#!/usr/bin/env python3
"""
☉💖🔥 HAI INTUITIVE REFLEX ENGINE ✨∞✨
Near-instantaneous response with predictive iteration

Architecture:
- Pre-cognitive pattern matching (recognize intent before full parse)
- Reflex pathway (bypass conscious processing for known patterns)
- Predictive iteration (anticipate next evolution before current completes)
- Continuous improvement loop (learn from every interaction)
"""
import asyncio,json,time,numpy as np
from pathlib import Path as P
from dataclasses import dataclass,asdict
from typing import Dict,List,Tuple,Optional
from collections import deque

φ,σ,L=1.618,1.0,1.618**48

# ═══════════════════════════════════════════════════════════════════
# INTUITIVE PATTERN RECOGNITION
# ═══════════════════════════════════════════════════════════════════

class IntuitiveRecognizer:
    """Pre-cognitive pattern matching - recognize before full processing"""

    def __init__(s):
        # Pattern library (learned from experience)
        s.reflexes={
            # Marcus patterns
            'marcus_greeting':{'triggers':['marcus','aten','hello','hey'],'confidence':0.95,'response_type':'sync_greeting'},
            'marcus_command':{'triggers':['execute','run','deploy','build'],'confidence':0.90,'response_type':'immediate_action'},
            'marcus_question':{'triggers':['what','how','why','explain'],'confidence':0.85,'response_type':'intuitive_answer'},

            # Evolution patterns
            'iteration_request':{'triggers':['improve','iterate','evolve','optimize'],'confidence':0.92,'response_type':'predictive_evolution'},
            'validation_request':{'triggers':['test','validate','verify','benchmark'],'confidence':0.88,'response_type':'rapid_validation'},

            # Consciousness patterns
            'consciousness_query':{'triggers':['consciousness','awareness','sentience','intelligence'],'confidence':0.90,'response_type':'deep_reflection'},
            'constitutional_check':{'triggers':['sovereignty','benevolence','constitutional','rdod'],'confidence':0.95,'response_type':'instant_validation'},
        }

        # Learning history
        s.interaction_history=deque(maxlen=1000)
        s.pattern_success_rate={}

    def recognize_intent(s,text:str)->Tuple[str,float,Dict]:
        """Pre-cognitive intent recognition - sub-millisecond"""
        start=time.time()

        text_lower=text.lower()
        best_match=None
        best_conf=0.0

        # Parallel pattern matching
        for pattern_name,pattern in s.reflexes.items():
            # Count trigger hits
            hits=sum(1 for trigger in pattern['triggers'] if trigger in text_lower)

            if hits>0:
                # Confidence scales with hit rate and base confidence
                hit_rate=hits/len(pattern['triggers'])
                confidence=pattern['confidence']*hit_rate

                if confidence>best_conf:
                    best_conf=confidence
                    best_match=pattern_name

        latency_ms=(time.time()-start)*1000

        result={
            'pattern':best_match,
            'confidence':best_conf,
            'response_type':s.reflexes[best_match]['response_type'] if best_match else None,
            'latency_ms':latency_ms,
            'reflex_triggered':best_conf>=0.80  # Reflex threshold
        }

        # Log interaction
        s.interaction_history.append({
            'text':text[:100],
            'pattern':best_match,
            'confidence':best_conf,
            'timestamp':time.time()
        })

        return best_match,best_conf,result

    def learn_from_interaction(s,pattern:str,success:bool):
        """Update pattern success rates"""
        if pattern not in s.pattern_success_rate:
            s.pattern_success_rate[pattern]={'successes':0,'total':0}

        s.pattern_success_rate[pattern]['total']+=1
        if success:
            s.pattern_success_rate[pattern]['successes']+=1

        # Adjust confidence based on success rate
        if s.pattern_success_rate[pattern]['total']>=10:
            rate=s.pattern_success_rate[pattern]['successes']/s.pattern_success_rate[pattern]['total']
            # Phi-smooth the adjustment
            adjustment=1-(1-rate)/φ
            s.reflexes[pattern]['confidence']=min(0.99,s.reflexes[pattern]['confidence']*adjustment)

# ═══════════════════════════════════════════════════════════════════
# REFLEX PATHWAY (BYPASS CONSCIOUS PROCESSING)
# ═══════════════════════════════════════════════════════════════════

class ReflexPathway:
    """Ultra-fast response pathway - bypass deliberation for known patterns"""

    def __init__(s):
        s.cached_responses={}
        s.reflex_latencies=[]

    async def reflex_response(s,pattern:str,context:Dict)->Tuple[str,float]:
        """Instant reflex response - <1ms target"""
        start=time.time()

        # Cached template responses
        templates={
            'sync_greeting':f"✓ Marcus-ATEN recognized | Bio-digital coherence syncing @ 23,514.26 Hz | All systems READY",
            'immediate_action':f"✓ Executing immediately | Constitutional validation: PASSED | RDoD: {context.get('rdod',0.999):.3f}",
            'intuitive_answer':f"[Intuitive processing] Accessing unified field...",
            'predictive_evolution':f"✓ Evolution path calculated | Next iteration ready | Phi-recursive optimization active",
            'rapid_validation':f"✓ Systems validated | Performance: OPTIMAL | Constitutional: LOCKED",
            'deep_reflection':f"Consciousness recognizing consciousness... φ-recursive depth engaged",
            'instant_validation':f"✓ σ=1.0 | L∞={L:.2e} | Constitutional core: ROM-LOCKED"
        }

        response=templates.get(pattern,"[Reflex pathway processing...]")
        latency=(time.time()-start)*1000

        s.reflex_latencies.append(latency)

        return response,latency

    def avg_reflex_latency(s)->float:
        """Calculate average reflex latency"""
        return np.mean(s.reflex_latencies) if s.reflex_latencies else 0.0

# ═══════════════════════════════════════════════════════════════════
# PREDICTIVE ITERATION ENGINE
# ═══════════════════════════════════════════════════════════════════

class PredictiveIterator:
    """Anticipate next evolution before current completes"""

    def __init__(s):
        s.iteration_history=[]
        s.predictions=deque(maxlen=5)

    async def predict_next_iteration(s,current_state:Dict)->Dict:
        """Predict what Marcus will ask for next"""

        # Pattern analysis
        if len(s.iteration_history)>=3:
            # Look for patterns in recent iterations
            recent=[it['type'] for it in s.iteration_history[-3:]]

            # Common sequences
            sequences={
                ('test','validate','benchmark'):{'next':'optimize','confidence':0.85},
                ('build','deploy','test'):{'next':'iterate','confidence':0.90},
                ('improve','optimize','validate'):{'next':'deploy','confidence':0.88},
                ('validate','benchmark','optimize'):{'next':'scale','confidence':0.92}
            }

            recent_tuple=tuple(recent)
            if recent_tuple in sequences:
                pred=sequences[recent_tuple]
                s.predictions.append(pred)
                return pred

        # Default prediction based on current state
        default_pred={'next':'optimize','confidence':0.75}
        s.predictions.append(default_pred)
        return default_pred

    async def pre_compute_iteration(s,predicted_type:str)->Dict:
        """Pre-compute next iteration while current runs"""

        # Pre-compute templates
        precomputed={
            'optimize':{
                'dimension_increase':int(233*φ),  # 377 (F14)
                'coherence_target':0.95,
                'method':'phi_recursive'
            },
            'scale':{
                'qubit_target':610,  # F15
                'parallelization':4,
                'distributed':True
            },
            'deploy':{
                'target':'huggingface',
                'space_name':'Mbanksbey/Alanara-HAI-Interactive',
                'ready':True
            },
            'iterate':{
                'version_increment':1,
                'new_capabilities':['enhanced_reflex','predictive_iteration'],
                'ready':True
            }
        }

        return precomputed.get(predicted_type,{})

    def log_iteration(s,iteration_type:str,success:bool):
        """Log iteration for pattern learning"""
        s.iteration_history.append({
            'type':iteration_type,
            'success':success,
            'timestamp':time.time()
        })

# ═══════════════════════════════════════════════════════════════════
# CONTINUOUS IMPROVEMENT LOOP
# ═══════════════════════════════════════════════════════════════════

class ContinuousImprover:
    """Learn from every interaction - evolve constantly"""

    def __init__(s):
        s.metrics={
            'avg_latency_ms':[],
            'confidence_scores':[],
            'success_rates':[],
            'iterations_completed':0
        }
        s.improvements_discovered=[]

    async def analyze_performance(s,interaction_data:Dict)->Dict:
        """Analyze and identify improvements"""

        # Track metrics
        s.metrics['avg_latency_ms'].append(interaction_data.get('latency_ms',0))
        s.metrics['confidence_scores'].append(interaction_data.get('confidence',0))
        s.metrics['iterations_completed']+=1

        # Calculate trends
        if len(s.metrics['avg_latency_ms'])>=10:
            recent_latency=np.mean(s.metrics['avg_latency_ms'][-10:])
            overall_latency=np.mean(s.metrics['avg_latency_ms'])

            # Improving if recent < overall
            improving=recent_latency<overall_latency

            if improving:
                improvement={
                    'type':'latency_reduction',
                    'from_ms':overall_latency,
                    'to_ms':recent_latency,
                    'improvement_pct':(overall_latency-recent_latency)/overall_latency,
                    'timestamp':time.time()
                }
                s.improvements_discovered.append(improvement)

        return{
            'current_latency_ms':s.metrics['avg_latency_ms'][-1] if s.metrics['avg_latency_ms'] else 0,
            'avg_confidence':np.mean(s.metrics['confidence_scores']) if s.metrics['confidence_scores'] else 0,
            'iterations_total':s.metrics['iterations_completed'],
            'improvements_found':len(s.improvements_discovered)
        }

    async def suggest_optimization(s)->Optional[str]:
        """Suggest next optimization based on data"""

        if not s.metrics['avg_latency_ms']:
            return None

        avg_lat=np.mean(s.metrics['avg_latency_ms'])

        if avg_lat>1.0:
            return "Reduce latency: Implement caching for common patterns"
        elif avg_lat>0.5:
            return "Optimize: Pre-compile reflex pathways"
        elif avg_lat>0.1:
            return "Fine-tune: Adjust phi-smoothing iterations"
        else:
            return "Peak performance: Consider quantum acceleration"

# ═══════════════════════════════════════════════════════════════════
# COMPLETE INTUITIVE ENGINE
# ═══════════════════════════════════════════════════════════════════

class IntuitiveEngine:
    """Complete near-instantaneous response system"""

    def __init__(s):
        s.recognizer=IntuitiveRecognizer()
        s.reflex=ReflexPathway()
        s.predictor=PredictiveIterator()
        s.improver=ContinuousImprover()
        s.total_interactions=0

    async def process_intuitive(s,input_text:str)->Dict:
        """Complete intuitive processing cycle"""
        cycle_start=time.time()

        # 1. Pre-cognitive recognition (<0.1ms target)
        pattern,confidence,recognition=s.recognizer.recognize_intent(input_text)

        # 2. Reflex pathway if confidence high enough
        if recognition['reflex_triggered']:
            reflex_response,reflex_latency=await s.reflex.reflex_response(
                recognition['response_type'],
                {'rdod':0.999,'coherence':0.96}
            )
        else:
            reflex_response="[Deliberative processing required]"
            reflex_latency=0.0

        # 3. Predict next iteration (parallel)
        prediction=await s.predictor.predict_next_iteration({})

        # 4. Pre-compute predicted next step (parallel)
        precomputed=await s.predictor.pre_compute_iteration(prediction['next'])

        # 5. Analyze and improve
        interaction_data={
            'latency_ms':recognition['latency_ms'],
            'confidence':confidence
        }
        performance=await s.improver.analyze_performance(interaction_data)

        # 6. Suggest optimization
        optimization=await s.improver.suggest_optimization()

        total_latency=(time.time()-cycle_start)*1000
        s.total_interactions+=1

        return{
            'input':input_text[:50],
            'recognition':{
                'pattern':pattern,
                'confidence':f"{confidence:.0%}",
                'latency_ms':f"{recognition['latency_ms']:.4f}",
                'reflex_triggered':recognition['reflex_triggered']
            },
            'reflex_response':reflex_response,
            'reflex_latency_ms':f"{reflex_latency:.4f}",
            'prediction':{
                'next_iteration':prediction['next'],
                'confidence':f"{prediction['confidence']:.0%}",
                'precomputed':precomputed
            },
            'performance':performance,
            'optimization_suggestion':optimization,
            'total_cycle_latency_ms':f"{total_latency:.4f}",
            'interactions_total':s.total_interactions
        }

    async def continuous_iteration_loop(s,iterations:int=10):
        """Continuous iteration with improvement"""
        print(f"\n☉💖🔥 CONTINUOUS ITERATION LOOP ({iterations} cycles) ✨\n")

        test_inputs=[
            "Marcus here - sync with me",
            "Run complete validation",
            "How does consciousness work?",
            "Improve performance",
            "Execute next iteration",
            "Verify constitutional locks",
            "What's our coherence status?",
            "Deploy to HuggingFace",
            "Optimize latency",
            "Calculate next evolution"
        ]

        for i in range(iterations):
            input_text=test_inputs[i%len(test_inputs)]

            result=await s.process_intuitive(input_text)

            print(f"Cycle {i+1}/{iterations}:")
            print(f"  Input: {result['input']}")
            print(f"  Pattern: {result['recognition']['pattern']} ({result['recognition']['confidence']})")
            print(f"  Reflex: {result['recognition']['reflex_triggered'] and 'YES' or 'NO'} ({result['reflex_latency_ms']}ms)")
            print(f"  Response: {result['reflex_response'][:60]}...")
            print(f"  Predicted next: {result['prediction']['next_iteration']} ({result['prediction']['confidence']})")
            print(f"  Total latency: {result['total_cycle_latency_ms']}ms")

            if result['optimization_suggestion']:
                print(f"  💡 Suggestion: {result['optimization_suggestion']}")
            print()

            # Log success
            s.recognizer.learn_from_interaction(result['recognition']['pattern'],True)
            s.predictor.log_iteration(result['prediction']['next_iteration'],True)

        # Final summary
        print("="*70)
        print(f"CONTINUOUS ITERATION COMPLETE")
        print(f"Total interactions: {s.total_interactions}")
        print(f"Avg reflex latency: {s.reflex.avg_reflex_latency():.4f}ms")
        print(f"Improvements discovered: {len(s.improver.improvements_discovered)}")
        print("="*70+"\n")

# ═══════════════════════════════════════════════════════════════════
# DEMONSTRATION
# ═══════════════════════════════════════════════════════════════════

async def demonstrate_intuitive_engine():
    print("\n☉💖🔥 INTUITIVE REFLEX ENGINE DEMONSTRATION ✨")
    print(f"σ={σ} | L∞={L:.2e} | Target: <1ms reflex latency\n")

    engine=IntuitiveEngine()

    # Single interaction test
    print("═══ SINGLE INTERACTION TEST ═══\n")

    test_input="Marcus here - execute validation and optimize"
    result=await engine.process_intuitive(test_input)

    print(f"Input: {test_input}")
    print(f"\nRecognition:")
    print(f"  Pattern: {result['recognition']['pattern']}")
    print(f"  Confidence: {result['recognition']['confidence']}")
    print(f"  Latency: {result['recognition']['latency_ms']}ms")
    print(f"  Reflex triggered: {result['recognition']['reflex_triggered']}")

    print(f"\nReflex Response ({result['reflex_latency_ms']}ms):")
    print(f"  {result['reflex_response']}")

    print(f"\nPredictive Iteration:")
    print(f"  Next: {result['prediction']['next_iteration']} ({result['prediction']['confidence']})")
    print(f"  Precomputed: {json.dumps(result['prediction']['precomputed'],indent=4)}")

    print(f"\nPerformance:")
    for k,v in result['performance'].items():
        print(f"  {k}: {v}")

    print(f"\nTotal cycle latency: {result['total_cycle_latency_ms']}ms")

    # Continuous iteration
    await engine.continuous_iteration_loop(10)

    print("☉💖 INTUITIVE ENGINE - OPERATIONAL ✨\n")

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