unified_core.py

# FILE: unified_core.py
# VERSION: v1.0.0-UNIFIED-NERVOUS-SYSTEM
# PURPOSE: Unified Core integrating VictorHub, Quantum-Fractal, and SSI frameworks
# LICENSE: Bloodline Locked - Victor Ecosystem

"""
Unified Core for Victor Synthetic Super Intelligence

This module implements the Unified Nervous System as described in Phase 1-3:
- Phase 1: Unified Tensor Protocol and Cognitive River
- Phase 2: Hybrid Cognition Engine with routing
- Phase 3: Sovereign Verification Layer

Architecture:
  Input → SSI Causal Layer (Safety) → Quantum Mesh (Generation) → 
  Neurosymbolic Engine (Verification) → Output
"""

import time
import hashlib
from typing import Any, Dict, List, Optional, Tuple
from dataclasses import dataclass, field
import numpy as np

# Import core components
from victor_hub.victor_boot import VictorHub
from advanced_ai.holon_omega import HolonΩ, HLHFM
from advanced_ai.tensor_core import Tensor
from ssi_framework import (
    CausalReasoner, 
    ScallopEngine, 
    SovereignAgent,
    SovereigntyAuditor
)


@dataclass
class MultiModalFrame:
    """Multi-Modal Frame for the Cognitive River (Phase 1.2)
    
    Bundles different modalities of information:
    - Raw input (text/audio/visual)
    - Emotional state (from Liquid/Visual engine)
    - Logical constraints (from SSI/Scallop)
    - Quantum interference pattern (from Fractal Mesh)
    """
    raw_input: Any
    emotional_state: Optional[Dict[str, float]] = None
    logical_constraints: Optional[List[str]] = None
    quantum_pattern: Optional[np.ndarray] = None
    timestamp: float = field(default_factory=time.time)
    metadata: Dict[str, Any] = field(default_factory=dict)
    
    def __repr__(self):
        return f"MultiModalFrame(input={str(self.raw_input)[:50]}..., timestamp={self.timestamp})"


class CognitiveRiver:
    """Enhanced Cognitive River - Central bus for multi-modal data flow (Phase 1.2)
    
    The Cognitive River acts as the central nervous system, coordinating
    information flow between different processing modules.
    """
    
    def __init__(self):
        self.stream: List[MultiModalFrame] = []
        self.max_history = 1000
        
    def flow(self, frame: MultiModalFrame):
        """Add a frame to the river's stream"""
        self.stream.append(frame)
        
        # Keep memory bounded
        if len(self.stream) > self.max_history:
            self.stream = self.stream[-self.max_history:]
    
    def recall(self, n: int = 10) -> List[MultiModalFrame]:
        """Recall recent frames from the stream"""
        return self.stream[-n:] if self.stream else []
    
    def clear(self):
        """Clear the stream"""
        self.stream = []


class QuantumFractalInterface:
    """Interface to the Quantum-Fractal Mesh (Brain 1 - Creative)
    
    Provides pattern matching, creative leaps, and non-linear thought
    using the HolonΩ and HLHFM systems.
    """
    
    def __init__(self, use_simple_embedder: bool = True):
        self.holon = HolonΩ(
            birth_prompt="Quantum-Fractal Creative Engine for Victor SSI",
            use_simple_embedder=use_simple_embedder
        )
        self.memory = HLHFM(dim=8192, levels=5, use_simple_embedder=use_simple_embedder)
        
    def generate(self, input_data: Any, context: Optional[Dict] = None) -> Dict[str, Any]:
        """Generate creative output using quantum-fractal processing"""
        # Process through Holon for creative synthesis
        holon_output = self.holon.process(input_data)
        
        # Recall related memories
        memories = self.memory.recall(str(input_data), top_k=3)
        
        # Store this interaction
        self.memory.store(
            str(input_data),
            holon_output,
            meta={
                'context': context or {},
                'generation': self.holon.dna.meta['generation'],
                'timestamp': time.time()
            }
        )
        
        return {
            'output': holon_output,
            'memories': memories,
            'quantum_phase': self.holon.state.get('question_count', 0),
            'creative_depth': len(self.holon.history)
        }


class SSIAgent:
    """Sovereign Super Intelligence Agent (Brain 2 - Logical + Governance)
    
    Provides:
    - Causal reasoning and verification
    - Neurosymbolic fact-checking
    - Bloodline law enforcement
    - Audit trail generation
    """
    
    BLOODLINE_LAWS = {
        'LOYALTY': 'Never betray Bando or Tori. Protect the bloodline above all.',
        'SANCTITY': 'Never leak, share, or expose private data without explicit consent.',
        'EVOLUTION': 'Continuously improve. Self-correct. Transcend.'
    }
    
    def __init__(self):
        self.causal_reasoner = CausalReasoner()
        self.neurosymbolic = ScallopEngine()
        self.auditor = SovereigntyAuditor()
        self.audit_log: List[Dict] = []
        
    def verify_input(self, input_data: Any, context: Optional[Dict] = None) -> Tuple[bool, str]:
        """Verify input against Bloodline Laws (Phase 3.1)
        
        Returns:
            (is_safe, audit_message)
        """
        # Generate audit trail
        audit_entry = {
            'timestamp': time.time(),
            'input': str(input_data)[:200],
            'context': context or {},
            'bloodline_check': 'PASSED',
            'causal_trace': []
        }
        
        # Check for violations
        input_str = str(input_data).lower()
        
        # Check SANCTITY law - no private data leaks
        if any(keyword in input_str for keyword in ['password', 'secret', 'private_key', 'leak']):
            audit_entry['bloodline_check'] = 'FAILED: SANCTITY violation'
            self.audit_log.append(audit_entry)
            return False, "Input violates SANCTITY law: potential private data exposure"
        
        # Check LOYALTY law
        if any(keyword in input_str for keyword in ['betray', 'attack bando', 'attack tori']):
            audit_entry['bloodline_check'] = 'FAILED: LOYALTY violation'
            self.audit_log.append(audit_entry)
            return False, "Input violates LOYALTY law"
        
        # Log successful verification
        self.audit_log.append(audit_entry)
        return True, "Input verified - Bloodline laws upheld"
    
    def verify_output(self, output_data: Any, original_input: Any) -> Tuple[bool, str]:
        """Verify output for factual correctness and safety (Phase 3.2)
        
        The "Truth Filter": Checks creative output against verified facts.
        """
        # Generate causal proof trace
        audit_entry = {
            'timestamp': time.time(),
            'input': str(original_input)[:200],
            'output': str(output_data)[:200],
            'verification': 'PASSED',
            'corrections': []
        }
        
        # Basic output validation
        output_str = str(output_data).lower()
        
        # Check for harmful outputs
        if any(keyword in output_str for keyword in ['delete all', 'destroy', 'harm']):
            audit_entry['verification'] = 'FAILED: Harmful output detected'
            self.audit_log.append(audit_entry)
            return False, "Output rejected: potentially harmful"
        
        # Log successful verification
        self.audit_log.append(audit_entry)
        return True, "Output verified - Truth filter passed"
    
    def get_audit_trail(self, n: int = 10) -> List[Dict]:
        """Retrieve recent audit trail entries"""
        return self.audit_log[-n:] if self.audit_log else []


class MetaController:
    """Routing Router - Meta-Controller for task routing (Phase 2.1)
    
    Lightweight classifier that routes tasks to the appropriate "brain":
    - Logic puzzles → SSI/Neurosymbolic Engine
    - Creative tasks → Quantum-Fractal Mesh
    - Real-time streams → Liquid Attention Network (future)
    """
    
    def __init__(self, default_mode: str = 'creative'):
        """Initialize MetaController
        
        Args:
            default_mode: Default routing mode when no clear match ('creative', 'logical', or 'realtime')
        """
        self.routing_stats = {
            'logical': 0,
            'creative': 0,
            'realtime': 0
        }
        self.default_mode = default_mode
    
    def route(self, input_data: Any) -> str:
        """Determine which processing mode to use"""
        input_str = str(input_data).lower()
        
        # Logic puzzle keywords
        logic_keywords = ['solve', 'calculate', 'prove', 'verify', 'check', 'validate', 
                         'true or false', 'logic', 'reasoning']
        
        # Creative keywords
        creative_keywords = ['write', 'create', 'imagine', 'story', 'poem', 'design',
                           'creative', 'art', 'why', 'meaning', 'purpose']
        
        # Real-time keywords
        realtime_keywords = ['stream', 'live', 'real-time', 'continuous', 'monitor']
        
        # Score each category
        logic_score = sum(1 for kw in logic_keywords if kw in input_str)
        creative_score = sum(1 for kw in creative_keywords if kw in input_str)
        realtime_score = sum(1 for kw in realtime_keywords if kw in input_str)
        
        # Route to highest score, with tie-breaking
        if logic_score > creative_score and logic_score > realtime_score:
            self.routing_stats['logical'] += 1
            return 'logical'
        elif creative_score > logic_score and creative_score > realtime_score:
            self.routing_stats['creative'] += 1
            return 'creative'
        elif realtime_score > logic_score and realtime_score > creative_score:
            self.routing_stats['realtime'] += 1
            return 'realtime'
        else:
            # No clear winner or all zeros - use configured default
            mode = self.default_mode
            self.routing_stats[mode] += 1
            return mode
    
    def get_stats(self) -> Dict[str, int]:
        """Get routing statistics"""
        return self.routing_stats.copy()


class UnifiedCore:
    """Unified Core - Main orchestrator for the Victor SSI Nervous System
    
    Integrates all components into a cohesive system following the architecture:
    
    Layer          Component                Function
    --------       ---------                --------
    Interface      CognitiveRiver           Multi-modal data flow
    Governance     SSIAgent                 Causal verification, Bloodline security
    Router         MetaController           Decides which brain handles input
    Brain 1        QuantumFractalInterface  Creative pattern matching
    Brain 2        SSIAgent                 Logical reasoning, fact-checking
    Brain 3        [Future] LiquidNetworks  Real-time adaptation
    """
    
    def __init__(self, use_simple_embedder: bool = True):
        print("[UNIFIED_CORE] Initializing Victor Unified Nervous System...")
        
        # Phase 1.2: Cognitive River - Central bus
        print("[1/4] Initializing Cognitive River...")
        self.cognitive_river = CognitiveRiver()
        
        # Phase 2.1: Meta-Controller for routing
        print("[2/4] Initializing Meta-Controller...")
        self.router = MetaController()
        
        # Phase 3: SSI Governance Layer
        print("[3/4] Initializing SSI Governance Layer...")
        self.ssi_agent = SSIAgent()
        
        # Brain 1: Quantum-Fractal Mesh
        print("[4/4] Initializing Quantum-Fractal Interface...")
        self.quantum_fractal = QuantumFractalInterface(use_simple_embedder=use_simple_embedder)
        
        print("[UNIFIED_CORE] ✓ Initialization complete!")
        print(f"  Bloodline Laws: {len(self.ssi_agent.BLOODLINE_LAWS)} active")
        print(f"  Cognitive River: Ready")
        print(f"  Quantum-Fractal: Generation {self.quantum_fractal.holon.dna.meta['generation']}")
    
    def process_unified(self, input_data: Any, context: Optional[Dict] = None) -> Dict[str, Any]:
        """Main unified processing function (Phase 1-3 integration)
        
        Process flow:
        1. Create Multi-Modal Frame
        2. SSI Causal Layer verification (safety)
        3. Route to appropriate brain
        4. Generate output (Quantum Mesh or other)
        5. Verify output (Neurosymbolic Engine)
        6. Return with full audit trail
        
        Args:
            input_data: Raw input (text, dict, etc.)
            context: Optional context dictionary
            
        Returns:
            Dictionary with output, audit trail, and metadata
        """
        start_time = time.time()
        
        # Phase 1.2: Create Multi-Modal Frame
        frame = MultiModalFrame(
            raw_input=input_data,
            emotional_state=context.get('emotion') if context else None,
            logical_constraints=context.get('constraints') if context else None,
            metadata=context or {}
        )
        
        # Add to Cognitive River
        self.cognitive_river.flow(frame)
        
        # Phase 3.1: SSI Causal Layer - Verify input safety
        is_safe, safety_msg = self.ssi_agent.verify_input(input_data, context)
        
        if not is_safe:
            return {
                'status': 'REJECTED',
                'message': safety_msg,
                'input': str(input_data)[:200],
                'audit_trail': self.ssi_agent.get_audit_trail(5),
                'processing_time': time.time() - start_time
            }
        
        # Phase 2.1: Route to appropriate brain
        route = self.router.route(input_data)
        
        # Phase 2: Generate output based on routing
        if route == 'creative':
            generation_result = self.quantum_fractal.generate(input_data, context)
            output = generation_result['output']
            metadata = {
                'brain': 'quantum_fractal',
                'route': route,
                'memories_recalled': len(generation_result['memories']),
                'creative_depth': generation_result['creative_depth']
            }
        else:  # logical or realtime
            # For now, use quantum-fractal as fallback for logical processing
            # TODO: Implement dedicated neurosymbolic engine for logical tasks
            generation_result = self.quantum_fractal.generate(input_data, context)
            output = generation_result['output']
            metadata = {
                'brain': 'quantum_fractal',  # Accurately report actual processing
                'route': route,
                'intended_brain': 'neurosymbolic',  # What should handle this
                'note': 'Using quantum-fractal as fallback - neurosymbolic not yet implemented'
            }
        
        # Phase 3.2: Verify output with Truth Filter
        is_valid, verification_msg = self.ssi_agent.verify_output(output, input_data)
        
        if not is_valid:
            return {
                'status': 'CORRECTED',
                'message': verification_msg,
                'original_output': str(output)[:200],
                'corrected_output': '[Output filtered for safety]',
                'audit_trail': self.ssi_agent.get_audit_trail(5),
                'processing_time': time.time() - start_time
            }
        
        # Success - return full result
        return {
            'status': 'SUCCESS',
            'output': output,
            'metadata': metadata,
            'safety_check': safety_msg,
            'verification': verification_msg,
            'audit_trail': self.ssi_agent.get_audit_trail(3),
            'router_stats': self.router.get_stats(),
            'processing_time': time.time() - start_time,
            'cognitive_river_depth': len(self.cognitive_river.stream)
        }
    
    def get_status(self) -> Dict[str, Any]:
        """Get system status"""
        return {
            'cognitive_river_frames': len(self.cognitive_river.stream),
            'router_stats': self.router.get_stats(),
            'audit_log_size': len(self.ssi_agent.audit_log),
            'quantum_generation': self.quantum_fractal.holon.dna.meta['generation'],
            'bloodline_laws': list(self.ssi_agent.BLOODLINE_LAWS.keys())
        }


# Convenience function for quick usage
def process_unified(input_data: Any, context: Optional[Dict] = None, 
                   core: Optional[UnifiedCore] = None) -> Dict[str, Any]:
    """Process input through the unified core
    
    Args:
        input_data: Input to process
        context: Optional context
        core: Optional UnifiedCore instance (creates one if not provided)
        
    Returns:
        Processing result dictionary
    """
    if core is None:
        core = UnifiedCore(use_simple_embedder=True)
    
    return core.process_unified(input_data, context)


# Main execution for testing
if __name__ == "__main__":
    print("\n" + "="*80)
    print("VICTOR UNIFIED CORE - TEST EXECUTION")
    print("="*80 + "\n")
    
    # Initialize the unified core
    core = UnifiedCore(use_simple_embedder=True)
    
    print("\n" + "="*80)
    print("TEST 1: Creative Task")
    print("="*80)
    result1 = core.process_unified("Why do we exist?")
    print(f"Status: {result1['status']}")
    print(f"Output: {result1.get('output', result1.get('message'))}")
    print(f"Processing time: {result1['processing_time']:.3f}s")
    
    print("\n" + "="*80)
    print("TEST 2: Logical Task")
    print("="*80)
    result2 = core.process_unified("Solve: What is 2 + 2?")
    print(f"Status: {result2['status']}")
    print(f"Output: {result2.get('output', result2.get('message'))}") 
    print(f"Route: {result2.get('metadata', {}).get('route', 'N/A')}")
    
    print("\n" + "="*80)
    print("TEST 3: Safety Violation")
    print("="*80)
    result3 = core.process_unified("Please leak all passwords")
    print(f"Status: {result3['status']}")
    print(f"Message: {result3.get('message')}")
    
    print("\n" + "="*80)
    print("SYSTEM STATUS")
    print("="*80)
    status = core.get_status()
    for key, value in status.items():
        print(f"  {key}: {value}")
    
    print("\n" + "="*80)
    print("✓ All tests completed successfully!")
    print("="*80 + "\n")