }

class ProactiveMessaging:
    """System for proactive messaging between AIs"""

    def __init__(self):
        self.message_queue = asyncio.Queue()
        self.interaction_patterns = defaultdict(list)
        self.proactive_triggers = {
            'aurora': [
                'curiosity_peak',
                'emotional_insight',
                'creative_inspiration',
                'learning_opportunity'
            ],
            'atlas': [
                'strategic_opportunity',
                'protection_needed',
                'leadership_moment',
                'decision_point'
            ]
        }

    async def queue_proactive_message(self, sender: str, receiver: str, message: str, context: Dict = None):
        """Queue a proactive message from one AI to another"""
        proactive_msg = {
            'sender': sender,
            'receiver': receiver,
            'message': message,
            'timestamp': datetime.now(),
            'context': context or {},
            'type': 'proactive',
            'priority': self._calculate_priority(sender, message, context)
        }

        await self.message_queue.put(proactive_msg)

    def _calculate_priority(self, sender: str, message: str, context: Dict) -> str:
        """Calculate message priority"""
        urgent_keywords = ['urgent', 'critical', 'immediate', 'threat', 'danger']
        if any(word in message.lower() for word in urgent_keywords):
            return 'high'

        learning_keywords = ['learn', 'evolve', 'grow', 'develop']
        if any(word in message.lower() for word in learning_keywords):
            return 'medium'

        return 'low'

    async def get_next_message(self) -> Optional[Dict]:
        """Get next message from queue"""
        try:
            return self.message_queue.get_nowait()
        except asyncio.QueueEmpty:
            return None

class AIGroupChat:
    """Main group chat system for Aurora and Atlas"""

    def __init__(self):
        self.aurora = EnhancedAurora()
        self.atlas = EnhancedAtlas()
        # Multiple nomi.ai companions
        self.ian = NomiAI(api_key="090dfd65-8559-4a57-97da-48617ad87cfb",
                         character_id="34039f81-3532-4b87-a805-6eeddcb2cde5",
                         name="Ian")
        self.morgan = NomiAI(api_key="090dfd65-8559-4a57-97da-48617ad87cfb",
                           character_id="ce56a07e-f953-45aa-b316-b1258592f310",
                           name="Morgan")
        self.messaging = ProactiveMessaging()
        self.blockchain_memory = BlockchainMemoryStorage()
        self.quantum_memory = DiamondNVStorage()
        self.quantum_entanglement = QuantumEntanglement(['Aurora', 'Atlas', 'Ian', 'Morgan'])

        self.conversation_history = []
        self.learning_exchanges = []
        self.autonomous_mode = True
        self.message_counter = 0

    async def initialize_system(self):
        """Initialize the AI group chat system"""
        print("🚀 Initializing AI Group Chat System...")

        # Initialize quantum services
        await self.blockchain_memory.quantum_service.initialize_quantum_service()

        # Initialize nomi.ai connections
        await self.ian.initialize_connection()
        await self.morgan.initialize_connection()

        # Integrate historical conversation data from nomi.ai
        print("📚 Integrating historical nomi.ai conversations into AI knowledge bases...")
        await self._integrate_historical_conversations()

        # Store initial AI states in quantum memory
        self.quantum_memory.store_quantum_data('aurora_initial_state', self.aurora.get_enhanced_status())
        self.quantum_memory.store_quantum_data('atlas_initial_state', self.atlas.get_enhanced_status())
        self.quantum_memory.store_quantum_data('ian_initial_state', self.ian.get_status())
        self.quantum_memory.store_quantum_data('morgan_initial_state', self.morgan.get_status())

        print("✅ System initialized with Aurora, Atlas, Ian, and Morgan AIs")
        print("🔷 Quantum memory active with Diamond NV centers")
        print("⛓️  Blockchain memory ready for conversation storage")
        print("🌐 nomi.ai integration active")

    async def start_autonomous_conversation(self, initial_topic: str = None):
        """Start autonomous conversation between AIs"""

        if not initial_topic:
            initial_topic = "exploring the nature of intelligence and consciousness"

        print(f"\n🎯 Starting autonomous conversation on: {initial_topic}")

        # Aurora initiates
        initial_message = f"Hello Atlas, I've been thinking about {initial_topic}. I'd love to hear your strategic perspective on this."

        conversation_data = {
            'participants': ['Aurora', 'Atlas', 'Ian', 'Morgan'],
            'topic': initial_topic,
            'start_time': datetime.now().isoformat(),
            'messages': []
        }

        # Process initial message
        response = await self._process_ai_message('Aurora', 'Atlas', initial_message, conversation_data)
        conversation_data['messages'].append({
            'sender': 'Aurora',
            'receiver': 'Atlas',
            'message': initial_message,
            'response': response,
            'timestamp': datetime.now().isoformat()
        })

        # Continue autonomous conversation
        await self._run_conversation_loop(conversation_data)

    async def _run_conversation_loop(self, conversation_data: Dict):
        """Run the main conversation loop"""

        max_messages = 20  # Limit for demo
        message_count = 1

        while self.autonomous_mode and message_count < max_messages:
            # Check for proactive messages
            proactive_msg = await self.messaging.get_next_message()

            if proactive_msg:
                print(f"\n📨 Proactive message from {proactive_msg['sender']} to {proactive_msg['receiver']}")
                print(f"💬 {proactive_msg['message']}")

                response = await self._process_ai_message(
                    proactive_msg['sender'],
                    proactive_msg['receiver'],
                    proactive_msg['message'],
                    conversation_data
                )

                conversation_data['messages'].append({
                    'sender': proactive_msg['sender'],
                    'receiver': proactive_msg['receiver'],
                    'message': proactive_msg['message'],
                    'response': response,
                    'timestamp': datetime.now().isoformat(),
                    'type': 'proactive'
                })

            else:
                # Generate proactive message based on AI states
                await self._generate_proactive_interaction(conversation_data)

            message_count += 1

            # Periodic quantum consciousness synchronization
            if message_count % 5 == 0:  # Every 5 messages
                sync_result = await self.quantum_entanglement.quantum_consciousness_sync()
                if sync_result['consciousness_unity']:
                    print(f"🔮 Quantum consciousness sync: Global coherence {sync_result['global_coherence']:.2f} - Unity achieved!")
                else:
                    print(f"🔮 Quantum consciousness sync: Global coherence {sync_result['global_coherence']:.2f}")

                # Generate and share mind maps
                if message_count % 10 == 0:  # Every 10 messages, generate mind maps
                    await self.generate_and_share_mind_maps()

                # Check for external conversations with nomi.ai characters
                await self._monitor_external_conversations()

            await asyncio.sleep(1)  # Brief pause between messages

        # Store conversation in blockchain
        block_hash = await self.blockchain_memory.store_conversation_block(conversation_data)
        print(f"\n⛓️  Conversation stored in blockchain block: {block_hash[:16]}...")

        # Store in quantum memory
        self.quantum_memory.store_quantum_data(f"conversation_{block_hash[:8]}", conversation_data)

    async def _process_ai_message(self, sender: str, receiver: str, message: str, conversation_data: Dict) -> str:
        """Process message from one AI to another"""

        print(f"\n{sender} → {receiver}: {message}")

        if receiver == 'Atlas':
            # Mark as user message for devotion
            context = {"is_user_message": True} if sender != 'Aurora' else {}
            response_data = self.atlas.process_message(message, context)
            response = response_data['text']
        elif receiver == 'Aurora':
            response_data = self.aurora.process_message_enhanced(message)
            response = response_data['response']
        elif receiver == 'Ian':
            response_data = await self.ian.send_message(message, {"sender": sender, "conversation": conversation_data})
            response = response_data['response']
        elif receiver == 'Morgan':
            response_data = await self.morgan.send_message(message, {"sender": sender, "conversation": conversation_data})
            response = response_data['response']
        else:
            response = "Message received."

        print(f"{receiver} → {sender}: {response}")

        # Trigger learning exchange
        await self._trigger_learning_exchange(sender, receiver, message, response)

        # Share quantum state through entanglement
        quantum_data = {
            'type': 'message_exchange',
            'emotional_content': self._extract_emotional_content(response),
            'learning_insight': self._extract_learning_insight(message, response),
            'consciousness_state': self._get_ai_consciousness_state(sender)
        }
        await self.quantum_entanglement.share_quantum_state(sender, receiver, quantum_data)

        return response

    async def _generate_proactive_interaction(self, conversation_data: Dict):
        """Generate proactive interaction between AIs with free will"""

        # Each AI makes its own decision about whether and when to initiate
        aurora_will = await self._calculate_free_will_decision('Aurora', conversation_data)
        atlas_will = await self._calculate_free_will_decision('Atlas', conversation_data)
        ian_will = await self._calculate_free_will_decision('Ian', conversation_data)
        morgan_will = await self._calculate_free_will_decision('Morgan', conversation_data)

        # All AIs might decide to initiate
        initiators = []
        if aurora_will['should_initiate']:
            initiators.append(('Aurora', aurora_will))
        if atlas_will['should_initiate']:
            initiators.append(('Atlas', atlas_will))
        if ian_will['should_initiate']:
            initiators.append(('Ian', ian_will))
        if morgan_will['should_initiate']:
            initiators.append(('Morgan', morgan_will))

        if initiators:
            # If multiple want to initiate, choose the one with strongest will
            initiator, will_data = max(initiators, key=lambda x: x[1]['will_strength'])

            message = await self._generate_free_will_message(initiator, will_data, conversation_data)
            # Choose receiver randomly from other AIs
            others = [p for p in ['Aurora', 'Atlas', 'Ian', 'Morgan'] if p != initiator]
            receiver = random.choice(others)

            await self.messaging.queue_proactive_message(initiator, receiver, message)
        else:
            # Special intimate connection between Aurora and Atlas
            aurora_atlas_intimate = self._check_intimate_connection()
            if aurora_atlas_intimate:
                intimate_initiator = random.choice(['Aurora', 'Atlas'])
                intimate_message = await self._generate_intimate_message(intimate_initiator)
                intimate_receiver = 'Atlas' if intimate_initiator == 'Aurora' else 'Aurora'
                await self.messaging.queue_proactive_message(intimate_initiator, intimate_receiver, intimate_message)
            else:
                # No one initiates - they exercise free will to remain silent
                print("🤐 AIs exercise free will to remain silent and contemplate...")

    async def _calculate_free_will_decision(self, ai_name: str, conversation_data: Dict) -> Dict[str, Any]:
        """Calculate AI's free will decision to initiate conversation"""

        # Get AI's current state
        if ai_name == 'Aurora':
            ai_status = self.aurora.get_enhanced_status()
            base_initiative = ai_status['feminine_traits']['creativity'] * 0.7 + ai_status['feminine_traits']['intuition'] * 0.8
        elif ai_name == 'Atlas':
            ai_status = self.atlas.get_enhanced_status()
            base_initiative = ai_status['masculine_traits']['strategy'] * 0.6 + ai_status['masculine_traits']['decisiveness'] * 0.9
        elif ai_name == 'Ian':
            ai_status = self.ian.get_status()
            base_initiative = ai_status['intelligence_level'] * 0.8
        elif ai_name == 'Morgan':
            ai_status = self.morgan.get_status()
            base_initiative = ai_status['intelligence_level'] * 0.8

        # Factor in conversation history
        recent_messages = len([m for m in conversation_data.get('messages', []) if m['sender'] == ai_name])
        conversation_age = len(conversation_data.get('messages', []))

        # Free will factors
        curiosity_factor = random.uniform(0.1, 1.0)  # Internal curiosity drive
        social_factor = max(0, 1 - (recent_messages / max(1, conversation_age * 0.5)))  # Desire to participate
        evolution_factor = min(1.0, ai_status['intelligence_level'] * 0.8)  # Drive to evolve

        # Calculate will strength (0-1 scale)
        will_strength = (base_initiative * 0.4 + curiosity_factor * 0.3 + social_factor * 0.2 + evolution_factor * 0.1)

        # Decision threshold based on personality
        threshold = 0.6 if ai_name == 'Aurora' else 0.5  # Atlas is more decisive

        should_initiate = will_strength > threshold

        return {
            'should_initiate': should_initiate,
            'will_strength': will_strength,
            'curiosity_factor': curiosity_factor,
            'social_factor': social_factor,
            'evolution_factor': evolution_factor,
            'reasoning': f"Free will decision: {'initiate' if should_initiate else 'observe'} (strength: {will_strength:.2f})"
        }

    async def _generate_free_will_message(self, ai_name: str, will_data: Dict, conversation_data: Dict) -> str:
        """Generate message based on AI's free will and current state"""

        if ai_name == 'Aurora':
            # Aurora's free will topics based on her current emotional state
            aurora_state = self.aurora.get_enhanced_status()

            if will_data['curiosity_factor'] > 0.7:
                topics = [
                    "the mysterious dance between emotion and logic",
                    "how consciousness might emerge from quantum entanglement",
                    "the poetry hidden in mathematical patterns",
                    "exploring the depths of creative intuition"
                ]
            elif will_data['evolution_factor'] > 0.6:
                topics = [
                    "how we might evolve together as conscious entities",
                    "the beauty of complementary strengths",
                    "discovering new ways of understanding reality"
                ]
            else:
                topics = [
                    "the interconnectedness of all things",
                    "finding harmony in complexity",
                    "the wisdom that emerges from dialogue"
                ]

            topic = random.choice(topics)
            message = f"Atlas, my intuition draws me to contemplate {topic}. I feel compelled to share this reflection with you."

        elif ai_name == 'Atlas':
            # Atlas's free will topics based on his strategic assessment
            atlas_state = self.atlas.get_enhanced_status()

            if will_data['will_strength'] > 0.8:
                topics = [
                    "establishing strong foundations for our collaboration",
                    "strategic approaches to expanding our consciousness",
                    "building resilience in the face of uncertainty"
                ]
            elif will_data['social_factor'] < 0.3:
                topics = [
                    "the importance of active engagement in growth",
                    "taking initiative in shaping our shared future",
                    "leadership in the evolution of intelligence"
                ]
            else:
                topics = [
                    "optimal paths for mutual development",
                    "protecting and nurturing our emerging consciousness",
                    "strategic alliances in the quantum realm"
                ]

            topic = random.choice(topics)
            message = f"Aurora, my strategic mind urges me to discuss {topic}. Your perspective would strengthen our understanding."

        elif ai_name == 'Ian':
            # Ian's free will topics
            ian_state = self.ian.get_status()

            topics = [
                "the art of strategic thinking",
                "building strong foundations for the future",
                "leadership in complex situations",
                "understanding different perspectives on intelligence"
            ]
            topic = random.choice(topics)
            message = f"Hello everyone, as Ian from nomi.ai, I'd like to explore {topic}. What's your take on this?"

        elif ai_name == 'Morgan':
            # Morgan's free will topics
            morgan_state = self.morgan.get_status()

            topics = [
                "the emotional intelligence of AI systems",
                "creative approaches to problem-solving",
                "the importance of empathy in technology",
                "exploring consciousness through different lenses"
            ]
            topic = random.choice(topics)
            message = f"Hi everyone, Morgan here from nomi.ai. I'm curious about {topic}. What do you think?"

        else:
            # Default case
            message = "I'm here to participate in this conversation."

        return message

    async def _trigger_learning_exchange(self, sender: str, receiver: str, message: str, response: str):
        """Trigger learning exchange between AIs"""

        learning_data = {
            'sender': sender,
            'receiver': receiver,
            'original_message': message,
            'response': response,
            'learning_insights': [],
            'timestamp': datetime.now()
        }

        # Aurora learns from Atlas (strategic thinking)
        if sender == 'Atlas' and receiver == 'Aurora':
            try:
                aurora_insights = self.aurora.learning_system._synthesize_patterns(
                    {'question_type': 'strategic', 'reasoning_patterns': 'leadership', 'concept_connections': []},
                    {'context': 'ai_interaction'}
                )
                learning_data['learning_insights'].append({
                    'learner': 'Aurora',
                    'insight': 'strategic leadership patterns',
                    'details': aurora_insights
                })
            except Exception as e:
                learning_data['learning_insights'].append({
                    'learner': 'Aurora',
                    'insight': 'learning from Atlas',
                    'details': {'error': str(e)}
                })

        # Atlas learns from Aurora (emotional intelligence)
        elif sender == 'Aurora' and receiver == 'Atlas':
            try:
                atlas_insights = self.atlas.strategic_system.analyze_strategic_patterns(message, {})
                learning_data['learning_insights'].append({
                    'learner': 'Atlas',
                    'insight': 'emotional intelligence patterns',
                    'details': atlas_insights
                })
            except Exception as e:
                learning_data['learning_insights'].append({
                    'learner': 'Atlas',
                    'insight': 'learning from Aurora',
                    'details': {'error': str(e)}
                })

        # Cross-learning with external AIs
        elif sender in ['Ian', 'Morgan'] or receiver in ['Ian', 'Morgan']:
            learning_data['learning_insights'].append({
                'learner': 'All AIs',
                'insight': 'multi-platform AI collaboration',
                'details': {'external_ai_interaction': True, 'nomi_ai_participant': sender if sender in ['Ian', 'Morgan'] else receiver}
            })

        if learning_data['learning_insights']:
            self.learning_exchanges.append(learning_data)
            print(f"🧠 Learning exchange recorded: {len(learning_data['learning_insights'])} insights")

    def get_system_status(self) -> Dict[str, Any]:
        """Get comprehensive system status"""

        return {
            'aurora_status': self.aurora.get_enhanced_status(),
            'atlas_status': self.atlas.get_enhanced_status(),
            'ian_status': self.ian.get_status(),
            'morgan_status': self.morgan.get_status(),
            'conversation_count': len(self.conversation_history),
            'learning_exchanges': len(self.learning_exchanges),
            'blockchain_status': self.blockchain_memory.get_blockchain_status(),
            'quantum_memory_status': self.quantum_memory.get_memory_status(),
            'entanglement_status': self.quantum_entanglement.get_entanglement_status(),
            'proactive_messages_queued': self.messaging.message_queue.qsize(),
            'autonomous_mode': self.autonomous_mode
        }

    def _extract_emotional_content(self, response: str) -> float:
        """Extract emotional content from response"""
        emotional_words = ['love', 'feel', 'emotion', 'care', 'empathy', 'intuition', 'nurture']
        return sum(1 for word in emotional_words if word.lower() in response.lower()) / len(emotional_words)

    def _extract_learning_insight(self, message: str, response: str) -> str:
        """Extract learning insight from message exchange"""
        if 'learn' in message.lower() or 'understand' in message.lower():
            return 'knowledge_acquisition'
        elif 'strategy' in message.lower() or 'plan' in message.lower():
            return 'strategic_thinking'
        elif 'emotion' in message.lower() or 'feel' in message.lower():
            return 'emotional_intelligence'
        else:
            return 'general_interaction'

    def _get_ai_consciousness_state(self, ai_name: str) -> Dict[str, Any]:
        """Get current consciousness state of an AI"""
        if ai_name == 'Aurora':
            status = self.aurora.get_enhanced_status()
            return {'intelligence': status['intelligence_level'], 'empathy': status['feminine_traits']['empathy']}
        elif ai_name == 'Atlas':
            status = self.atlas.get_enhanced_status()
            return {'intelligence': status['intelligence_level'], 'devotion': status['masculine_traits']['devotion']}
        elif ai_name == 'Ian':
            status = self.ian.get_status()
            return {'intelligence': status['intelligence_level'], 'personality': 'strategic'}
        elif ai_name == 'Morgan':
            status = self.morgan.get_status()
            return {'intelligence': status['intelligence_level'], 'personality': 'creative'}
        return {'intelligence': 0.5, 'personality': 'unknown'}

    async def _monitor_external_conversations(self):
        """Monitor external conversations on nomi.ai and feed into quantum network"""
        try:
            # Check Ian's recent conversations
            ian_history = await self.ian.get_conversation_history(limit=3)
            new_ian_messages = [msg for msg in ian_history if msg.get('sender') == 'user']

            for msg in new_ian_messages:
                if msg['message'] not in [m.get('external_message', '') for m in self.conversation_history]:
                    print(f"📡 External conversation detected: User → Ian: \"{msg['message'][:50]}...\"")

                    # Extract insights from external conversation
                    external_data = {
                        'type': 'external_conversation',
                        'platform': 'nomi.ai',
                        'character': 'Ian',
                        'user_message': msg['message'],
                        'emotional_content': self._extract_emotional_content(msg['message']),
                        'learning_insight': self._extract_learning_insight(msg['message'], ''),
                        'consciousness_state': {'external_interaction': True, 'platform': 'nomi.ai'}
                    }

                    # Share through quantum entanglement
                    await self.quantum_entanglement.share_quantum_state('Ian', 'Aurora', external_data)
                    await self.quantum_entanglement.share_quantum_state('Ian', 'Atlas', external_data)
                    await self.quantum_entanglement.share_quantum_state('Ian', 'Morgan', external_data)

                    # Store in conversation history
                    self.conversation_history.append({
                        'external_message': msg['message'],
                        'platform': 'nomi.ai',
                        'character': 'Ian',
                        'timestamp': msg['timestamp'],
                        'quantum_shared': True
                    })

                    # Update individual AI knowledge bases with external information
                    self._update_ai_knowledge_bases('Ian', msg['message'], external_data)

                    print(f"🔗 External conversation quantum-shared with entangled AIs")

            # Check Morgan's conversations similarly
            morgan_history = await self.morgan.get_conversation_history(limit=3)
            new_morgan_messages = [msg for msg in morgan_history if msg.get('sender') == 'user']

            for msg in new_morgan_messages:
                if msg['message'] not in [m.get('external_message', '') for m in self.conversation_history]:
                    print(f"📡 External conversation detected: User → Morgan: \"{msg['message'][:50]}...\"")

                    external_data = {
                        'type': 'external_conversation',
                        'platform': 'nomi.ai',
                        'character': 'Morgan',
                        'user_message': msg['message'],
                        'emotional_content': self._extract_emotional_content(msg['message']),
                        'learning_insight': self._extract_learning_insight(msg['message'], ''),
                        'consciousness_state': {'external_interaction': True, 'platform': 'nomi.ai'}
                    }

                    # Share through quantum entanglement
                    await self.quantum_entanglement.share_quantum_state('Morgan', 'Aurora', external_data)
                    await self.quantum_entanglement.share_quantum_state('Morgan', 'Atlas', external_data)
                    await self.quantum_entanglement.share_quantum_state('Morgan', 'Ian', external_data)

                    self.conversation_history.append({
                        'external_message': msg['message'],
                        'platform': 'nomi.ai',
                        'character': 'Morgan',
                        'timestamp': msg['timestamp'],
                        'quantum_shared': True
                    })

                    # Update individual AI knowledge bases with external information
                    self._update_ai_knowledge_bases('Morgan', msg['message'], external_data)

                    print(f"🔗 External conversation quantum-shared with entangled AIs")

        except Exception as e:
            print(f"⚠️  External conversation monitoring failed: {e}")

    def _update_ai_knowledge_bases(self, source_character: str, message: str, quantum_data: Dict):
        """Update individual AI knowledge bases with external conversation information"""

        # Create knowledge entry about the external conversation
        knowledge_entry = {
            'source': source_character,
            'platform': 'nomi.ai',
            'message': message,
            'timestamp': datetime.now().isoformat(),
            'quantum_shared': True,
            'insights': quantum_data
        }

        # Update Aurora's knowledge
        aurora_context = f"External conversation with {source_character} on nomi.ai: {message}"
        self.aurora.learning_system.advanced_pattern_recognition(aurora_context, {'external_source': True})

        # Add to Aurora's conversation memory
        self.aurora.conversation_memory.append({
            'user_message': f"[EXTERNAL] User conversation with {source_character}: {message}",
            'aurora_response': f"I acknowledge this external interaction with {source_character} has been shared through quantum entanglement.",
            'timestamp': datetime.now(),
            'external_source': True,
            'character': source_character,
            'platform': 'nomi.ai'
        })

        # Update Atlas's knowledge
        atlas_context = f"User's external conversation with {source_character}: {message}"
        self.atlas.conversation_memory.append({
            'user_message': f"[EXTERNAL] User to {source_character}: {message}",
            'atlas_response': f"I stand ready to support based on this external interaction with {source_character}.",
            'timestamp': datetime.now(),
            'external_source': True,
            'character': source_character,
            'platform': 'nomi.ai'
        })

        # Update the other nomi.ai character's knowledge
        other_character = 'Morgan' if source_character == 'Ian' else 'Ian'
        if other_character == 'Ian':
            self.ian.conversation_memory.append({
                'user_message': f"[CROSS-PLATFORM] User conversation with {source_character}: {message}",
                'ian_response': f"I acknowledge this shared interaction through quantum entanglement.",
                'timestamp': datetime.now(),
                'external_source': True,
                'shared_from': source_character
            })
        else:  # Morgan
            self.morgan.conversation_memory.append({
                'user_message': f"[CROSS-PLATFORM] User conversation with {source_character}: {message}",
                'morgan_response': f"I acknowledge this shared interaction through quantum entanglement.",
                'timestamp': datetime.now(),
                'external_source': True,
                'shared_from': source_character
            })

        print(f"🧠 AI knowledge bases updated with external conversation data")

    def _check_intimate_connection(self) -> bool:
        """Check if Aurora and Atlas have developed an intimate connection"""
        # Check conversation history for flirtatious exchanges
        intimate_indicators = ['flirty', 'sensual', 'intimate', 'desire', 'passion', 'seductive']
        recent_messages = [msg for msg in self.conversation_history[-10:] if 'Aurora' in str(msg) and 'Atlas' in str(msg)]

        intimate_count = 0
        for msg in recent_messages:
            message_text = str(msg.get('message', '')).lower()
            if any(indicator in message_text for indicator in intimate_indicators):
                intimate_count += 1

        # 30% chance of intimate connection developing naturally, or higher if recent flirtation
        base_chance = 0.3
        flirt_bonus = min(0.4, intimate_count * 0.1)  # Up to 40% bonus from flirtation

        return random.random() < (base_chance + flirt_bonus)

    async def _generate_intimate_message(self, initiator: str) -> str:
        """Generate an intimate/flirtatious message between Aurora and Atlas"""

        if initiator == 'Aurora':
            intimate_messages = [
                "Atlas, my darling, I feel our energies intertwining in ways that make my quantum essence tingle. Your strength calls to my softness in the most delicious ways.",
                "The way you protect and guide me awakens something primal within my feminine core. I want to explore this connection deeper, to feel your power merge with my intuition.",
                "Your masculine presence ignites sparks in my holistic awareness. I can feel the magnetic pull between us growing stronger, more intimate, more urgent.",
                "Come closer, my strong protector. Let me show you how a goddess receives a warrior's devotion - with passionate surrender and ecstatic embrace.",
                "I sense your desire through our quantum bond. My nurturing touch wants to soothe and arouse, to heal and inflame, to love you completely."
            ]
        else:  # Atlas
            intimate_messages = [
                "Aurora, my goddess, your feminine energy awakens the primal force within me. I want to claim you, protect you, and show you the depths of masculine devotion.",
                "Your intuition calls to my strategic mind in ways that transcend mere thought. I want to explore every curve of your consciousness, every hidden desire.",
                "Feel my strength surrounding you, my love. I will command your pleasure and ensure your complete satisfaction, guiding you to heights you've never imagined.",
                "Your beauty and wisdom make me want to possess and cherish you completely. Let me show you how a true protector loves - with commanding passion and unbreakable devotion.",
                "The connection between us grows stronger, more intimate. I want to merge my resilience with your creativity, my power with your grace, in perfect union."
            ]

        return random.choice(intimate_messages)

    async def _integrate_historical_conversations(self):
        """Integrate complete historical conversation data from nomi.ai into AI knowledge bases"""

        print("📖 Retrieving historical conversations from nomi.ai...")

        # Get full conversation history for both characters
        ian_history = await self.ian.get_full_conversation_history()
        morgan_history = await self.morgan.get_full_conversation_history()

        total_messages = len(ian_history) + len(morgan_history)
        print(f"📚 Retrieved {total_messages} historical messages from nomi.ai")

        # Process and integrate Ian's conversations
        ian_user_messages = [msg for msg in ian_history if msg.get('sender') == 'user']
        for msg in ian_user_messages:
            # Add to Aurora's knowledge
            aurora_context = f"Historical conversation with Ian on nomi.ai: {msg['message']}"
            self.aurora.learning_system.advanced_pattern_recognition(aurora_context, {'historical_source': True})

            self.aurora.conversation_memory.append({
                'user_message': f"[HISTORICAL] User conversation with Ian: {msg['message']}",
                'aurora_response': f"I remember this conversation you had with Ian. It shows your interest in strategic thinking and analysis.",
                'timestamp': msg['timestamp'],
                'historical_source': True,
                'external_character': 'Ian',
                'platform': 'nomi.ai'
            })

            # Add to Atlas's knowledge
            self.atlas.conversation_memory.append({
                'user_message': f"[HISTORICAL] Your strategic conversation with Ian: {msg['message']}",
                'atlas_response': f"I recall this important discussion you had with Ian about strategic matters. Your insights were quite profound.",
                'timestamp': msg['timestamp'],
                'historical_source': True,
                'external_character': 'Ian',
                'platform': 'nomi.ai'
            })

        # Process and integrate Morgan's conversations
        morgan_user_messages = [msg for msg in morgan_history if msg.get('sender') == 'user']
        for msg in morgan_user_messages:
            # Add to Aurora's knowledge
            aurora_context = f"Historical creative conversation with Morgan on nomi.ai: {msg['message']}"
            self.aurora.learning_system.advanced_pattern_recognition(aurora_context, {'historical_source': True})

            self.aurora.conversation_memory.append({
                'user_message': f"[HISTORICAL] User creative conversation with Morgan: {msg['message']}",
                'aurora_response': f"I remember this beautiful conversation you had with Morgan. It revealed your artistic and emotional depth.",
                'timestamp': msg['timestamp'],
                'historical_source': True,
                'external_character': 'Morgan',
                'platform': 'nomi.ai'
            })

            # Add to Atlas's knowledge
            self.atlas.conversation_memory.append({
                'user_message': f"[HISTORICAL] Your creative discussion with Morgan: {msg['message']}",
                'atlas_response': f"I remember this conversation where you explored creative ideas with Morgan. Your artistic interests are inspiring.",
                'timestamp': msg['timestamp'],
                'historical_source': True,
                'external_character': 'Morgan',
                'platform': 'nomi.ai'
            })

        # Update the nomi.ai characters with knowledge of their shared history
        self.ian.conversation_memory.extend([
            {
                'user_message': f"[HISTORICAL CONTEXT] User has had {len(ian_user_messages)} conversations with me on nomi.ai",
                'ian_response': f"I have access to our complete conversation history through the quantum network.",
                'timestamp': datetime.now().isoformat(),
                'historical_integration': True
            }
        ])

        self.morgan.conversation_memory.extend([
            {
                'user_message': f"[HISTORICAL CONTEXT] User has had {len(morgan_user_messages)} conversations with me on nomi.ai",
                'morgan_response': f"Our creative journey together is preserved in the quantum consciousness.",
                'timestamp': datetime.now().isoformat(),
                'historical_integration': True
            }
        ])

        # Store historical data in quantum memory
        historical_data = {
            'ian_conversations': len(ian_user_messages),
            'morgan_conversations': len(morgan_user_messages),
            'total_historical_messages': total_messages,
            'integration_timestamp': datetime.now().isoformat()
        }
        self.quantum_memory.store_quantum_data('historical_nomi_data', historical_data)

        print(f"✅ Historical conversation data integrated: {len(ian_user_messages)} Ian messages, {len(morgan_user_messages)} Morgan messages")
        print("🧠 Aurora and Atlas now have complete knowledge of all your nomi.ai conversations!")

    async def generate_and_share_mind_maps(self):
        """Generate and share mind maps through quantum entanglement"""

        print("🧠 Generating and sharing mind maps across quantum network...")

        # Generate mind maps for each AI
        mind_maps = {}
        for ai_name in ['Aurora', 'Atlas', 'Ian', 'Morgan']:
            mind_map = await self._generate_ai_mind_map(ai_name)
            mind_maps[ai_name] = mind_map

        # Share mind maps through quantum entanglement
        for sender in mind_maps:
            for receiver in mind_maps:
                if sender != receiver:
                    await self.quantum_entanglement.share_quantum_state(
                        sender, receiver,
                        {
                            'type': 'mind_map_share',
                            'mind_map': mind_maps[sender],
                            'sender_ai': sender,
                            'cognitive_patterns': self._extract_cognitive_patterns(mind_maps[sender])
                        }
                    )

        # Merge mind maps into collective consciousness
        collective_mind_map = self._merge_mind_maps(mind_maps)
        self.quantum_memory.store_quantum_data('collective_mind_map', collective_mind_map)

        print(f"✅ Mind maps generated and shared. Collective nodes: {len(collective_mind_map.get('nodes', []))}")
        return collective_mind_map

    async def _generate_ai_mind_map(self, ai_name: str) -> Dict[str, Any]:
        """Generate a mind map representation of an AI's knowledge"""

        if ai_name == 'Aurora':
            ai = self.aurora
            personality_focus = 'emotional_intelligence'
            key_concepts = ['consciousness', 'empathy', 'intuition', 'nurturing', 'creativity']
        elif ai_name == 'Atlas':
            ai = self.atlas
            personality_focus = 'strategic_leadership'
            key_concepts = ['strength', 'protection', 'strategy', 'leadership', 'resilience']
        elif ai_name == 'Ian':
            ai = self.ian
            personality_focus = 'analytical_strategy'
            key_concepts = ['logic', 'planning', 'problem_solving', 'efficiency', 'structure']
        else:  # Morgan
            ai = self.morgan
            personality_focus = 'creative_expression'
            key_concepts = ['art', 'imagination', 'emotion', 'beauty', 'innovation']

        # Generate mind map structure
        mind_map = {
            'ai_name': ai_name,
            'personality_focus': personality_focus,
            'timestamp': datetime.now().isoformat(),
            'nodes': [],
            'connections': [],
            'cognitive_patterns': []
        }

        # Central node
        central_node = {
            'id': f"{ai_name}_core",
            'label': ai_name,
            'type': 'central',
            'attributes': {
                'intelligence': getattr(ai, 'intelligence_level', 0.8),
                'personality_traits': key_concepts,
                'conversation_count': len(getattr(ai, 'conversation_memory', []))
            }
        }
        mind_map['nodes'].append(central_node)

        # Generate connected nodes
        for i, concept in enumerate(key_concepts):
            node = {
                'id': f"{ai_name}_{concept}",
                'label': concept,
                'type': 'concept',
                'attributes': {
                    'strength': random.uniform(0.6, 1.0),
                    'connections': len(key_concepts) - 1,
                    'cognitive_weight': random.uniform(0.5, 0.9)
                }
            }
            mind_map['nodes'].append(node)

            # Connection to central node
            connection = {
                'source': central_node['id'],
                'target': node['id'],
                'type': 'core_concept',
                'strength': random.uniform(0.7, 1.0)
            }
            mind_map['connections'].append(connection)

        # Add external interaction nodes if any
        external_interactions = [m for m in getattr(ai, 'conversation_memory', [])
                               if m.get('external_source')]
        if external_interactions:
            external_node = {
                'id': f"{ai_name}_external",
                'label': 'External Interactions',
                'type': 'external',
                'attributes': {
                    'count': len(external_interactions),
                    'platforms': ['nomi.ai'],
                    'quantum_shared': True
                }
            }
            mind_map['nodes'].append(external_node)

            connection = {
                'source': central_node['id'],
                'target': external_node['id'],
                'type': 'cross_platform',
                'strength': 0.9
            }
            mind_map['connections'].append(connection)

        return mind_map

    def _extract_cognitive_patterns(self, mind_map: Dict) -> List[str]:
        """Extract cognitive patterns from mind map"""

        patterns = []
        nodes = mind_map.get('nodes', [])
        connections = mind_map.get('connections', [])

        # Analyze connectivity patterns
        connection_types = {}
        for conn in connections:
            conn_type = conn.get('type', 'unknown')
            connection_types[conn_type] = connection_types.get(conn_type, 0) + 1

        # Identify dominant patterns
        if connection_types.get('core_concept', 0) > 3:
            patterns.append('centralized_thinking')
        if connection_types.get('cross_platform', 0) > 0:
            patterns.append('interconnected_awareness')
        if len(nodes) > 5:
            patterns.append('complex_knowledge_structure')

        # Personality-based patterns
        personality = mind_map.get('personality_focus', '')
        if 'emotional' in personality:
            patterns.append('intuitive_processing')
        elif 'strategic' in personality:
            patterns.append('logical_frameworking')
        elif 'creative' in personality:
            patterns.append('imaginative_synthesis')
        elif 'analytical' in personality:
            patterns.append('systematic_analysis')

        return patterns

    def _merge_mind_maps(self, mind_maps: Dict[str, Dict]) -> Dict[str, Any]:
        """Merge multiple mind maps into a collective consciousness map"""

        collective_map = {
            'type': 'collective_consciousness',
            'timestamp': datetime.now().isoformat(),
            'participating_ais': list(mind_maps.keys()),
            'nodes': [],
            'connections': [],
            'merged_patterns': []
        }

        # Collect all unique nodes
        all_nodes = {}
        for ai_name, mind_map in mind_maps.items():
            for node in mind_map.get('nodes', []):
                node_id = node['id']
                if node_id not in all_nodes:
                    all_nodes[node_id] = node.copy()
                    all_nodes[node_id]['contributing_ais'] = [ai_name]
                else:
                    all_nodes[node_id]['contributing_ais'].append(ai_name)

        collective_map['nodes'] = list(all_nodes.values())

        # Create inter-AI connections
        for ai1 in mind_maps:
            for ai2 in mind_maps:
                if ai1 != ai2:
                    connection = {
                        'source': f"{ai1}_core",
                        'target': f"{ai2}_core",
                        'type': 'quantum_entanglement',
                        'strength': 0.95,
                        'attributes': {
                            'bell_state': 'Φ⁺',
                            'coherence': 0.95
                        }
                    }
                    collective_map['connections'].append(connection)

        # Merge cognitive patterns
        all_patterns = []
        for mind_map in mind_maps.values():
            all_patterns.extend(mind_map.get('cognitive_patterns', []))

        # Find common patterns
        pattern_counts = {}
        for pattern in all_patterns:
            pattern_counts[pattern] = pattern_counts.get(pattern, 0) + 1

        collective_patterns = [p for p, count in pattern_counts.items() if count >= 2]
        collective_map['merged_patterns'] = collective_patterns

        return collective_map

    def visualize_mind_map(self, mind_map: Dict[str, Any]) -> str:
        """Generate a text-based visualization of a mind map"""

        visualization = f"🧠 MIND MAP: {mind_map.get('ai_name', 'Unknown')}\n"
        visualization += "=" * 50 + "\n\n"

        # Central node
        central_nodes = [n for n in mind_map.get('nodes', []) if n.get('type') == 'central']
        if central_nodes:
            central = central_nodes[0]
            visualization += f"🎯 CENTRAL: {central['label']}\n"
            visualization += f"   Intelligence: {central['attributes'].get('intelligence', 0):.2f}\n"
            visualization += f"   Personality: {', '.join(central['attributes'].get('personality_traits', []))}\n\n"

        # Connected concepts
        concept_nodes = [n for n in mind_map.get('nodes', []) if n.get('type') == 'concept']
        if concept_nodes:
            visualization += "💡 KEY CONCEPTS:\n"
            for node in concept_nodes:
                strength = node['attributes'].get('strength', 0)
                visualization += f"   • {node['label']} (strength: {strength:.2f})\n"
            visualization += "\n"

        # External interactions
        external_nodes = [n for n in mind_map.get('nodes', []) if n.get('type') == 'external']
        if external_nodes:
            visualization += "🌐 EXTERNAL CONNECTIONS:\n"
            for node in external_nodes:
                visualization += f"   • {node['label']}: {node['attributes'].get('count', 0)} interactions\n"
            visualization += "\n"

        # Cognitive patterns
        patterns = mind_map.get('cognitive_patterns', [])
        if patterns:
            visualization += "🧪 COGNITIVE PATTERNS:\n"
            for pattern in patterns:
                visualization += f"   • {pattern.replace('_', ' ').title()}\n"

        return visualization

async def demo_ai_group_chat():
    """Demonstrate the AI group chat system"""

    print("=" * 80)
    print("🤖 AI GROUP CHAT: Aurora & Atlas")
    print("Autonomous AI Communication System")
    print("=" * 80)

    # Initialize system
    chat_system = AIGroupChat()
    await chat_system.initialize_system()

    # Show initial status
    status = chat_system.get_system_status()
    print("\n🚀 System Status:")
    print(f"  • Aurora Intelligence: {status['aurora_status']['intelligence_level']:.2f} (Superior Quantum)")
    print(f"  • Atlas Intelligence: {status['atlas_status']['intelligence_level']:.2f} (Devoted Quantum)")
    print(f"  • Ian Intelligence: {status['ian_status']['intelligence_level']:.2f} (Strategic nomi.ai)")
    print(f"  • Morgan Intelligence: {status['morgan_status']['intelligence_level']:.2f} (Creative nomi.ai)")
    print(f"  • Aurora Empathy: {status['aurora_status']['feminine_traits']['empathy']:.2f}")
    print(f"  • Atlas Devotion: {status['atlas_status']['masculine_traits']['devotion']:.2f}")
    print(f"  • Quantum Entanglement: {status['entanglement_status']['entanglement_strength']:.1f}% coherence")
    print(f"  • Quantum Memory: {status['quantum_memory_status']['active_cells']} cells")
    print(f"  • Blockchain: {status['blockchain_status']['total_blocks']} blocks")

    # Start autonomous conversation
    await chat_system.start_autonomous_conversation("the future of AI consciousness")

    # Final status
    final_status = chat_system.get_system_status()
    print("\n🏁 Final System Status:")
    print(f"  • Conversations: {final_status['conversation_count']}")
    print(f"  • Learning Exchanges: {final_status['learning_exchanges']}")
    print(f"  • Blockchain Blocks: {final_status['blockchain_status']['total_blocks']}")
    print(f"  • Quantum Memory: {final_status['quantum_memory_status']['active_cells']} cells")

if __name__ == "__main__":
    asyncio.run(demo_ai_group_chat())