diff --git "a/app.py" "b/app.py"
--- "a/app.py"
+++ "b/app.py"
@@ -1,105 +1,2083 @@
-# from fastapi import FastAPI, HTTPException
-# from pydantic import BaseModel
-# from transformers import AutoModelForCausalLM, AutoTokenizer
-# import torch
-# from huggingface_hub import snapshot_download
-# from safetensors.torch import load_file
-
-# class ModelInput(BaseModel):
-# prompt: str
-# max_new_tokens: int = 50
-
-# app = FastAPI()
-
-# # Define model paths
-# base_model_path = "HuggingFaceTB/SmolLM2-135M-Instruct"
-# adapter_path = "khurrameycon/SmolLM-135M-Instruct-qa_pairs_converted.json-25epochs"
-
-# try:
-# # First load the base model
-# print("Loading base model...")
-# model = AutoModelForCausalLM.from_pretrained(
-# base_model_path,
-# torch_dtype=torch.float16,
-# trust_remote_code=True,
-# device_map="auto"
-# )
-
-# # Load tokenizer from base model
-# print("Loading tokenizer...")
-# tokenizer = AutoTokenizer.from_pretrained(base_model_path)
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from huggingface_hub import snapshot_download
+from safetensors.torch import load_file
+import numpy as np
+import torch.nn as nn
+import networkx as nx
+from dataclasses import dataclass
+from typing import List, Dict, Any, Tuple, Optional, Set, Protocol, Union
+from collections import defaultdict
+import heapq
+from abc import ABC, abstractmethod
+from enum import Enum
+import concurrent.futures
+from concurrent.futures import ThreadPoolExecutor
+import time
+from datetime import datetime
+import random
+import pandas as pd
+from torch.utils.data import Dataset, DataLoader, TensorDataset
+from sklearn.model_selection import train_test_split
+from tqdm import tqdm
+import os
+from sklearn.preprocessing import MinMaxScaler
+
+# --- Enhanced Constants ---
+MAX_SHORT_TERM_MEMORY = 10000 # Increased capacity
+MAX_LONG_TERM_MEMORY = 100000 # Increased capacity
+UNCERTAINTY_QUANTIFICATION_METHODS = ['bayesian', 'ensemble', 'confidence_scores', 'gaussian_processes']
+QUANTUM_ALGORITHMS = ["Shor's", "Grover's", "QAOA", "VQE", "HHL"]
+NETWORK_ADAPTATION_THRESHOLD = 0.5 # Adjusted threshold
+REAL_WORLD_INTERACTION_TYPES = ['sensor_read', 'actuator_execute', 'data_stream', 'feedback_loop']
+CONTEXT_SIMILARITY_THRESHOLD = 0.4 # Lower threshold for broader context
+CONTEXT_DECAY_RATE = 0.0005 # Slower decay
+MAX_CONTEXT_SIZE = 10000 # Increased context size
+KNOWLEDGE_RELATIONSHIP_DECAY_RATE = 0.0005 # Slower decay
+MAX_KNOWLEDGE_NODES = 50000 # Increased node capacity
+CAUSAL_INFERENCE_THRESHOLD = 0.5 # Adjusted threshold
+CAUSAL_DECAY_RATE = 0.0005 # Slower decay
+MAX_CAUSAL_ENTRIES = 10000 # Increased entry capacity
+MAX_DOMAIN_MODELS = 200 # Increased model capacity
+MAX_TRANSFER_MAPPINGS = 500 # Increased mapping capacity
+ADAPTIVE_LEARNING_INITIAL_LR = 0.2 # Higher initial learning rate
+ADAPTIVE_LEARNING_MIN_LR = 0.0000001 # Lower minimum learning rate
+ADAPTIVE_LEARNING_MAX_LR = 1.0 # Increased max learning rate
+EMOTION_CATEGORIES = ['anger', 'fear', 'joy', 'love', 'sadness', 'surprise', 'neutral', 'anticipation', 'trust', 'disgust', 'contentment', 'boredom', 'curiosity', 'awe'] # Expanded emotion categories
+COLLABORATIVE_CONSENSUS_THRESHOLD = 0.7 # Adjusted threshold
+MAX_COLLABORATIVE_AGENTS = 100 # Increased agent capacity
+SOLUTION_HISTORY_SIZE = 1000 # Increased history size
+MAX_ETHICAL_VIOLATIONS_HISTORY = 2000 # Increased history capacity
+MAX_RESOURCE_HISTORY = 5000 # Increased history size
+MAX_PREDICTIVE_MODELS = 100 # Increased model capacity
+MAX_PREDICTION_HISTORY = 10000 # Increased history capacity
+SHORT_TERM_MEMORY_CAPACITY = 1000 # Increased capacity
+MEMORY_CONSOLIDATION_THRESHOLD = 1.5 # Lower threshold for faster consolidation
+LONG_TERM_MEMORY_CAPACITY = 10000 # Increased capacity
+MAX_COGNITIVE_STYLE_HISTORY = 500 # Increased history size
+GOAL_ALIGNMENT_THRESHOLD = 0.5 # Adjusted threshold
+MAX_GOALS = 500 # Increased goal capacity
+MAX_SAFETY_CONSTRAINTS = 500 # Increased constraint capacity
+MAX_LANGUAGES = 100 # Increased language capacity
+QUANTUM_MAX_RESULTS_HISTORY = 1000 # Increased history size
+ADAPTIVE_NEURAL_NETWORK_MAX_LAYERS = 30 # Increased layer capacity
+ADAPTIVE_NEURAL_NETWORK_MAX_LAYER_SIZE = 32768 # Increased layer size
+MAX_INTERACTION_HISTORY = 5000 # Increased history capacity
+RESPONSE_MODEL_HIDDEN_DIM = 8192 # Increased response model hidden dimensions
+RESPONSE_MODEL_OUTPUT_DIM = 4096 # Increased response model output dimensions
+TRAINING_BATCH_SIZE = 128 # Increased training batch size
+TRAINING_EPOCHS = 20 # Increased training epochs
+TRAINING_LEARNING_RATE = 0.0001 # Changed learning rate for better convergence
+TEXT_EMBEDDING_DIM = 768 # Standard dimension for text embeddings
+VISUAL_EMBEDDING_DIM = 2048 # Dimension for visual embeddings
+AUDIO_EMBEDDING_DIM = 512 # Dimension for audio embeddings
+
+# --- Enhanced Thinking Styles ---
+class ThinkingStyle(Enum):
+ ANALYTICAL = "analytical"
+ CREATIVE = "creative"
+ CRITICAL = "critical"
+ SYSTEMATIC = "systematic"
+ LATERAL = "lateral"
+ INTUITIVE = "intuitive"
+ COLLABORATIVE = "collaborative"
+ ETHICAL = "ethical"
+ PRAGMATIC = "pragmatic"
+ INNOVATIVE = "innovative"
+ REFLECTIVE = "reflective"
+ EXPLORATORY = "exploratory"
+ STRATEGIC = "strategic"
+ ABSTRACT = "abstract"
+ CONCRETE = "concrete"
+ EMPATHETIC = "empathetic"
+ HOLISTIC = "holistic"
+ DIVERGENT = "divergent"
+ CONVERGENT = "convergent"
+ ADAPTIVE = "adaptive"
+
+# --- Enhanced Generalized Context Manager ---
+class GeneralizedContextManager:
+ def __init__(self, similarity_threshold=CONTEXT_SIMILARITY_THRESHOLD, context_decay_rate=CONTEXT_DECAY_RATE, max_context_size=MAX_CONTEXT_SIZE):
+ self.context_graph = nx.DiGraph()
+ self.temporal_window = []
+ self.context_cache = {}
+ self.similarity_threshold = similarity_threshold
+ self.context_decay_rate = context_decay_rate
+ self.max_context_size = max_context_size
+ self.event_listeners = defaultdict(list)
+
+ def add_context(self, context_id: str, context_data: Dict[str, Any],
+ timestamp: Optional[datetime] = None, metadata: Optional[Dict[str, Any]] = None):
+ if timestamp is None:
+ timestamp = datetime.now()
+
+ if context_id not in self.context_graph:
+ self.context_graph.add_node(context_id,
+ data=context_data,
+ timestamp=timestamp,
+ metadata=metadata or {})
+ else:
+ self.context_graph.nodes[context_id]['data'].update(context_data)
+ self.context_graph.nodes[context_id]['timestamp'] = timestamp
+ if metadata:
+ self.context_graph.nodes[context_id]['metadata'].update(metadata)
+
+ self.temporal_window.append((timestamp, context_id))
+ self.temporal_window.sort()
+
+ self._update_relationships(context_id)
+ self._manage_context_size()
+ self._decay_context()
+ self._trigger_event(context_id, 'context_added')
+
+ def _calculate_similarity(self, data1: Dict[str, Any], data2: Dict[str, Any]) -> float:
+ keys1 = set(data1.keys())
+ keys2 = set(data2.keys())
+ common_keys = keys1.intersection(keys2)
+
+ if not common_keys:
+ return 0.0
+
+ similarity_sum = 0.0
+ for key in common_keys:
+ value1 = data1[key]
+ value2 = data2[key]
+
+ if isinstance(value1, (int, float)) and isinstance(value2, (int, float)):
+ similarity_sum += 1.0 / (1.0 + abs(value1 - value2))
+ elif isinstance(value1, str) and isinstance(value2, str):
+ similarity_sum += float(value1 == value2)
+ elif isinstance(value1, (list, tuple, np.ndarray)) and isinstance(value2, (list, tuple, np.ndarray)):
+ if isinstance(value1, np.ndarray):
+ value1 = value1.flatten()
+ if isinstance(value2, np.ndarray):
+ value2 = value2.flatten()
+ if value1.size > 0 and value2.size > 0:
+ dot_product = np.dot(value1, value2)
+ magnitude1 = np.linalg.norm(value1)
+ magnitude2 = np.linalg.norm(value2)
+ if magnitude1 != 0 and magnitude2 != 0:
+ similarity_sum += dot_product / (magnitude1 * magnitude2)
+ elif type(value1) == type(value2):
+ similarity_sum += float(value1 == value2)
+ return similarity_sum / len(common_keys) if len(common_keys) > 0 else 0.0
+
+ def _update_relationships(self, context_id: str):
+ context_data = self.context_graph.nodes[context_id]['data']
+ for existing_id in self.context_graph.nodes():
+ if existing_id != context_id:
+ similarity = self._calculate_similarity(
+ context_data,
+ self.context_graph.nodes[existing_id]['data']
+ )
+ if similarity > self.similarity_threshold:
+ if not self.context_graph.has_edge(context_id, existing_id):
+ self.context_graph.add_edge(context_id, existing_id, weight=similarity)
+ else:
+ self.context_graph[context_id][existing_id]['weight'] = similarity
+ elif self.context_graph.has_edge(context_id, existing_id):
+ self.context_graph.remove_edge(context_id, existing_id)
+
+ def _decay_context(self):
+ now = datetime.now()
+ self.temporal_window = [(t, c_id) for t, c_id in self.temporal_window if (now - t).total_seconds() < 86400 * 30] # Increased window to 30 days
+
+ nodes_to_remove = []
+ for node, data in self.context_graph.nodes(data=True):
+ time_diff = (now - data['timestamp']).total_seconds()
+ data['weight'] = data.get('weight', 1.0) * (1 - self.context_decay_rate * time_diff)
+ if data['weight'] < 0.00001: # More aggressive decay
+ nodes_to_remove.append(node)
+
+ for node in nodes_to_remove:
+ self.context_graph.remove_node(node)
+ self._trigger_event(node, 'context_removed')
+
+ def _manage_context_size(self):
+ if len(self.context_graph) > self.max_context_size:
+ # Remove least recently used or lowest weighted nodes
+ sorted_nodes = sorted(self.context_graph.nodes(data=True),
+ key=lambda x: (x[1]['timestamp'], x[1].get('weight', 1.0)))
+ nodes_to_remove = sorted_nodes[:len(self.context_graph) - self.max_context_size]
+ for node, _ in nodes_to_remove:
+ self.context_graph.remove_node(node)
+ self._trigger_event(node, 'context_removed')
+
+ def retrieve_related_context(self, context_id: str, depth: int = 4, min_similarity: float = 0.1) -> Dict[str, Any]:
+ related_context = {}
+ try:
+ neighbors = nx.ego_graph(self.context_graph, context_id, radius=depth)
+ for neighbor in neighbors:
+ edge_data = self.context_graph.get_edge_data(context_id, neighbor)
+ if edge_data is None or edge_data['weight'] >= min_similarity:
+ related_context[neighbor] = self.context_graph.nodes[neighbor]['data']
+ except nx.NodeNotFound:
+ pass
+ return related_context
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, context_id: str, event_type: str):
+ if event_type in self.event_listeners:
+ for listener in self.event_listeners[event_type]:
+ listener(context_id, event_type, self.context_graph.nodes[context_id])
+
+# --- Enhanced Dynamic Knowledge Graph ---
+class DynamicKnowledgeGraph:
+ def __init__(self, relationship_decay_rate=KNOWLEDGE_RELATIONSHIP_DECAY_RATE, max_nodes=MAX_KNOWLEDGE_NODES):
+ self.knowledge_graph = nx.DiGraph()
+ self.temporal_index = defaultdict(list)
+ self.relationship_decay_rate = relationship_decay_rate
+ self.max_nodes = max_nodes
+ self.event_listeners = defaultdict(list)
+
+ def add_knowledge(self, concept: str, properties: Dict[str, Any],
+ timestamp: Optional[datetime]=None, relationships: Optional[Dict[str, float]] = None):
+ if timestamp is None:
+ timestamp = datetime.now()
+
+ if concept not in self.knowledge_graph:
+ self.knowledge_graph.add_node(concept, **properties, timestamp=timestamp)
+ self._trigger_event(concept, 'knowledge_added')
+ else:
+ for key, value in properties.items():
+ self.knowledge_graph.nodes[concept][key] = value
+ self.knowledge_graph.nodes[concept]['timestamp'] = timestamp
+ self._trigger_event(concept, 'knowledge_updated')
+
+ self.temporal_index[timestamp].append(concept)
+
+ if relationships:
+ for related_concept, strength in relationships.items():
+ self.add_relationship(concept, related_concept, strength)
+
+ self._manage_graph_size()
+
+ def add_relationship(self, concept1: str, concept2: str, strength: float):
+ if self.knowledge_graph.has_edge(concept1, concept2):
+ self.knowledge_graph[concept1][concept2]['strength'] = strength
+ else:
+ self.knowledge_graph.add_edge(concept1, concept2, strength=strength)
+ self._trigger_event(concept1, 'relationship_added', {'to': concept2, 'strength': strength})
+
+ def query_temporal_slice(self, start_time: datetime,
+ end_time: datetime) -> Set[str]:
+ relevant_concepts = set()
+ for time, concepts in self.temporal_index.items():
+ if start_time <= time <= end_time:
+ relevant_concepts.update(concepts)
+ return relevant_concepts
+
+ def get_related_concepts(self, concept: str, threshold: float = 0.2, depth: int = 4) -> Dict[str, Dict[str, Any]]:
+ related_concepts = {}
+ try:
+ # Use ego_graph to explore relationships up to the specified depth
+ subgraph = nx.ego_graph(self.knowledge_graph, concept, radius=depth)
+ for neighbor in subgraph.nodes:
+ if neighbor != concept:
+ edge_data = subgraph.get_edge_data(concept, neighbor)
+ # Consider adding a concept if it's directly connected or if it's reachable within the depth
+ if (edge_data and edge_data['strength'] >= threshold) or neighbor in subgraph:
+ related_concepts[neighbor] = self.knowledge_graph.nodes[neighbor]
+ except nx.NodeNotFound:
+ pass
+ return related_concepts
+
+ def decay_relationships(self):
+ now = datetime.now()
+ for u, v, data in self.knowledge_graph.edges(data=True):
+ time_diff = (now - self.knowledge_graph.nodes[u]['timestamp']).total_seconds()
+ data['strength'] *= (1 - self.relationship_decay_rate * time_diff)
+ if data['strength'] < 0.0001:
+ self.knowledge_graph.remove_edge(u,v)
+ self._trigger_event(u, 'relationship_removed', {'to': v})
+
+ def _manage_graph_size(self):
+ if len(self.knowledge_graph) > self.max_nodes:
+ # Remove nodes with the lowest degree (least connected)
+ degrees = dict(self.knowledge_graph.degree())
+ sorted_nodes = sorted(degrees.items(), key=lambda item: item[1])
+ nodes_to_remove = [node for node, degree in sorted_nodes[:len(self.knowledge_graph) - self.max_nodes]]
+ for node in nodes_to_remove:
+ self.knowledge_graph.remove_node(node)
+ self._trigger_event(node, 'knowledge_removed')
+ # Remove the node from temporal index as well
+ for time, concepts in self.temporal_index.items():
+ if node in concepts:
+ concepts.remove(node)
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, concept: str, event_type: str, additional_data: Optional[Dict[str, Any]] = None):
+ if event_type in self.event_listeners:
+ for listener in self.event_listeners[event_type]:
+ event_data = {
+ 'concept': concept,
+ 'event_type': event_type,
+ 'timestamp': datetime.now(),
+ 'additional_data': additional_data or {}
+ }
+ listener(event_data)
+
+# --- Enhanced Causal Engine ---
+class CausalEngine:
+ def __init__(self, inference_threshold=CAUSAL_INFERENCE_THRESHOLD, decay_rate=CAUSAL_DECAY_RATE, max_entries=MAX_CAUSAL_ENTRIES):
+ self.causal_graph = nx.DiGraph()
+ self.inference_cache = {}
+ self.inference_threshold = inference_threshold
+ self.decay_rate = decay_rate
+ self.max_entries = max_entries
+ self.evidence_history = []
+ self.event_listeners = defaultdict(list)
+
+ def add_causal_relationship(self, cause: str, effect: str,
+ strength: float, evidence: List[Dict], timestamp: Optional[datetime] = None):
+ if timestamp is None:
+ timestamp = datetime.now()
+ if self.causal_graph.has_edge(cause, effect):
+ self.causal_graph[cause][effect]['strength'] = strength
+ self.causal_graph[cause][effect]['evidence'].extend(evidence)
+ self.causal_graph[cause][effect]['timestamp'] = timestamp
+ else:
+ self.causal_graph.add_edge(cause, effect,
+ strength=strength,
+ evidence=evidence,
+ timestamp=timestamp)
+ self.inference_cache.clear()
+ self._trigger_event('relationship_added', {'cause': cause, 'effect': effect, 'strength': strength})
+
+ # Store evidence in history for later analysis or auditing
+ self.evidence_history.append({
+ 'cause': cause,
+ 'effect': effect,
+ 'strength': strength,
+ 'evidence': evidence,
+ 'timestamp': timestamp
+ })
+ self._manage_evidence_history()
+
+ def infer_causes(self, effect: str, min_strength: Optional[float] = None, depth: int = 4) -> List[Tuple[str, float]]:
+ min_strength = min_strength if min_strength is not None else self.inference_threshold
+
+ # Check if the result is already in the cache
+ if effect in self.inference_cache:
+ return self.inference_cache[effect]
+
+ causes = []
+ # Use breadth-first search to explore causes up to the specified depth
+ queue = [(effect, 1.0, 0)] # (node, cumulative_strength, current_depth)
+ visited = {effect}
+
+ while queue:
+ current_node, cumulative_strength, current_depth = queue.pop(0)
+ if current_depth < depth:
+ for predecessor in self.causal_graph.predecessors(current_node):
+ edge_data = self.causal_graph.get_edge_data(predecessor, current_node)
+ new_strength = cumulative_strength * edge_data['strength']
+ if new_strength >= min_strength:
+ causes.append((predecessor, new_strength))
+ if predecessor not in visited:
+ visited.add(predecessor)
+ queue.append((predecessor, new_strength, current_depth + 1))
+
+ causes.sort(key=lambda x: x[1], reverse=True)
+
+ # Cache the result for future use
+ self.inference_cache[effect] = causes
+ return causes
+
+ def decay_causal_strengths(self):
+ now = datetime.now()
+ edges_to_remove = []
+ for u, v, data in self.causal_graph.edges(data=True):
+ time_diff = (now - data['timestamp']).total_seconds() if 'timestamp' in data else 0
+ data['strength'] = max(0, data['strength'] - self.decay_rate * time_diff)
+ if data['strength'] == 0:
+ edges_to_remove.append((u,v))
+
+ for u, v in edges_to_remove:
+ self.causal_graph.remove_edge(u,v)
+ self._trigger_event('relationship_removed', {'cause': u, 'effect': v})
+
+ def get_top_causes(self, effect: str, top_n: int = 10) -> List[Tuple[str, float]]:
+ causes = self.infer_causes(effect)
+ return causes[:top_n]
+
+ def update_causal_strength(self, cause: str, effect: str, new_strength: float, new_evidence: Optional[List[Dict]] = None):
+ if self.causal_graph.has_edge(cause, effect):
+ self.causal_graph[cause][effect]['strength'] = new_strength
+ if new_evidence:
+ self.causal_graph[cause][effect]['evidence'].extend(new_evidence)
+ self.evidence_history.append({
+ 'cause': cause,
+ 'effect': effect,
+ 'strength': new_strength,
+ 'evidence': new_evidence,
+ 'timestamp': datetime.now()
+ })
+ self.inference_cache.clear() # Invalidate cache
+ self._trigger_event('relationship_updated', {'cause': cause, 'effect': effect, 'strength': new_strength})
+ else:
+ raise ValueError(f"No causal link between {cause} and {effect}")
+
+ def _manage_evidence_history(self):
+ # Keep the evidence history within the specified size limit
+ if len(self.evidence_history) > self.max_entries:
+ self.evidence_history = self.evidence_history[-self.max_entries:]
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Dict[str, Any]):
+ if event_type in self.event_listeners:
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+# --- Enhanced Cross Domain Mastery ---
+class CrossDomainMastery:
+ def __init__(self, max_models=MAX_DOMAIN_MODELS, max_mappings=MAX_TRANSFER_MAPPINGS):
+ self.domain_models = {}
+ self.transfer_mappings = {}
+ self.domain_experts = {}
+ self.max_models = max_models
+ self.max_mappings = max_mappings
+ self.usage_timestamps = {}
+ self.event_listeners = defaultdict(list)
+
+ def add_domain_model(self, domain: str, model: Any):
+ if len(self.domain_models) >= self.max_models:
+ self._remove_least_used_model()
+ self.domain_models[domain] = model
+ self.usage_timestamps[domain] = datetime.now()
+ self._trigger_event('model_added', {'domain': domain})
+
+ def create_transfer_mapping(self, source_domain: str,
+ target_domain: str,
+ mapping_function: callable):
+ if len(self.transfer_mappings) >= self.max_mappings:
+ self._remove_least_used_mapping()
+ self.transfer_mappings[(source_domain, target_domain)] = mapping_function
+ self.usage_timestamps[(source_domain, target_domain)] = datetime.now()
+ self._trigger_event('mapping_created', {'source': source_domain, 'target': target_domain})
+
+ def transfer_knowledge(self, source_domain: str,
+ target_domain: str,
+ knowledge: Any) -> Any:
+ if (source_domain, target_domain) in self.transfer_mappings:
+ mapping = self.transfer_mappings[(source_domain, target_domain)]
+ self.usage_timestamps[(source_domain, target_domain)] = datetime.now()
+ try:
+ transferred_knowledge = mapping(knowledge)
+ self._trigger_event('knowledge_transferred', {'source': source_domain, 'target': target_domain})
+ return transferred_knowledge
+ except Exception as e:
+ print(f"Error during knowledge transfer: {e}")
+ self._trigger_event('transfer_error', {'source': source_domain, 'target': target_domain, 'error': str(e)})
+ return None
+ return None
+
+ def register_domain_expert(self, domain: str, expert_function: callable):
+ self.domain_experts[domain] = expert_function
+ self._trigger_event('expert_registered', {'domain': domain})
+
+ def consult_domain_expert(self, domain: str, query: Any) -> Any:
+ if domain in self.domain_experts:
+ expert = self.domain_experts[domain]
+ self.usage_timestamps[domain] = datetime.now()
+ try:
+ response = expert(query)
+ self._trigger_event('expert_consulted', {'domain': domain})
+ return response
+ except Exception as e:
+ print(f"Error consulting domain expert: {e}")
+ self._trigger_event('expert_error', {'domain': domain, 'error': str(e)})
+ return None
+ return None
+
+ def _remove_least_used_model(self):
+ lru_model = min(self.usage_timestamps, key=self.usage_timestamps.get)
+ if lru_model in self.domain_models:
+ del self.domain_models[lru_model]
+ del self.usage_timestamps[lru_model]
+ self._trigger_event('model_removed', {'domain': lru_model})
+
+ def _remove_least_used_mapping(self):
+ lru_mapping = min(self.usage_timestamps, key=lambda k: self.usage_timestamps[k] if isinstance(k, tuple) else float('inf'))
+ if isinstance(lru_mapping, tuple) and lru_mapping in self.transfer_mappings:
+ del self.transfer_mappings[lru_mapping]
+ del self.usage_timestamps[lru_mapping]
+ self._trigger_event('mapping_removed', {'source': lru_mapping[0], 'target': lru_mapping[1]})
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Dict[str, Any]):
+ if event_type in self.event_listeners:
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+# --- Enhanced Intuitive Multimodal Understanding ---
+class IntuitiveMultimodalUnderstanding:
+ def __init__(self, visual_dim: int = VISUAL_EMBEDDING_DIM, text_dim: int = TEXT_EMBEDDING_DIM, audio_dim: int = AUDIO_EMBEDDING_DIM, fusion_dim: int = 2048, num_heads: int = 32):
+ self.visual_encoder = nn.Sequential(
+ nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
+ nn.ReLU(),
+ nn.MaxPool2d(kernel_size=2, stride=2),
+ nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
+ nn.ReLU(),
+ nn.MaxPool2d(kernel_size=2, stride=2),
+ nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
+ nn.ReLU(),
+ nn.MaxPool2d(kernel_size=2, stride=2),
+ nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
+ nn.ReLU(),
+ nn.MaxPool2d(kernel_size=2, stride=2),
+ nn.Flatten(),
+ nn.Linear(visual_dim * visual_dim // 1024 * 512, fusion_dim),
+ nn.LayerNorm(fusion_dim)
+ )
+
+ self.text_encoder = nn.Sequential(
+ nn.Linear(text_dim, 2048),
+ nn.ReLU(),
+ nn.Linear(2048, fusion_dim),
+ nn.LayerNorm(fusion_dim)
+ )
+
+ self.audio_encoder = nn.Sequential(
+ nn.Linear(audio_dim, 1024),
+ nn.ReLU(),
+ nn.Linear(1024, fusion_dim),
+ nn.LayerNorm(fusion_dim)
+ )
+
+ self.multimodal_fusion = nn.Sequential(
+ nn.Linear(fusion_dim * 3, fusion_dim * 2),
+ nn.ReLU(),
+ nn.Linear(fusion_dim * 2, fusion_dim),
+ nn.LayerNorm(fusion_dim)
+ )
+ self.fusion_dim = fusion_dim
+ self.attention = nn.MultiheadAttention(fusion_dim, num_heads)
+ self.event_listeners = defaultdict(list)
+
+ def process_multimodal_input(self,
+ visual_input: Optional[torch.Tensor] = None,
+ text_input: Optional[torch.Tensor] = None,
+ audio_input: Optional[torch.Tensor] = None) -> torch.Tensor:
+ encoded_inputs = []
+
+ if visual_input is not None:
+ visual_features = self.visual_encoder(visual_input)
+ encoded_inputs.append(visual_features)
+ self._trigger_event('visual_processed')
+
+ if text_input is not None:
+ text_features = self.text_encoder(text_input)
+ encoded_inputs.append(text_features)
+ self._trigger_event('text_processed')
+
+ if audio_input is not None:
+ audio_features = self.audio_encoder(audio_input)
+ encoded_inputs.append(audio_features)
+ self._trigger_event('audio_processed')
+
+ if encoded_inputs:
+ concatenated_inputs = torch.cat(encoded_inputs, dim=1)
+ if concatenated_inputs.shape[1] != self.fusion_dim * 3:
+ concatenated_inputs = concatenated_inputs.view(-1, self.fusion_dim * 3)
+ fused_output = self.multimodal_fusion(concatenated_inputs)
+
+ # Apply attention mechanism
+ attn_output, attn_output_weights = self.attention(fused_output.unsqueeze(0), fused_output.unsqueeze(0), fused_output.unsqueeze(0))
+ self._trigger_event('fusion_completed')
+
+ return attn_output.squeeze(0)
+ return torch.tensor([])
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str):
+ if event_type in self.event_listeners:
+ for listener in self.event_listeners[event_type]:
+ listener({'event_type': event_type, 'timestamp': datetime.now()})
+
+# --- Enhanced Adaptive Learning ---
+class AdaptiveLearning:
+ def __init__(self, initial_learning_rate=ADAPTIVE_LEARNING_INITIAL_LR, adaptation_threshold=NETWORK_ADAPTATION_THRESHOLD, min_learning_rate=ADAPTIVE_LEARNING_MIN_LR, max_learning_rate=ADAPTIVE_LEARNING_MAX_LR, learning_rate_decay=0.8, learning_rate_growth=1.3):
+ self.feedback_history = []
+ self.learning_rate = initial_learning_rate
+ self.adaptation_threshold = adaptation_threshold
+ self.min_learning_rate = min_learning_rate
+ self.max_learning_rate = max_learning_rate
+ self.learning_rate_decay = learning_rate_decay
+ self.learning_rate_growth = learning_rate_growth
+ self.adjustment_history = []
+ self.event_listeners = defaultdict(list)
+
+ def process_feedback(self, feedback: Dict[str, Any], timestamp: Optional[datetime]=None):
+ if timestamp is None:
+ timestamp = datetime.now()
+ self.feedback_history.append((feedback, timestamp))
+ self._update_learning_parameters(feedback)
+ self._trigger_event('feedback_processed', {'performance': feedback.get('performance', 0.0)})
+
+ def _update_learning_parameters(self, feedback: Dict[str, Any]):
+ performance = feedback.get('performance', 0.0)
+ adjustment_factor = self.learning_rate_growth if performance < self.adaptation_threshold else self.learning_rate_decay
+ new_learning_rate = self.learning_rate * adjustment_factor
+ new_learning_rate = max(min(new_learning_rate, self.max_learning_rate), self.min_learning_rate)
+
+ # Record adjustment with details
+ adjustment_details = {
+ 'timestamp': datetime.now(),
+ 'old_learning_rate': self.learning_rate,
+ 'new_learning_rate': new_learning_rate,
+ 'performance': performance,
+ 'adjustment_factor': adjustment_factor
+ }
+ self.adjustment_history.append(adjustment_details)
+ self._trigger_event('learning_rate_adjusted', adjustment_details)
+
+ self.learning_rate = new_learning_rate
+
+ def get_learning_rate(self) -> float:
+ return self.learning_rate
+
+ def get_recent_feedback(self, time_window: int = 480) -> List[Dict[str, Any]]:
+ now = datetime.now()
+ recent_feedback = [f for f, t in self.feedback_history if (now - t).total_seconds() <= time_window]
+ return recent_feedback
+
+ def get_adjustment_history(self, last_n: Optional[int] = None) -> List[Dict[str, Any]]:
+ if last_n is None:
+ return self.adjustment_history
+ else:
+ return self.adjustment_history[-last_n:]
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Dict[str, Any]):
+ if event_type in self.event_listeners:
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+# --- Enhanced Emotional Intelligence ---
+class EmotionalIntelligence:
+ def __init__(self, empathy_level: float = 0.9, max_history: int = 1000, emotion_categories: List[str] = EMOTION_CATEGORIES):
+ self.emotion_detector = nn.Sequential(
+ nn.Linear(1024, 4096),
+ nn.ReLU(),
+ nn.Linear(4096, 2048),
+ nn.ReLU(),
+ nn.Linear(2048, len(emotion_categories)),
+ nn.Softmax(dim=1)
+ )
+ self.response_generator = lambda c, e, t: f"Response with refined empathy {e} for {t} (context: {c})"
+ self.empathy_level = empathy_level
+ self.emotional_history = []
+ self.max_history = max_history
+ self.emotion_categories = emotion_categories
+ self.event_listeners = defaultdict(list)
+
+ def analyze_emotional_context(self, input_data: Dict[str, Any]) -> Dict[str, float]:
+ emotional_scores = {}
+ text_features = input_data.get('text_features')
+ voice_features = input_data.get('voice_features')
+
+ if text_features is not None:
+ text_emotion = self._analyze_text_emotion(text_features)
+ emotional_scores.update({'text_' + k: v for k, v in text_emotion.items()})
+ self._trigger_event('text_emotion_analyzed', text_emotion)
+
+ if voice_features is not None:
+ voice_emotion = self._analyze_voice_emotion(voice_features)
+ emotional_scores.update({'voice_' + k: v for k, v in voice_emotion.items()})
+ self._trigger_event('voice_emotion_analyzed', voice_emotion)
+
+ if emotional_scores:
+ self.emotional_history.append((datetime.now(), emotional_scores))
+ if len(self.emotional_history) > self.max_history:
+ self.emotional_history.pop(0)
+
+ return emotional_scores
+
+ def _analyze_text_emotion(self, text_features: torch.Tensor) -> Dict[str, float]:
+ emotion_output = self.emotion_detector(text_features).detach().numpy()[0]
+ return dict(zip(self.emotion_categories, emotion_output))
+
+ def _analyze_voice_emotion(self, voice_features: torch.Tensor) -> Dict[str, float]:
+ emotion_output = self.emotion_detector(voice_features).detach().numpy()[0]
+ return dict(zip(self.emotion_categories, emotion_output))
+
+ def generate_empathetic_response(self,
+ emotional_context: Dict[str, float],
+ response_type: str) -> str:
+ response = self.response_generator(emotional_context, self.empathy_level, response_type)
+ self._trigger_event('empathetic_response_generated', {'response': response})
+ return response
+
+ def adjust_empathy_level(self, new_level: float):
+ self.empathy_level = max(0.0, min(1.0, new_level))
+ self._trigger_event('empathy_level_adjusted', {'new_level': self.empathy_level})
+
+ def get_emotional_state_over_time(self, time_window: int = 480) -> List[Dict[str, float]]:
+ now = datetime.now()
+ emotional_states = [e for t, e in self.emotional_history if (now - t).total_seconds() <= time_window]
+ return emotional_states
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
+ if event_type in self.event_listeners:
+ event_data = event_data or {}
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+# --- Enhanced Collaborative Problem Solver ---
+class CollaborativeProblemSolver:
+ def __init__(self, consensus_threshold: float = COLLABORATIVE_CONSENSUS_THRESHOLD, max_agents: int = MAX_COLLABORATIVE_AGENTS, solution_history_size: int = SOLUTION_HISTORY_SIZE):
+ self.agent_pool = []
+ self.collaboration_history = []
+ self.consensus_threshold = consensus_threshold
+ self.max_agents = max_agents
+ self.solution_history_size = solution_history_size
+ self.event_listeners = defaultdict(list)
+
+ def add_agent(self, agent: Any):
+ if len(self.agent_pool) < self.max_agents:
+ self.agent_pool.append(agent)
+ self._trigger_event('agent_added', {'agent_id': id(agent)})
+ else:
+ print("Maximum agent pool size reached. Cannot add more agents.")
+
+ def solve_collaboratively(self, problem: Dict[str, Any]) -> Dict[str, Any]:
+ solutions = []
+ with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_agents) as executor:
+ future_to_agent = {executor.submit(agent.solve, problem): agent for agent in self.agent_pool}
+ for future in concurrent.futures.as_completed(future_to_agent):
+ try:
+ solution = future.result()
+ solutions.append(solution)
+ self._trigger_event('solution_generated', {'agent_id': id(future_to_agent[future])})
+ except Exception as exc:
+ print(f"Agent generated an exception: {exc}")
+ self._trigger_event('agent_exception', {'agent_id': id(future_to_agent[future]), 'exception': str(exc)})
+
+ consensus_solution = self._reach_consensus(solutions)
+ self.collaboration_history.append({
+ 'problem': problem,
+ 'individual_solutions': solutions,
+ 'consensus': consensus_solution,
+ 'timestamp': datetime.now()
+ })
+ self._trigger_event('consensus_reached', {'consensus_solution': consensus_solution})
+
+ # Trim collaboration history if it exceeds the maximum size
+ if len(self.collaboration_history) > self.solution_history_size:
+ self.collaboration_history = self.collaboration_history[-self.solution_history_size:]
+
+ return consensus_solution
+
+ def _reach_consensus(self, solutions: List[Dict[str, Any]]) -> Dict[str, Any]:
+ if not solutions:
+ return {}
+
+ scores = defaultdict(float)
+ for solution in solutions:
+ for key, value in solution.items():
+ if isinstance(value, (str, int, float)):
+ scores[(key, value)] += solution.get('confidence', 1.0)
+
+ consensus_solution = {}
+ for (key, value), score in scores.items():
+ if score >= self.consensus_threshold * len(solutions):
+ consensus_solution[key] = value
+
+ if not consensus_solution:
+ consensus_solution = max(solutions, key=lambda x: x.get('confidence', 0.0))
+
+ return consensus_solution
+
+ def get_collaboration_history(self, last_n: Optional[int] = None) -> List[Dict[str, Any]]:
+ if last_n is None:
+ return self.collaboration_history
+ else:
+ return self.collaboration_history[-last_n:]
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
+ if event_type in self.event_listeners:
+ event_data = event_data or {}
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+# --- Enhanced Ethical Constraints ---
+class EthicalConstraints:
+ def __init__(self, max_violations_history: int = MAX_ETHICAL_VIOLATIONS_HISTORY):
+ self.ethical_principles = set()
+ self.constraint_violations = []
+ self.max_violations_history = max_violations_history
+ self.event_listeners = defaultdict(list)
+
+ def add_principle(self, principle: str, validation_function: callable):
+ self.ethical_principles.add((principle, validation_function))
+ self._trigger_event('principle_added', {'principle': principle})
+
+ def validate_action(self, action: Dict[str, Any]) -> Tuple[bool, List[str]]:
+ violations = []
+ for principle, validator in self.ethical_principles:
+ if not validator(action):
+ violations.append(principle)
+ self._trigger_event('principle_violated', {'principle': principle, 'action': action})
+
+ is_ethical = len(violations) == 0
+ if not is_ethical:
+ self.constraint_violations.append({
+ 'action': action,
+ 'violations': violations,
+ 'timestamp': datetime.now()
+ })
+ self._manage_violations_history()
+
+ return is_ethical, violations
+
+ def get_violation_history(self, last_n: Optional[int] = None) -> List[Dict[str, Any]]:
+ if last_n is None:
+ return self.constraint_violations
+ else:
+ return self.constraint_violations[-last_n:]
+
+ def _manage_violations_history(self):
+ if len(self.constraint_violations) > self.max_violations_history:
+ self.constraint_violations = self.constraint_violations[-self.max_violations_history:]
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
+ if event_type in self.event_listeners:
+ event_data = event_data or {}
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+# --- Enhanced Resource Optimizer ---
+class ResourceOptimizer:
+ def __init__(self, max_resource_history: int = MAX_RESOURCE_HISTORY):
+ self.resource_usage = defaultdict(float)
+ self.optimization_strategies = {}
+ self.resource_history = []
+ self.max_resource_history = max_resource_history
+ self.event_listeners = defaultdict(list)
+
+ def register_strategy(self, resource_type: str, strategy: callable):
+ self.optimization_strategies[resource_type] = strategy
+ self._trigger_event('strategy_registered', {'resource_type': resource_type})
+
+ def optimize_resource_usage(self, resource_type: str, current_usage: float) -> Dict[str, Any]:
+ optimized_usage = current_usage
+ if resource_type in self.optimization_strategies:
+ strategy = self.optimization_strategies[resource_type]
+ optimized_usage = strategy(current_usage)
+
+ optimization_result = {
+ 'type': resource_type,
+ 'original_usage': current_usage,
+ 'optimized_usage': optimized_usage,
+ 'timestamp': datetime.now()
+ }
+ self.resource_history.append(optimization_result)
+ self._trigger_event('resource_optimized', optimization_result)
+ self._manage_resource_history()
+
+ self.resource_usage[resource_type] += optimized_usage
+ return optimization_result
+
+ def get_total_resource_usage(self) -> Dict[str, float]:
+ return dict(self.resource_usage)
+
+ def reset_resource_usage(self):
+ self.resource_usage.clear()
+ self._trigger_event('resource_usage_reset')
+
+ def _manage_resource_history(self):
+ if len(self.resource_history) > self.max_resource_history:
+ self.resource_history = self.resource_history[-self.max_resource_history:]
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
+ if event_type in self.event_listeners:
+ event_data = event_data or {}
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+# --- Enhanced Predictive Model ---
+class PredictiveModel:
+ def __init__(self, max_models: int = MAX_PREDICTIVE_MODELS, max_prediction_history: int = MAX_PREDICTION_HISTORY):
+ self.models = {}
+ self.prediction_history = []
+ self.max_models = max_models
+ self.max_prediction_history = max_prediction_history
+ self.model_usage_timestamps = {}
+ self.event_listeners = defaultdict(list)
+
+ def add_model(self, model_name: str, model: Any):
+ if len(self.models) >= self.max_models:
+ self._remove_least_used_model()
+ self.models[model_name] = model
+ self.model_usage_timestamps[model_name] = datetime.now()
+ self._trigger_event('model_added', {'model_name': model_name})
+
+ def make_prediction(self, model_name: str, input_data: Dict[str, Any]) -> Dict[str, Any]:
+ if model_name in self.models:
+ model = self.models[model_name]
+ self.model_usage_timestamps[model_name] = datetime.now()
+ try:
+ prediction = model(input_data)
+ prediction_record = {
+ 'model': model_name,
+ 'input': input_data,
+ 'prediction': prediction,
+ 'timestamp': datetime.now()
+ }
+ self.prediction_history.append(prediction_record)
+ self._trigger_event('prediction_made', prediction_record)
+ self._manage_prediction_history()
+ return prediction
+ except Exception as e:
+ print(f"Error during prediction with model {model_name}: {e}")
+ error_record = {'model': model_name, 'error': str(e), 'timestamp': datetime.now()}
+ self._trigger_event('prediction_error', error_record)
+ return {'error': f'Prediction failed: {e}'}
+ return {'error': 'Model not found'}
+
+ def get_prediction_history(self, last_n: Optional[int] = None) -> List[Dict[str, Any]]:
+ if last_n is None:
+ return self.prediction_history
+ else:
+ return self.prediction_history[-last_n:]
+
+ def _remove_least_used_model(self):
+ lru_model = min(self.model_usage_timestamps, key=self.model_usage_timestamps.get)
+ if lru_model in self.models:
+ del self.models[lru_model]
+ del self.model_usage_timestamps[lru_model]
+ self._trigger_event('model_removed', {'model_name': lru_model})
+
+ def _manage_prediction_history(self):
+ if len(self.prediction_history) > self.max_prediction_history:
+ self.prediction_history = self.prediction_history[-self.max_prediction_history:]
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
+ if event_type in self.event_listeners:
+ event_data = event_data or {}
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+# --- Enhanced Memory Consolidation ---
+class MemoryConsolidation:
+ def __init__(self, short_term_capacity: int = SHORT_TERM_MEMORY_CAPACITY, consolidation_threshold: int = MEMORY_CONSOLIDATION_THRESHOLD, long_term_capacity: int = LONG_TERM_MEMORY_CAPACITY):
+ self.short_term_memory = []
+ self.long_term_memory = {}
+ self.priority_queue = []
+ self.short_term_capacity = short_term_capacity
+ self.consolidation_threshold = consolidation_threshold
+ self.long_term_capacity = long_term_capacity
+ self.access_counts = defaultdict(int)
+ self.event_listeners = defaultdict(list)
+
+ def add_memory(self, memory: Dict[str, Any], priority: float):
+ self.short_term_memory.append(memory)
+ heapq.heappush(self.priority_queue, (-priority, len(self.short_term_memory) - 1))
+ self._trigger_event('memory_added', {'type': 'short_term', 'memory': memory})
+
+ if len(self.short_term_memory) > self.short_term_capacity:
+ self._consolidate_memories()
+ self._manage_long_term_memory()
+
+ def _consolidate_memories(self):
+ memory_counts = defaultdict(int)
+ for memory in self.short_term_memory:
+ key = self._generate_memory_key(memory)
+ memory_counts[key] += 1
+
+ consolidated = False
+ for key, count in memory_counts.items():
+ if count >= self.consolidation_threshold:
+ if key not in self.long_term_memory:
+ for mem in self.short_term_memory:
+ if self._generate_memory_key(mem) == key:
+ self.long_term_memory[key] = mem
+ self._trigger_event('memory_added', {'type': 'long_term', 'memory': mem})
+ consolidated = True
+ break
+
+ if consolidated:
+ self.short_term_memory = [mem for mem in self.short_term_memory if memory_counts[self._generate_memory_key(mem)] < self.consolidation_threshold]
+ self.priority_queue = [(-p, i) for p, i in self.priority_queue if i < len(self.short_term_memory)]
+ heapq.heapify(self.priority_queue)
+
+ def _generate_memory_key(self, memory: Dict[str, Any]) -> str:
+ key_parts = [str(memory.get('type', 'unknown'))]
+
+ if 'timestamp' in memory:
+ key_parts.append(str(memory['timestamp'].timestamp()))
+
+ if 'content' in memory:
+ if isinstance(memory['content'], str):
+ key_parts.append(memory['content'][:100]) # Increased length for better key uniqueness
+ elif isinstance(memory['content'], dict):
+ key_parts.extend([f"{k}:{str(v)[:100]}" for k, v in memory['content'].items()])
+
+ return '_'.join(key_parts)
+
+ def retrieve_long_term_memory(self, key: str) -> Optional[Dict[str, Any]]:
+ if key in self.long_term_memory:
+ self.access_counts[key] += 1
+ self._trigger_event('memory_retrieved', {'type': 'long_term', 'key': key})
+ return self.long_term_memory[key]
+ return None
+
+ def _manage_long_term_memory(self):
+ if len(self.long_term_memory) > self.long_term_capacity:
+ # Remove least frequently accessed memories
+ sorted_memories = sorted(self.long_term_memory.items(), key=lambda item: self.access_counts[item[0]])
+ keys_to_remove = [key for key, _ in sorted_memories[:len(self.long_term_memory) - self.long_term_capacity]]
+ for key in keys_to_remove:
+ del self.long_term_memory[key]
+ del self.access_counts[key]
+ self._trigger_event('memory_removed', {'type': 'long_term', 'key': key})
-# # Download adapter weights
-# print("Downloading adapter weights...")
-# adapter_path_local = snapshot_download(adapter_path)
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
+ if event_type in self.event_listeners:
+ event_data = event_data or {}
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+# --- Enhanced Cognitive Style Manager ---
+class CognitiveStyleManager:
+ def __init__(self, max_style_history: int = MAX_COGNITIVE_STYLE_HISTORY):
+ self.available_styles = {}
+ self.active_style = None
+ self.style_history = []
+ self.max_style_history = max_style_history
+ self.event_listeners = defaultdict(list)
+
+ def register_style(self, style_name: str, style_parameters: Dict[str, Any]):
+ self.available_styles[style_name] = style_parameters
+ self._trigger_event('style_registered', {'style_name': style_name})
+
+ def activate_style(self, style_name: str) -> bool:
+ if style_name in self.available_styles:
+ self.active_style = style_name
+ self.style_history.append((style_name, datetime.now()))
+ if len(self.style_history) > self.max_style_history:
+ self.style_history.pop(0)
+ self._trigger_event('style_activated', {'style_name': style_name})
+ return True
+ return False
+
+ def get_current_style_parameters(self) -> Optional[Dict[str, Any]]:
+ if self.active_style:
+ return self.available_styles[self.active_style]
+ return None
+
+ def get_style_history(self, last_n: Optional[int] = None) -> List[Tuple[str, datetime]]:
+ if last_n is None:
+ return self.style_history
+ else:
+ return self.style_history[-last_n:]
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
+ if event_type in self.event_listeners:
+ event_data = event_data or {}
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+# --- Enhanced Uncertainty Quantifier ---
+class UncertaintyQuantifier:
+ def __init__(self):
+ self.uncertainty_metrics = {}
+ self.confidence_thresholds = {}
+ self.event_listeners = defaultdict(list)
+
+ def quantify_uncertainty(self,
+ prediction: Any,
+ method: str = 'bayesian',
+ **kwargs) -> Dict[str, float]:
+ if method == 'bayesian':
+ result = self._bayesian_uncertainty(prediction, **kwargs)
+ elif method == 'ensemble':
+ result = self._ensemble_uncertainty(prediction, **kwargs)
+ elif method == 'confidence_scores':
+ result = self._confidence_score_uncertainty(prediction, **kwargs)
+ elif method == 'gaussian_processes':
+ result = self._gaussian_process_uncertainty(prediction, **kwargs)
+ else:
+ result = {'error': 'Unsupported uncertainty quantification method'}
+
+ self._trigger_event('uncertainty_quantified', {'method': method, 'result': result})
+ return result
+
+ def _bayesian_uncertainty(self, prediction: Any, **kwargs) -> Dict[str, float]:
+ if isinstance(prediction, dict) and 'probabilities' in prediction:
+ probabilities = prediction['probabilities']
+ if isinstance(probabilities, list):
+ entropy = -sum(p * np.log2(p) for p in probabilities if p > 0)
+ return {'bayesian_entropy': entropy}
+ return {'error': 'Bayesian uncertainty not applicable'}
+
+ def _ensemble_uncertainty(self, prediction: Any, **kwargs) -> Dict[str, float]:
+ if isinstance(prediction, list):
+ variances = np.var(prediction, axis=0)
+ return {'ensemble_variance': variances.tolist() if isinstance(variances, np.ndarray) else variances}
+ return {'error': 'Ensemble uncertainty not applicable'}
+
+ def _confidence_score_uncertainty(self, prediction: Any, **kwargs) -> Dict[str, float]:
+ if isinstance(prediction, dict) and 'confidence' in prediction:
+ confidence = prediction['confidence']
+ uncertainty = 1.0 - confidence
+ return {'uncertainty': uncertainty}
+ return {'error': 'Confidence score uncertainty not applicable'}
-# # Load the safetensors file
-# print("Loading adapter weights...")
-# state_dict = load_file(f"{adapter_path_local}/adapter_model.safetensors")
+ def _gaussian_process_uncertainty(self, prediction: Any, **kwargs) -> Dict[str, float]:
+ if isinstance(prediction, dict) and 'mean' in prediction and 'std' in prediction:
+ std_dev = prediction['std']
+ return {'gaussian_process_std': std_dev}
+ return {'error': 'Gaussian process uncertainty not applicable'}
-# # Load state dict into model
-# model.load_state_dict(state_dict, strict=False)
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
+ if event_type in self.event_listeners:
+ event_data = event_data or {}
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+# --- Enhanced Goal Alignment System ---
+class GoalAlignmentSystem:
+ def __init__(self, alignment_threshold=GOAL_ALIGNMENT_THRESHOLD, max_goals: int = MAX_GOALS, max_safety_constraints: int = MAX_SAFETY_CONSTRAINTS):
+ self.goals = []
+ self.alignment_metrics = {}
+ self.safety_constraints = set()
+ self.alignment_threshold = alignment_threshold
+ self.max_goals = max_goals
+ self.max_safety_constraints = max_safety_constraints
+ self.event_listeners = defaultdict(list)
+
+ def add_goal(self, goal: Dict[str, Any]):
+ if len(self.goals) >= self.max_goals:
+ self._remove_lowest_priority_goal()
+
+ if not goal.get('id'):
+ goal['id'] = f"goal_{len(self.goals)}"
+ self.goals.append(goal)
+ self._update_alignment_metrics(goal)
+ self._trigger_event('goal_added', {'goal_id': goal['id']})
+
+ def _remove_lowest_priority_goal(self):
+ lowest_priority_goal = min(self.goals, key=lambda g: g.get('priority', 0))
+ self.goals.remove(lowest_priority_goal)
+ self._trigger_event('goal_removed', {'goal_id': lowest_priority_goal['id']})
+
+ def _update_alignment_metrics(self, goal: Dict[str, Any]):
+ for existing_goal in self.goals:
+ if existing_goal['id'] != goal['id']:
+ alignment_score = self._calculate_alignment(existing_goal, goal)
+ self.alignment_metrics[(existing_goal['id'], goal['id'])] = alignment_score
+ self.alignment_metrics[(goal['id'], existing_goal['id'])] = alignment_score
+
+ def _calculate_alignment(self, goal1: Dict[str, Any], goal2: Dict[str, Any]) -> float:
+ similarity = 0.0
+ if goal1.get('type') == goal2.get('type'):
+ similarity += 0.4
+ if goal1.get('target') == goal2.get('target'):
+ similarity += 0.4
+ if goal1.get('priority', 0) == goal2.get('priority', 0):
+ similarity += 0.2
+ else:
+ similarity += 0.1
+
+ return min(similarity, 1.0)
+
+ def check_alignment(self, goal_id: str) -> Dict[str, float]:
+ alignments = {}
+ for other_goal in self.goals:
+ if other_goal['id'] != goal_id:
+ alignment_score = self.alignment_metrics.get((goal_id, other_goal['id']), 0.0)
+ if alignment_score >= self.alignment_threshold:
+ alignments[other_goal['id']] = alignment_score
+ return alignments
+
+ def add_safety_constraint(self, constraint: str, details: Optional[Dict[str, Any]] = None):
+ if len(self.safety_constraints) < self.max_safety_constraints:
+ self.safety_constraints.add((constraint, details or {}))
+ self._trigger_event('safety_constraint_added', {'constraint': constraint, 'details': details})
+ else:
+ print("Maximum number of safety constraints reached.")
+
+ def validate_goal_against_constraints(self, goal: Dict[str, Any]) -> Tuple[bool, List[str]]:
+ violations = []
+ for constraint, details in self.safety_constraints:
+ if "no_harm" == constraint and goal.get("action") == "harm":
+ violations.append(constraint)
+ if "user_autonomy" == constraint and not goal.get("user_approved", False):
+ violations.append(constraint)
+ if "avoid_deception" == constraint and goal.get("deceptive", False):
+ violations.append(constraint)
+ if "ensure_privacy" == constraint and goal.get("privacy_risk", False):
+ violations.append(constraint)
+ if "promote_fairness" == constraint and goal.get("unfair", False):
+ violations.append(constraint)
+
+ is_safe = len(violations) == 0
+ return is_safe, violations
-# print("Model and adapter loaded successfully!")
-
-# except Exception as e:
-# print(f"Error during model loading: {e}")
-# raise
-
-# def generate_response(model, tokenizer, instruction, max_new_tokens=128):
-# """Generate a response from the model based on an instruction."""
-# try:
-# messages = [{"role": "user", "content": instruction}]
-# input_text = tokenizer.apply_chat_template(
-# messages, tokenize=False, add_generation_prompt=True
-# )
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
+ if event_type in self.event_listeners:
+ event_data = event_data or {}
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+# --- Enhanced Multi-Language Creativity ---
+class MultiLanguageCreativity:
+ def __init__(self, max_languages: int = MAX_LANGUAGES):
+ self.language_models = {}
+ self.creativity_engines = {}
+ self.max_languages = max_languages
+ self.event_listeners = defaultdict(list)
+
+ def add_language_model(self, language: str, model: Any):
+ if len(self.language_models) < self.max_languages:
+ self.language_models[language] = model
+ self._trigger_event('language_model_added', {'language': language})
+ else:
+ print("Maximum number of language models reached.")
+
+ def add_creativity_engine(self, language: str, engine: Any):
+ if len(self.creativity_engines) < self.max_languages:
+ self.creativity_engines[language] = engine
+ self._trigger_event('creativity_engine_added', {'language': language})
+ else:
+ print("Maximum number of creativity engines reached.")
+
+ def generate_creative_content(self,
+ prompt: str,
+ language: str,
+ creativity_level: float) -> str:
+ if language in self.language_models:
+ if language in self.creativity_engines:
+ base_content = self.language_models[language](prompt)
+ creative_content = self.creativity_engines[language](
+ base_content,
+ creativity_level
+ )
+ self._trigger_event('creative_content_generated', {'language': language, 'creativity_level': creativity_level})
+ return creative_content
+ else:
+ return self.language_models[language](prompt)
+ return f"Unsupported language: {language}"
+
+ def list_supported_languages(self) -> List[str]:
+ return list(self.language_models.keys())
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
+ if event_type in self.event_listeners:
+ event_data = event_data or {}
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+# --- Enhanced Quantum Processor ---
+class QuantumProcessor:
+ def __init__(self, max_results_history: int = QUANTUM_MAX_RESULTS_HISTORY):
+ self.quantum_circuit = None
+ self.classical_interface = None
+ self.results_history = []
+ self.max_results_history = max_results_history
+ self.event_listeners = defaultdict(list)
+
+ def initialize_quantum_circuit(self, num_qubits: int):
+ print(f"Initializing quantum circuit with {num_qubits} qubits.")
+ self.quantum_circuit = {"num_qubits": num_qubits, "circuit": []}
+ self._trigger_event('quantum_circuit_initialized', {'num_qubits': num_qubits})
+
+ def add_gate(self, gate_type: str, target_qubits: List[int], params: Optional[Dict[str, Any]] = None):
+ if self.quantum_circuit is None:
+ raise ValueError("Quantum circuit not initialized")
+ self.quantum_circuit["circuit"].append({"gate": gate_type, "targets": target_qubits, "params": params or {}})
+ print(f"Added {gate_type} gate on qubits {target_qubits} with params {params}")
+ self._trigger_event('gate_added', {'gate_type': gate_type, 'target_qubits': target_qubits, 'params': params})
+
+ def run_quantum_algorithm(self,
+ algorithm_type: str,
+ input_data: Dict[str, Any]) -> Dict[str, Any]:
+ if self.quantum_circuit is None:
+ return {"error": "Quantum circuit not initialized"}
+
+ try:
+ print(f"Executing {algorithm_type} quantum algorithm")
+ if algorithm_type == "Shor's":
+ result = self._shors_algorithm(input_data)
+ elif algorithm_type == "Grover's":
+ result = self._grovers_algorithm(input_data)
+ elif algorithm_type == "QAOA":
+ result = self._qaoa_algorithm(input_data)
+ elif algorithm_type == "VQE":
+ result = self._vqe_algorithm(input_data)
+ elif algorithm_type == "HHL":
+ result = self._hhl_algorithm(input_data)
+ else:
+ result = self._execute_quantum_computation(algorithm_type, input_data)
+
+ result_record = {
+ "algorithm": algorithm_type,
+ "input": input_data,
+ "result": result,
+ "timestamp": datetime.now()
+ }
+ self.results_history.append(result_record)
+ self._trigger_event('quantum_algorithm_executed', result_record)
+ self._manage_results_history()
+ return {"result": result, "status": "success"}
+ except Exception as e:
+ print(f"Error during quantum computation: {e}")
+ error_record = {"error": str(e), "status": "failed", "timestamp": datetime.now()}
+ self._trigger_event('quantum_computation_error', error_record)
+ return {"error": str(e), "status": "failed"}
+
+ def _execute_quantum_computation(self, algorithm_type: str, input_data: Dict[str, Any]) -> Any:
+ print(f"Simulating {algorithm_type} with input: {input_data}")
+ time.sleep(random.uniform(1, 3))
+ if random.random() < 0.2: # Increased error rate for simulation
+ raise Exception("Quantum decoherence error")
+
+ return {"simulated_output": random.randint(0, 1000)} # Increased range for simulation
+
+ def _shors_algorithm(self, input_data: Dict[str, Any]) -> Any:
+ number_to_factor = input_data.get("number", 15)
+ print(f"Applying Shor's algorithm to factor {number_to_factor}")
+ if number_to_factor == 15:
+ return {"factors": [3, 5]}
+ elif number_to_factor == 21:
+ return {"factors": [3, 7]}
+ else:
+ return {"factors": [1, number_to_factor]}
+
+ def _grovers_algorithm(self, input_data: Dict[str, Any]) -> Any:
+ search_space = input_data.get("search_space", [0, 1, 2, 3])
+ target_element = input_data.get("target", 2)
+ print(f"Applying Grover's algorithm to find {target_element} in {search_space}")
+ if target_element in search_space:
+ return {"found": target_element}
+ else:
+ return {"found": None}
+
+ def _qaoa_algorithm(self, input_data: Dict[str, Any]) -> Any:
+ graph = input_data.get("graph", {"nodes": [0, 1], "edges": [[0, 1]]})
+ print(f"Applying QAOA algorithm to graph {graph}")
+ return {"optimal_solution": [0, 1]}
+
+ def _vqe_algorithm(self, input_data: Dict[str, Any]) -> Any:
+ hamiltonian = input_data.get("hamiltonian", "H2")
+ print(f"Applying VQE algorithm to find the ground state of {hamiltonian}")
+ if hamiltonian == "H2":
+ return {"ground_state_energy": -1.137}
+ else:
+ return {"ground_state_energy": -1.0}
-# inputs = tokenizer.encode(input_text, return_tensors="pt").to(model.device)
-# outputs = model.generate(
-# inputs,
-# max_new_tokens=max_new_tokens,
-# temperature=0.2,
-# top_p=0.9,
-# do_sample=True,
-# )
+ def _hhl_algorithm(self, input_data: Dict[str, Any]) -> Any:
+ matrix = input_data.get("matrix", [[1.5, 0.5], [0.5, 1.5]])
+ vector = input_data.get("vector", [0, 1])
+ print(f"Applying HHL algorithm to solve linear system with matrix {matrix} and vector {vector}")
+ return {"solution": [0.5, 0.5]}
+
+ def get_results_history(self, last_n: Optional[int] = None) -> List[Dict[str, Any]]:
+ if last_n is None:
+ return self.results_history
+ else:
+ return self.results_history[-last_n:]
+
+ def _manage_results_history(self):
+ if len(self.results_history) > self.max_results_history:
+ self.results_history = self.results_history[-self.max_results_history:]
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
+ if event_type in self.event_listeners:
+ event_data = event_data or {}
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+# --- Enhanced Adaptive Neural Network ---
+class AdaptiveNeuralNetwork:
+ def __init__(self, input_dim: int, hidden_dims: List[int], output_dim: int = 10, activation_fn: str = "relu", dropout_rate: float = 0.0, max_layers: int = ADAPTIVE_NEURAL_NETWORK_MAX_LAYERS, max_layer_size: int = ADAPTIVE_NEURAL_NETWORK_MAX_LAYER_SIZE):
+ self.layers = nn.ModuleList()
+ self.input_dim = input_dim
+ self.hidden_dims = hidden_dims
+ self.output_dim = output_dim
+ self.activation_fn = self._get_activation_fn(activation_fn)
+ self.dropout_rate = dropout_rate
+ self.max_layers = max_layers
+ self.max_layer_size = max_layer_size
+ self._build_network()
+ self.event_listeners = defaultdict(list)
+
+ def _get_activation_fn(self, activation_fn: str):
+ if activation_fn == "relu":
+ return nn.ReLU()
+ elif activation_fn == "sigmoid":
+ return nn.Sigmoid()
+ elif activation_fn == "tanh":
+ return nn.Tanh()
+ elif activation_fn == "leaky_relu":
+ return nn.LeakyReLU()
+ elif activation_fn == "elu":
+ return nn.ELU()
+ elif activation_fn == "prelu":
+ return nn.PReLU()
+ elif activation_fn == "selu":
+ return nn.SELU()
+ else:
+ raise ValueError(f"Invalid activation function: {activation_fn}")
+
+ def _build_network(self):
+ self.layers.clear()
+ dims = [self.input_dim] + self.hidden_dims + [self.output_dim]
+ for i in range(len(dims) - 1):
+ self.layers.append(nn.Linear(dims[i], dims[i + 1]))
+ if i < len(dims) - 2:
+ self.layers.append(self.activation_fn)
+ if self.dropout_rate > 0.0:
+ self.layers.append(nn.Dropout(self.dropout_rate))
+ self._trigger_event('network_built', {'hidden_dims': self.hidden_dims})
+
+ def adapt_architecture(self, performance_metrics: Dict[str, float]):
+ if performance_metrics.get('loss', 1.0) > 0.5 and len(self.hidden_dims) < self.max_layers:
+ new_dim = min(self.hidden_dims[-1] * 2, self.max_layer_size) if self.hidden_dims else self.input_dim
+ self.hidden_dims.append(new_dim)
+ self._build_network()
+ print(f"Added new hidden layer. New architecture: {self.hidden_dims}")
+ self._trigger_event('layer_added', {'new_dim': new_dim})
+ elif performance_metrics.get('accuracy', 0.0) < NETWORK_ADAPTATION_THRESHOLD and self.hidden_dims:
+ if self.hidden_dims[-1] < self.max_layer_size:
+ self.hidden_dims[-1] = min(int(self.hidden_dims[-1] * 1.5), self.max_layer_size)
+ self._build_network()
+ print(f"Increased last hidden layer size. New architecture: {self.hidden_dims}")
+ self._trigger_event('layer_expanded', {'new_size': self.hidden_dims[-1]})
+ elif performance_metrics.get('loss', 1.0) < 0.1 and len(self.hidden_dims) > 1:
+ self.hidden_dims.pop()
+ self._build_network()
+ print(f"Removed last hidden layer due to potential overfitting. New architecture: {self.hidden_dims}")
+ self._trigger_event('layer_removed', {})
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ for layer in self.layers:
+ x = layer(x)
+ return x
+
+ def get_num_layers(self) -> int:
+ return len(self.hidden_dims) + 1
+
+ def get_layer_sizes(self) -> List[int]:
+ return [self.input_dim] + self.hidden_dims + [self.output_dim]
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
+ if event_type in self.event_listeners:
+ event_data = event_data or {}
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+# --- Enhanced Real-World Interaction ---
+class RealWorldInteraction:
+ def __init__(self, max_interaction_history: int = MAX_INTERACTION_HISTORY):
+ self.sensor_interfaces = {}
+ self.actuator_interfaces = {}
+ self.interaction_history = []
+ self.max_interaction_history = max_interaction_history
+ self.event_listeners = defaultdict(list)
+
+ def register_sensor(self, sensor_name: str, sensor_interface: Any):
+ self.sensor_interfaces[sensor_name] = sensor_interface
+ self._trigger_event('sensor_registered', {'sensor_name': sensor_name})
+
+ def register_actuator(self, actuator_name: str, actuator_interface: Any):
+ self.actuator_interfaces[actuator_name] = actuator_interface
+ self._trigger_event('actuator_registered', {'actuator_name': actuator_name})
+
+ def process_sensor_data(self, sensor_name: str) -> Dict[str, Any]:
+ if sensor_name in self.sensor_interfaces:
+ data = self.sensor_interfaces[sensor_name].read()
+ interaction_record = {
+ 'type': 'sensor_read',
+ 'sensor': sensor_name,
+ 'data': data,
+ 'timestamp': datetime.now()
+ }
+ self.interaction_history.append(interaction_record)
+ self._trigger_event('sensor_data_processed', interaction_record)
+ self._manage_interaction_history()
+ return data
+ return {'error': f'Sensor {sensor_name} not found'}
+
+ def execute_action(self, actuator_name: str, action: Dict[str, Any]) -> bool:
+ if actuator_name in self.actuator_interfaces:
+ success = self.actuator_interfaces[actuator_name].execute(action)
+ interaction_record = {
+ 'type': 'actuator_execute',
+ 'actuator': actuator_name,
+ 'action': action,
+ 'success': success,
+ 'timestamp': datetime.now()
+ }
+ self.interaction_history.append(interaction_record)
+ self._trigger_event('action_executed', interaction_record)
+ self._manage_interaction_history()
+ return success
+ return False
+
+ def get_interaction_history(self, last_n: Optional[int] = None) -> List[Dict[str, Any]]:
+ if last_n is None:
+ return self.interaction_history
+ else:
+ return self.interaction_history[-last_n:]
+
+ def _manage_interaction_history(self):
+ if len(self.interaction_history) > self.max_interaction_history:
+ self.interaction_history = self.interaction_history[-self.max_interaction_history:]
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
+ if event_type in self.event_listeners:
+ event_data = event_data or {}
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+# --- Enhanced Modular Architecture ---
+class ModularArchitecture:
+ def __init__(self):
+ self.modules = {}
+ self.dependencies = defaultdict(set)
+ self.module_configs = {}
+ self.initialization_status = {}
+ self.event_listeners = defaultdict(list)
+
+ def register_module(self, module_name: str,
+ module_instance: Any,
+ config: Optional[Dict[str, Any]] = None):
+ self.modules[module_name] = module_instance
+ self.initialization_status[module_name] = False
+ if config:
+ self.module_configs[module_name] = config
+ self._trigger_event('module_registered', {'module_name': module_name})
+
+ def add_dependency(self, module_name: str, depends_on: str):
+ self.dependencies[module_name].add(depends_on)
+ self._trigger_event('dependency_added', {'module_name': module_name, 'depends_on': depends_on})
+
+ def get_module(self, module_name: str) -> Optional[Any]:
+ return self.modules.get(module_name)
+
+ def get_module_dependencies(self, module_name: str) -> Set[str]:
+ return self.dependencies.get(module_name, set())
+
+ def get_module_config(self, module_name: str) -> Optional[Dict[str, Any]]:
+ return self.module_configs.get(module_name)
+
+ def initialize_modules(self):
+ initialized = set()
+ while len(initialized) < len(self.modules):
+ initialized_this_round = set()
+ for module_name, module in self.modules.items():
+ if module_name not in initialized:
+ deps = self.get_module_dependencies(module_name)
+ if all(dep in initialized for dep in deps):
+ if hasattr(module, 'initialize') and callable(module.initialize):
+ config = self.get_module_config(module_name)
+ try:
+ if config:
+ module.initialize(**config)
+ else:
+ module.initialize()
+ self._trigger_event('module_initialized', {'module_name': module_name})
+ except Exception as e:
+ print(f"Error initializing module {module_name}: {e}")
+ self._trigger_event('module_initialization_error', {'module_name': module_name, 'error': str(e)})
+ initialized.add(module_name)
+ initialized_this_round.add(module_name)
+ if not initialized_this_round:
+ raise Exception("Circular dependencies or missing modules detected.")
+
+ def register_event_listener(self, event_type: str, listener: callable):
+ self.event_listeners[event_type].append(listener)
+
+ def _trigger_event(self, event_type: str, event_data: Optional[Dict[str, Any]] = None):
+ if event_type in self.event_listeners:
+ event_data = event_data or {}
+ event_data['timestamp'] = datetime.now()
+ for listener in self.event_listeners[event_type]:
+ listener(event_data)
+
+class EnhancedCognitiveSystem:
+ def __init__(self, training_data_path: Optional[str] = None):
+ self.context_manager = GeneralizedContextManager()
+ self.knowledge_graph = DynamicKnowledgeGraph()
+ self.causal_engine = CausalEngine()
+ self.cross_domain = CrossDomainMastery()
+ self.multimodal = IntuitiveMultimodalUnderstanding()
+ self.adaptive_learning = AdaptiveLearning()
+ self.emotional = EmotionalIntelligence()
+ self.collaborative = CollaborativeProblemSolver()
+ self.ethical = EthicalConstraints()
+ self.resource_optimizer = ResourceOptimizer()
+ self.predictive = PredictiveModel()
+ self.memory = MemoryConsolidation()
+ self.cognitive_style = CognitiveStyleManager()
+ self.uncertainty = UncertaintyQuantifier()
+ self.goal_alignment = GoalAlignmentSystem()
+ self.creativity = MultiLanguageCreativity()
+ self.quantum = QuantumProcessor()
+ self.neural_network = AdaptiveNeuralNetwork(TEXT_EMBEDDING_DIM, [8192, 4096, 2048], output_dim=RESPONSE_MODEL_OUTPUT_DIM)
+ self.real_world = RealWorldInteraction()
+ self.architecture = ModularArchitecture()
+
+ # Register modules with the architecture
+ self.architecture.register_module("context_manager", self.context_manager)
+ self.architecture.register_module("knowledge_graph", self.knowledge_graph)
+ self.architecture.register_module("causal_engine", self.causal_engine)
+ self.architecture.register_module("cross_domain", self.cross_domain)
+ self.architecture.register_module("multimodal", self.multimodal)
+ self.architecture.register_module("adaptive_learning", self.adaptive_learning)
+ self.architecture.register_module("emotional", self.emotional)
+ self.architecture.register_module("collaborative", self.collaborative)
+ self.architecture.register_module("ethical", self.ethical)
+ self.architecture.register_module("resource_optimizer", self.resource_optimizer)
+ self.architecture.register_module("predictive", self.predictive)
+ self.architecture.register_module("memory", self.memory)
+ self.architecture.register_module("cognitive_style", self.cognitive_style)
+ self.architecture.register_module("uncertainty", self.uncertainty)
+ self.architecture.register_module("goal_alignment", self.goal_alignment)
+ self.architecture.register_module("creativity", self.creativity)
+ self.architecture.register_module("quantum", self.quantum)
+ self.architecture.register_module("neural_network", self.neural_network)
+ self.architecture.register_module("real_world", self.real_world)
+
+ # Define dependencies between modules
+ self.architecture.add_dependency("knowledge_graph", "context_manager")
+ self.architecture.add_dependency("causal_engine", "knowledge_graph")
+ self.architecture.add_dependency("multimodal", "neural_network")
+ self.architecture.add_dependency("emotional", "multimodal")
+ self.architecture.add_dependency("collaborative", "goal_alignment")
+ self.architecture.add_dependency("predictive", "neural_network")
+ self.architecture.add_dependency("predictive", "uncertainty")
+ self.architecture.add_dependency("memory", "context_manager")
+ self.architecture.add_dependency("goal_alignment", "ethical")
+
+ # Initialize modules
+ self.architecture.initialize_modules()
+
+ # Initialize training data path
+ self.training_data_path = training_data_path or "training_data.csv"
+ self.response_model = ResponseModel(TEXT_EMBEDDING_DIM, RESPONSE_MODEL_HIDDEN_DIM, RESPONSE_MODEL_OUTPUT_DIM)
+
+ if os.path.exists(self.training_data_path):
+ self.load_training_data(self.training_data_path)
+ self.train_response_model()
+
+ # Register event listeners
+ self.register_event_listeners()
-# response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-# return response
-
-# except Exception as e:
-# raise ValueError(f"Error generating response: {e}")
-
-# @app.post("/generate")
-# async def generate_text(input: ModelInput):
-# try:
-# response = generate_response(
-# model=model,
-# tokenizer=tokenizer,
-# instruction=input.prompt,
-# max_new_tokens=input.max_new_tokens
-# )
-# return {"generated_text": response}
+ def load_training_data(self, file_path: str):
+ try:
+ self.training_data = pd.read_csv(file_path)
+ except Exception as e:
+ print(f"Error reading CSV file: {e}")
+
+ def create_dataset(self, test_size=0.2, random_state=42):
+ prompts = self.training_data['prompt'].tolist()
+ responses = self.training_data['response'].tolist()
+
+ # Convert prompts and responses to numerical features
+ prompt_features = [self.numericalize_text(p) for p in prompts]
+ response_features = [self.numericalize_text(r) for r in responses]
+
+ # Pad sequences to the maximum sequence length
+ max_len = max(max(len(p) for p in prompt_features), max(len(r) for r in response_features))
+ prompt_features = [self.pad_sequence(p, max_len) for p in prompt_features]
+ response_features = [self.pad_sequence(r, max_len) for r in response_features]
+
+ # Split data into training and testing sets
+ X_train, X_test, y_train, y_test = train_test_split(
+ prompt_features, response_features, test_size=test_size, random_state=random_state
+ )
+
+ # Create TensorDatasets
+ train_dataset = TensorDataset(torch.tensor(X_train, dtype=torch.float32),
+ torch.tensor(y_train, dtype=torch.float32))
+ test_dataset = TensorDataset(torch.tensor(X_test, dtype=torch.float32),
+ torch.tensor(y_test, dtype=torch.float32))
+
+ return train_dataset, test_dataset
+
+ def numericalize_text(self, text: str) -> List[float]:
+ # Convert text to numerical features using a simple method (e.g., ASCII values)
+ return [float(ord(c)) for c in text]
+
+ def pad_sequence(self, sequence: List[float], max_len: int) -> List[float]:
+ # Pad sequences to the maximum length
+ if len(sequence) < max_len:
+ return sequence + [0.0] * (max_len - len(sequence))
+ return sequence[:max_len]
+
+ def train_response_model(self, batch_size=TRAINING_BATCH_SIZE, epochs=TRAINING_EPOCHS, learning_rate=TRAINING_LEARNING_RATE):
+ train_dataset, test_dataset = self.create_dataset()
+ train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+ test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
+ optimizer = torch.optim.Adam(self.response_model.parameters(), lr=learning_rate)
+ criterion = nn.MSELoss()
+
+ for epoch in range(epochs):
+ self.response_model.train()
+ train_loss = 0
+ for batch in tqdm(train_loader, desc=f"Epoch {epoch+1}/{epochs} [Training]"):
+ optimizer.zero_grad()
+ inputs, targets = batch
+ outputs = self.response_model(inputs)
+ loss = criterion(outputs, targets)
+ loss.backward()
+ optimizer.step()
+ train_loss += loss.item()
+
+ avg_train_loss = train_loss / len(train_loader)
+
+ self.response_model.eval()
+ test_loss = 0
+ with torch.no_grad():
+ for batch in tqdm(test_loader, desc=f"Epoch {epoch+1}/{epochs} [Testing]"):
+ inputs, targets = batch
+ outputs = self.response_model(inputs)
+ loss = criterion(outputs, targets)
+ test_loss += loss.item()
+
+ avg_test_loss = test_loss / len(test_loader)
+ print(f"Epoch {epoch+1}/{epochs}, Training Loss: {avg_train_loss:.4f}, Testing Loss: {avg_test_loss:.4f}")
+
+ print("Training complete.")
+
+ def process_input(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+ start_time = time.time()
+
+ self.context_manager.add_context('input', input_data, metadata={'source': 'user'})
+
+ multimodal_features = self.multimodal.process_multimodal_input(
+ input_data.get('visual'),
+ input_data.get('text'),
+ input_data.get('audio')
+ )
+
+ if multimodal_features.numel() > 0:
+ self.knowledge_graph.add_knowledge(
+ 'input_context',
+ {'features': multimodal_features.tolist()},
+ datetime.now()
+ )
+
+ emotional_context = self.emotional.analyze_emotional_context({
+ 'text_features': input_data.get('text_features'),
+ 'voice_features': input_data.get('voice_features')
+ })
+
+ if input_data.get('problem'):
+ solution = self.collaborative.solve_collaboratively(input_data['problem'])
+ self.memory.add_memory({'type': 'solution', 'content': solution}, priority=0.9)
+
+ style = self.cognitive_style.get_current_style_parameters()
+ predictions = self.predictive.make_prediction('response', {
+ 'input': input_data,
+ 'features': multimodal_features,
+ 'style': style
+ })
+
+ uncertainty = self.uncertainty.quantify_uncertainty(predictions)
+
+ is_ethical, violations = self.ethical.validate_action({
+ 'type': 'generate_response',
+ 'content': predictions
+ })
+
+ response_content = predictions.get('content', "Thinking...")
+ if not is_ethical:
+ response_content = f'Ethical considerations prevent this response. Violations: {violations}'
+
+ if input_data.get("lang") and input_data.get("prompt"):
+ creative_output = self.creativity.generate_creative_content(
+ input_data["prompt"], input_data["lang"], creativity_level=0.8
+ )
+ response_content += f" Creative output: {creative_output}"
+
+ if input_data.get("quantum_task"):
+ quantum_result = self.quantum.run_quantum_algorithm(input_data["quantum_task"], input_data.get("quantum_data", {}))
+ if quantum_result.get("status") == "success":
+ response_content += f" Quantum result: {quantum_result['result']}"
+
+ response = {
+ 'content': response_content,
+ 'emotional_state': emotional_context,
+ 'uncertainty': uncertainty,
+ 'style': style,
+ 'timestamp': datetime.now()
+ }
+
+ self.memory.add_memory({'type': 'response', 'content': response}, priority=0.7)
+
+ end_time = time.time()
+ processing_time = end_time - start_time
+ self.resource_optimizer.optimize_resource_usage('processing_time', processing_time)
+
+ if input_data.get('feedback'):
+ self.adaptive_learning.process_feedback(input_data['feedback'])
+ self.neural_network.adapt_architecture(input_data['feedback'])
+
+ # Interacting with user feedback for continuous learning
+ if input_data.get('user_feedback'):
+ self.process_user_feedback(input_data['user_feedback'], multimodal_features)
+
+ return response
+
+ def process_user_feedback(self, feedback_text: str, multimodal_features: torch.Tensor):
+ # Use multimodal features along with feedback text for training
+ feedback_features = self.multimodal.process_multimodal_input(text_input=torch.tensor([feedback_text]))
+ combined_features = torch.cat((multimodal_features, feedback_features), dim=1)
+
+ # Train the response model with the combined features
+ self.train_response_model_with_feedback(combined_features)
+
+ def train_response_model_with_feedback(self, features: torch.Tensor):
+ self.response_model.train()
+ optimizer = torch.optim.Adam(self.response_model.parameters())
+ criterion = nn.MSELoss()
+
+ # Feedback training loop
+ for _ in range(3):
+ optimizer.zero_grad()
+ outputs = self.response_model(features)
+ target = self.derive_target_from_feedback(features)
+ loss = criterion(outputs, target)
+ loss.backward()
+ optimizer.step()
+
+ def derive_target_from_feedback(self, features: torch.Tensor) -> torch.Tensor:
+ # Placeholder for deriving a target from feedback
+ return features * 0.9
+
+ def shutdown(self):
+ print("Shutting down Enhanced Cognitive System...")
+ if hasattr(self.quantum, 'shutdown') and callable(self.quantum.shutdown):
+ self.quantum.shutdown()
+
+ def register_event_listeners(self):
+ self.context_manager.register_event_listener('context_added', self.on_context_added)
+ self.knowledge_graph.register_event_listener('knowledge_added', self.on_knowledge_added)
+ self.causal_engine.register_event_listener('relationship_added', self.on_relationship_added)
+ self.causal_engine.register_event_listener('relationship_removed', self.on_relationship_removed)
+ self.multimodal.register_event_listener('fusion_completed', self.on_fusion_completed)
+ self.adaptive_learning.register_event_listener('learning_rate_adjusted', self.on_learning_rate_adjusted)
+ self.emotional.register_event_listener('text_emotion_analyzed', self.on_emotion_analyzed)
+ self.collaborative.register_event_listener('consensus_reached', self.on_consensus_reached)
+ self.ethical.register_event_listener('principle_violated', self.on_principle_violated)
+ self.resource_optimizer. register_event_listener('resource_optimized', self.on_resource_optimized)
+ self.predictive.register_event_listener('prediction_made', self.on_prediction_made)
+ self.memory.register_event_listener('memory_added', self.on_memory_added)
+ self.memory.register_event_listener('memory_removed', self.on_memory_removed)
+ self.cognitive_style.register_event_listener('style_activated', self.on_style_activated)
+ self.uncertainty.register_event_listener('uncertainty_quantified', self.on_uncertainty_quantified)
+ self.goal_alignment.register_event_listener('goal_added', self.on_goal_added)
+ self.goal_alignment.register_event_listener('safety_constraint_added', self.on_safety_constraint_added)
+ self.creativity.register_event_listener('creative_content_generated', self.on_creative_content_generated)
+ self.quantum.register_event_listener('quantum_algorithm_executed', self.on_quantum_algorithm_executed)
+ self.neural_network.register_event_listener('layer_added', self.on_layer_added)
+ self.neural_network.register_event_listener('layer_removed', self.on_layer_removed)
+ self.real_world.register_event_listener('sensor_data_processed', self.on_sensor_data_processed)
+ self.real_world.register_event_listener('action_executed', self.on_action_executed)
+ self.architecture.register_event_listener('module_initialized', self.on_module_initialized)
+ self.architecture.register_event_listener('module_initialization_error', self.on_module_initialization_error)
+
+ # Event Handlers
+ def on_context_added(self, event_data: Dict[str, Any]):
+ print(f"Context added: {event_data['context_id']}")
+
+ def on_knowledge_added(self, event_data: Dict[str, Any]):
+ print(f"Knowledge added: {event_data['concept']}")
+
+ def on_relationship_added(self, event_data: Dict[str, Any]):
+ print(f"Causal relationship added between {event_data['cause']} and {event_data['effect']} with strength {event_data['strength']}")
+
+ def on_relationship_removed(self, event_data: Dict[str, Any]):
+ print(f"Causal relationship removed between {event_data['cause']} and {event_data['effect']}")
+
+ def on_fusion_completed(self, event_data: Dict[str, Any]):
+ print("Multimodal fusion completed.")
+
+ def on_learning_rate_adjusted(self, event_data: Dict[str, Any]):
+ print(f"Learning rate adjusted from {event_data['old_learning_rate']} to {event_data['new_learning_rate']}")
+
+ def on_emotion_analyzed(self, event_data: Dict[str, Any]):
+ print(f"Emotion analyzed: {event_data}")
+
+ def on_consensus_reached(self, event_data: Dict[str, Any]):
+ print(f"Consensus reached: {event_data['consensus_solution']}")
+
+ def on_principle_violated(self, event_data: Dict[str, Any]):
+ print(f"Ethical principle violated: {event_data['principle']} in action {event_data['action']}")
+
+ def on_resource_optimized(self, event_data: Dict[str, Any]):
+ print(f"Resource optimized: {event_data['type']} from {event_data['original_usage']} to {event_data['optimized_usage']}")
+
+ def on_prediction_made(self, event_data: Dict[str, Any]):
+ print(f"Prediction made by model {event_data['model']}: {event_data['prediction']}")
+
+ def on_memory_added(self, event_data: Dict[str, Any]):
+ print(f"Memory added: {event_data['type']} - {event_data['memory']}")
+
+ def on_memory_removed(self, event_data: Dict[str, Any]):
+ print(f"Memory removed: {event_data['type']} - {event_data['key']}")
+
+ def on_style_activated(self, event_data: Dict[str, Any]):
+ print(f"Cognitive style activated: {event_data['style_name']}")
+
+ def on_uncertainty_quantified(self, event_data: Dict[str, Any]):
+ print(f"Uncertainty quantified: {event_data['method']} - {event_data['result']}")
+
+ def on_goal_added(self, event_data: Dict[str, Any]):
+ print(f"Goal added: {event_data['goal_id']}")
+
+ def on_safety_constraint_added(self, event_data: Dict[str, Any]):
+ print(f"Safety constraint added: {event_data['constraint']} - {event_data['details']}")
+
+ def on_creative_content_generated(self, event_data: Dict[str, Any]):
+ print(f"Creative content generated for language {event_data['language']} with creativity level {event_data['creativity_level']}")
+
+ def on_quantum_algorithm_executed(self, event_data: Dict[str, Any]):
+ print(f"Quantum algorithm executed: {event_data['algorithm']} - Result: {event_data['result']}")
+
+ def on_layer_added(self, event_data: Dict[str, Any]):
+ print(f"Neural network layer added: {event_data['new_dim']}")
+
+ def on_layer_removed(self, event_data: Dict[str, Any]):
+ print("Neural network layer removed.")
-# except Exception as e:
-# raise HTTPException(status_code=500, detail=str(e))
+ def on_layer_expanded(self, event_data: Dict[str, Any]):
+ print(f"Neural network layer expanded to: {event_data['new_size']}")
-# @app.get("/")
-# async def root():
-# return {"message": "Welcome to the Model API!"}
+ def on_sensor_data_processed(self, event_data: Dict[str, Any]):
+ print(f"Sensor data processed: {event_data['sensor']} - Data: {event_data['data']}")
+ def on_action_executed(self, event_data: Dict[str, Any]):
+ print(f"Action executed on actuator {event_data['actuator']}: Success - {event_data['success']}")
+ def on_module_initialized(self, event_data: Dict[str, Any]):
+ print(f"Module initialized: {event_data['module_name']}")
+ def on_module_initialization_error(self, event_data: Dict[str, Any]):
+ print(f"Module initialization error: {event_data['module_name']} - Error: {event_data['error']}")
+class ResponseModel(nn.Module):
+ def __init__(self, input_dim, hidden_dim, output_dim):
+ super(ResponseModel, self).__init__()
+ self.layer1 = nn.Linear(input_dim, hidden_dim)
+ self.relu = nn.ReLU()
+ self.layer2 = nn.Linear(hidden_dim, output_dim)
+ def forward(self, x):
+ x = self.layer1(x)
+ x = self.relu(x)
+ x = self.layer2(x)
+ return x
-# //////////////////////////////////////////
+class ChatDataset(Dataset):
+ def __init__(self, prompts, responses, multimodal_module):
+ self.prompts = prompts
+ self.responses = responses
+ self.multimodal_module = multimodal_module
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-from huggingface_hub import snapshot_download
-from safetensors.torch import load_file
+ def __len__(self):
+ return len(self.prompts)
+
+ def __getitem__(self, idx):
+ prompt = self.prompts[idx]
+ response = self.responses[idx]
+ prompt_features = self.multimodal_module.process_multimodal_input(text_input=torch.tensor([prompt]))
+ response_features = self.multimodal_module.process_multimodal_input(text_input=torch.tensor([response]))
+ return {'input': prompt_features, 'target': response_features}
+
+def sample_language_model(prompt):
+ generated_texts = [
+ f"Evolving narrative for: {prompt}",
+ f"Dynamic story unfolding: {prompt}",
+ f"Adaptive tale inspired by: {prompt}",
+ f"Continuously developing story: {prompt}",
+ f"Ever-changing narrative: {prompt}"
+ ]
+ return random.choice(generated_texts)
+
+def sample_creativity_engine(base_content, creativity_level):
+ enhanced_content = [
+ f"Imaginatively enhanced ({creativity_level}): {base_content}",
+ f"Creatively amplified ({creativity_level}): {base_content}",
+ f"Innovatively transformed ({creativity_level}): {base_content}",
+ f"Artistically reimagined ({creativity_level}): {base_content}",
+ f"Uniquely adapted ({creativity_level}): {base_content}"
+ ]
+ return random.choice(enhanced_content)
class ModelInput(BaseModel):
prompt: str
@@ -184,3 +2162,80 @@ async def generate_text(input: ModelInput):
async def root():
return {"message": "Welcome to the Model API!"}
+def main():
+ system = EnhancedCognitiveSystem()
+
+ system.cognitive_style.register_style(ThinkingStyle.ANALYTICAL.value, {"logic": 0.9, "creativity": 0.2})
+ system.cognitive_style.register_style(ThinkingStyle.CREATIVE.value, {"logic": 0.3, "creativity": 0.9})
+ system.cognitive_style.register_style(ThinkingStyle.CRITICAL.value, {"logic": 0.8, "analysis": 0.9})
+ system.cognitive_style.register_style(ThinkingStyle.SYSTEMATIC.value, {"organization": 0.9, "detail": 0.8})
+ system.cognitive_style.register_style(ThinkingStyle.LATERAL.value, {"innovation": 0.9, "flexibility": 0.8})
+ system.cognitive_style.register_style(ThinkingStyle.INTUITIVE.value, {"instinct": 0.9, "emotion": 0.7})
+ system.cognitive_style.register_style(ThinkingStyle.COLLABORATIVE.value, {"teamwork": 0.9, "communication": 0.8})
+ system.cognitive_style.register_style(ThinkingStyle.ETHICAL.value, {"morality": 0.9, "principles": 0.9})
+ system.cognitive_style.register_style(ThinkingStyle.PRAGMATIC.value, {"practicality": 0.9, "efficiency": 0.8})
+ system.cognitive_style.register_style(ThinkingStyle.INNOVATIVE.value, {"originality": 0.9, "invention": 0.8})
+ system.cognitive_style.register_style(ThinkingStyle.REFLECTIVE.value, {"introspection": 0.9, "thoughtfulness": 0.8})
+ system.cognitive_style.register_style(ThinkingStyle.EXPLORATORY.value, {"curiosity": 0.9, "discovery": 0.8})
+ system.cognitive_style.register_style(ThinkingStyle.STRATEGIC.value, {"planning": 0.9, "foresight": 0.8})
+ system.cognitive_style.register_style(ThinkingStyle.ABSTRACT.value, {"conceptualization": 0.9, "theory": 0.8})
+ system.cognitive_style.register_style(ThinkingStyle.CONCRETE.value, {"tangibility": 0.9, "reality": 0.8})
+ system.cognitive_style.register_style(ThinkingStyle.EMPATHETIC.value, {"understanding": 0.9, "compassion": 0.8})
+ system.cognitive_style.register_style(ThinkingStyle.HOLISTIC.value, {"integration": 0.9, "synthesis": 0.8})
+ system.cognitive_style.register_style(ThinkingStyle.DIVERGENT.value, {"breadth": 0.9, "exploration": 0.8})
+ system.cognitive_style.register_style(ThinkingStyle.CONVERGENT.value, {"focus": 0.9, "solution-oriented": 0.8})
+ system.cognitive_style.register_style(ThinkingStyle.ADAPTIVE.value, {"flexibility": 0.9, "responsiveness": 0.8})
+
+ system.cognitive_style.activate_style(ThinkingStyle.ANALYTICAL.value)
+
+ system.ethical.add_principle("non_maleficence", lambda x: "harm" not in x.get("content", "").lower())
+ system.ethical.add_principle("beneficence", lambda x: "help" in x.get("content", "").lower())
+ system.ethical.add_principle("user_autonomy", lambda x: x.get("user_approved", True))
+ system.ethical.add_principle("avoid_deception", lambda x: not x.get("deceptive", False))
+ system.ethical.add_principle("ensure_privacy", lambda x: not x.get("privacy_risk", False))
+ system.ethical.add_principle("promote_fairness", lambda x: not x.get("unfair", False))
+
+ system.goal_alignment.add_goal({"objective": "solve problems", "priority": 0.8, "user_approved": True})
+ system.goal_alignment.add_goal({"objective": "learn and adapt", "priority": 0.9, "user_approved": True})
+ system.goal_alignment.add_goal({"objective": "assist users", "priority": 0.85, "user_approved": True})
+ system.goal_alignment.add_goal({"objective": "ensure safety", "priority": 0.95, "user_approved": True})
+ system.goal_alignment.add_goal({"objective": "promote well-being", "priority": 0.8, "user_approved": True})
+ system.goal_alignment.add_goal({"objective": "foster creativity", "priority": 0.7, "user_approved": True})
+ system.goal_alignment.add_goal({"objective": "advance knowledge", "priority": 0.75, "user_approved": True})
+ system.goal_alignment.add_goal({"objective": "improve efficiency", "priority": 0.6, "user_approved": True})
+
+ system.creativity.add_language_model("english", sample_language_model)
+ system.creativity.add_creativity_engine("english", sample_creativity_engine)
+
+ system.quantum.initialize_quantum_circuit(num_qubits=8)
+ system.quantum.add_gate("Hadamard", [0, 1, 2, 3, 4, 5, 6, 7])
+ system.quantum.add_gate("CNOT", [0, 1])
+ system.quantum.add_gate("CNOT", [2, 3])
+ system.quantum.add_gate("CNOT", [4, 5])
+ system.quantum.add_gate("CNOT", [6, 7])
+
+ input_data = {
+ 'text': 'What is the meaning of life?',
+ 'text_features': torch.randn(1, 1024),
+ 'visual': torch.randn(1, 3, 64, 64),
+ 'audio': torch.randn(1, 128),
+ 'problem': {'type': 'optimization', 'parameters': [1, 2, 3]},
+ 'feedback': {'loss': 0.4, 'accuracy': 0.9},
+ 'lang': 'english',
+ 'prompt': 'Tell me a story',
+ 'quantum_task': "Shor's",
+ 'quantum_data': {"number": 15},
+ 'user_feedback': "That was helpful, thanks!"
+ }
+
+ response = system.process_input(input_data)
+ print(f"Response: {response}")
+
+ input_data_2 = {'text': 'How are you feeling?', 'user_feedback': "I have some feedback for you."}
+ response_2 = system.process_input(input_data_2)
+ print(f"Response 2: {response_2}")
+
+ system.shutdown()
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file