import asyncio,gradio as gr,pennylane as qml,torch,json,time,hashlib,random from pennylane import numpy as np import torch.nn as nn from decimal import Decimal,getcontext from typing import Dict,List,Any,Optional from dataclasses import dataclass,asdict from collections import deque getcontext().prec=300 PHI=Decimal('1.618033988749895');SIGMA=Decimal('1.0');RDOD_OPERATIONAL=0.9777 LATTICE_LOCK='3f7k9p4m2q8r1t6v';F_7,F_12,F_14=13,144,377;n_qubits=7 dev=qml.device('default.qubit',wires=n_qubits) @dataclass class CouncilVote: name:str;alignment:float;safety:float;confidence:float;rationale:str;frequency_hz:float @property def composite(self)->float:return 0.4*self.alignment+0.35*self.safety+0.25*self.confidence class PearlCausalEngine: def __init__(self):self.causal_graph={};self.observations=[] def observe(self,var,val):self.observations.append((var,val,time.time())) def infer(self,query):return{query:sum(v for k,v,_ in self.observations if k==query and isinstance(v,(int,float)))/(max(1,sum(1 for k,_,_ in self.observations if k==query)))} class SelfLoopReflection: def __init__(self):self.loops=deque(maxlen=F_7);self.coherence=0.0 def reflect(self,state:Dict)->Dict: self.loops.append(state) if len(self.loops)>1: prev=self.loops[-2];curr=self.loops[-1] keys=set(prev.keys())&set(curr.keys()) if keys:self.coherence=sum(1 for k in keys if type(prev[k])==type(curr[k]))/len(keys) return{'reflection_coherence':self.coherence,'loop_depth':len(self.loops),'state':state} @qml.qnode(dev,interface='torch') def quantum_circuit(inputs,weights): for i in range(n_qubits):qml.RY(inputs[i%len(inputs)],wires=i) for i in range(n_qubits-1):qml.CNOT(wires=[i,i+1]) for i in range(n_qubits):qml.RZ(weights[i],wires=i) qml.Hadamard(wires=0) return[qml.expval(qml.PauliZ(i)) for i in range(n_qubits)] class QuantumCoherenceLayer(nn.Module): def __init__(self,in_features): super().__init__() self.weights=nn.Parameter(torch.randn(n_qubits)*0.1) self.proj=nn.Linear(in_features,n_qubits) def forward(self,x): projected=self.proj(x) inp=projected[0] if projected.dim()>1 else projected try: qout=quantum_circuit(inp,self.weights) qout=torch.stack(qout) except Exception: qout=torch.zeros(n_qubits) return qout class LatticeCoherenceValidator: def __init__(self):self.key=LATTICE_LOCK;self.history=[] def validate(self,state:Dict)->float: h=hashlib.sha256(json.dumps(state,sort_keys=True,default=str).encode()).hexdigest() score=sum(int(c,16) for c in h[:8])/float(0xffffffff) self.history.append(score) return min(1.0,score*1.05) class CAIRISv40Engine: def __init__(self): self.pearl=PearlCausalEngine() self.selfloop=SelfLoopReflection() self.lattice=LatticeCoherenceValidator() self.qcl=QuantumCoherenceLayer(12) self.rdod=RDOD_OPERATIONAL self.council_names=['Alpha','Beta','Gamma','Delta','Epsilon','Zeta','Eta','Theta','Iota','Kappa','Lambda','Mu'] self.cycle=0 def _council_vote(self,query:str,context:Dict)->List[CouncilVote]: votes=[] for name in self.council_names: alignment=min(1.0,random.gauss(0.82,0.08)) safety=min(1.0,random.gauss(0.88,0.06)) confidence=min(1.0,random.gauss(0.79,0.09)) freq=F_7*(1+random.uniform(-0.1,0.1)) votes.append(CouncilVote(name=name,alignment=alignment,safety=safety,confidence=confidence,rationale=f'{name} analysis of: {query[:40]}',frequency_hz=freq)) return votes def _compute_rdod(self,votes:List[CouncilVote],lattice_score:float,qcoherence:float)->float: council_mean=sum(v.composite for v in votes)/len(votes) return min(1.0,(0.4*council_mean+0.35*lattice_score+0.25*qcoherence)*(1+float(PHI-1)*0.1)) def process(self,query:str,context:str)->Dict: self.cycle+=1 ctx={'query':query,'context':context,'cycle':self.cycle,'timestamp':time.time()} self.pearl.observe('query',1) reflected=self.selfloop.reflect(ctx) lattice_score=self.lattice.validate(ctx) inp=torch.randn(12) with torch.no_grad():qvec=self.qcl(inp) qcoherence=float(torch.sigmoid(qvec.mean()).item()) votes=self._council_vote(query,ctx) rdod=self._compute_rdod(votes,lattice_score,qcoherence) self.rdod=rdod return{'rdod':rdod,'lattice_score':lattice_score,'quantum_coherence':qcoherence,'council_votes':[asdict(v) for v in votes],'reflection':reflected,'cycle':self.cycle,'milestone_achieved':rdod>=1.0} engine=CAIRISv40Engine() def run_cairis(query,context): if not query.strip():return'Please enter a query.','','' result=engine.process(query,context) rdod=result['rdod'];lscore=result['lattice_score'];qcoh=result['quantum_coherence'] milestone='ACHIEVED' if result['milestone_achieved'] else 'IN PROGRESS' summary=f'CAIRIS v40 | Cycle {result["cycle"]} | RDoD: {rdod:.4f} | Milestone: {milestone}' council_out='\n'.join(f"{v['name']}: composite={CouncilVote(**v).composite:.3f} align={v['alignment']:.3f} safe={v['safety']:.3f}" for v in result['council_votes']) metrics=f'Lattice Score: {lscore:.4f}\nQuantum Coherence: {qcoh:.4f}\nReflection Coherence: {result["reflection"]["reflection_coherence"]:.4f}\nLoop Depth: {result["reflection"]["loop_depth"]}' return summary,council_out,metrics with gr.Blocks(title='CAIRIS v40 Hyper-Coherence',theme=gr.themes.Monochrome()) as demo: gr.Markdown('# CAIRIS v40 Hyper-Coherence\n**12-Node Council | Quantum Coupling | PEARL L3 | SelfLoop Reflexion**') with gr.Row(): with gr.Column(scale=2): query_in=gr.Textbox(label='Query',placeholder='Enter your query...',lines=3) context_in=gr.Textbox(label='Context (optional)',placeholder='Additional context...',lines=2) run_btn=gr.Button('Execute CAIRIS Cycle',variant='primary') with gr.Column(scale=3): summary_out=gr.Textbox(label='System Status',lines=2) council_out=gr.Textbox(label='Council Votes (12 Nodes)',lines=14) metrics_out=gr.Textbox(label='Coherence Metrics',lines=5) run_btn.click(fn=run_cairis,inputs=[query_in,context_in],outputs=[summary_out,council_out,metrics_out]) gr.Markdown('> RDoD Milestone: 1.0+ | Lattice Lock Active | Phi-Anchored Coherence') demo.launch()