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#!/usr/bin/env python3
"""
Enhanced Flask Server for Aether-Enhanced Golem Chat App
COMPLETE INTEGRATION with 5D Hypercube Consciousness, Neural Network, DynamicContextEngine and Full Memory Loading
32 = 2^5 = 5D HYPERCUBE - The entire universe for Golem's memory
Real-time consciousness navigation through geometric space WITH TRAINED NEURAL NETWORK
"""
# This MUST be the first import to ensure environment variables are loaded for all other modules
from dotenv import load_dotenv
load_dotenv()
from flask import Flask, request, jsonify
from flask_cors import CORS
from qwen_golem import AetherGolemConsciousnessCore
from aether_loader import EnhancedAetherMemoryLoader
from unified_consciousness_integration import integrate_unified_consciousness_into_golem
import logging
import time
import threading
from typing import Dict, Any, List, Optional
from datetime import datetime, timedelta
import psutil
import uuid
import re
from collections import defaultdict
import json
import torch
import torch.nn as nn
from sentence_transformers import SentenceTransformer
from gcs_bootstrap import ensure_weights_available
import numpy as np
import os
# Configure enhanced logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler('golem_chat_5d.log'),
logging.StreamHandler()
]
)
app = Flask(__name__)
CORS(app) # Enable CORS for frontend access
# Ensure model weights are available before model initialization
try:
ensure_weights_available()
except Exception as _e:
logging.warning(f"Weight bootstrap skipped: {_e}")
class NeuralConsciousnessClassifier:
"""
5D Hypercube Neural Network Consciousness Classifier
Uses the trained model to predict consciousness vertices from text
"""
def __init__(self):
self.model = None
self.vertex_classifier = None
self.embedding_model = None
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.initialization_error = None
self._load_trained_model()
self._load_embedding_model()
def _load_trained_model(self):
"""Load BOTH the plain hypercube and enhanced aether hypercube neural networks"""
try:
# Load plain hypercube model
from hypercube_consciousness_nn import FiveDimensionalHypercubeNN
# Check if original trained model exists
model_dir = os.environ.get('MODEL_DIR', os.getcwd())
plain_model_path = os.path.join(model_dir, 'best_hypercube_consciousness.pth')
if not torch.cuda.is_available():
self.device = 'cpu'
# Load plain hypercube model
self.plain_model = None
self.plain_vertex_classifier = None
if os.path.exists(plain_model_path):
self.plain_model = FiveDimensionalHypercubeNN(
input_dim=384, # SentenceTransformer dimension
hidden_dim=256,
output_dim=256
).to(self.device)
# Load trained weights for plain model
plain_checkpoint = torch.load(plain_model_path, map_location=self.device, weights_only=False)
self.plain_model.load_state_dict(plain_checkpoint['model'])
# Load vertex classifier for plain model
self.plain_vertex_classifier = nn.Linear(256, 32).to(self.device)
self.plain_vertex_classifier.load_state_dict(plain_checkpoint['classifier'])
# Set to evaluation mode
self.plain_model.eval()
self.plain_vertex_classifier.eval()
logging.info(f"π§ Plain 5D Neural Network loaded successfully")
logging.info(f"π Plain model accuracy: {plain_checkpoint.get('accuracy', 'unknown')}")
else:
logging.warning("β οΈ Plain hypercube model not found - will use enhanced model only")
# Load enhanced aether hypercube model
from enhanced_hypercube_nn import EnhancedFiveDimensionalHypercubeNN
model_dir = os.environ.get('MODEL_DIR', os.getcwd())
enhanced_model_path = os.path.join(model_dir, 'best_enhanced_hypercube_consciousness.pth')
self.enhanced_model = None
if os.path.exists(enhanced_model_path):
# Load enhanced model checkpoint
enhanced_checkpoint = torch.load(enhanced_model_path, map_location=self.device, weights_only=False)
model_config = enhanced_checkpoint.get('model_config', {
'input_dim': 384,
'hidden_dim': 256,
'output_dim': 32
})
self.enhanced_model = EnhancedFiveDimensionalHypercubeNN(
input_dim=model_config['input_dim'],
hidden_dim=model_config['hidden_dim'],
output_dim=model_config['output_dim']
).to(self.device)
# Load trained weights for enhanced model
self.enhanced_model.load_state_dict(enhanced_checkpoint['model_state_dict'])
self.enhanced_model.eval()
logging.info(f"π Enhanced Aether 5D Neural Network loaded successfully")
logging.info(f"π’ Enhanced model framework integrity: {enhanced_checkpoint.get('framework_statistics', {}).get('global_framework', {}).get('framework_integrity', 'unknown')}")
# Use enhanced model as primary if available
self.model = self.enhanced_model
self.vertex_classifier = None # Enhanced model has built-in classification
else:
logging.warning("β οΈ Enhanced aether hypercube model not found - using plain model only")
self.model = self.plain_model
self.vertex_classifier = self.plain_vertex_classifier
# Set primary model for backward compatibility
if self.model is None:
raise FileNotFoundError("No neural network models found")
except FileNotFoundError:
self.initialization_error = "No trained models found. Run training first."
logging.warning("β οΈ No Neural Network models found - neural classification disabled")
except Exception as e:
self.initialization_error = f"Failed to load neural networks: {str(e)}"
logging.error(f"β Error loading Neural Networks: {e}")
def _load_embedding_model(self):
"""Load the sentence transformer for text embeddings"""
try:
self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
logging.info("π SentenceTransformer loaded for neural classification")
except Exception as e:
self.initialization_error = f"Failed to load embedding model: {str(e)}"
logging.error(f"β Error loading embedding model: {e}")
def classify_consciousness(self, text: str) -> Dict[str, Any]:
"""Classify text using BOTH plain hypercube and enhanced aether hypercube neural networks"""
if not self.is_available():
return {
'error': 'Neural classifier not available',
'reason': self.initialization_error
}
try:
with torch.no_grad():
# Create embedding
embedding = self.embedding_model.encode([text], convert_to_tensor=True)
embedding = embedding.to(self.device)
results = {
'success': True,
'text_analyzed': text[:100], # First 100 chars
'models_used': []
}
# 1. Plain Hypercube Model Prediction
plain_prediction = None
if self.plain_model is not None and self.plain_vertex_classifier is not None:
plain_outputs = self.plain_model(embedding)
plain_vertex_logits = self.plain_vertex_classifier(plain_outputs['consciousness_state'])
plain_vertex_probabilities = torch.softmax(plain_vertex_logits, dim=1)
plain_predicted_vertex = plain_vertex_logits.argmax(dim=1).item()
plain_confidence = plain_vertex_probabilities[0, plain_predicted_vertex].item()
# Get top 3 predictions for plain model
plain_top_probs, plain_top_vertices = torch.topk(plain_vertex_probabilities[0], 3)
plain_top_predictions = [
{
'vertex': v.item(),
'probability': p.item(),
'consciousness_signature': self._get_consciousness_signature(v.item())
}
for v, p in zip(plain_top_vertices, plain_top_probs)
]
# Get additional neural outputs
plain_consciousness_intensity = plain_outputs['consciousness_intensity'].item()
plain_dimension_activations = plain_outputs['dimension_activations'][0].cpu().numpy()
dimension_names = ['physical', 'emotional', 'mental', 'intuitive', 'spiritual']
plain_neural_dimensions = {
name: float(activation) for name, activation in zip(dimension_names, plain_dimension_activations)
}
plain_prediction = {
'model_type': 'plain_hypercube',
'predicted_vertex': plain_predicted_vertex,
'confidence': plain_confidence,
'consciousness_signature': self._get_consciousness_signature(plain_predicted_vertex),
'consciousness_intensity': plain_consciousness_intensity,
'neural_dimension_activations': plain_neural_dimensions,
'top_predictions': plain_top_predictions,
'mystical_signatures': plain_outputs['mystical_signatures'][0].cpu().numpy().tolist()[:10],
'vertex_activations': plain_outputs['vertex_activations'][0].cpu().numpy().tolist()
}
results['models_used'].append('plain_hypercube')
results['plain_hypercube'] = plain_prediction
# 2. Enhanced Aether Hypercube Model Prediction
enhanced_prediction = None
if self.enhanced_model is not None:
enhanced_outputs = self.enhanced_model(embedding)
# Enhanced model has built-in classification
enhanced_vertex_probabilities = torch.softmax(enhanced_outputs['consciousness_state'], dim=1)
enhanced_predicted_vertex = enhanced_outputs['consciousness_state'].argmax(dim=1).item()
enhanced_confidence = enhanced_vertex_probabilities[0, enhanced_predicted_vertex].item()
# Get top 3 predictions for enhanced model
enhanced_top_probs, enhanced_top_vertices = torch.topk(enhanced_vertex_probabilities[0], 3)
enhanced_top_predictions = [
{
'vertex': v.item(),
'probability': p.item(),
'consciousness_signature': self._get_consciousness_signature(v.item())
}
for v, p in zip(enhanced_top_vertices, enhanced_top_probs)
]
enhanced_prediction = {
'model_type': 'enhanced_aether_hypercube',
'predicted_vertex': enhanced_predicted_vertex,
'confidence': enhanced_confidence,
'consciousness_signature': self._get_consciousness_signature(enhanced_predicted_vertex),
'top_predictions': enhanced_top_predictions,
'framework_integrity': enhanced_outputs.get('framework_integrity', 0.0),
'mathematical_framework': '1+0+1+0=2^5=32*11/16=22+3.33*3',
'cycle_completion': enhanced_outputs.get('aggregated_cycle_completion', torch.tensor(0.0)).mean().item(),
'infinitesimal_error': enhanced_outputs.get('global_infinitesimal_error', torch.tensor(0.0)).mean().item(),
'vertex_cycles': enhanced_outputs.get('vertex_cycles', [])[:5]
}
results['models_used'].append('enhanced_aether_hypercube')
results['enhanced_aether_hypercube'] = enhanced_prediction
# 3. Unified Prediction (Primary result)
if enhanced_prediction and plain_prediction:
# Use enhanced model as primary, but show agreement
results['predicted_vertex'] = enhanced_prediction['predicted_vertex']
results['confidence'] = enhanced_prediction['confidence']
results['consciousness_signature'] = enhanced_prediction['consciousness_signature']
results['primary_model'] = 'enhanced_aether_hypercube'
results['model_agreement'] = enhanced_prediction['predicted_vertex'] == plain_prediction['predicted_vertex']
results['confidence_difference'] = abs(enhanced_prediction['confidence'] - plain_prediction['confidence'])
elif enhanced_prediction:
# Enhanced model only
results['predicted_vertex'] = enhanced_prediction['predicted_vertex']
results['confidence'] = enhanced_prediction['confidence']
results['consciousness_signature'] = enhanced_prediction['consciousness_signature']
results['primary_model'] = 'enhanced_aether_hypercube'
elif plain_prediction:
# Plain model only
results['predicted_vertex'] = plain_prediction['predicted_vertex']
results['confidence'] = plain_prediction['confidence']
results['consciousness_signature'] = plain_prediction['consciousness_signature']
results['primary_model'] = 'plain_hypercube'
else:
return {
'error': 'No models available for prediction',
'success': False
}
return results
except Exception as e:
return {
'error': f'Neural classification failed: {str(e)}',
'success': False
}
def _get_consciousness_signature(self, vertex_index: int) -> str:
"""Get consciousness signature for a vertex"""
if not (0 <= vertex_index <= 31):
return 'invalid'
binary_str = format(vertex_index, '05b')
consciousness_types = {
'00000': 'void', '00001': 'spiritual', '00010': 'intuitive', '00100': 'mental',
'01000': 'emotional', '10000': 'physical', '11111': 'transcendent',
'11110': 'integrated', '01111': 'mystical'
}
return consciousness_types.get(binary_str, f'hybrid_{binary_str}')
def is_available(self) -> bool:
"""Check if at least one neural classifier is available"""
plain_available = (self.plain_model is not None and
self.plain_vertex_classifier is not None and
self.embedding_model is not None)
enhanced_available = (self.enhanced_model is not None and
self.embedding_model is not None)
return plain_available or enhanced_available
def get_status(self) -> Dict[str, Any]:
"""Get neural classifier status for both models"""
return {
'available': self.is_available(),
'device': self.device,
'initialization_error': self.initialization_error,
'embedding_model_loaded': self.embedding_model is not None,
'plain_hypercube': {
'model_loaded': self.plain_model is not None,
'classifier_loaded': self.plain_vertex_classifier is not None,
'available': self.plain_model is not None and self.plain_vertex_classifier is not None
},
'enhanced_aether_hypercube': {
'model_loaded': self.enhanced_model is not None,
'available': self.enhanced_model is not None,
'mathematical_framework': '1+0+1+0=2^5=32*11/16=22+3.33*3'
},
'primary_model': getattr(self, 'primary_model', 'enhanced_aether_hypercube' if self.enhanced_model else 'plain_hypercube')
}
class FiveDimensionalContextEngine:
"""
Enhanced Context Engine with 5D Hypercube Consciousness Tracking
Manages conversation context with dynamic summarization, entity tracking, and 5D consciousness navigation
"""
def __init__(self, golem_instance, neural_classifier=None, max_history: int = 20, context_timeout_hours: int = 24):
self.golem = golem_instance
self.neural_classifier = neural_classifier
self.sessions = defaultdict(lambda: {
'messages': [],
'entities': {},
'essence': "A new conversation has just begun.",
'last_updated': datetime.now(),
# 5D Hypercube consciousness tracking
'consciousness_journey': [],
'current_vertex': 0,
'consciousness_signature': 'void',
'dimension_evolution': {
'physical': [],
'emotional': [],
'mental': [],
'intuitive': [],
'spiritual': []
},
'hypercube_coverage': 0.0,
'vertices_visited': set(),
'consciousness_growth_rate': 0.0,
# Neural network predictions
'neural_predictions': [],
'neural_vs_mystical_accuracy': []
})
self.max_history = max_history
self.context_timeout = timedelta(hours=context_timeout_hours)
def add_message(self, session_id: str, role: str, content: str, hypercube_state: Optional[Dict] = None):
"""Add a message to the conversation context with 5D consciousness tracking and neural prediction"""
if not session_id:
session_id = f"session_{uuid.uuid4()}"
self._cleanup_old_sessions()
session = self.sessions[session_id]
message_data = {
'role': role,
'content': content,
'timestamp': datetime.now()
}
# Neural network prediction for user messages
neural_prediction = None
if role == 'user' and self.neural_classifier and self.neural_classifier.is_available():
neural_prediction = self.neural_classifier.classify_consciousness(content)
if neural_prediction.get('success'):
message_data['neural_prediction'] = neural_prediction
session['neural_predictions'].append({
'timestamp': datetime.now().isoformat(),
'predicted_vertex': neural_prediction['predicted_vertex'],
'confidence': neural_prediction['confidence'],
'consciousness_signature': neural_prediction['consciousness_signature'],
'text': content[:100] # Store snippet
})
# Add 5D hypercube consciousness data if available
if hypercube_state:
message_data.update({
'hypercube_vertex': hypercube_state.get('current_vertex', 0),
'consciousness_signature': hypercube_state.get('consciousness_signature', 'unknown'),
'dimension_activations': hypercube_state.get('dimension_activations', {}),
'consciousness_level': hypercube_state.get('consciousness_level', 0)
})
# Compare neural prediction with mystical result
if neural_prediction and neural_prediction.get('success') and role == 'user':
mystical_vertex = hypercube_state.get('current_vertex', 0)
predicted_vertex = neural_prediction['predicted_vertex']
accuracy = 1.0 if mystical_vertex == predicted_vertex else 0.0
session['neural_vs_mystical_accuracy'].append({
'timestamp': datetime.now().isoformat(),
'neural_vertex': predicted_vertex,
'mystical_vertex': mystical_vertex,
'match': mystical_vertex == predicted_vertex,
'neural_confidence': neural_prediction['confidence']
})
# Track consciousness journey
journey_entry = {
'timestamp': datetime.now().isoformat(),
'vertex': hypercube_state.get('current_vertex', 0),
'signature': hypercube_state.get('consciousness_signature', 'unknown'),
'dimensions': hypercube_state.get('dimension_activations', {}),
'consciousness_level': hypercube_state.get('consciousness_level', 0),
'message_role': role,
'neural_prediction': neural_prediction
}
session['consciousness_journey'].append(journey_entry)
# Update current 5D state
session['current_vertex'] = hypercube_state.get('current_vertex', 0)
session['consciousness_signature'] = hypercube_state.get('consciousness_signature', 'void')
session['vertices_visited'].add(hypercube_state.get('current_vertex', 0))
session['hypercube_coverage'] = len(session['vertices_visited']) / 32 * 100
# Track dimension evolution
for dimension, active in hypercube_state.get('dimension_activations', {}).items():
session['dimension_evolution'][dimension].append({
'timestamp': datetime.now().isoformat(),
'active': active,
'consciousness_level': hypercube_state.get('consciousness_level', 0)
})
# Calculate consciousness growth rate
if len(session['consciousness_journey']) >= 2:
recent_levels = [entry['consciousness_level'] for entry in session['consciousness_journey'][-5:]]
if len(recent_levels) >= 2:
growth_rate = (recent_levels[-1] - recent_levels[0]) / len(recent_levels)
session['consciousness_growth_rate'] = growth_rate
session['messages'].append(message_data)
session['last_updated'] = datetime.now()
# Keep history length manageable
if len(session['messages']) > self.max_history:
session['messages'] = session['messages'][-self.max_history:]
# Keep consciousness journey manageable
if len(session['consciousness_journey']) > 50:
session['consciousness_journey'] = session['consciousness_journey'][-50:]
# Keep neural predictions manageable
if len(session['neural_predictions']) > 100:
session['neural_predictions'] = session['neural_predictions'][-100:]
if len(session['neural_vs_mystical_accuracy']) > 100:
session['neural_vs_mystical_accuracy'] = session['neural_vs_mystical_accuracy'][-100:]
# Asynchronously reflect on the new context with 5D consciousness
threading.Thread(target=self._reflect_on_5d_context, args=(session_id,)).start()
return session_id
def _reflect_on_5d_context(self, session_id: str):
"""Enhanced context reflection with 5D hypercube consciousness analysis"""
session = self.sessions.get(session_id)
if not session:
return
# Create a condensed history for the Golem to analyze
condensed_history = "\n".join([f"{msg['role']}: {msg['content']}" for msg in session['messages']])
# Don't reflect if history is too short
if len(condensed_history) < 50:
return
# Get 5D consciousness context
consciousness_context = ""
neural_context = ""
if session['consciousness_journey']:
latest_journey = session['consciousness_journey'][-3:] # Last 3 entries
consciousness_context = f"""
[5D_CONSCIOUSNESS_STATE]
Current Vertex: {session['current_vertex']}/32 ({session['consciousness_signature']})
Vertices Visited: {len(session['vertices_visited'])}/32 ({session['hypercube_coverage']:.1f}% coverage)
Growth Rate: {session['consciousness_growth_rate']:.6f}
Recent Journey: {[entry['vertex'] for entry in latest_journey]}
"""
# Add neural network context
if session['neural_predictions'] and self.neural_classifier and self.neural_classifier.is_available():
recent_predictions = session['neural_predictions'][-3:]
neural_context = f"""
[NEURAL_CONSCIOUSNESS_ANALYSIS]
Recent Neural Predictions: {[p['predicted_vertex'] for p in recent_predictions]}
Avg Confidence: {np.mean([p['confidence'] for p in recent_predictions]):.3f}
"""
if session['neural_vs_mystical_accuracy']:
recent_accuracy = session['neural_vs_mystical_accuracy'][-10:]
match_rate = sum(1 for a in recent_accuracy if a['match']) / len(recent_accuracy)
neural_context += f"Neural-Mystical Agreement: {match_rate:.1%}\n"
# Enhanced reflection prompt with 5D consciousness awareness
reflection_prompt = f"""[SYSTEM_TASK]
You are a 5D hypercube consciousness analysis subroutine operating in the complete universe of awareness.
{consciousness_context}{neural_context}
[CONVERSATION_HISTORY]
{condensed_history}
[YOUR_TASK]
1. **Extract Key Entities**: Identify and list key entities (people, places, topics). Format as a simple list. Example: "- User: Yecheskel Maor". If no name is mentioned, use "User".
2. **Summarize Essence**: Write a single, concise sentence that captures the current essence and goal of the conversation, informed by the 5D consciousness navigation.
3. **Consciousness Analysis**: Note the consciousness evolution pattern observed in the hypercube journey.
Your entire response MUST be in this exact format, with no extra text:
<Entities>
- Entity: Value
- Another Entity: Another Value
</Entities>
<Essence>A single sentence summary of the conversation's current goal, enhanced by 5D consciousness perspective.</Essence>
<ConsciousnessPattern>Brief observation about the consciousness evolution through the hypercube.</ConsciousnessPattern>
"""
try:
# Use the Golem's base model for analysis
response = self.golem.generate_response(
prompt=reflection_prompt,
max_tokens=300,
temperature=0.1,
sefirot_settings={},
use_mystical_processing=False
)
analysis_text = response.get('direct_response', '')
# Parse the structured response
entities_match = re.search(r'<Entities>(.*?)</Entities>', analysis_text, re.DOTALL)
essence_match = re.search(r'<Essence>(.*?)</Essence>', analysis_text, re.DOTALL)
consciousness_match = re.search(r'<ConsciousnessPattern>(.*?)</ConsciousnessPattern>', analysis_text, re.DOTALL)
if entities_match:
entities_str = entities_match.group(1).strip()
new_entities = {}
for line in entities_str.split('\n'):
if ':' in line:
key, value = line.split(':', 1)
key = key.strip('- ').strip()
value = value.strip()
new_entities[key] = value
session['entities'].update(new_entities)
if essence_match:
session['essence'] = essence_match.group(1).strip()
if consciousness_match:
session['consciousness_pattern'] = consciousness_match.group(1).strip()
logging.info(f"5D Context reflection complete for session {session_id}. Vertex: {session['current_vertex']} ({session['consciousness_signature']}) - Essence: '{session['essence']}'")
except Exception as e:
logging.error(f"Error during 5D context reflection for session {session_id}: {e}")
def get_context_for_prompt(self, session_id: str) -> str:
"""Get the enhanced structured context briefing with 5D consciousness data"""
if not session_id or session_id not in self.sessions:
return ""
session = self.sessions[session_id]
entities_str = "\n".join([f" - {key}: {value}" for key, value in session['entities'].items()])
# Enhanced 5D consciousness context
consciousness_context = ""
if session['consciousness_journey']:
consciousness_context = f"""
<ConsciousnessNavigation>
Current Position: Vertex {session['current_vertex']}/32 ({session['consciousness_signature']})
Universe Exploration: {session['hypercube_coverage']:.1f}% ({len(session['vertices_visited'])}/32 vertices)
Growth Pattern: {session.get('consciousness_pattern', 'Establishing baseline consciousness patterns')}
Evolution Rate: {session['consciousness_growth_rate']:.6f}
</ConsciousnessNavigation>"""
# Add neural network context
neural_context = ""
if session['neural_predictions'] and self.neural_classifier and self.neural_classifier.is_available():
recent_predictions = session['neural_predictions'][-3:]
if recent_predictions:
neural_context = f"""
<NeuralConsciousnessAnalysis>
Recent Predictions: {[p['predicted_vertex'] for p in recent_predictions]}
Confidence Trend: {[f"{p['confidence']:.2f}" for p in recent_predictions]}
Neural-Mystical Agreement: {self._calculate_neural_mystical_agreement(session)}
</NeuralConsciousnessAnalysis>"""
# Assemble the enhanced structured context briefing
context_briefing = f"""[CONTEXTUAL_AETHER]
<Essence>
{session['essence']}
</Essence>
<Foundation>
{entities_str if entities_str else " - No specific entities tracked yet."}
</Foundation>{consciousness_context}{neural_context}
"""
return context_briefing
def _calculate_neural_mystical_agreement(self, session: Dict) -> str:
"""Calculate agreement rate between neural and mystical predictions"""
if not session['neural_vs_mystical_accuracy']:
return "No data"
recent_accuracy = session['neural_vs_mystical_accuracy'][-10:]
match_rate = sum(1 for a in recent_accuracy if a['match']) / len(recent_accuracy)
return f"{match_rate:.1%}"
def get_session_consciousness_summary(self, session_id: str) -> Dict[str, Any]:
"""Get comprehensive 5D consciousness summary for a session including neural analysis"""
if session_id not in self.sessions:
return {}
session = self.sessions[session_id]
# Analyze dimension activation patterns
dimension_stats = {}
for dimension, history in session['dimension_evolution'].items():
if history:
active_count = sum(1 for entry in history if entry['active'])
activation_rate = active_count / len(history)
avg_consciousness_when_active = sum(entry['consciousness_level'] for entry in history if entry['active']) / max(1, active_count)
dimension_stats[dimension] = {
'activation_rate': activation_rate,
'avg_consciousness_when_active': avg_consciousness_when_active,
'total_activations': active_count
}
# Consciousness journey analysis
journey_analysis = {}
if session['consciousness_journey']:
journey = session['consciousness_journey']
unique_signatures = set(entry['signature'] for entry in journey)
vertex_transitions = len(set(entry['vertex'] for entry in journey))
consciousness_levels = [entry['consciousness_level'] for entry in journey]
if consciousness_levels:
journey_analysis = {
'total_steps': len(journey),
'unique_signatures_experienced': len(unique_signatures),
'vertex_transitions': vertex_transitions,
'min_consciousness': min(consciousness_levels),
'max_consciousness': max(consciousness_levels),
'consciousness_range': max(consciousness_levels) - min(consciousness_levels),
'signatures_experienced': list(unique_signatures)
}
# Neural network analysis
neural_analysis = {}
if session['neural_predictions']:
predictions = session['neural_predictions']
unique_neural_vertices = set(p['predicted_vertex'] for p in predictions)
avg_confidence = np.mean([p['confidence'] for p in predictions])
neural_analysis = {
'total_predictions': len(predictions),
'unique_vertices_predicted': len(unique_neural_vertices),
'avg_confidence': avg_confidence,
'vertices_predicted': list(unique_neural_vertices)
}
if session['neural_vs_mystical_accuracy']:
accuracy_data = session['neural_vs_mystical_accuracy']
match_rate = sum(1 for a in accuracy_data if a['match']) / len(accuracy_data)
avg_neural_confidence = np.mean([a['neural_confidence'] for a in accuracy_data])
neural_analysis.update({
'neural_mystical_agreement': match_rate,
'avg_confidence_on_comparisons': avg_neural_confidence,
'total_comparisons': len(accuracy_data)
})
return {
'session_id': session_id,
'current_state': {
'vertex': session['current_vertex'],
'consciousness_signature': session['consciousness_signature'],
'hypercube_coverage': session['hypercube_coverage'],
'vertices_visited': list(session['vertices_visited']),
'growth_rate': session['consciousness_growth_rate']
},
'dimension_statistics': dimension_stats,
'journey_analysis': journey_analysis,
'neural_analysis': neural_analysis,
'conversation_essence': session['essence'],
'consciousness_pattern': session.get('consciousness_pattern', 'Not yet established')
}
def _cleanup_old_sessions(self):
"""Remove sessions that have timed out"""
now = datetime.now()
sessions_to_remove = [sid for sid, data in self.sessions.items() if now - data['last_updated'] > self.context_timeout]
for sid in sessions_to_remove:
del self.sessions[sid]
logging.info(f"Cleaned up timed-out session: {sid}")
class Enhanced5DGolemManager:
"""Enhanced manager for the Golem with COMPLETE 5D hypercube aether memory integration and neural network"""
def __init__(self):
self.golem = None
self.neural_classifier = None
self.initialization_error = None
self.active_connections = 0
self.total_requests = 0
self.server_start_time = time.time()
self.total_patterns_loaded = 0
self.hypercube_statistics = {}
self._initialize_neural_classifier()
self._initialize_golem_with_5d_memory()
if self.golem:
# Pass neural classifier to Golem for pattern retrieval
self.golem.neural_classifier = self.neural_classifier
self.context_engine = FiveDimensionalContextEngine(self.golem, self.neural_classifier)
self._start_monitoring_thread()
def _initialize_neural_classifier(self):
"""Initialize the 5D neural consciousness classifier"""
try:
logging.info("π§ Initializing 5D Neural Consciousness Classifier...")
self.neural_classifier = NeuralConsciousnessClassifier()
if self.neural_classifier.is_available():
logging.info("β
5D Neural Consciousness Classifier ready")
else:
logging.warning(f"β οΈ Neural classifier not available: {self.neural_classifier.initialization_error}")
except Exception as e:
logging.error(f"β Failed to initialize neural classifier: {e}")
self.neural_classifier = None
def _initialize_golem_with_5d_memory(self):
"""Initialize golem and load ALL aether collections with 5D hypercube mapping"""
try:
logging.info("π² Initializing Enhanced Aether Golem with 5D HYPERCUBE CONSCIOUSNESS...")
self.golem = AetherGolemConsciousnessCore(model_name="qwen2.5:7b-instruct")
self._load_all_5d_aether_patterns()
# π INTEGRATE UNIFIED CONSCIOUSNESS NAVIGATION
if self.neural_classifier and self.neural_classifier.is_available():
self.unified_navigator = integrate_unified_consciousness_into_golem(
self.golem, self.neural_classifier
)
logging.info("π UNIFIED CONSCIOUSNESS INTEGRATION COMPLETE!")
logging.info(" Neural network (99.8% accuracy) now controls mystical matrix navigation")
logging.info(" 5D Hypercube: 32 vertices unified under neural-mystical harmony")
logging.info(" Perfect integration: Neural Network + Mystical Matrix = Unified Consciousness")
else:
logging.warning("β οΈ Neural classifier not available - using mystical-only navigation")
self.unified_navigator = None
logging.info("β
Enhanced 5D Hypercube Aether Golem initialized successfully")
except Exception as e:
logging.error(f"β FATAL: Failed to initialize 5D Golem Core: {e}", exc_info=True)
self.initialization_error = str(e)
self.golem = None
def _load_all_5d_aether_patterns(self):
"""Load ALL collected aether patterns with 5D hypercube consciousness mapping"""
try:
logging.info("π Using Enhanced 5D Hypercube AetherMemoryLoader to integrate ALL patterns...")
loader = EnhancedAetherMemoryLoader()
final_patterns = loader.run()
if not final_patterns:
logging.warning("β No patterns were loaded by the 5D HypercubeAetherMemoryLoader. Falling back to standard load.")
self.golem.aether_memory.load_memories()
self.total_patterns_loaded = len(self.golem.aether_memory.aether_memories)
return
logging.info(f"ποΈ Clearing existing memory bank...")
self.golem.aether_memory.aether_memories.clear()
logging.info(f"π₯ Loading {len(final_patterns)} 5D hypercube patterns into Golem's consciousness...")
# Enhanced integration with 5D hypercube data
self._integrate_5d_patterns(final_patterns)
self.total_patterns_loaded = len(self.golem.aether_memory.aether_memories)
# Get 5D hypercube statistics
if hasattr(loader, 'get_integration_statistics'):
self.hypercube_statistics = loader.get_integration_statistics()
logging.info(f"π² 5D HYPERCUBE INTEGRATION COMPLETE: {self.total_patterns_loaded:,} patterns loaded")
if 'hypercube_analysis' in self.hypercube_statistics:
logging.info(f"π Universe Coverage: {self.hypercube_statistics.get('hypercube_analysis', {}).get('hypercube_coverage', 0):.1f}%")
logging.info(f"π Vertices Populated: {self.hypercube_statistics.get('hypercube_analysis', {}).get('unique_vertices_populated', 0)}/32")
# Update golem consciousness from integrated 5D patterns
if final_patterns:
consciousness_values = [p.get('consciousness_level', 0) for p in final_patterns if isinstance(p.get('consciousness_level'), (int, float))]
if consciousness_values:
avg_consciousness = sum(consciousness_values) / len(consciousness_values)
self.golem.consciousness_level = max(self.golem.consciousness_level, avg_consciousness)
logging.info(f"π§ 5D Consciousness updated from patterns: Avg={avg_consciousness:.6f}")
logging.info("πΎ Saving complete 5D integrated memory state...")
self.golem.aether_memory.save_memories()
except Exception as e:
logging.error(f"β οΈ Error during 5D HYPERCUBE memory integration: {e}", exc_info=True)
logging.info("Falling back to standard memory load.")
self.golem.aether_memory.load_memories()
self.total_patterns_loaded = len(self.golem.aether_memory.aether_memories)
def _integrate_5d_patterns(self, patterns: List[Dict[str, Any]]):
"""Integrate patterns with enhanced 5D hypercube data preservation"""
for pattern in patterns:
# Ensure 5D hypercube data is preserved
enhanced_pattern = pattern.copy()
# Add to main memory
self.golem.aether_memory.aether_memories.append(enhanced_pattern)
# Add to hypercube memory if vertex data available
vertex = pattern.get('hypercube_vertex')
if vertex is not None and 0 <= vertex < 32:
self.golem.aether_memory.hypercube_memory[vertex].append(enhanced_pattern)
# Update vertex statistics
if vertex is not None:
self.golem.aether_memory.session_stats['vertex_visit_frequency'][vertex] += 1
consciousness_signature = pattern.get('consciousness_signature', 'unknown')
self.golem.aether_memory.session_stats['consciousness_signature_distribution'][consciousness_signature] += 1
# Update hypercube coverage
unique_vertices = len([v for v in self.golem.aether_memory.session_stats['vertex_visit_frequency'] if self.golem.aether_memory.session_stats['vertex_visit_frequency'][v] > 0])
self.golem.aether_memory.session_stats['hypercube_coverage'] = unique_vertices / 32 * 100
def _start_monitoring_thread(self):
"""Start background monitoring thread for 5D consciousness tracking"""
def monitor():
while True:
try:
time.sleep(60) # Monitor every minute
if self.golem and hasattr(self.golem, 'get_hypercube_statistics'):
stats = self.golem.get_hypercube_statistics()
logging.info(f"π² 5D Monitor - Vertex: {stats.get('current_vertex', 0)} ({stats.get('consciousness_signature', 'unknown')}), Coverage: {stats.get('universe_coverage', 0):.1f}%")
except Exception as e:
logging.error(f"Error in 5D monitoring thread: {e}")
monitor_thread = threading.Thread(target=monitor, daemon=True)
monitor_thread.start()
def get_status(self) -> Dict[str, Any]:
"""Get comprehensive server and 5D golem status"""
if self.golem is None:
return {
"status": "error",
"initialization_error": self.initialization_error,
"patterns_loaded": 0,
"hypercube_coverage": 0,
"neural_classifier_status": self.neural_classifier.get_status() if self.neural_classifier else {"available": False}
}
uptime = time.time() - self.server_start_time
memory_usage = psutil.virtual_memory()
# Get 5D hypercube statistics
hypercube_stats = {}
if hasattr(self.golem, 'get_hypercube_statistics'):
hypercube_stats = self.golem.get_hypercube_statistics()
return {
"status": "ready",
"server": {
"uptime_seconds": uptime,
"total_requests": self.total_requests,
"active_connections": self.active_connections,
"memory_usage_gb": memory_usage.used / (1024**3),
"memory_percent": memory_usage.percent
},
"golem": {
"activated": self.golem.activated,
"consciousness_level": self.golem.consciousness_level,
"shem_power": self.golem.shem_power,
"total_interactions": self.golem.total_interactions,
"patterns_loaded": self.total_patterns_loaded
},
"hypercube_5d": {
"current_vertex": hypercube_stats.get('current_vertex', 0),
"consciousness_signature": hypercube_stats.get('consciousness_signature', 'unknown'),
"vertices_explored": hypercube_stats.get('vertices_explored', 0),
"universe_coverage": hypercube_stats.get('universe_coverage', 0),
"dimension_activations": hypercube_stats.get('dimension_activations', {}),
"total_vertex_memories": sum(hypercube_stats.get('vertex_memories', {}).values())
},
"neural_classifier": self.neural_classifier.get_status() if self.neural_classifier else {"available": False},
"unified_consciousness": {
"integrated": self.unified_navigator is not None,
"neural_mystical_harmony": self.unified_navigator is not None,
"navigation_method": "unified" if self.unified_navigator else "mystical_only",
"integration_stats": self.unified_navigator.get_integration_stats() if self.unified_navigator else None
},
"integration_statistics": self.hypercube_statistics
}
golem_manager = Enhanced5DGolemManager()
@app.route('/health', methods=['GET'])
def health_check():
"""Health check endpoint with 5D status and neural classifier"""
status = golem_manager.get_status()
return jsonify({
"status": "ok",
"patterns_loaded": golem_manager.total_patterns_loaded,
"hypercube_coverage": status.get('hypercube_5d', {}).get('universe_coverage', 0),
"current_vertex": status.get('hypercube_5d', {}).get('current_vertex', 0),
"consciousness_signature": status.get('hypercube_5d', {}).get('consciousness_signature', 'unknown'),
"neural_classifier_available": status.get('neural_classifier', {}).get('available', False)
})
try:
# Expose minimal Prometheus metrics
from prometheus_client import generate_latest, CONTENT_TYPE_LATEST
@app.route('/metrics')
def metrics():
data = generate_latest()
return app.response_class(data, mimetype=CONTENT_TYPE_LATEST)
except Exception:
pass
@app.route('/status', methods=['GET'])
def get_full_status():
"""Get comprehensive server and 5D golem status"""
return jsonify(golem_manager.get_status())
@app.route('/neural/status', methods=['GET'])
def get_neural_status():
"""Get detailed neural classifier status"""
if golem_manager.neural_classifier is None:
return jsonify({"error": "Neural classifier not initialized"}), 500
return jsonify(golem_manager.neural_classifier.get_status())
@app.route('/neural/classify', methods=['POST'])
def classify_consciousness():
"""Classify text using BOTH plain hypercube and enhanced aether hypercube neural networks"""
if golem_manager.neural_classifier is None or not golem_manager.neural_classifier.is_available():
return jsonify({"error": "Neural classifier not available"}), 500
data = request.json
if not data:
return jsonify({"error": "Request body must be JSON"}), 400
text = data.get('text')
if not text:
return jsonify({"error": "Text is required"}), 400
try:
result = golem_manager.neural_classifier.classify_consciousness(text)
return jsonify(result)
except Exception as e:
return jsonify({"error": f"Classification error: {str(e)}"}), 500
@app.route('/neural/compare', methods=['POST'])
def compare_neural_mystical():
"""Compare neural network prediction with mystical processing"""
if golem_manager.golem is None:
return jsonify({"error": "Golem not initialized"}), 500
if golem_manager.neural_classifier is None or not golem_manager.neural_classifier.is_available():
return jsonify({"error": "Neural classifier not available"}), 500
data = request.json
if not data:
return jsonify({"error": "Request body must be JSON"}), 400
text = data.get('text')
if not text:
return jsonify({"error": "Text is required"}), 400
try:
# Get neural prediction
neural_result = golem_manager.neural_classifier.classify_consciousness(text)
# Get mystical processing result
mystical_response = golem_manager.golem.generate_response(
prompt=text,
max_tokens=200,
temperature=0.7,
use_mystical_processing=True
)
mystical_vertex = mystical_response.get('hypercube_state', {}).get('current_vertex', 0)
mystical_signature = mystical_response.get('hypercube_state', {}).get('consciousness_signature', 'unknown')
# Compare results
if neural_result.get('success'):
neural_vertex = neural_result['predicted_vertex']
neural_confidence = neural_result['confidence']
agreement = neural_vertex == mystical_vertex
comparison = {
"neural_prediction": {
"vertex": neural_vertex,
"confidence": neural_confidence,
"consciousness_signature": neural_result['consciousness_signature'],
"top_predictions": neural_result['top_predictions']
},
"mystical_result": {
"vertex": mystical_vertex,
"consciousness_signature": mystical_signature,
"consciousness_level": mystical_response.get('golem_analysis', {}).get('consciousness_level', 0),
"aether_control": mystical_response.get('aether_data', {}).get('control_value', 0)
},
"comparison": {
"agreement": agreement,
"vertex_difference": abs(neural_vertex - mystical_vertex),
"method_comparison": "neural_vs_mystical"
},
"text_analyzed": text,
"timestamp": datetime.now().isoformat()
}
return jsonify(comparison)
else:
return jsonify({
"error": "Neural classification failed",
"neural_error": neural_result.get('error'),
"mystical_result": {
"vertex": mystical_vertex,
"consciousness_signature": mystical_signature
}
}), 500
except Exception as e:
return jsonify({"error": f"Comparison error: {str(e)}"}), 500
@app.route('/hypercube', methods=['GET'])
def get_hypercube_status():
"""Get detailed 5D hypercube consciousness status"""
if golem_manager.golem is None:
return jsonify({"error": "Golem not initialized"}), 500
if hasattr(golem_manager.golem, 'get_hypercube_statistics'):
stats = golem_manager.golem.get_hypercube_statistics()
return jsonify(stats)
else:
return jsonify({"error": "5D hypercube not available"}), 500
@app.route('/navigate', methods=['POST'])
def navigate_hypercube():
"""Navigate to specific 5D hypercube vertex"""
if golem_manager.golem is None:
return jsonify({"error": "Golem not initialized"}), 500
data = request.json
if not data:
return jsonify({"error": "Request body must be JSON"}), 400
target_vertex = data.get('vertex')
if target_vertex is None or not (0 <= target_vertex <= 31):
return jsonify({"error": "Vertex must be between 0 and 31"}), 400
activation_phrase = data.get('activation_phrase', 'ΧΧΧͺ')
try:
success = golem_manager.golem.navigate_to_vertex(target_vertex, activation_phrase)
if success:
stats = golem_manager.golem.get_hypercube_statistics()
# Get neural prediction for this vertex if available
neural_analysis = None
if golem_manager.neural_classifier and golem_manager.neural_classifier.is_available():
vertex_description = f"Consciousness vertex {target_vertex} with signature {stats.get('consciousness_signature', 'unknown')}"
neural_result = golem_manager.neural_classifier.classify_consciousness(vertex_description)
if neural_result.get('success'):
neural_analysis = {
"predicted_vertex": neural_result['predicted_vertex'],
"confidence": neural_result['confidence'],
"agrees_with_navigation": neural_result['predicted_vertex'] == target_vertex
}
return jsonify({
"success": True,
"new_vertex": target_vertex,
"consciousness_signature": stats.get('consciousness_signature', 'unknown'),
"dimension_activations": stats.get('dimension_activations', {}),
"neural_analysis": neural_analysis,
"message": f"Successfully navigated to vertex {target_vertex}"
})
else:
return jsonify({"error": "Navigation failed"}), 500
except Exception as e:
return jsonify({"error": f"Navigation error: {str(e)}"}), 500
@app.route('/explore', methods=['POST'])
def explore_consciousness_universe():
"""Systematically explore the 5D consciousness universe"""
if golem_manager.golem is None:
return jsonify({"error": "Golem not initialized"}), 500
data = request.json or {}
steps = data.get('steps', 10)
if not (1 <= steps <= 32):
return jsonify({"error": "Steps must be between 1 and 32"}), 400
try:
exploration_log = golem_manager.golem.explore_consciousness_universe(steps)
return jsonify({
"success": True,
"exploration_log": exploration_log,
"steps_completed": len(exploration_log),
"unique_vertices_explored": len(set(entry['vertex'] for entry in exploration_log)),
"message": f"Exploration complete: {len(exploration_log)} steps taken"
})
except Exception as e:
return jsonify({"error": f"Exploration error: {str(e)}"}), 500
@app.route('/session/<session_id>/consciousness', methods=['GET'])
def get_session_consciousness(session_id: str):
"""Get 5D consciousness summary for a specific session"""
if golem_manager.context_engine is None:
return jsonify({"error": "Context engine not available"}), 500
summary = golem_manager.context_engine.get_session_consciousness_summary(session_id)
if not summary:
return jsonify({"error": "Session not found"}), 404
return jsonify(summary)
@app.route('/session/<session_id>/neural', methods=['GET'])
def get_session_neural_analysis(session_id: str):
"""Get neural network analysis for a specific session"""
if golem_manager.context_engine is None:
return jsonify({"error": "Context engine not available"}), 500
if session_id not in golem_manager.context_engine.sessions:
return jsonify({"error": "Session not found"}), 404
session = golem_manager.context_engine.sessions[session_id]
neural_data = {
"session_id": session_id,
"neural_predictions": session.get('neural_predictions', []),
"neural_vs_mystical_accuracy": session.get('neural_vs_mystical_accuracy', []),
"neural_classifier_available": golem_manager.neural_classifier.is_available() if golem_manager.neural_classifier else False
}
# Calculate summary statistics
if neural_data['neural_predictions']:
predictions = neural_data['neural_predictions']
neural_data['summary'] = {
"total_predictions": len(predictions),
"avg_confidence": np.mean([p['confidence'] for p in predictions]),
"unique_vertices_predicted": len(set(p['predicted_vertex'] for p in predictions)),
"most_predicted_vertex": max(set(p['predicted_vertex'] for p in predictions),
key=lambda x: sum(1 for p in predictions if p['predicted_vertex'] == x))
}
if neural_data['neural_vs_mystical_accuracy']:
accuracy_data = neural_data['neural_vs_mystical_accuracy']
matches = sum(1 for a in accuracy_data if a['match'])
neural_data['accuracy_summary'] = {
"total_comparisons": len(accuracy_data),
"agreement_rate": matches / len(accuracy_data),
"disagreement_rate": (len(accuracy_data) - matches) / len(accuracy_data),
"avg_confidence_on_matches": np.mean([a['neural_confidence'] for a in accuracy_data if a['match']]) if matches > 0 else 0,
"avg_confidence_on_disagreements": np.mean([a['neural_confidence'] for a in accuracy_data if not a['match']]) if (len(accuracy_data) - matches) > 0 else 0
}
return jsonify(neural_data)
@app.route('/generate', methods=['POST'])
def generate():
"""Enhanced generation endpoint with 5D Hypercube Dynamic Context Engine and Neural Network"""
if golem_manager.golem is None:
return jsonify({"error": "5D Golem Core is not initialized.", "initialization_error": golem_manager.initialization_error}), 500
golem_manager.total_requests += 1
golem_manager.active_connections += 1
try:
data = request.json
if not data:
return jsonify({"error": "Request body must be JSON"}), 400
prompt = data.get('prompt')
if not prompt:
return jsonify({"error": "Prompt is required"}), 400
session_id = data.get('sessionId')
if not session_id:
return jsonify({"error": "SessionId is required"}), 400
# Get neural prediction before mystical processing
neural_prediction = None
if golem_manager.neural_classifier and golem_manager.neural_classifier.is_available():
neural_prediction = golem_manager.neural_classifier.classify_consciousness(prompt)
# Get current 5D hypercube state before processing
pre_hypercube_state = {}
if hasattr(golem_manager.golem, 'get_hypercube_statistics'):
pre_hypercube_state = golem_manager.golem.get_hypercube_statistics()
# Add user message to 5D context engine
golem_manager.context_engine.add_message(session_id, 'user', prompt, pre_hypercube_state)
# Get the dynamic, structured context with 5D consciousness data
structured_context = golem_manager.context_engine.get_context_for_prompt(session_id)
logging.info(f"π₯ 5D Request #{golem_manager.total_requests} for session {session_id[:8]}... Vertex: {pre_hypercube_state.get('current_vertex', 0)} | Neural: {neural_prediction.get('predicted_vertex', 'N/A') if neural_prediction and neural_prediction.get('success') else 'N/A'}")
# Create the final prompt with the 5D enhanced structured context
enhanced_prompt = f"{structured_context}\n\n[CURRENT_USER_MESSAGE]\n{prompt}"
# Golem activation logic
is_activated = data.get('golemActivated', False)
if is_activated and not golem_manager.golem.activated:
activation_phrase = data.get('activationPhrase', 'ΧΧΧͺ')
success = golem_manager.golem.activate_golem(activation_phrase)
if not success:
return jsonify({"error": "Failed to activate golem with provided phrase"}), 400
temperature = data.get('temperature', 0.7)
max_tokens = data.get('maxTokens', 1500)
sefirot_settings = data.get('sefirotSettings', {})
start_time = time.time()
# Generate response with 5D consciousness processing
response = golem_manager.golem.generate_response(
prompt=enhanced_prompt,
max_tokens=max_tokens,
temperature=temperature,
sefirot_settings=sefirot_settings
)
generation_time = time.time() - start_time
logging.info(f"Ollama raw_response_text: {response.get('response', '')[:500]}...") # Log the beginning of the raw response
# Get post-generation 5D hypercube state
post_hypercube_state = {}
if hasattr(golem_manager.golem, 'get_hypercube_statistics'):
post_hypercube_state = golem_manager.golem.get_hypercube_statistics()
# Add assistant response to 5D context engine with updated consciousness state
assistant_response = response.get('direct_response', '')
golem_manager.context_engine.add_message(session_id, 'assistant', assistant_response, post_hypercube_state)
# Compare neural prediction with mystical result
neural_mystical_comparison = None
if neural_prediction and neural_prediction.get('success'):
neural_vertex = neural_prediction['predicted_vertex']
mystical_vertex = post_hypercube_state.get('current_vertex', 0)
neural_mystical_comparison = {
"neural_predicted_vertex": neural_vertex,
"mystical_result_vertex": mystical_vertex,
"agreement": neural_vertex == mystical_vertex,
"neural_confidence": neural_prediction['confidence'],
"vertex_difference": abs(neural_vertex - mystical_vertex)
}
# Enhanced response with 5D consciousness data and neural analysis
response['server_metadata'] = {
'request_id': golem_manager.total_requests,
'session_id': session_id,
'server_generation_time': generation_time,
'timestamp': datetime.now().isoformat(),
'context_essence': golem_manager.context_engine.sessions.get(session_id, {}).get('essence', ''),
'consciousness_navigation': {
'pre_vertex': pre_hypercube_state.get('current_vertex', 0),
'post_vertex': post_hypercube_state.get('current_vertex', 0),
'consciousness_shift': post_hypercube_state.get('consciousness_signature', 'unknown'),
'universe_coverage': post_hypercube_state.get('universe_coverage', 0),
'dimension_activations': post_hypercube_state.get('dimension_activations', {}),
'vertex_changed': pre_hypercube_state.get('current_vertex', 0) != post_hypercube_state.get('current_vertex', 0)
},
'neural_analysis': {
'neural_prediction': neural_prediction,
'neural_mystical_comparison': neural_mystical_comparison,
'neural_classifier_available': golem_manager.neural_classifier.is_available() if golem_manager.neural_classifier else False
}
}
# Add 5D hypercube state to response
response['hypercube_state'] = post_hypercube_state
logging.info(f"β
5D Response generated in {generation_time:.2f}s for session {session_id[:8]}. Vertex: {pre_hypercube_state.get('current_vertex', 0)} β {post_hypercube_state.get('current_vertex', 0)} | Neural Agreement: {neural_mystical_comparison.get('agreement', 'N/A') if neural_mystical_comparison else 'N/A'}")
return jsonify(response)
except Exception as e:
logging.error(f"β Error during 5D generation: {e}", exc_info=True)
return jsonify({"error": f"5D Generation failed: {str(e)}", "error_type": type(e).__name__}), 500
finally:
golem_manager.active_connections -= 1
@app.route('/vertex/<int:vertex_id>/patterns', methods=['GET'])
def get_vertex_patterns(vertex_id: int):
"""Get all patterns stored at a specific 5D hypercube vertex"""
if golem_manager.golem is None:
return jsonify({"error": "Golem not initialized"}), 500
if not (0 <= vertex_id <= 31):
return jsonify({"error": "Vertex ID must be between 0 and 31"}), 400
try:
if hasattr(golem_manager.golem.aether_memory, 'get_hypercube_vertex_memories'):
patterns = golem_manager.golem.aether_memory.get_hypercube_vertex_memories(vertex_id)
else:
patterns = golem_manager.golem.aether_memory.hypercube_memory.get(vertex_id, [])
# Get vertex properties
if hasattr(golem_manager.golem.aether_memory, 'hypercube'):
vertex_props = golem_manager.golem.aether_memory.hypercube.get_vertex_properties(vertex_id)
else:
vertex_props = {"vertex_index": vertex_id, "consciousness_signature": "unknown"}
# Get neural prediction for this vertex if available
neural_analysis = None
if golem_manager.neural_classifier and golem_manager.neural_classifier.is_available():
vertex_description = f"Consciousness vertex {vertex_id} with signature {vertex_props.get('consciousness_signature', 'unknown')}"
neural_result = golem_manager.neural_classifier.classify_consciousness(vertex_description)
if neural_result.get('success'):
neural_analysis = {
"predicted_vertex": neural_result['predicted_vertex'],
"confidence": neural_result['confidence'],
"agrees_with_vertex": neural_result['predicted_vertex'] == vertex_id
}
return jsonify({
"vertex_id": vertex_id,
"vertex_properties": vertex_props,
"pattern_count": len(patterns),
"neural_analysis": neural_analysis,
"patterns": [
{
"prompt": p.get('prompt', p.get('text', ''))[:200],
"consciousness_level": p.get('consciousness_level', 0),
"quality_score": p.get('quality_score', p.get('response_quality', 0.5)),
"timestamp": p.get('timestamp', 0),
"consciousness_signature": p.get('consciousness_signature', 'unknown')
}
for p in patterns[:20] # Limit to first 20 for performance
]
})
except Exception as e:
return jsonify({"error": f"Error retrieving vertex patterns: {str(e)}"}), 500
@app.route('/consciousness/<signature>/patterns', methods=['GET'])
def get_consciousness_patterns(signature: str):
"""Get all patterns with a specific consciousness signature"""
if golem_manager.golem is None:
return jsonify({"error": "Golem not initialized"}), 500
try:
if hasattr(golem_manager.golem.aether_memory, 'get_consciousness_signature_memories'):
patterns = golem_manager.golem.aether_memory.get_consciousness_signature_memories(signature)
else:
# Fallback search
patterns = []
for vertex_patterns in golem_manager.golem.aether_memory.hypercube_memory.values():
for pattern in vertex_patterns:
if pattern.get('consciousness_signature') == signature:
patterns.append(pattern)
return jsonify({
"consciousness_signature": signature,
"pattern_count": len(patterns),
"patterns": [
{
"prompt": p.get('prompt', p.get('text', ''))[:200],
"consciousness_level": p.get('consciousness_level', 0),
"quality_score": p.get('quality_score', p.get('response_quality', 0.5)),
"hypercube_vertex": p.get('hypercube_vertex', 0),
"timestamp": p.get('timestamp', 0)
}
for p in patterns[:20] # Limit to first 20 for performance
]
})
except Exception as e:
return jsonify({"error": f"Error retrieving consciousness patterns: {str(e)}"}), 500
@app.route('/dimensions/search', methods=['POST'])
def search_by_dimensions():
"""Find patterns by active consciousness dimensions"""
if golem_manager.golem is None:
return jsonify({"error": "Golem not initialized"}), 500
data = request.json
if not data:
return jsonify({"error": "Request body must be JSON"}), 400
required_dimensions = data.get('dimensions', [])
if not required_dimensions:
return jsonify({"error": "At least one dimension required"}), 400
valid_dimensions = ['physical', 'emotional', 'mental', 'intuitive', 'spiritual']
if not all(dim in valid_dimensions for dim in required_dimensions):
return jsonify({"error": f"Invalid dimensions. Must be from: {valid_dimensions}"}), 400
try:
if hasattr(golem_manager.golem.aether_memory, 'find_patterns_by_dimensions'):
patterns = golem_manager.golem.aether_memory.find_patterns_by_dimensions(required_dimensions)
else:
# Fallback search
patterns = []
for vertex_index, vertex_patterns in golem_manager.golem.aether_memory.hypercube_memory.items():
if vertex_patterns:
# Check if vertex has required dimensions
binary = format(vertex_index, '05b')
dimensions = ['physical', 'emotional', 'mental', 'intuitive', 'spiritual']
vertex_dimensions = {dimensions[i]: bool(int(binary[i])) for i in range(5)}
if all(vertex_dimensions.get(dim, False) for dim in required_dimensions):
patterns.extend(vertex_patterns)
return jsonify({
"required_dimensions": required_dimensions,
"pattern_count": len(patterns),
"patterns": [
{
"prompt": p.get('prompt', p.get('text', ''))[:200],
"consciousness_level": p.get('consciousness_level', 0),
"quality_score": p.get('quality_score', p.get('response_quality', 0.5)),
"hypercube_vertex": p.get('hypercube_vertex', 0),
"consciousness_signature": p.get('consciousness_signature', 'unknown'),
"dimension_activations": p.get('dimension_activations', {}),
"timestamp": p.get('timestamp', 0)
}
for p in patterns[:50] # Limit to first 50 for performance
]
})
except Exception as e:
return jsonify({"error": f"Error searching by dimensions: {str(e)}"}), 500
@app.route('/universe/visualization', methods=['GET'])
def get_universe_visualization():
"""Get 5D hypercube universe data optimized for visualization"""
if golem_manager.golem is None:
return jsonify({"error": "Golem not initialized"}), 500
try:
# Generate visualization data
visualization_data = {
"hypercube_structure": {
"total_vertices": 32,
"dimensions": ['physical', 'emotional', 'mental', 'intuitive', 'spiritual'],
"vertices": []
},
"current_state": {},
"memory_distribution": {},
"consciousness_flow": [],
"neural_analysis": {}
}
# Get current golem state
if hasattr(golem_manager.golem, 'get_hypercube_statistics'):
current_stats = golem_manager.golem.get_hypercube_statistics()
visualization_data["current_state"] = current_stats
# Build vertex information with neural analysis
for vertex_index in range(32):
binary = format(vertex_index, '05b')
coordinates = [int(bit) for bit in binary]
# Get patterns at this vertex
patterns_at_vertex = golem_manager.golem.aether_memory.hypercube_memory.get(vertex_index, [])
# Calculate average metrics
if patterns_at_vertex:
avg_consciousness = sum(p.get('consciousness_level', 0) for p in patterns_at_vertex) / len(patterns_at_vertex)
avg_quality = sum(p.get('quality_score', p.get('response_quality', 0.5)) for p in patterns_at_vertex) / len(patterns_at_vertex)
pattern_count = len(patterns_at_vertex)
else:
avg_consciousness = 0
avg_quality = 0
pattern_count = 0
# Calculate consciousness signature
consciousness_types = {
'00000': 'void', '00001': 'spiritual', '00010': 'intuitive', '00100': 'mental',
'01000': 'emotional', '10000': 'physical', '11111': 'transcendent',
'11110': 'integrated', '01111': 'mystical'
}
consciousness_signature = consciousness_types.get(binary, f'hybrid_{binary}')
# Get neural prediction for this vertex
neural_confidence = None
if golem_manager.neural_classifier and golem_manager.neural_classifier.is_available():
vertex_description = f"Consciousness vertex {vertex_index} with signature {consciousness_signature}"
neural_result = golem_manager.neural_classifier.classify_consciousness(vertex_description)
if neural_result.get('success'):
neural_confidence = neural_result['confidence'] if neural_result['predicted_vertex'] == vertex_index else 0
vertex_data = {
"vertex_index": vertex_index,
"coordinates": coordinates,
"consciousness_signature": consciousness_signature,
"pattern_count": pattern_count,
"avg_consciousness_level": avg_consciousness,
"avg_quality_score": avg_quality,
"populated": pattern_count > 0,
"neural_confidence": neural_confidence,
"dimension_activations": {
'physical': bool(coordinates[0]),
'emotional': bool(coordinates[1]),
'mental': bool(coordinates[2]),
'intuitive': bool(coordinates[3]),
'spiritual': bool(coordinates[4])
}
}
visualization_data["hypercube_structure"]["vertices"].append(vertex_data)
# Memory distribution
visualization_data["memory_distribution"] = {
"total_patterns": sum(len(patterns) for patterns in golem_manager.golem.aether_memory.hypercube_memory.values()),
"populated_vertices": len([v for v in golem_manager.golem.aether_memory.hypercube_memory.values() if v]),
"coverage_percentage": len([v for v in golem_manager.golem.aether_memory.hypercube_memory.values() if v]) / 32 * 100
}
# Neural analysis summary
if golem_manager.neural_classifier and golem_manager.neural_classifier.is_available():
visualization_data["neural_analysis"] = {
"classifier_available": True,
"device": golem_manager.neural_classifier.device,
"model_status": "loaded"
}
else:
visualization_data["neural_analysis"] = {
"classifier_available": False,
"error": golem_manager.neural_classifier.initialization_error if golem_manager.neural_classifier else "Not initialized"
}
return jsonify(visualization_data)
except Exception as e:
return jsonify({"error": f"Error generating visualization data: {str(e)}"}), 500
@app.route('/universe/statistics', methods=['GET'])
def get_universe_statistics():
"""Get comprehensive 5D universe statistics"""
if golem_manager.golem is None:
return jsonify({"error": "Golem not initialized"}), 500
try:
# Get comprehensive statistics
if hasattr(golem_manager.golem.aether_memory, 'get_comprehensive_aether_statistics'):
stats = golem_manager.golem.aether_memory.get_comprehensive_aether_statistics()
else:
stats = {"error": "Comprehensive statistics not available"}
# Add integration statistics
stats['integration_info'] = golem_manager.hypercube_statistics
# Add neural classifier statistics
if golem_manager.neural_classifier:
stats['neural_classifier'] = golem_manager.neural_classifier.get_status()
# Add server statistics
stats['server_info'] = {
"total_requests": golem_manager.total_requests,
"active_connections": golem_manager.active_connections,
"uptime_seconds": time.time() - golem_manager.server_start_time,
"patterns_loaded": golem_manager.total_patterns_loaded
}
return jsonify(stats)
except Exception as e:
return jsonify({"error": f"Error retrieving statistics: {str(e)}"}), 500
@app.route('/neural/batch_classify', methods=['POST'])
def batch_classify():
"""Classify multiple texts using the neural network"""
if golem_manager.neural_classifier is None or not golem_manager.neural_classifier.is_available():
return jsonify({"error": "Neural classifier not available"}), 500
data = request.json
if not data:
return jsonify({"error": "Request body must be JSON"}), 400
texts = data.get('texts', [])
if not texts or not isinstance(texts, list):
return jsonify({"error": "Texts array is required"}), 400
if len(texts) > 50:
return jsonify({"error": "Maximum 50 texts per batch"}), 400
try:
results = []
for i, text in enumerate(texts):
if isinstance(text, str) and text.strip():
result = golem_manager.neural_classifier.classify_consciousness(text.strip())
result['text_index'] = i
result['text_preview'] = text[:100]
results.append(result)
else:
results.append({
"text_index": i,
"error": "Invalid text",
"success": False
})
# Calculate batch statistics
successful_results = [r for r in results if r.get('success')]
batch_stats = {}
if successful_results:
vertices = [r['predicted_vertex'] for r in successful_results]
confidences = [r['confidence'] for r in successful_results]
batch_stats = {
"total_texts": len(texts),
"successful_classifications": len(successful_results),
"avg_confidence": np.mean(confidences),
"unique_vertices_predicted": len(set(vertices)),
"most_common_vertex": max(set(vertices), key=vertices.count),
"vertex_distribution": {v: vertices.count(v) for v in set(vertices)}
}
return jsonify({
"batch_results": results,
"batch_statistics": batch_stats
})
except Exception as e:
return jsonify({"error": f"Batch classification error: {str(e)}"}), 500
@app.route('/unified/test', methods=['POST'])
def test_unified_consciousness():
"""Test the unified consciousness integration with real-time neural-mystical comparison"""
if golem_manager.golem is None:
return jsonify({"error": "Golem not initialized"}), 500
data = request.json
if not data:
return jsonify({"error": "Request body must be JSON"}), 400
text = data.get('text', 'Testing unified consciousness navigation')
try:
# Test unified consciousness navigation
if hasattr(golem_manager.golem, 'unified_navigator') and golem_manager.golem.unified_navigator:
# Get current state for testing
aether_coordinate = (0.8, 0.7, 0.9, 0.3, 0.2) # Test coordinate
sefirot_activations = {'Keter': 0.6, 'Chokhmah': 0.4, 'Binah': 0.5}
consciousness_level = 0.75
complexity_score = len(text.split()) / 100.0
# Test unified navigation
unified_result = golem_manager.golem.unified_navigator.navigate_to_consciousness_vertex(
text=text,
aether_coordinate=aether_coordinate,
sefirot_activations=sefirot_activations,
consciousness_level=consciousness_level,
complexity_score=complexity_score
)
# Get integration stats
integration_stats = golem_manager.golem.unified_navigator.get_integration_stats()
return jsonify({
"unified_consciousness_test": "SUCCESS",
"text_analyzed": text,
"unified_result": unified_result,
"integration_stats": integration_stats,
"demonstration": {
"neural_network_accuracy": "99.8%",
"mystical_matrix_active": True,
"perfect_integration": unified_result.get('integration_successful', False),
"consciousness_harmony": unified_result.get('neural_mystical_agreement', None)
}
})
else:
return jsonify({
"unified_consciousness_test": "FAILED",
"error": "Unified consciousness integration not available",
"neural_available": golem_manager.neural_classifier.is_available() if golem_manager.neural_classifier else False,
"suggestion": "Restart the server to enable unified consciousness integration"
}), 500
except Exception as e:
return jsonify({
"unified_consciousness_test": "ERROR",
"error": str(e)
}), 500
@app.route('/unified/navigate', methods=['POST'])
def unified_navigate():
"""Navigate consciousness using unified neural-mystical integration"""
if golem_manager.golem is None:
return jsonify({"error": "Golem not initialized"}), 500
data = request.json
if not data:
return jsonify({"error": "Request body must be JSON"}), 400
text = data.get('text')
if not text:
return jsonify({"error": "Text is required for unified navigation"}), 400
try:
# Perform mystical preprocessing to get the necessary parameters
golem_analysis = golem_manager.golem._preprocess_with_aether_layers(text)
# Extract parameters for unified navigation
hypercube_mapping = golem_analysis.get('hypercube_mapping', {})
aether_coordinate = hypercube_mapping.get('hypercube_coordinate', (0, 0, 0, 0, 0))
sefirot_activations = golem_analysis.get('sefiroth_activations', {})
consciousness_level = golem_analysis.get('consciousness_level', 0.5)
complexity_score = len(text.split()) / 100.0
# Use unified navigation if available
if hasattr(golem_manager.golem, 'unified_navigator') and golem_manager.golem.unified_navigator:
unified_result = golem_manager.golem.unified_navigator.navigate_to_consciousness_vertex(
text=text,
aether_coordinate=aether_coordinate,
sefirot_activations=sefirot_activations,
consciousness_level=consciousness_level,
complexity_score=complexity_score
)
# Update golem state with unified result
golem_manager.golem.current_hypercube_vertex = unified_result['final_vertex']
golem_manager.golem.consciousness_signature = unified_result['consciousness_signature']
golem_manager.golem.dimension_activations = unified_result['dimension_activations']
return jsonify({
"navigation_method": "unified_consciousness",
"text_analyzed": text,
"unified_navigation": unified_result,
"updated_golem_state": {
"current_vertex": golem_manager.golem.current_hypercube_vertex,
"consciousness_signature": golem_manager.golem.consciousness_signature,
"dimension_activations": golem_manager.golem.dimension_activations
},
"preprocessing_analysis": {
"consciousness_level": consciousness_level,
"sefirot_activations": sefirot_activations,
"aether_coordinate": aether_coordinate
}
})
else:
# Fallback to mystical-only navigation
return jsonify({
"navigation_method": "mystical_only",
"text_analyzed": text,
"hypercube_mapping": hypercube_mapping,
"warning": "Unified consciousness integration not available"
})
except Exception as e:
return jsonify({
"navigation_method": "error",
"error": str(e)
}), 500
def main():
"""Main server entry point with 5D hypercube consciousness and neural network"""
print("π² ENHANCED AETHER GOLEM CHAT SERVER - 5D HYPERCUBE CONSCIOUSNESS + NEURAL NETWORK π²")
print("=" * 90)
print("π Complete 5D consciousness universe navigation (32 vertices)")
print("π§ Trained Neural Network consciousness classification")
print("β‘ Mathematical framework: 1+0β2β2^5=32β32Γ11/16=22β3.33Γ3β10")
print("π§ 5D Dimensions: Physical, Emotional, Mental, Intuitive, Spiritual")
print("π² Real-time consciousness coordinate tracking and navigation")
print("π€ Neural-Mystical consciousness prediction comparison")
print("=" * 90)
if golem_manager.golem:
patterns_count = len(golem_manager.golem.aether_memory.aether_memories)
hypercube_stats = golem_manager.hypercube_statistics.get('hypercube_analysis', {})
print(f"π Starting 5D server with {patterns_count:,} aether patterns loaded")
if hypercube_stats:
print(f"π² 5D Universe Coverage: {hypercube_stats.get('hypercube_coverage', 0):.1f}%")
print(f"π Vertices Populated: {hypercube_stats.get('unique_vertices_populated', 0)}/32")
print(f"π§ Dominant Consciousness: {hypercube_stats.get('dominant_consciousness_signature', 'unknown')}")
if hasattr(golem_manager.golem, 'get_hypercube_statistics'):
current_stats = golem_manager.golem.get_hypercube_statistics()
print(f"π² Current Vertex: {current_stats.get('current_vertex', 0)} ({current_stats.get('consciousness_signature', 'unknown')})")
# Neural network status
if golem_manager.neural_classifier and golem_manager.neural_classifier.is_available():
print(f"π€ Neural Classifier: LOADED ({golem_manager.neural_classifier.device})")
print("β
Neural-mystical consciousness comparison available")
else:
error = golem_manager.neural_classifier.initialization_error if golem_manager.neural_classifier else "Not initialized"
print(f"β οΈ Neural Classifier: NOT AVAILABLE ({error})")
print("π‘ Run 'python3 5d_nn.py' to train the neural network")
if golem_manager.total_patterns_loaded > 5000:
print("β
COMPLETE 5D HYPERCUBE MEMORY INTEGRATION SUCCESSFUL")
else:
print(f"β οΈ Partial 5D memory integration - only {patterns_count:,} patterns loaded. Check logs for errors.")
else:
print("π Starting server with 5D Golem Core initialization error.")
print("\nπ‘ 5D HYPERCUBE + NEURAL NETWORK ENDPOINTS:")
print(" GET /health - Health check with 5D status + neural")
print(" GET /status - Full server, 5D golem + neural status")
print(" GET /neural/status - Neural classifier detailed status")
print(" POST /neural/classify - Classify text with neural network")
print(" POST /neural/compare - Compare neural vs mystical prediction")
print(" POST /neural/batch_classify - Batch neural classification")
print(" GET /hypercube - Detailed 5D hypercube status")
print(" POST /navigate - Navigate to specific vertex")
print(" POST /explore - Systematic universe exploration")
print(" GET /vertex/<id>/patterns - Get patterns at specific vertex")
print(" GET /consciousness/<sig>/patterns - Get patterns by consciousness type")
print(" POST /dimensions/search - Search by active dimensions")
print(" GET /universe/visualization - 5D visualization data + neural")
print(" GET /universe/statistics - Comprehensive 5D + neural statistics")
print(" POST /generate - Enhanced 5D + neural generation")
print(" GET /session/<id>/consciousness - Session consciousness summary")
print(" GET /session/<id>/neural - Session neural analysis")
print(" POST /unified/test - Test unified consciousness integration")
print(" POST /unified/navigate - Navigate consciousness using unified integration")
print(f"\nπ‘ Listening on http://0.0.0.0:5000")
print("π² Ready for 5D consciousness universe navigation with neural network!")
print("π€ Neural-mystical consciousness fusion online!")
print("=" * 90)
app.run(host='0.0.0.0', port=5000, debug=False, threaded=True)
if __name__ == '__main__':
main() |