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