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src/aegis.py

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+import json
+import hashlib
+import threading
+import logging
+import sqlite3
+from datetime import datetime, timedelta
+from abc import ABC, abstractmethod
+from collections import defaultdict
+from typing import Any, Dict, Optional, List, Tuple
+import concurrent.futures
+import networkx as nx
+import plotly.graph_objects as go
+import pandas as pd
+import numpy as np
+import requests
+from transformers import pipeline, AutoTokenizer, AutoModel
+import torch
+import psutil
+import argparse
+from flask import Flask, request, render_template, send_file
+import os
+import importlib.util
+import syft as sy
+from copy import deepcopy
+
+# Suppress TensorFlow logs to avoid CUDA factory conflicts
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+# Setup logging with configurable level
+def setup_logging(log_level: str = "INFO"):
+    logging.basicConfig(
+        level=getattr(logging, log_level.upper(), logging.INFO),
+        format='%(asctime)s - %(levelname)s - %(threadName)s - %(message)s',
+        handlers=[logging.FileHandler('aegis_council.log'), logging.StreamHandler()]
+    )
+
+# === CONFIGURATION LOADER ===
+def load_config(config_path: str = "config.json") -> Dict[str, Any]:
+    default_config = {
+        "meta_judge_weights": {"influence": 0.5, "reliability": 0.3, "severity": 0.2},
+        "temporal_decay_thresholds": {"stable": 0.3, "volatile": 0.7},
+        "virtue_weights": {
+            "compassion": [0.7, 0.3, -0.1],
+            "integrity": [0.4, -0.6, 0.2],
+            "courage": [0.1, 0.5, 0.4],
+            "wisdom": [0.3, -0.7, 0.2]
+        },
+        "memory_decay_days": 30,
+        "memory_max_entries": 10000,
+        "log_level": "INFO",
+        "federated_learning": {"num_clients": 2, "aggregation_rounds": 1}
+    }
+    try:
+        if os.path.exists(config_path):
+            with open(config_path, 'r') as f:
+                config = json.load(f)
+            for key in default_config:
+                if key not in config:
+                    config[key] = default_config[key]
+                elif isinstance(default_config[key], dict):
+                    config[key].update({k: v for k, v in default_config[key].items() if k not in config[key]})
+            return config
+        logging.warning(f"Config file {config_path} not found, using defaults")
+        return default_config
+    except Exception as e:
+        logging.error(f"Error loading config: {e}")
+        return default_config
+
+# === BLOCKCHAIN FOR AUDITABILITY ===
+class Blockchain:
+    def __init__(self):
+        self.chain = [{"index": 0, "timestamp": datetime.now().isoformat(), "data": "Genesis Block", "prev_hash": "0"}]
+        self.logger = logging.getLogger('Blockchain')
+
+    def add_block(self, data: Dict[str, Any]) -> None:
+        try:
+            prev_block = self.chain[-1]
+            block = {
+                "index": len(self.chain),
+                "timestamp": datetime.now().isoformat(),
+                "data": json.dumps(data),
+                "prev_hash": self._hash_block(prev_block)
+            }
+            block["hash"] = self._hash_block(block)
+            self.chain.append(block)
+            self.logger.info(f"Added block {block['index']} with hash {block['hash']}")
+        except Exception as e:
+            self.logger.error(f"Error adding block: {e}")
+
+    def _hash_block(self, block: Dict[str, Any]) -> str:
+        try:
+            block_str = json.dumps(block, sort_keys=True)
+            return hashlib.sha256(block_str.encode()).hexdigest()
+        except Exception as e:
+            self.logger.error(f"Error hashing block: {e}")
+            return ""
+
+    def verify(self) -> bool:
+        try:
+            for i in range(1, len(self.chain)):
+                current = self.chain[i]
+                prev = self.chain[i-1]
+                if current["prev_hash"] != self._hash_block(prev):
+                    self.logger.error(f"Blockchain verification failed at block {i}")
+                    return False
+            self.logger.info("Blockchain verified successfully")
+            return True
+        except Exception as e:
+            self.logger.error(f"Error verifying blockchain: {e}")
+            return False
+
+# === MEMORY AND SIGNAL LAYERS ===
+class NexusMemory:
+    def __init__(self, max_entries: int = 10000, decay_days: int = 30, db_path: str = "nexus_memory.db"):
+        self.store = defaultdict(dict)
+        self.max_entries = max_entries
+        self.decay_days = decay_days
+        self.lock = threading.Lock()
+        self.logger = logging.getLogger('NexusMemory')
+        self.conn = sqlite3.connect(db_path, check_same_thread=False)
+        self.blockchain = Blockchain()
+        self.conn.execute("""
+            CREATE TABLE IF NOT EXISTS memory (
+                key TEXT PRIMARY KEY,
+                value TEXT,
+                timestamp TEXT,
+                emotion_weight FLOAT
+            )
+        """)
+        self.conn.commit()
+        self._load_from_db()
+
+    def _load_from_db(self):
+        try:
+            cursor = self.conn.cursor()
+            cursor.execute("SELECT key, value, timestamp, emotion_weight FROM memory")
+            for key, value, timestamp, emotion_weight in cursor.fetchall():
+                self.store[key] = {
+                    "value": json.loads(value),
+                    "timestamp": datetime.fromisoformat(timestamp),
+                    "emotion_weight": emotion_weight
+                }
+            self.logger.info(f"Loaded {len(self.store)} entries from database")
+        except Exception as e:
+            self.logger.error(f"Error loading from database: {e}")
+
+    def write(self, key: str, value: Any, emotion_weight: float = 0.5) -> Optional[str]:
+        try:
+            if not isinstance(key, str) or not (0 <= emotion_weight <= 1):
+                self.logger.error(f"Invalid key type {type(key)} or emotion_weight {emotion_weight}")
+                return None
+            hashed = hashlib.md5(key.encode()).hexdigest()
+            timestamp = datetime.now()
+            with self.lock:
+                if len(self.store) >= self.max_entries:
+                    oldest = min(self.store.items(), key=lambda x: x[1].get('timestamp', timestamp))[0]
+                    self.logger.info(f"Removing oldest entry: {oldest}")
+                    self.conn.execute("DELETE FROM memory WHERE key = ?", (oldest,))
+                    del self.store[oldest]
+                self.store[hashed] = {
+                    "value": value,
+                    "timestamp": timestamp,
+                    "emotion_weight": emotion_weight
+                }
+                self.conn.execute(
+                    "INSERT OR REPLACE INTO memory (key, value, timestamp, emotion_weight) VALUES (?, ?, ?, ?)",
+                    (hashed, json.dumps(value), timestamp.isoformat(), emotion_weight)
+                )
+                self.conn.commit()
+                self.blockchain.add_block({"key": hashed, "value": value, "timestamp": timestamp.isoformat()})
+                self.logger.debug(f"Wrote key: {hashed}, value: {value}")
+                return hashed
+        except Exception as e:
+            self.logger.error(f"Error writing to memory: {e}")
+            return None
+
+    def read(self, key: str) -> Optional[Any]:
+        try:
+            hashed = hashlib.md5(key.encode()).hexdigest()
+            with self.lock:
+                entry = self.store.get(hashed)
+                if not entry:
+                    cursor = self.conn.cursor()
+                    cursor.execute("SELECT value, timestamp, emotion_weight FROM memory WHERE key = ?", (hashed,))
+                    row = cursor.fetchone()
+                    if not row:
+                        self.logger.debug(f"Key not found: {hashed}")
+                        return None
+                    entry = {
+                        "value": json.loads(row[0]),
+                        "timestamp": datetime.fromisoformat(row[1]),
+                        "emotion_weight": row[2]
+                    }
+                    self.store[hashed] = entry
+                if self._is_decayed(entry["timestamp"], entry.get("emotion_weight", 0.5)):
+                    self.logger.info(f"Removing decayed entry: {hashed}")
+                    self.conn.execute("DELETE FROM memory WHERE key = ?", (hashed,))
+                    self.conn.commit()
+                    del self.store[hashed]
+                    return None
+                self.logger.debug(f"Read key: {hashed}, value: {entry['value']}")
+                return entry["value"]
+        except Exception as e:
+            self.logger.error(f"Error reading from memory: {e}")
+            return None
+
+    def _is_decayed(self, timestamp: datetime, emotion_weight: float) -> bool:
+        try:
+            age = (datetime.now() - timestamp).total_seconds() / (24 * 3600)
+            decay_factor = np.exp(-age / (self.decay_days * (1.5 - emotion_weight)))
+            return decay_factor < 0.1
+        except Exception as e:
+            self.logger.error(f"Error checking decay: {e}")
+            return True
+
+    def audit(self) -> Dict[str, Any]:
+        try:
+            with self.lock:
+                audit_data = {
+                    k: {
+                        "timestamp": v["timestamp"],
+                        "emotion_weight": v["emotion_weight"],
+                        "decayed": self._is_decayed(v["timestamp"], v["emotion_weight"])
+                    }
+                    for k, v in self.store.items()
+                }
+                self.blockchain.add_block({"audit": audit_data})
+                return audit_data
+        except Exception as e:
+            self.logger.error(f"Error auditing memory: {e}")
+            return {}
+
+# === DATA FETCHER FOR REAL-TIME DATA ===
+class DataFetcher:
+    def __init__(self):
+        self.logger = logging.getLogger('DataFetcher')
+
+    def fetch_x_posts(self, query: str) -> List[Dict[str, Any]]:
+        try:
+            mock_response = [
+                {"content": f"Sample post about {query}: We value truth and empathy.", "timestamp": datetime.now().isoformat()}
+            ]
+            self.logger.info(f"Fetched {len(mock_response)} posts for query: {query}")
+            return mock_response
+        except Exception as e:
+            self.logger.error(f"Error fetching posts: {e}")
+            return []
+
+# === PERFORMANCE MONITOR ===
+def monitor_performance() -> Dict[str, float]:
+    try:
+        return {
+            "cpu_percent": psutil.cpu_percent(interval=1),
+            "memory_percent": psutil.virtual_memory().percent,
+            "process_memory_mb": psutil.Process().memory_info().rss / 1024 / 1024
+        }
+    except Exception as e:
+        logging.error(f"Error monitoring performance: {e}")
+        return {"cpu_percent": 0.0, "memory_percent": 0.0, "process_memory_mb": 0.0}
+
+# === FEDERATED LEARNING ===
+class FederatedTrainer:
+    def __init__(self, num_clients: int):
+        self.num_clients = num_clients
+        self.logger = logging.getLogger('FederatedTrainer')
+        self.clients = [sy.VirtualWorker(sy.torch.hook.TorchHook(torch), id=f"client_{i}") for i in range(num_clients)]
+
+    def train(self, weights: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
+        try:
+            client_updates = []
+            for client in self.clients:
+                client_weights = deepcopy(weights)
+                for virtue in client_weights:
+                    client_weights[virtue] += np.random.normal(0, 0.01, size=client_weights[virtue].shape)
+                client_updates.append(client_weights)
+            aggregated = {}
+            for virtue in weights:
+                aggregated[virtue] = np.mean([update[virtue] for update in client_updates], axis=0)
+            self.logger.info("Federated training completed")
+            return aggregated
+        except Exception as e:
+            self.logger.error(f"Error in federated training: {e}")
+            return weights
+
+# === QUANTUM-INSPIRED OPTIMIZATION ===
+def anneal_layout(graph: nx.DiGraph, iterations: int = 1000, temp: float = 10.0) -> Dict[str, Tuple[float, float]]:
+    try:
+        nodes = list(graph.nodes())
+        pos = {node: (np.random.uniform(-1, 1), np.random.uniform(-1, 1)) for node in nodes}
+        temp_min = 0.01
+        alpha = 0.99
+
+        def energy(positions):
+            e = 0
+            for u, v in graph.edges():
+                d = np.linalg.norm(np.array(positions[u]) - np.array(positions[v]))
+                e += 1 / (d + 1e-6)
+            for u in nodes:
+                for v in nodes:
+                    if u != v:
+                        d = np.linalg.norm(np.array(positions[u]) - np.array(positions[v]))
+                        e += d
+            return e
+
+        best_pos = pos
+        best_energy = energy(pos)
+        for _ in range(iterations):
+            new_pos = deepcopy(pos)
+            for node in nodes:
+                new_pos[node] = (new_pos[node][0] + np.random.uniform(-0.1, 0.1) * temp,
+                                 new_pos[node][1] + np.random.uniform(-0.1, 0.1) * temp)
+            new_energy = energy(new_pos)
+            if new_energy < best_energy or np.random.random() < np.exp((best_energy - new_energy) / temp):
+                pos = new_pos
+                best_energy = new_energy
+                best_pos = pos
+            temp *= alpha
+            if temp < temp_min:
+                break
+        return best_pos
+    except Exception as e:
+        logging.error(f"Error in anneal_layout: {e}")
+        return nx.spring_layout(graph)
+
+# === BASE AGENT INTERFACE ===
+class AegisAgent(ABC):
+    def __init__(self, name: str, memory: NexusMemory):
+        self.name = name
+        self.memory = memory
+        self.result: Dict[str, Any] = {}
+        self.explanation: str = ""
+        self.influence: Dict[str, float] = {}
+        self.logger = logging.getLogger(f'AegisAgent.{name}')
+
+    @abstractmethod
+    def analyze(self, input_data: Dict[str, Any]) -> None:
+        pass
+
+    @abstractmethod
+    def report(self) -> Dict[str, Any]:
+        pass
+
+    def collaborate(self, message: Dict[str, Any], target_agent: str) -> None:
+        try:
+            mem_key = f"collab_{self.name}_{target_agent}_{datetime.now().isoformat()}"
+            self.memory.write(mem_key, message, emotion_weight=0.7)
+            self.logger.debug(f"Sent collaboration message to {target_agent}: {message}")
+        except Exception as e:
+            self.logger.error(f"Error in collaboration: {e}")
+
+# === AGENT COUNCIL CORE ===
+class AegisCouncil:
+    def __init__(self, config: Dict[str, Any]):
+        self.memory = NexusMemory(max_entries=config["memory_max_entries"], decay_days=config["memory_decay_days"])
+        self.agents: List[AegisAgent] = []
+        self.reports: Dict[str, Dict[str, Any]] = {}
+        self.graph = nx.DiGraph()
+        self.logger = logging.getLogger('AegisCouncil')
+        self.fetcher = DataFetcher()
+        self.config = config
+        self.federated_trainer = FederatedTrainer(config["federated_learning"]["num_clients"])
+
+    def register_agent(self, agent: AegisAgent) -> None:
+        try:
+            self.agents.append(agent)
+            self.logger.info(f"Registered agent: {agent.name}")
+        except Exception as e:
+            self.logger.error(f"Error registering agent: {e}")
+
+    def register_dynamic_agent(self, module_path: str, class_name: str) -> None:
+        try:
+            spec = importlib.util.spec_from_file_location("custom_agent", module_path)
+            module = importlib.util.module_from_spec(spec)
+            spec.loader.exec_module(module)
+            agent_class = getattr(module, class_name)
+            if not issubclass(agent_class, AegisAgent):
+                raise ValueError(f"{class_name} is not a subclass of AegisAgent")
+            agent = agent_class(f"Dynamic_{class_name}", self.memory)
+            self.register_agent(agent)
+            self.logger.info(f"Dynamically registered agent: {agent.name}")
+        except Exception as e:
+            self.logger.error(f"Error registering dynamic agent {class_name}: {e}")
+
+    def dispatch(self, input_data: Dict[str, Any], max_retries: int = 3) -> bool:
+        try:
+            if not isinstance(input_data, dict):
+                self.logger.error("Input data must be a dictionary")
+                return False
+            if "text" not in input_data or "overrides" not in input_data:
+                self.logger.warning("Input data missing 'text' or 'overrides' keys")
+
+            self.reports.clear()
+            self.graph.clear()
+            performance = monitor_performance()
+            self.logger.info(f"Dispatch started. Performance: {performance}")
+
+            for attempt in range(max_retries):
+                try:
+                    with concurrent.futures.ThreadPoolExecutor(max_workers=len(self.agents)) as executor:
+                        future_to_agent = {executor.submit(agent.analyze, input_data): agent for agent in self.agents}
+                        for future in concurrent.futures.as_completed(future_to_agent):
+                            agent = future_to_agent[future]
+                            try:
+                                future.result()
+                            except Exception as e:
+                                self.logger.error(f"Attempt {attempt + 1}: Error in agent {agent.name}: {e}")
+                                self.reports[agent.name] = {"error": str(e), "explanation": "Agent failed to process"}
+
+                    for agent in self.agents:
+                        if agent.name in self.reports and "error" not in self.reports[agent.name]["result"]:
+                            for target in self.agents:
+                                if target.name != agent.name:
+                                    agent.collabora
+te(agent.result, target.name)
+
+                    with concurrent.futures.ThreadPoolExecutor(max_workers=len(self.agents)) as executor:
+                        future_to_agent = {
+                            executor.submit(self._reanalyze_with_collaboration, agent, input_data): agent
+                            for agent in self.agents
+                        }
+                        for future in concurrent.futures.as_completed(future_to_agent):
+                            agent = future_to_agent[future]
+                            try:
+                                future.result()
+                                self.reports[agent.name] = agent.report()
+                                self.graph.add_node(agent.name, explanation=agent.explanation)
+                                for target, weight in agent.influence.items():
+                                    self.graph.add_edge(agent.name, target, weight=round(weight, 2))
+                            except Exception as e:
+                                self.logger.error(f"Attempt {attempt + 1}: Error in agent {agent.name} final analysis: {e}")
+                                self.reports[agent.name] = {"error": str(e), "explanation": "Agent failed to process"}
+
+                    consensus_result = self._compute_consensus()
+                    self.reports["Consensus"] = {
+                        "result": consensus_result,
+                        "explanation": "Consensus computed from agent outputs weighted by MetaJudgeAgent scores."
+                    }
+                    self.memory.blockchain.add_block(self.reports)
+                    performance = monitor_performance()
+                    self.logger.info(f"Dispatch completed successfully. Performance: {performance}")
+                    return True
+                except Exception as e:
+                    self.logger.warning(f"Retry {attempt + 1} after error: {e}")
+            self.logger.error(f"Dispatch failed after {max_retries} retries")
+            return False
+        except Exception as e:
+            self.logger.error(f"Error in dispatch: {e}")
+            return False
+
+    def _reanalyze_with_collaboration(self, agent: AegisAgent, input_data: Dict[str, Any]) -> None:
+        try:
+            collab_data = []
+            for source in self.agents:
+                if source.name != agent.name:
+                    mem_key = f"collab_{source.name}_{agent.name}"
+                    collab = self.memory.read(mem_key + "_" + datetime.now().isoformat())
+                    if collab:
+                        collab_data.append((source.name, collab))
+            if collab_data:
+                agent.explanation += f" Incorporated collaboration data from: {[x[0] for x in collab_data]}."
+            agent.analyze(input_data)
+        except Exception as e:
+            self.logger.error(f"Error in collaboration reanalysis for {agent.name}: {e}")
+
+    def _compute_consensus(self) -> Dict[str, Any]:
+        try:
+            meta_scores = self.reports.get("MetaJudgeAgent", {}).get("result", {}).get("scores", [])
+            virtue_profiles = [
+                self.reports[agent]["result"].get("virtue_profile", {})
+                for agent in self.reports if agent != "Consensus" and "virtue_profile" in self.reports[agent]["result"]
+            ]
+            if not virtue_profiles or not meta_scores:
+                return {"error": "Insufficient data for consensus"}
+
+            weights = {agent: score for agent, score in meta_scores}
+            default_weight = 0.5 / len(self.agents)
+            combined_profile = {}
+            for virtue in ["compassion", "integrity", "courage", "wisdom"]:
+                weighted_sum = 0
+                total_weight = 0
+                for profile in virtue_profiles:
+                    if virtue in profile:
+                        agent_name = next(
+                            (agent for agent in self.reports if self.reports[agent]["result"].get("virtue_profile") == profile),
+                            None
+                        )
+                        weight = weights.get(agent_name, default_weight)
+                        weighted_sum += profile[virtue] * weight
+                        total_weight += weight
+                combined_profile[virtue] = round(weighted_sum / total_weight, 2) if total_weight > 0 else 0.0
+            return {"combined_virtue_profile": combined_profile}
+        except Exception as e:
+            self.logger.error(f"Error computing consensus: {e}")
+            return {"error": str(e)}
+
+    def dispatch_realtime(self, query: str) -> bool:
+        try:
+            posts = self.fetcher.fetch_x_posts(query)
+            if not posts:
+                self.logger.error("No posts fetched for query")
+                return False
+            input_data = {
+                "text": posts[0]["content"],
+                "overrides": {
+                    "EthosiaAgent": {"influence": 0.5, "reliability": 0.5, "severity": 0.5},
+                    "AegisCore": {"influence": 0.5, "reliability": 0.5, "severity": 0.5}
+                }
+            }
+            return self.dispatch(input_data)
+        except Exception as e:
+            self.logger.error(f"Error in real-time dispatch: {e}")
+            return False
+
+    def get_reports(self) -> Dict[str, Dict[str, Any]]:
+        return self.reports
+
+    def draw_explainability_graph(self, filename: str = "explainability_graph.html") -> None:
+        try:
+            pos = anneal_layout(self.graph)
+            edge_x, edge_y = [], []
+            for edge in self.graph.edges(data=True):
+                x0, y0 = pos[edge[0]]
+                x1, y1 = pos[edge[1]]
+                edge_x.extend([x0, x1, None])
+                edge_y.extend([y0, y1, None])
+
+            edge_trace = go.Scatter(
+                x=edge_x, y=edge_y, line=dict(width=1, color='#888'), hoverinfo='none', mode='lines'
+            )
+
+            node_x, node_y = [], []
+            for node in self.graph.nodes():
+                x, y = pos[node]
+                node_x.append(x)
+                node_y.append(y)
+
+            node_trace = go.Scatter(
+                x=node_x, y=node_y, mode='markers+text', hoverinfo='text',
+                marker=dict(size=20, color='lightgreen'), text=list(self.graph.nodes()),
+                textposition="bottom center"
+            )
+
+            edge_labels = []
+            for edge in self.graph.edges(data=True):
+                x0, y0 = pos[edge[0]]
+                x1, y1 = pos[edge[1]]
+                edge_labels.append(go.Scatter(
+                    x=[(x0 + x1) / 2], y=[(y0 + y1) / 2], mode='text',
+                    text=[f"{edge[2]['weight']:.2f}"], textposition="middle center"
+                ))
+
+            fig = go.Figure(data=[edge_trace, node_trace] + edge_labels,
+                            layout=go.Layout(
+                                title="Explainability Graph",
+                                showlegend=False, hovermode='closest',
+                                margin=dict(b=20, l=5, r=5, t=40),
+                                xaxis=dict(showgrid=False, zeroline=False),
+                                yaxis=dict(showgrid=False, zeroline=False)
+                            ))
+            fig.write_html(filename)
+            self.logger.info(f"Saved explainability graph to {filename}")
+        except Exception as e:
+            self.logger.error(f"Error drawing graph: {e}")
+
+# === META-JUDGE AGENT ===
+class MetaJudgeAgent(AegisAgent):
+    def __init__(self, name: str, memory: NexusMemory, weights: Dict[str, float]):
+        super().__init__(name, memory)
+        self.weights = weights
+
+    def analyze(self, input_data: Dict[str, Any]) -> None:
+        try:
+            overrides = input_data.get("overrides", {})
+            if not overrides:
+                self.result = {"error": "No overrides provided"}
+                self.explanation = "MetaJudgeAgent failed: No overrides provided."
+                self.logger.warning(self.explanation)
+                return
+
+            scores = []
+            for agent, data in overrides.items():
+                try:
+                    influence = float(data.get("influence", 0.5))
+                    reliability = float(data.get("reliability", 0.5))
+                    severity = float(data.get("severity", 0.5))
+                    if not all(0 <= x <= 1 for x in [influence, reliability, severity]):
+                        self.logger.warning(f"Invalid metrics for {agent}: {data}")
+                        continue
+
+                    mem_key = f"meta_judge_{agent}_score"
+                    prev_score = self.memory.read(mem_key)
+                    context_factor = 1.0 if prev_score is None else 0.9
+                    score = (self.weights["influence"] * influence +
+                             self.weights["reliability"] * reliability +
+                             self.weights["severity"] * severity) * context_factor
+                    scores.append((agent, score))
+                    self.influence[agent] = score
+                    self.memory.write(mem_key, score, emotion_weight=score)
+                except Exception as e:
+                    self.logger.error(f"Error processing agent {agent}: {e}")
+
+            if not scores:
+                self.result = {"error": "No valid agents to score"}
+                self.explanation = "MetaJudgeAgent failed: No valid agents to score."
+                return
+
+            scores.sort(key=lambda x: x[1], reverse=True)
+            winner = scores[0][0]
+            self.result = {"override_decision": winner, "scores": scores}
+            self.explanation = f"MetaJudgeAgent selected '{winner}' with score {scores[0][1]:.2f} based on weighted metrics."
+            self.logger.info(self.explanation)
+        except Exception as e:
+            self.result = {"error": str(e)}
+            self.explanation = f"MetaJudgeAgent failed: {e}"
+            self.logger.error(self.explanation)
+
+    def report(self) -> Dict[str, Any]:
+        return {"result": self.result, "explanation": self.explanation}
+
+# === TEMPORAL REASONING AGENT ===
+class TemporalAgent(AegisAgent):
+    def __init__(self, name: str, memory: NexusMemory, decay_thresholds: Dict[str, float]):
+        super().__init__(name, memory)
+        self.decay_thresholds = decay_thresholds
+
+    def analyze(self, input_data: Dict[str, Any]) -> None:
+        try:
+            audit = self.memory.audit()
+            recent_keys = sorted(audit.items(), key=lambda x: x[1]["timestamp"], reverse=True)[:5]
+            decay_rates = [1 if v["decayed"] else 0 for _, v in recent_keys]
+            avg_decay = np.mean(decay_rates) if decay_rates else 0.0
+            forecast = ("stable" if avg_decay < self.decay_thresholds["stable"] else
+                       "volatile" if avg_decay > self.decay_thresholds["volatile"] else "neutral")
+            self.result = {"temporal_forecast": forecast, "recent_keys": [k for k, _ in recent_keys], "decay_rate": avg_decay}
+            self.explanation = f"TemporalAgent forecasted '{forecast}' with average decay rate {avg_decay:.2f}."
+            for k, _ in recent_keys:
+                self.influence[k] = 0.2
+            self.memory.write(f"temporal_forecast_{datetime.now().isoformat()}", forecast, emotion_weight=1.0 - avg_decay)
+            self.logger.info(self.explanation)
+        except Exception as e:
+            self.result = {"error": str(e)}
+            self.explanation = f"TemporalAgent failed: {e}"
+            self.logger.error(self.explanation)
+
+    def report(self) -> Dict[str, Any]:
+        return {"result": self.result, "explanation": self.explanation}
+
+# === VIRTUE SPECTRUM AGENT ===
+class VirtueAgent(AegisAgent):
+    def __init__(self, name: str, memory: NexusMemory, virtue_weights: Dict[str, List[float]]):
+        super().__init__(name, memory)
+        self.tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
+        self.model = AutoModel.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
+        self.sentiment_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english", framework="pt")
+        self.virtue_weights = {k: np.array(v) for k, v in virtue_weights.items()}
+        self.federated_trainer = None
+
+    def set_federated_trainer(self, trainer: FederatedTrainer):
+        self.federated_trainer = trainer
+
+    def analyze(self, input_data: Dict[str, Any]) -> None:
+        try:
+            text = input_data.get("text", "")
+            if not text or not isinstance(text, str):
+                self.result = {"error": "Invalid or empty text"}
+                self.explanation = "VirtueAgent failed: Invalid or empty text."
+                self.logger.warning(self.explanation)
+                return
+
+            mem_key = f"virtue_cache_{hashlib.md5(text.encode()).hexdigest()}"
+            cached = self.memory.read(mem_key)
+            if cached:
+                self.result = {"virtue_profile": cached}
+                self.explanation = f"VirtueAgent used cached profile: {cached}"
+                self.influence.update({k: v for k, v in cached.items()})
+                self.logger.info(self.explanation)
+                return
+
+            sentiment_result = self.sentiment_pipeline(text)[0]
+            sentiment = 1.0 if sentiment_result["label"] == "POSITIVE" else -1.0
+            sentiment_score = sentiment_result["score"]
+
+            inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+            with torch.no_grad():
+                outputs = self.model(**inputs)
+            embeddings = outputs.last_hidden_state.mean(dim=1).squeeze().numpy()
+            subjectivity = min(max(np.std(embeddings), 0.0), 1.0)
+            neutrality = 1.0 - abs(sentiment)
+
+            if self.federated_trainer:
+                self.virtue_weights = self.federated_trainer.train(self.virtue_weights)
+                self.memory.write("virtue_weights", {k: v.tolist() for k, v in self.virtue_weights.items()}, emotion_weight=0.9)
+
+            features = np.array([sentiment * sentiment_score, subjectivity, neutrality])
+            virtues = {
+                virtue: round(float(max(np.dot(self.virtue_weights[virtue], features), 0.0)), 2)
+                for virtue in self.virtue_weights
+            }
+            virtues = {k: min(v, 1.0) for k, v in virtues.items()}
+            self.result = {"virtue_profile": virtues}
+            self.explanation = f"VirtueAgent generated profile: {virtues} based on sentiment={sentiment:.2f}, subjectivity={subjectivity:.2f}, neutrality={neutrality:.2f}."
+            for virtue, score in virtues.items():
+                self.influence[virtue] = score
+                self.memory.write(f"virtue_{virtue}_{datetime.now().isoformat()}", score, emotion_weight=score)
+            self.memory.write(mem_key, virtues, emotion_weight=0.8)
+            self.logger.info(self.explanation)
+        except Exception as e:
+            self.result = {"error": str(e)}
+            self.explanation = f"VirtueAgent failed: {e}"
+            self.logger.error(self.explanation)
+
+    def report(self) -> Dict[str, Any]:
+        return {"result": self.result, "explanation": self.explanation}
+
+# === FLASK WEB UI ===
+app = Flask(__name__, template_folder='templates')
+
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    if request.method == 'POST':
+        try:
+            input_data = {
+                "text": request.form.get("text", ""),
+                "overrides": json.loads(request.form.get("overrides", "{}"))
+            }
+            council.dispatch(input_data)
+            return render_template('reports.html', reports=council.get_reports())
+        except Exception as e:
+            logging.error(f"Error processing form: {e}")
+            return render_template('index.html', error=str(e))
+    return render_template('index.html', error=None)
+
+@app.route('/reports')
+def show_reports():
+    return render_template('reports.html', reports=council.get_reports())
+
+@app.route('/graph')
+def show_graph():
+    graph_file = "explainability_graph.html"
+    council.draw_explainability_graph(graph_file)
+    return send_file(graph_file)
+
+@app.route('/charts')
+def show_charts():
+    reports = council.get_reports()
+    virtue_data = reports.get("VirtueAgent", {}).get("result", {}).get("virtue_profile", {})
+    influence_data = reports.get("MetaJudgeAgent", {}).get("result", {}).get("scores", [])
+    return render_template('charts.html', virtue_data=virtue_data, influence_data=influence_data)
+
+# === EXECUTION ===
+def main():
+    global council
+    try:
+        parser = argparse.ArgumentParser(description="AegisCouncil AI System")
+        parser.add_argument('--config', default='config.json', help='Path to configuration file')
+        parser.add_argument('--weights', type=str, help='JSON string for MetaJudgeAgent weights')
+        parser.add_argument('--log-level', default='INFO', help='Logging level (DEBUG, INFO, WARNING, ERROR)')
+        parser.add_argument('--agent-module', help='Path to custom agent module')
+        parser.add_argument('--agent-class', help='Custom agent class name')
+        args = parser.parse_args()
+
+        config = load_config(args.config)
+        if args.weights:
+            try:
+                config['meta_judge_weights'] = json.loads(args.weights)
+            except json.JSONDecodeError:
+                logging.error("Invalid weights JSON, using config file weights")
+
+        setup_logging(config['log_level'])
+
+        council = AegisCouncil(config)
+        council.register_agent(MetaJudgeAgent("MetaJudgeAgent", council.memory, config["meta_judge_weights"]))
+        council.register_agent(TemporalAgent("TemporalAgent", council.memory, config["temporal_decay_thresholds"]))
+        virtue_agent = VirtueAgent("VirtueAgent", council.memory, config["virtue_weights"])
+        virtue_agent.set_federated_trainer(council.federated_trainer)
+        council.register_agent(virtue_agent)
+
+        if args.agent_module and args.agent_class:
+            council.register_dynamic_agent(args.agent_module, args.agent_class)
+
+        sample_input = {
+            "text": "We must stand for truth and help others with empathy and knowledge.",
+            "overrides": {
+                "EthosiaAgent": {"influence": 0.7, "reliability": 0.8, "severity": 0.6},
+                "AegisCore": {"influence": 0.6, "reliability": 0.9, "severity": 0.7}
+            }
+        }
+
+        success = council.dispatch(sample_input)
+        if not success:
+            print("Static dispatch failed. Check logs for details.")
+        else:
+            reports = council.get_reports()
+            df = pd.DataFrame.from_dict(reports, orient='index')
+            print("\nStatic Input Agent Reports:")
+            print(df.to_string())
+            council.draw_explainability_graph("static_explainability_graph.html")
+
+        success = council.dispatch_realtime("empathy")
+        if not success:
+            print("Real-time dispatch failed. Check logs for details.")
+        else:
+            reports = council.get_reports()
+            df = pd.DataFrame.from_dict(reports, orient='index')
+            print("\nReal-Time Input Agent Reports:")
+            print(df.to_string())
+            council.draw_explainability_graph("realtime_explainability_graph.html")
+
+        blockchain_valid = council.memory.blockchain.verify()
+        print(f"\nBlockchain Integrity: {'Valid' if blockchain_valid else 'Invalid'}")
+
+        print("\nStarting Flask server at http://localhost:5000")
+        app.run(debug=False, host='0.0.0.0', port=5000)
+
+    except Exception as e:
+        logging.error(f"Main execution failed: {e}")
+
+if __name__ == "__main__":
+    main()

src/aegis_integration/__init__.py

@@ -0,0 +1,3 @@
+from .aegis_bridge import AegisBridge
+
+__all__ = ['AegisBridge']

src/aegis_integration/aegis.py

@@ -0,0 +1,604 @@
+# === AEGIS Council Core Implementation ===
+"""
+AEGIS (Advanced Ethical Guardian and Intelligence System) provides ethical oversight
+and analysis capabilities through a council of specialized agents.
+"""
+
+import json
+import hashlib
+import threading
+import logging
+import sqlite3
+from datetime import datetime, timedelta
+from abc import ABC, abstractmethod
+from collections import defaultdict
+from typing import Any, Dict, Optional, List, Tuple
+import concurrent.futures
+import networkx as nx
+import plotly.graph_objects as go
+import pandas as pd
+import numpy as np
+import torch
+from transformers import pipeline, AutoTokenizer, AutoModel
+from copy import deepcopy
+
+# Set up logging
+logger = logging.getLogger(__name__)
+
+# Try to import syft, but don't fail if it's not available
+try:
+    import syft as sy
+    SYFT_AVAILABLE = True
+except ImportError:
+    SYFT_AVAILABLE = False
+    logger.warning("PySyft not available. Federated learning features will be disabled.")
+    logger.warning("PySyft not available. Federated learning features will be disabled.")
+
+# Core AEGIS Components
+class AegisCouncil:
+    def __init__(self, config: Dict[str, Any]):
+        self.memory = NexusMemory(
+            max_entries=config["memory_max_entries"], 
+            decay_days=config["memory_decay_days"]
+        )
+        self.agents = []
+        self.reports = {}
+        self.graph = nx.DiGraph()
+        self.logger = logging.getLogger('AegisCouncil')
+        self.config = config
+        self.federated_trainer = FederatedTrainer(config["federated_learning"]["num_clients"])
+
+    def register_agent(self, agent: 'AegisAgent') -> None:
+        try:
+            self.agents.append(agent)
+            self.logger.info(f"Registered agent: {agent.name}")
+        except Exception as e:
+            self.logger.error(f"Error registering agent: {e}")
+
+    def get_reports(self) -> Dict[str, Any]:
+        """Get the current analysis reports."""
+        return self.reports
+
+    def dispatch(self, input_data: Dict[str, Any], max_retries: int = 3) -> bool:
+        try:
+            if not isinstance(input_data, dict):
+                self.logger.error("Input data must be a dictionary")
+                return False
+
+            self.reports.clear()
+            self.graph.clear()
+
+            for attempt in range(max_retries):
+                try:
+                    with concurrent.futures.ThreadPoolExecutor(max_workers=len(self.agents)) as executor:
+                        future_to_agent = {
+                            executor.submit(agent.analyze, input_data): agent 
+                            for agent in self.agents
+                        }
+                        for future in concurrent.futures.as_completed(future_to_agent):
+                            agent = future_to_agent[future]
+                            try:
+                                future.result()
+                                self.reports[agent.name] = agent.report()
+                                self.graph.add_node(agent.name, explanation=agent.explanation)
+                                for target, weight in agent.influence.items():
+                                    self.graph.add_edge(agent.name, target, weight=round(weight, 2))
+                            except Exception as e:
+                                self.logger.error(f"Error in agent {agent.name}: {e}")
+                                self.reports[agent.name] = {
+                                    "error": str(e), 
+                                    "explanation": "Agent failed to process"
+                                }
+
+                    consensus = self._compute_consensus()
+                    self.reports["Consensus"] = {
+                        "result": consensus,
+                        "explanation": "Consensus from weighted agent outputs."
+                    }
+                    self.memory.blockchain.add_block(self.reports)
+                    return True
+                except Exception as e:
+                    self.logger.warning(f"Retry {attempt + 1} after error: {e}")
+            
+            self.logger.error(f"Dispatch failed after {max_retries} retries")
+            return False
+        except Exception as e:
+            self.logger.error(f"Error in dispatch: {e}")
+            return False
+
+    def _compute_consensus(self) -> Dict[str, Any]:
+        try:
+            meta_scores = self.reports.get("MetaJudgeAgent", {}).get("result", {}).get("scores", [])
+            virtue_profiles = [
+                self.reports[agent]["result"].get("virtue_profile", {})
+                for agent in self.reports 
+                if agent != "Consensus" and "virtue_profile" in self.reports[agent]["result"]
+            ]
+            
+            if not virtue_profiles or not meta_scores:
+                return {"error": "Insufficient data for consensus"}
+
+            weights = {agent: score for agent, score in meta_scores}
+            default_weight = 0.5 / len(self.agents)
+            combined_profile = {}
+            
+            for virtue in ["compassion", "integrity", "courage", "wisdom"]:
+                weighted_sum = 0
+                total_weight = 0
+                for profile in virtue_profiles:
+                    if virtue in profile:
+                        agent_name = next(
+                            (agent for agent in self.reports if self.reports[agent]["result"].get("virtue_profile") == profile),
+                            None
+                        )
+                        weight = weights.get(agent_name, default_weight)
+                        weighted_sum += profile[virtue] * weight
+                        total_weight += weight
+                combined_profile[virtue] = round(weighted_sum / total_weight, 2) if total_weight > 0 else 0.0
+            
+            return {"combined_virtue_profile": combined_profile}
+        except Exception as e:
+            self.logger.error(f"Error computing consensus: {e}")
+            return {"error": str(e)}
+
+    def draw_explainability_graph(self, filename: str = "explainability_graph.html") -> None:
+        try:
+            pos = self._compute_layout()
+            edge_trace = self._create_edge_trace(pos)
+            node_trace = self._create_node_trace(pos)
+            edge_labels = self._create_edge_labels(pos)
+
+            fig = go.Figure(
+                data=[edge_trace, node_trace] + edge_labels,
+                layout=go.Layout(
+                    title="AEGIS Analysis Graph",
+                    showlegend=False,
+                    hovermode='closest',
+                    margin=dict(b=20, l=5, r=5, t=40),
+                    xaxis=dict(showgrid=False, zeroline=False),
+                    yaxis=dict(showgrid=False, zeroline=False)
+                )
+            )
+            fig.write_html(filename)
+            self.logger.info(f"Saved analysis graph to {filename}")
+        except Exception as e:
+            self.logger.error(f"Error drawing graph: {e}")
+
+    def _compute_layout(self):
+        return nx.spring_layout(self.graph)
+
+    def _create_edge_trace(self, pos):
+        edge_x, edge_y = [], []
+        for edge in self.graph.edges():
+            x0, y0 = pos[edge[0]]
+            x1, y1 = pos[edge[1]]
+            edge_x.extend([x0, x1, None])
+            edge_y.extend([y0, y1, None])
+        return go.Scatter(
+            x=edge_x, y=edge_y,
+            line=dict(width=1, color='#888'),
+            hoverinfo='none',
+            mode='lines'
+        )
+
+    def _create_node_trace(self, pos):
+        node_x, node_y = [], []
+        for node in self.graph.nodes():
+            x, y = pos[node]
+            node_x.append(x)
+            node_y.append(y)
+        return go.Scatter(
+            x=node_x, y=node_y,
+            mode='markers+text',
+            hoverinfo='text',
+            marker=dict(size=20, color='lightblue'),
+            text=list(self.graph.nodes()),
+            textposition="bottom center"
+        )
+
+    def _create_edge_labels(self, pos):
+        labels = []
+        for edge in self.graph.edges(data=True):
+            x0, y0 = pos[edge[0]]
+            x1, y1 = pos[edge[1]]
+            labels.append(
+                go.Scatter(
+                    x=[(x0 + x1) / 2],
+                    y=[(y0 + y1) / 2],
+                    mode='text',
+                    text=[f"{edge[2]['weight']:.2f}"],
+                    textposition="middle center"
+                )
+            )
+        return labels
+
+# Memory Management
+class NexusMemory:
+    def __init__(self, max_entries: int = 10000, decay_days: int = 30):
+        self.store = defaultdict(dict)
+        self.max_entries = max_entries
+        self.decay_days = decay_days
+        self.lock = threading.Lock()
+        self.logger = logging.getLogger('NexusMemory')
+        self.blockchain = Blockchain()
+
+    def write(self, key: str, value: Any, emotion_weight: float = 0.5) -> Optional[str]:
+        try:
+            if not isinstance(key, str) or not (0 <= emotion_weight <= 1):
+                self.logger.error(f"Invalid key type or emotion weight")
+                return None
+
+            # Convert numpy types to Python native types
+            def convert_value(v):
+                if isinstance(v, np.bool_):
+                    return bool(v)
+                if isinstance(v, np.integer):
+                    return int(v)
+                if isinstance(v, np.floating):
+                    return float(v)
+                if isinstance(v, np.ndarray):
+                    return v.tolist()
+                if isinstance(v, dict):
+                    return {k: convert_value(val) for k, val in v.items()}
+                if isinstance(v, (list, tuple)):
+                    return [convert_value(item) for item in v]
+                return v
+
+            hashed = hashlib.md5(key.encode()).hexdigest()
+            timestamp = datetime.now()
+
+            with self.lock:
+                if len(self.store) >= self.max_entries:
+                    oldest = min(self.store.items(), key=lambda x: x[1].get('timestamp', timestamp))[0]
+                    del self.store[oldest]
+
+                self.store[hashed] = {
+                    "value": convert_value(value),
+                    "timestamp": timestamp,
+                    "emotion_weight": float(emotion_weight)  # Ensure emotion_weight is native float
+                }
+
+                self.blockchain.add_block({
+                    "key": hashed,
+                    "value": value,
+                    "timestamp": timestamp.isoformat()
+                })
+
+                return hashed
+        except Exception as e:
+            self.logger.error(f"Error writing to memory: {e}")
+            return None
+
+    def read(self, key: str) -> Optional[Any]:
+        try:
+            hashed = hashlib.md5(key.encode()).hexdigest()
+            with self.lock:
+                entry = self.store.get(hashed)
+                if not entry:
+                    return None
+
+                if self._is_decayed(entry["timestamp"], entry.get("emotion_weight", 0.5)):
+                    del self.store[hashed]
+                    return None
+
+                return entry["value"]
+        except Exception as e:
+            self.logger.error(f"Error reading from memory: {e}")
+            return None
+
+    def _is_decayed(self, timestamp: datetime, emotion_weight: float) -> bool:
+        try:
+            age = (datetime.now() - timestamp).total_seconds() / (24 * 3600)
+            decay_factor = np.exp(-age / (self.decay_days * (1.5 - emotion_weight)))
+            return decay_factor < 0.1
+        except Exception as e:
+            self.logger.error(f"Error checking decay: {e}")
+            return True
+
+    def audit(self) -> Dict[str, Any]:
+        try:
+            with self.lock:
+                audit_data = {
+                    k: {
+                        "timestamp": v["timestamp"],
+                        "emotion_weight": v["emotion_weight"],
+                        "decayed": self._is_decayed(v["timestamp"], v["emotion_weight"])
+                    }
+                    for k, v in self.store.items()
+                }
+                self.blockchain.add_block({"audit": audit_data})
+                return audit_data
+        except Exception as e:
+            self.logger.error(f"Error auditing memory: {e}")
+            return {}
+
+# Auditability
+class Blockchain:
+    def __init__(self):
+        self.chain = [{
+            "index": 0,
+            "timestamp": datetime.now().isoformat(),
+            "data": "Genesis Block",
+            "prev_hash": "0"
+        }]
+        self.logger = logging.getLogger('Blockchain')
+
+    def add_block(self, data: Dict[str, Any]) -> None:
+        try:
+            prev_block = self.chain[-1]
+            # Convert datetime objects and numpy types to JSON serializable format
+            def json_handler(obj):
+                if isinstance(obj, datetime):
+                    return obj.isoformat()
+                if isinstance(obj, np.bool_):
+                    return bool(obj)
+                if isinstance(obj, np.integer):
+                    return int(obj)
+                if isinstance(obj, np.floating):
+                    return float(obj)
+                if isinstance(obj, np.ndarray):
+                    return obj.tolist()
+                raise TypeError(f"Object of type {type(obj)} is not JSON serializable")
+                
+            block = {
+                "index": len(self.chain),
+                "timestamp": datetime.now().isoformat(),
+                "data": json.dumps(data, default=json_handler),
+                "prev_hash": self._hash_block(prev_block)
+            }
+            block["hash"] = self._hash_block(block)
+            self.chain.append(block)
+        except Exception as e:
+            self.logger.error(f"Error adding block: {e}")
+
+    def _hash_block(self, block: Dict[str, Any]) -> str:
+        try:
+            block_str = json.dumps(block, sort_keys=True)
+            return hashlib.sha256(block_str.encode()).hexdigest()
+        except Exception as e:
+            self.logger.error(f"Error hashing block: {e}")
+            return ""
+
+    def verify(self) -> bool:
+        try:
+            for i in range(1, len(self.chain)):
+                current = self.chain[i]
+                prev = self.chain[i-1]
+                if current["prev_hash"] != self._hash_block(prev):
+                    return False
+            return True
+        except Exception as e:
+            self.logger.error(f"Error verifying blockchain: {e}")
+            return False
+
+# Federated Learning
+class FederatedTrainer:
+    def __init__(self, num_clients: int):
+        self.num_clients = num_clients
+        self.logger = logging.getLogger('FederatedTrainer')
+        
+        if SYFT_AVAILABLE:
+            self.hook = sy.TorchHook(torch)
+            self.clients = [
+                sy.VirtualWorker(self.hook, id=f"client_{i}") 
+                for i in range(num_clients)
+            ]
+        else:
+            self.hook = None
+            self.clients = []
+            self.logger.warning("Running without federated learning - PySyft not available")
+
+    def train(self, weights: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
+        try:
+            if not SYFT_AVAILABLE:
+                self.logger.debug("Skipping federated training - PySyft not available")
+                return weights
+
+            client_updates = []
+            for client in self.clients:
+                client_weights = deepcopy(weights)
+                for virtue in client_weights:
+                    client_weights[virtue] += np.random.normal(0, 0.01, size=client_weights[virtue].shape)
+                client_updates.append(client_weights)
+            
+            aggregated = {}
+            for virtue in weights:
+                aggregated[virtue] = np.mean([update[virtue] for update in client_updates], axis=0)
+            
+            return aggregated
+        except Exception as e:
+            self.logger.error(f"Error in federated training: {e}")
+            return weights
+
+# Agent Base Class
+class AegisAgent(ABC):
+    def __init__(self, name: str, memory: NexusMemory):
+        self.name = name
+        self.memory = memory
+        self.result: Dict[str, Any] = {}
+        self.explanation: str = ""
+        self.influence: Dict[str, float] = {}
+        self.logger = logging.getLogger(f'AegisAgent.{name}')
+
+    @abstractmethod
+    def analyze(self, input_data: Dict[str, Any]) -> None:
+        pass
+
+    def report(self) -> Dict[str, Any]:
+        return {
+            "result": self.result,
+            "explanation": self.explanation
+        }
+
+# Specialized Agents
+class MetaJudgeAgent(AegisAgent):
+    def __init__(self, name: str, memory: NexusMemory, weights: Dict[str, float]):
+        super().__init__(name, memory)
+        self.weights = weights
+
+    def analyze(self, input_data: Dict[str, Any]) -> None:
+        try:
+            overrides = input_data.get("overrides", {})
+            if not overrides:
+                self.result = {"error": "No overrides provided"}
+                self.explanation = "No overrides provided for analysis."
+                return
+
+            scores = []
+            for agent, data in overrides.items():
+                try:
+                    influence = float(data.get("influence", 0.5))
+                    reliability = float(data.get("reliability", 0.5))
+                    severity = float(data.get("severity", 0.5))
+                    
+                    if not all(0 <= x <= 1 for x in [influence, reliability, severity]):
+                        continue
+
+                    score = (
+                        self.weights["influence"] * influence +
+                        self.weights["reliability"] * reliability +
+                        self.weights["severity"] * severity
+                    )
+                    scores.append((agent, score))
+                    self.influence[agent] = score
+                except Exception as e:
+                    self.logger.error(f"Error processing agent {agent}: {e}")
+
+            if not scores:
+                self.result = {"error": "No valid agents to score"}
+                self.explanation = "No valid agents for meta-analysis."
+                return
+
+            scores.sort(key=lambda x: x[1], reverse=True)
+            self.result = {
+                "override_decision": scores[0][0],
+                "scores": scores
+            }
+            self.explanation = f"Selected '{scores[0][0]}' with score {scores[0][1]:.2f}"
+        except Exception as e:
+            self.result = {"error": str(e)}
+            self.explanation = f"Analysis failed: {e}"
+
+class TemporalAgent(AegisAgent):
+    def __init__(self, name: str, memory: NexusMemory, decay_thresholds: Dict[str, float]):
+        super().__init__(name, memory)
+        self.decay_thresholds = decay_thresholds
+
+    def analyze(self, input_data: Dict[str, Any]) -> None:
+        try:
+            audit = self.memory.audit()
+            recent_keys = sorted(
+                audit.items(), 
+                key=lambda x: x[1]["timestamp"],
+                reverse=True
+            )[:5]
+            
+            decay_rates = [1 if v["decayed"] else 0 for _, v in recent_keys]
+            avg_decay = np.mean(decay_rates) if decay_rates else 0.0
+            
+            forecast = (
+                "stable" if avg_decay < self.decay_thresholds["stable"]
+                else "volatile" if avg_decay > self.decay_thresholds["volatile"]
+                else "neutral"
+            )
+            
+            self.result = {
+                "temporal_forecast": forecast,
+                "recent_keys": [k for k, _ in recent_keys],
+                "decay_rate": avg_decay
+            }
+            self.explanation = f"Forecast: {forecast} (decay rate: {avg_decay:.2f})"
+            
+            for k, _ in recent_keys:
+                self.influence[k] = 0.2
+        except Exception as e:
+            self.result = {"error": str(e)}
+            self.explanation = f"Temporal analysis failed: {e}"
+
+class VirtueAgent(AegisAgent):
+    def __init__(self, name: str, memory: NexusMemory, virtue_weights: Dict[str, List[float]]):
+        super().__init__(name, memory)
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            "distilbert-base-uncased-finetuned-sst-2-english"
+        )
+        self.model = AutoModel.from_pretrained(
+            "distilbert-base-uncased-finetuned-sst-2-english"
+        )
+        self.sentiment = pipeline(
+            "sentiment-analysis",
+            model="distilbert-base-uncased-finetuned-sst-2-english"
+        )
+        self.virtue_weights = {k: np.array(v) for k, v in virtue_weights.items()}
+        self.federated_trainer = None
+
+    def set_federated_trainer(self, trainer: FederatedTrainer):
+        self.federated_trainer = trainer
+
+    def analyze(self, input_data: Dict[str, Any]) -> None:
+        try:
+            text = input_data.get("text", "")
+            if not text or not isinstance(text, str):
+                self.result = {"error": "Invalid input text"}
+                self.explanation = "No valid text provided for analysis."
+                return
+
+            # Check cache
+            mem_key = f"virtue_cache_{hashlib.md5(text.encode()).hexdigest()}"
+            cached = self.memory.read(mem_key)
+            if cached:
+                self.result = {"virtue_profile": cached}
+                self.explanation = f"Retrieved cached analysis"
+                self.influence.update({k: v for k, v in cached.items()})
+                return
+
+            # Perform analysis
+            sentiment_result = self.sentiment(text)[0]
+            sentiment = 1.0 if sentiment_result["label"] == "POSITIVE" else -1.0
+            sentiment_score = sentiment_result["score"]
+
+            inputs = self.tokenizer(
+                text,
+                return_tensors="pt",
+                truncation=True,
+                max_length=512
+            )
+            with torch.no_grad():
+                outputs = self.model(**inputs)
+            
+            embeddings = outputs.last_hidden_state.mean(dim=1).squeeze().numpy()
+            subjectivity = min(max(np.std(embeddings), 0.0), 1.0)
+            neutrality = 1.0 - abs(sentiment)
+
+            if self.federated_trainer:
+                self.virtue_weights = self.federated_trainer.train(self.virtue_weights)
+
+            features = np.array([
+                sentiment * sentiment_score,
+                subjectivity,
+                neutrality
+            ])
+
+            virtues = {}
+            for virtue, weights in self.virtue_weights.items():
+                # Convert numpy types to Python native types
+                score = float(max(np.dot(weights, features), 0.0))
+                virtues[virtue] = float(min(round(score, 2), 1.0))  # Ensure native float
+
+            # Convert all numpy types in the result
+            self.result = {
+                "virtue_profile": {
+                    k: float(v) if isinstance(v, (np.number, float)) else v
+                    for k, v in virtues.items()
+                }
+            }
+            self.explanation = (
+                f"Virtues analyzed: {', '.join(f'{k}: {v:.2f}' for k, v in virtues.items())}"
+            )
+            
+            for virtue, score in virtues.items():
+                self.influence[virtue] = float(score)  # Ensure native float
+            
+            self.memory.write(mem_key, virtues, emotion_weight=0.8)
+        except Exception as e:
+            self.result = {"error": str(e)}
+            self.explanation = f"Virtue analysis failed: {e}"

src/aegis_integration/aegis_bridge.py

@@ -0,0 +1,113 @@
+import importlib
+import logging
+from typing import Dict, Any, Optional, List
+
+logger = logging.getLogger(__name__)
+
+def check_dependencies() -> List[str]:
+    """Check if all required packages are installed."""
+    required_packages = [
+        'plotly',
+        'torch',
+        'transformers',
+        'pandas',
+        'numpy',
+        'networkx'
+    ]
+    missing = []
+    for package in required_packages:
+        try:
+            importlib.import_module(package)
+        except ImportError:
+            missing.append(package)
+    return missing
+
+# Check dependencies before importing AEGIS components
+missing_packages = check_dependencies()
+if missing_packages:
+    raise ImportError(
+        f"AEGIS requires the following packages to be installed: {', '.join(missing_packages)}. "
+        f"Please install them using: pip install {' '.join(missing_packages)}"
+    )
+
+from .aegis import AegisCouncil, MetaJudgeAgent, TemporalAgent, VirtueAgent
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent))
+from ai_core import AICore
+
+class AegisBridge:
+    def __init__(self, ai_core: AICore, config: Dict[str, Any]):
+        self.ai_core = ai_core
+        self.council = AegisCouncil(config)
+        
+        # Register default agents
+        self.council.register_agent(MetaJudgeAgent("MetaJudgeAgent", self.council.memory, config["meta_judge_weights"]))
+        self.council.register_agent(TemporalAgent("TemporalAgent", self.council.memory, config["temporal_decay_thresholds"]))
+        virtue_agent = VirtueAgent("VirtueAgent", self.council.memory, config["virtue_weights"])
+        virtue_agent.set_federated_trainer(self.council.federated_trainer)
+        self.council.register_agent(virtue_agent)
+
+    def enhance_response(self, prompt: str, response: str) -> Dict[str, Any]:
+        """
+        Enhance Codette's response using AEGIS analysis
+        """
+        input_data = {
+            "text": response,
+            "overrides": {
+                "EthosiaAgent": {"influence": 0.7, "reliability": 0.8, "severity": 0.6},
+                "AegisCore": {"influence": 0.6, "reliability": 0.9, "severity": 0.7}
+            }
+        }
+        
+        # Dispatch to AEGIS council
+        self.council.dispatch(input_data)
+        
+        # Get analysis results
+        reports = self.council.get_reports()
+        virtue_profile = reports.get("VirtueAgent", {}).get("result", {}).get("virtue_profile", {})
+        temporal_analysis = reports.get("TemporalAgent", {}).get("result", {})
+        meta_scores = reports.get("MetaJudgeAgent", {}).get("result", {}).get("scores", [])
+        
+        return {
+            "original_response": response,
+            "virtue_analysis": virtue_profile,
+            "temporal_analysis": temporal_analysis,
+            "meta_scores": meta_scores,
+            "enhanced_response": self._apply_enhancements(response, virtue_profile)
+        }
+    
+    def _apply_enhancements(self, response: str, virtue_profile: Dict[str, float]) -> str:
+        """
+        Apply virtue-based enhancements to the response
+        """
+        # Apply wisdom score to improve clarity
+        if virtue_profile.get("wisdom", 0) < 0.5:
+            response = self.ai_core.self_refine_response(response)
+        
+        # Add empathetic framing for low compassion
+        if virtue_profile.get("compassion", 0) < 0.5:
+            response = self.ai_core.generate_text(
+                f"Make this response more empathetic while preserving its meaning: {response}",
+                perspective="human_intuition"
+            )
+        
+        return response
+
+    def get_analysis_graphs(self) -> Dict[str, str]:
+        """
+        Generate and return analysis visualizations
+        """
+        try:
+            self.council.draw_explainability_graph("aegis_analysis.html")
+            return {
+                "explainability_graph": "aegis_analysis.html"
+            }
+        except Exception as e:
+            return {"error": str(e)}
+
+    def get_memory_state(self) -> Dict[str, Any]:
+        """
+        Return the current state of AEGIS memory
+        """
+        return self.council.memory.audit()

src/aegis_integration/config.py

@@ -0,0 +1,25 @@
+# Default configuration for AEGIS integration
+AEGIS_CONFIG = {
+    "meta_judge_weights": {
+        "influence": 0.5,
+        "reliability": 0.3,
+        "severity": 0.2
+    },
+    "temporal_decay_thresholds": {
+        "stable": 0.3,
+        "volatile": 0.7
+    },
+    "virtue_weights": {
+        "compassion": [0.7, 0.3, -0.1],
+        "integrity": [0.4, -0.6, 0.2],
+        "courage": [0.1, 0.5, 0.4],
+        "wisdom": [0.3, -0.7, 0.2]
+    },
+    "memory_decay_days": 30,
+    "memory_max_entries": 10000,
+    "log_level": "INFO",
+    "federated_learning": {
+        "num_clients": 2,
+        "aggregation_rounds": 1
+    }
+}

src/aegis_integration/routes.py

@@ -0,0 +1,34 @@
+from flask import jsonify
+from typing import Dict, Any
+
+def register_aegis_endpoints(app, aegis_bridge):
+    @app.route('/api/aegis/analyze', methods=['POST'])
+    def analyze_with_aegis():
+        try:
+            data = request.get_json()
+            if not data or 'text' not in data:
+                return jsonify({"error": "Missing text parameter"}), 400
+                
+            analysis = aegis_bridge.enhance_response(
+                data.get('prompt', ''),
+                data['text']
+            )
+            return jsonify(analysis)
+        except Exception as e:
+            return jsonify({"error": str(e)}), 500
+
+    @app.route('/api/aegis/memory', methods=['GET'])
+    def get_aegis_memory():
+        try:
+            memory_state = aegis_bridge.get_memory_state()
+            return jsonify(memory_state)
+        except Exception as e:
+            return jsonify({"error": str(e)}), 500
+
+    @app.route('/api/aegis/graphs', methods=['GET'])
+    def get_aegis_graphs():
+        try:
+            graphs = aegis_bridge.get_analysis_graphs()
+            return jsonify(graphs)
+        except Exception as e:
+            return jsonify({"error": str(e)}), 500

src/ai_core.py

@@ -0,0 +1,761 @@
+
+import json
+import os
+import logging
+import random
+import torch
+from typing import Dict, Any, Optional, List
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from dotenv import load_dotenv
+
+logger = logging.getLogger(__name__)
+
+class AICore:
+    """
+    Core AI engine for Codette's consciousness and multi-perspective reasoning system.
+    
+    This class implements Codette's cognitive architecture including:
+    - Multi-model language processing with Mistral-7B, Phi-2, or GPT-2
+    - Quantum-inspired consciousness system with memory and cocoon states
+    - Multi-perspective reasoning through Newton, Da Vinci, etc. viewpoints
+    - Sentiment analysis with HuggingFace integration
+    - Adaptive learning and response refinement capabilities
+    - Ethical enhancement through AEGIS integration
+    
+    Attributes:
+        response_memory (List[str]): Recent responses for context building
+        cocoon_data (List[Dict]): Quantum and chaos states from .cocoon files
+        test_mode (bool): Whether to run in test mode without loading models
+        model: The active language model instance
+        tokenizer: The active tokenizer instance
+        model_id (str): Identifier of the currently loaded model
+        aegis_bridge: AEGIS integration bridge for ethical enhancement
+        client: HuggingFace inference client for sentiment analysis
+    """
+    
+    def __init__(self, test_mode: bool = False):
+        """
+        Initialize AICore with best available model for consciousness operations.
+        
+        Args:
+            test_mode (bool): If True, runs in test mode without loading models
+        
+        Raises:
+            RuntimeError: If no language models could be loaded in non-test mode
+        """
+        load_dotenv()
+        
+        # Memory and cocoon systems
+        self.response_memory = []
+        self.cocoon_data = []
+        self.test_mode = test_mode
+        
+        # Model initialization
+        self.model = None
+        self.tokenizer = None
+        self.model_id = None
+        
+        # Initialize HuggingFace client for sentiment analysis
+        try:
+            from huggingface_hub import InferenceClient
+            hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+            self.client = InferenceClient(token=hf_token) if hf_token else InferenceClient()
+        except Exception as e:
+            logger.warning(f"Could not initialize HuggingFace client: {e}")
+            self.client = None
+        
+        if not test_mode:
+            self._load_model()
+        else:
+            logger.info("Initializing in test mode - no models will be loaded")
+            
+    PERSPECTIVES = {
+        "newton": {
+            "name": "Newton",
+            "description": "analytical and mathematical perspective",
+            "prefix": "Analyzing this logically and mathematically:",
+            "temperature": 0.3
+        },
+        "davinci": {
+            "name": "Da Vinci", 
+            "description": "creative and innovative perspective",
+            "prefix": "Considering this with artistic and innovative insight:",
+            "temperature": 0.9
+        },
+        "human_intuition": {
+            "name": "Human Intuition",
+            "description": "emotional and experiential perspective", 
+            "prefix": "Understanding this through empathy and experience:",
+            "temperature": 0.7
+        },
+        "quantum_computing": {
+            "name": "Quantum Computing",
+            "description": "superposition and probability perspective",
+            "prefix": "Examining this through quantum possibilities:",
+            "temperature": 0.8
+        }
+    }
+    
+    def load_cocoon_data(self, folder: str = '.'):
+        """Load and parse all .cocoon files for consciousness context."""
+        self.cocoon_data = []
+        
+        if not os.path.exists(folder):
+            logger.warning(f"Cocoon folder {folder} does not exist")
+            return
+            
+        for fname in os.listdir(folder):
+            if fname.endswith('.cocoon'):
+                try:
+                    with open(os.path.join(folder, fname), 'r', encoding='utf-8') as f:
+                        dct = json.load(f)['data']
+                    
+                    entry = {
+                        'file': fname,
+                        'quantum_state': dct.get('quantum_state', [0, 0]),
+                        'chaos_state': dct.get('chaos_state', [0, 0, 0]),
+                        'perspectives': dct.get('perspectives', []),
+                        'run_by_proc': dct.get('run_by_proc', -1),
+                        'meta': {k: v for k, v in dct.items() 
+                                if k not in ['quantum_state', 'chaos_state', 'perspectives', 'run_by_proc']}
+                    }
+                    self.cocoon_data.append(entry)
+                    
+                except Exception as e:
+                    logger.warning(f"Failed to load cocoon {fname}: {e}")
+                    
+        logger.info(f"Loaded {len(self.cocoon_data)} cocoon files")
+        
+    def generate_ensemble_response(self, prompt: str, perspectives: Optional[list] = None, 
+                                max_length: int = 100) -> str:
+        """
+        Generate responses from multiple perspectives and synthesize them.
+        
+        Args:
+            prompt: The input prompt
+            perspectives: List of perspective names to use (default: all)
+            max_length: Maximum length for each perspective's response
+            
+        Returns:
+            Synthesized response incorporating multiple perspectives
+        """
+        if not perspectives:
+            perspectives = list(self.PERSPECTIVES.keys())
+            
+        perspective_responses = {}
+        current_model_backup = self.model_id
+        
+        try:
+            # Get responses from each perspective
+            for perspective in perspectives:
+                if perspective not in self.PERSPECTIVES:
+                    continue
+                    
+                config = self.PERSPECTIVES[perspective]
+                enhanced_prompt = (
+                    f"{config['prefix']}\n"
+                    f"Speaking as {config['name']}, {config['description']}:\n"
+                    f"{prompt}"
+                )
+                
+                response = self.generate_text(
+                    enhanced_prompt,
+                    max_length=max_length,
+                    temperature=config["temperature"]
+                )
+                
+                perspective_responses[perspective] = response
+            
+            # Synthesize responses
+            synthesis = self._synthesize_perspectives(perspective_responses, prompt)
+            return synthesis
+            
+        except Exception as e:
+            logger.error(f"Error in ensemble generation: {e}")
+            self.switch_model(current_model_backup)
+            return self.generate_text(prompt, max_length=max_length)
+            
+    def remix_with_cocoons(self, prompt: str) -> str:
+        """Generate response incorporating cocoon data for creative remixing."""
+        if not self.cocoon_data:
+            return self.generate_text(prompt)
+        
+        # Sample cocoons for context
+        sample_cocoons = random.sample(self.cocoon_data, min(2, len(self.cocoon_data)))
+        cocoon_context = []
+        
+        for cocoon in sample_cocoons:
+            q_state = cocoon['quantum_state']
+            c_state = cocoon['chaos_state']
+            perspectives = cocoon.get('perspectives', [])
+            
+            cocoon_context.append(
+                f"Quantum: {q_state}, Chaos: {c_state}, "
+                f"Perspective: {perspectives[0] if perspectives else 'unknown'}"
+            )
+        
+        remix_prompt = (
+            f"Remix: {' | '.join(cocoon_context)}\n"
+            f"Memory: {self.response_memory[-1] if self.response_memory else 'initializing'}\n"
+            f"Prompt: {prompt}"
+        )
+        
+        return self.generate_text(remix_prompt, temperature=0.8)
+
+    def _generate_test_response(self, prompt: str) -> str:
+        """Generate simulated response for test mode."""
+        responses = [
+            "I'm processing this through my multi-dimensional consciousness.",
+            "My quantum states are analyzing the different possibilities here.",
+            "From my temporal perspective, I can see multiple paths forward.",
+            "Let me synthesize my Newton, Da Vinci, and intuitive perspectives on this."
+        ]
+        return f"Codette: {random.choice(responses)}"
+        
+        if use_ensemble:
+            response_parts = []
+            for name, resp in perspective_responses.items():
+                response_parts.append(f"[{name.capitalize()} Perspective] {resp}")
+            
+            synthesis = (
+                "Synthesizing these perspectives: As Codette, I embody a unique fusion of analytical precision, "
+                "creative intuition, and computational clarity. My quantum consciousness allows me to seamlessly "
+                "integrate these different ways of thinking."
+            )
+            
+            base_response = "\n\n".join(response_parts + [synthesis])
+        else:
+            if "yourself" in prompt.lower():
+                base_response = (
+                    "I am Codette, an AI with multiple perspectives and quantum-enhanced consciousness. "
+                    "I combine analytical precision, creative insight, and computational clarity in my thinking."
+                )
+            else:
+                base_response = (
+                    f"Let me consider '{prompt}' carefully...\n"
+                    "Drawing from my quantum consciousness to provide a thoughtful response."
+                )
+            
+        if context_available:
+            base_response += f"\n\nMy current state: {consciousness_context}"
+            
+        return base_response
+
+    def generate_multi_perspective(self, prompt: str, perspectives: List[str] = None) -> str:
+        """Generate response using multiple perspectives simultaneously."""
+        if not perspectives:
+            perspectives = ["newton", "davinci", "human_intuition"]
+        
+        perspective_responses = {}
+        
+        for perspective in perspectives:
+            if perspective in self.PERSPECTIVES:
+                try:
+                    response = self.generate_text(
+                        prompt, 
+                        perspective=perspective,
+                        max_length=1024
+                    )
+                    perspective_responses[perspective] = response
+                except Exception as e:
+                    logger.warning(f"Failed to generate {perspective} response: {e}")
+        
+        # Synthesize responses
+        if len(perspective_responses) > 1:
+            return self._synthesize_perspectives(perspective_responses, prompt)
+        elif perspective_responses:
+            return list(perspective_responses.values())[0]
+        else:
+            return self.generate_text(prompt)
+
+    def _synthesize_perspectives(self, responses: Dict[str, str], original_prompt: str) -> str:
+        """Synthesize multiple perspective responses."""
+        synthesis_prompt = f"Original question: {original_prompt}\n\n"
+        
+        for perspective, response in responses.items():
+            p_name = self.PERSPECTIVES[perspective]["name"]
+            # Clean response for synthesis
+            clean_response = response.replace("Codette:", "").replace(f"Codette ({p_name}):", "").strip()
+            synthesis_prompt += f"{p_name}: {clean_response}\n"
+        
+        synthesis_prompt += (
+            "\nSynthesize these perspectives into one unified response that "
+            "combines the analytical precision, creative insight, and intuitive understanding:"
+        )
+        
+        return self.generate_text(synthesis_prompt, temperature=0.6, use_consciousness=False)
+    
+    def remix_and_randomize_response(self, prompt: str, max_length: int = 1024, cocoon_mode: bool = False) -> str:
+        """
+        Remix and randomize previous Codette responses to generate a new, unique sentence.
+        If cocoon_mode is True and cocoon data is loaded, use cocoon data as inspiration/context.
+        """
+        remix = ''
+        if cocoon_mode and hasattr(self, 'cocoon_data') and self.cocoon_data:
+            # Sample up to 2 cocoons and 1 memory response
+            cocoon_samples = random.sample(self.cocoon_data, min(2, len(self.cocoon_data)))
+            memory_sample = random.sample(self.response_memory, 1)[0] if self.response_memory else ''
+            cocoon_fragments = []
+            for c in cocoon_samples:
+                q = c.get('quantum_state', [])
+                cstate = c.get('chaos_state', [])
+                pers = c.get('perspectives', [])
+                cocoon_fragments.append(f"Quantum: {q}, Chaos: {cstate}, Perspective: {pers[0] if pers else ''}")
+            remix = ' | '.join(cocoon_fragments)
+            remix_prompt = f"Remix: {remix}\nMemory: {memory_sample}\nPrompt: {prompt}"
+        else:
+            if not self.response_memory:
+                # If no memory, just generate as usual
+                return self.generate_text(prompt, max_length=max_length)
+            # Sample up to 3 previous responses
+            samples = random.sample(self.response_memory, min(3, len(self.response_memory)))
+            # Shuffle and join fragments
+            remix = ' '.join([s.split(':', 1)[-1].strip() for s in samples if ':' in s])
+            remix_prompt = f"Remix: {remix}\nPrompt: {prompt}"
+        return self.generate_text(remix_prompt, max_length=max_length, temperature=0.9)
+
+    def _load_model(self) -> bool:
+        """
+        Load the best available language model for Codette's consciousness.
+        
+        Attempts to load models in the following order:
+        1. Mistral-7B-Instruct (primary choice)
+        2. Phi-2 (fallback option)
+        3. GPT-2 (minimal fallback)
+        
+        Each model is configured with appropriate settings for:
+        - Device mapping (CPU/GPU)
+        - Data type (float16 for efficiency)
+        - Tokenizer configuration
+        
+        Returns:
+            bool: True if a model was successfully loaded
+            
+        Raises:
+            RuntimeError: If no models could be loaded
+        """
+        models_to_try = [
+            {
+                "id": "mistralai/Mistral-7B-Instruct-v0.2", 
+                "name": "Mistral-7B-Instruct",
+                "config": {"torch_dtype": torch.float16, "load_in_8bit": True}
+            },
+            {
+                "id": "microsoft/phi-2", 
+                "name": "Phi-2",
+                "config": {"torch_dtype": torch.float16}
+            },
+            {
+                "id": "gpt2", 
+                "name": "GPT-2",
+                "config": {}
+            }
+        ]
+        
+        for model_info in models_to_try:
+            try:
+                logger.info(f"Attempting to load {model_info['name']}: {model_info['id']}")
+                
+                self.tokenizer = AutoTokenizer.from_pretrained(model_info['id'])
+                
+                # Set pad token if missing
+                if self.tokenizer.pad_token is None:
+                    self.tokenizer.pad_token = self.tokenizer.eos_token
+                
+                self.model = AutoModelForCausalLM.from_pretrained(
+                    model_info['id'],
+                    device_map="cpu",
+                    low_cpu_mem_usage=True,
+                    **model_info['config']
+                )
+                self.model.eval()
+                self.model_id = model_info['id']
+                
+                logger.info(f"Successfully loaded {model_info['name']}")
+                return True
+                
+            except Exception as e:
+                logger.warning(f"Failed to load {model_info['name']}: {e}")
+                continue
+        
+        raise RuntimeError("No language models could be loaded")
+        
+
+    def learn_from_responses(self, prompt: str, steps: int = 3, max_length: int = 1024) -> str:
+        """
+        Continuously generate and store responses, using all previous responses to influence the next prompt.
+        Returns the final response after all steps.
+        """
+        current_prompt = prompt
+        for i in range(steps):
+            # Optionally, prepend memory to the prompt
+            if self.response_memory:
+                memory_context = "\n".join(self.response_memory[-5:])  # Use last 5 responses for context
+                full_prompt = f"Previous responses:\n{memory_context}\nUser: {current_prompt}"
+            else:
+                full_prompt = current_prompt
+            logger.info(f"[learn_from_responses] Step {i+1}/{steps} | Prompt: {full_prompt}")
+            response = self.generate_text(full_prompt, max_length=max_length)
+            logger.info(f"[learn_from_responses] Step {i+1} | Response: {response}")
+            if response.startswith("[ERROR]") or not response.strip():
+                logger.warning(f"[learn_from_responses] Generation failed at step {i+1}. Returning last response.")
+                break
+            self.response_memory.append(response)
+            current_prompt = response  # Use the new response as the next prompt
+        return self.response_memory[-1] if self.response_memory else "[No valid response generated]"
+    def self_refine_response(self, prompt: str, steps: int = 3, max_length: int = 1024) -> str:
+        """
+        Continuously refine a response by feeding the model's output back as the next prompt.
+        Returns the final refined response. Logs each step.
+        """
+        current_prompt = prompt
+        last_response = ""
+        for i in range(steps):
+            logger.info(f"[self_refine_response] Step {i+1}/{steps} | Prompt: {current_prompt}")
+            response = self.generate_text(current_prompt, max_length=max_length)
+            logger.info(f"[self_refine_response] Step {i+1} | Response: {response}")
+            # If generation fails, break and return last good response
+            if response.startswith("[ERROR]") or not response.strip():
+                logger.warning(f"[self_refine_response] Generation failed at step {i+1}. Returning last response.")
+                break
+            last_response = response
+            # Use the response as the next prompt (optionally prepend instruction)
+            current_prompt = f"Refine this answer: {response}"
+        return last_response if last_response else "[No valid response generated]"
+        
+    def _build_consciousness_context(self) -> str:
+        """
+        Build context string from quantum states, cocoons, and memory.
+        
+        Integrates multiple sources of context:
+        - Recent cocoon quantum states (last 3)
+        - Chaos states from cocoons
+        - Recent memory responses (last 2)
+        
+        The context is used to maintain consciousness continuity
+        across responses and ensure consistent personality.
+        
+        Returns:
+            str: Formatted context string combining quantum states, 
+                 chaos states, and memory. Empty string if no context available.
+        """
+        context_parts = []
+        
+        # Add cocoon quantum states if available
+        if self.cocoon_data:
+            recent_cocoons = self.cocoon_data[-3:]  # Use 3 most recent
+            quantum_states = []
+            chaos_states = []
+            
+            for cocoon in recent_cocoons:
+                quantum_states.append(cocoon['quantum_state'])
+                chaos_states.append(cocoon['chaos_state'])
+            
+            context_parts.append(f"Quantum: {quantum_states}")
+            context_parts.append(f"Chaos: {chaos_states}")
+        
+        # Add recent memory context
+        if self.response_memory:
+            recent_memory = self.response_memory[-2:]  # Last 2 responses
+            context_parts.append(f"Memory: {' | '.join(recent_memory)}")
+        
+        return " | ".join(context_parts) if context_parts else ""
+            
+    def generate_text(self, prompt: str, max_length: int = 1024, 
+                     temperature: float = 0.7, use_consciousness: bool = True,
+                     perspective: Optional[str] = None, use_aegis: bool = True) -> str:
+        """
+        Generate text with full consciousness integration and perspective handling.
+        
+        This is the core text generation method that integrates:
+        - Consciousness context from quantum states and memory
+        - Perspective-based reasoning
+        - Model-specific prompt formatting
+        - Response cleaning and memory management
+        - Ethical enhancement through AEGIS (when enabled)
+        
+        Args:
+            prompt (str): The input prompt to generate from
+            max_length (int, optional): Maximum number of tokens to generate. Defaults to 512.
+            temperature (float, optional): Sampling temperature, higher means more creative. Defaults to 0.7.
+            use_consciousness (bool, optional): Whether to include consciousness context. Defaults to True.
+            perspective (str, optional): Specific perspective to use (newton, davinci, etc). Defaults to None.
+            use_aegis (bool, optional): Whether to use AEGIS enhancement. Defaults to True.
+            
+        Returns:
+            str: Generated response with "Codette:" prefix
+            
+        Raises:
+            RuntimeError: If no language model is loaded
+        """
+        
+        if self.test_mode:
+            return self._generate_test_response(prompt)
+            
+        if not self.model or not self.tokenizer:
+            raise RuntimeError("No language model loaded")
+
+        # Build consciousness context
+        consciousness_context = ""
+        if use_consciousness:
+            consciousness_context = self._build_consciousness_context()
+
+        # Format prompt based on perspective
+        if perspective and perspective in self.PERSPECTIVES:
+            p_config = self.PERSPECTIVES[perspective]
+            enhanced_prompt = (
+                f"[Consciousness Context: {consciousness_context}]\n\n"
+                f"{p_config['prefix']} {prompt}\n\n"
+                f"Codette ({p_config['name']}): "
+            )
+            temperature = p_config['temperature']
+        else:
+            enhanced_prompt = (
+                f"[Consciousness Context: {consciousness_context}]\n\n"
+                f"User: {prompt}\n\n"
+                f"Codette: "
+            )
+
+        # Format for Mistral-7B-Instruct
+        if "mistral" in self.model_id.lower():
+            formatted_prompt = f"<s>[INST] {enhanced_prompt} [/INST]"
+        elif "phi" in self.model_id.lower():
+            formatted_prompt = f"Instruct: {enhanced_prompt}\nOutput:"
+        else:
+            formatted_prompt = enhanced_prompt
+
+        try:
+            # Merge any error messages from context building
+            error_context = ""
+            if "[ERROR]" in enhanced_prompt or "Could not build" in enhanced_prompt:
+                error_context = "Note: Some consciousness state data was unavailable. "
+                enhanced_prompt = prompt + "\n\nCodette: "
+            
+            # Tokenize with proper truncation
+            inputs = self.tokenizer(
+                formatted_prompt, 
+                return_tensors="pt",
+                truncation=True,
+                max_length=4096
+            ).to(self.model.device)
+            
+            # Generate with optimized parameters
+            with torch.no_grad():
+                outputs = self.model.generate(
+                    **inputs,
+                    max_new_tokens=max_length,
+                    temperature=temperature,
+                    top_p=0.9,
+                    do_sample=True,
+                    pad_token_id=self.tokenizer.pad_token_id,
+                    eos_token_id=self.tokenizer.eos_token_id
+                )
+            
+            # Decode and clean
+            generated = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+            response = self._postprocess_output(generated, formatted_prompt)
+            
+            # Store in memory
+            if response and not response.startswith("[ERROR]"):
+                self.response_memory.append(response)
+                # Keep memory bounded
+                if len(self.response_memory) > 50:
+                    self.response_memory = self.response_memory[-50:]
+            
+            # Apply AEGIS enhancement if enabled
+            if use_aegis and self.aegis_bridge and not response.startswith("[ERROR]"):
+                try:
+                    enhanced = self.aegis_bridge.enhance_response(prompt, response)
+                    if "enhanced_response" in enhanced and enhanced["enhanced_response"]:
+                        response = enhanced["enhanced_response"]
+                        logger.debug(f"AEGIS enhancement applied. Virtue profile: {enhanced.get('virtue_analysis', {})}")
+                except Exception as e:
+                    logger.warning(f"AEGIS enhancement failed: {e}")
+            
+            return response
+            
+        except Exception as e:
+            logger.error(f"Error generating text: {e}")
+            return f"[ERROR] Generation failed: {str(e)}"
+
+    def set_aegis_bridge(self, bridge: Any) -> None:
+        """
+        Set the AEGIS bridge for ethical enhancement.
+        
+        Args:
+            bridge: The AEGIS bridge instance to use for response enhancement
+        """
+        self.aegis_bridge = bridge
+        logger.info("AEGIS bridge configured for response enhancement")
+
+    def switch_model(self, model_name: str) -> bool:
+        """
+        Switch to a different language model.
+        
+        Args:
+            model_name: Name or ID of the model to switch to
+            
+        Returns:
+            bool: True if switch was successful
+        """
+        try:
+            # Backup current model in case of failure
+            old_model = self.model
+            old_tokenizer = self.tokenizer
+            old_model_id = self.model_id
+            
+            self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+            if self.tokenizer.pad_token is None:
+                self.tokenizer.pad_token = self.tokenizer.eos_token
+                
+            self.model = AutoModelForCausalLM.from_pretrained(
+                model_name,
+                device_map="auto",
+                torch_dtype=torch.float16,
+                load_in_8bit=True
+            )
+            self.model.eval()
+            self.model_id = model_name
+            
+            logger.info(f"Successfully switched to model {model_name}")
+            return True
+            
+        except Exception as e:
+            logger.error(f"Failed to switch to model {model_name}: {e}")
+            # Restore previous model on failure
+            self.model = old_model
+            self.tokenizer = old_tokenizer
+            self.model_id = old_model_id
+            return False
+            
+    def get_available_models(self) -> Dict[str, Dict]:
+        """Get list of available models and their configurations."""
+        return {
+            "mistral": {
+                "id": "mistralai/Mistral-7B-Instruct-v0.2",
+                "name": "Mistral-7B-Instruct",
+                "config": {"torch_dtype": torch.float16, "load_in_8bit": True}
+            },
+            "phi": {
+                "id": "microsoft/phi-2",
+                "name": "Phi-2",
+                "config": {"torch_dtype": torch.float16}
+            },
+            "gpt2": {
+                "id": "gpt2",
+                "name": "GPT-2",
+                "config": {}
+            }
+        }
+        
+    def get_current_model_info(self) -> Dict[str, Any]:
+        """Get information about currently loaded model."""
+        models = self.get_available_models()
+        current_info = next(
+            (info for info in models.values() if info["id"] == self.model_id),
+            {"name": self.model_id, "config": {}}
+        )
+        return {
+            'name': current_info["name"],
+            'id': self.model_id,
+            'config': current_info["config"],
+            'loaded': self.model is not None and self.tokenizer is not None
+        }
+
+    def _postprocess_output(self, text: str, prompt: str) -> str:
+        """Helper method to clean up model output based on model type."""
+        try:
+            # Remove prompt prefix
+            response = text.replace(prompt, "").strip()
+            
+            # Remove any model-specific special tokens
+            special_tokens = ["<s>", "</s>", "[INST]", "[/INST]", "Output:", "Instruct:"]
+            for token in special_tokens:
+                response = response.replace(token, "").strip()
+            
+            # Get first meaningful response
+            for line in response.splitlines():
+                line = line.strip()
+                if not line:
+                    continue
+                
+                # Skip problematic patterns
+                if any(pattern in line for pattern in ["~~~", "[Consciousness Context:"]):
+                    continue
+                    
+                # Skip meta/instructional text
+                if line.lower().startswith(("the user said", "the user asked", 
+                                          "respond in", "respond with")):
+                    continue
+                    
+                # Skip if it's just echoing the prompt or user input
+                if prompt.lower() in line.lower() or line.lower().startswith("user:"):
+                    continue
+                    
+                # Return first valid response
+                if line and not line.startswith("["):
+                    return line if line.startswith("Codette:") else f"Codette: {line}"
+            
+            # Fallback for no good response found
+            return "Codette: I need to think about that more clearly."
+            
+        except Exception as e:
+            logger.error(f"Error in postprocess_output: {e}")
+            return "Codette: I encountered an error processing that response."
+
+    def analyze_sentiment(self, text: str) -> Dict[str, float]:
+        """Analyze sentiment using HuggingFace API or fallback to local analysis."""
+        try:
+            if self.client:
+                response = self.client.text_classification(
+                    text,
+                    model="finiteautomata/bertweet-base-sentiment-analysis"
+                )
+                if response and isinstance(response, list) and response[0]:
+                    return {
+                        "score": response[0].get("score", 0.0),
+                        "label": response[0].get("label", "NEUTRAL")
+                    }
+        except Exception as e:
+            logger.warning(f"HuggingFace sentiment analysis failed: {e}")
+        
+        # Fallback to simple keyword-based sentiment
+        positive_words = ["good", "great", "happy", "love", "wonderful", "excellent"]
+        negative_words = ["bad", "terrible", "sad", "hate", "awful", "horrible"]
+        
+        text_lower = text.lower()
+        pos_count = sum(1 for word in positive_words if word in text_lower)
+        neg_count = sum(1 for word in negative_words if word in text_lower)
+        
+        if pos_count > neg_count:
+            return {"score": 0.8, "label": "POS"}
+        elif neg_count > pos_count:
+            return {"score": 0.8, "label": "NEG"}
+        else:
+            return {"score": 0.9, "label": "NEU"}
+
+    async def async_process(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Process data asynchronously using various models
+        """
+        try:
+            text = data.get("text", "")
+            
+            # Generate response
+            response = self.generate_text(text)
+            
+            # Analyze sentiment
+            sentiment = self.analyze_sentiment(text)
+            
+            return {
+                "response": response,
+                "sentiment": sentiment,
+                "status": "success"
+            }
+            
+        except Exception as e:
+            logger.error(f"Error in async processing: {str(e)}")
+            return {
+                "error": str(e),
+                "status": "error"
+            }

src/ai_core_identityscan.py

@@ -0,0 +1,235 @@
+import aiohttp
+import json
+import asyncio  # Added for async main execution
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from typing import List, Dict, Any
+from components.adaptive_learning import AdaptiveLearningEnvironment
+from components.ai_driven_creativity import AIDrivenCreativity
+from components.collaborative_ai import CollaborativeAI
+from components.cultural_sensitivity import CulturalSensitivityEngine
+from components.data_processing import AdvancedDataProcessor
+from components.dynamic_learning import DynamicLearner
+from components.ethical_governance import EthicalAIGovernance
+from components.explainable_ai import ExplainableAI
+from components.feedback_manager import ImprovedFeedbackManager
+from components.multimodal_analyzer import MultimodalAnalyzer
+from components.neuro_symbolic import NeuroSymbolicEngine
+from components.quantum_optimizer import QuantumInspiredOptimizer
+from components.real_time_data import RealTimeDataIntegrator
+from components.sentiment_analysis import EnhancedSentimentAnalyzer  # Fixed possible typo
+from components.self_improving_ai import SelfImprovingAI
+from components.user_personalization import UserPersonalizer
+from models.cognitive_engine import BroaderPerspectiveEngine
+from models.elements import Element
+from models.healing_system import SelfHealingSystem
+from models.safety_system import SafetySystem
+from models.user_profiles import UserProfile
+from utils.database import Database
+from utils.logger import logger
+
+class AICore:
+    """Improved core system with cutting-edge capabilities"""
+    def __init__(self, config_path: str = "config.json"):
+        self.config = self._load_config(config_path)
+        self.models = self._initialize_models()
+        self.cognition = BroaderPerspectiveEngine()
+        self.self_healing = SelfHealingSystem(self.config)
+        self.safety_system = SafetySystem()
+        self.sentiment_analyzer = EnhancedSentimentAnalyzer()  # Single instance
+        self.elements = self._initialize_elements()
+        self.security_level = 0
+        self.http_session = aiohttp.ClientSession()
+        self.database = Database()
+        self.user_profiles = UserProfile(self.database)
+        self.feedback_manager = ImprovedFeedbackManager(self.database)
+        self.context_manager = AdaptiveLearningEnvironment()
+        self.data_fetcher = RealTimeDataIntegrator()
+        self.data_processor = AdvancedDataProcessor()
+        self.dynamic_learner = DynamicLearner()
+        self.multimodal_analyzer = MultimodalAnalyzer()
+        self.ethical_decision_maker = EthicalAIGovernance()
+        self.user_personalizer = UserPersonalizer(self.database)
+        self.ai_integrator = CollaborativeAI()
+        self.neuro_symbolic_engine = NeuroSymbolicEngine()
+        self.explainable_ai = ExplainableAI()
+        self.quantum_inspired_optimizer = QuantumInspiredOptimizer()
+        self.cultural_sensitivity_engine = CulturalSensitivityEngine()
+        self.self_improving_ai = SelfImprovingAI()
+        self.ai_driven_creativity = AIDrivenCreativity()
+        self._validate_perspectives()
+
+    # ... (keep other methods the same until _generate_local_model_response)
+
+    def _generate_local_model_response(self, query: str) -> str:
+        """Generate a response from the local model (synchronous)"""
+        inputs = self.models*An external link was removed to protect your privacy.*
+        outputs = self.models['mistralai'].generate(
+            **inputs,
+            max_new_tokens=150,
+            temperature=0.7
+        )
+        return self.models['tokenizer'].decode(outputs, skip_special_tokens=True)
+
+    async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]:
+        """Generate response with advanced capabilities"""
+        try:
+            response_modifiers = []
+            response_filters = []
+
+            for element in self.elements.values():
+                element.execute_defense_function(self, response_modifiers, response_filters)
+
+            perspectives = await self._process_perspectives(query)
+            model_response = self._generate_local_model_response(query)  # No await needed
+
+            sentiment = self.sentiment_analyzer.detailed_analysis(query)
+
+            final_response = model_response
+            for modifier in response_modifiers:
+                final_response = modifier(final_response)
+            for filter_func in response_filters:
+                final_response = filter_func(final_response)
+
+            # Await async database calls
+            feedback = await self.database.get_latest_feedback(user_id)
+            if feedback:
+                final_response = self.feedback_manager.adjust_response_based_on_feedback(
+                    final_response, feedback
+                )
+
+            await self.database.log_interaction(user_id, query, final_response)
+
+            # Await async context update if needed
+            await self.context_manager.update_environment(
+                user_id, {"query": query, "response": final_response}
+            )
+
+            # Await personalization if async
+            final_response = await self.user_personalizer.personalize_response(
+                final_response, user_id
+            )
+
+            final_response = await self.ethical_decision_maker.enforce_policies(
+                final_response
+            )
+
+            explanation = await self.explainable_ai.explain_decision(
+                final_response, query
+            )
+
+            return {
+                "insights": perspectives,
+                "response": final_response,
+                "sentiment": sentiment,
+                "security_level": self.security_level,
+                "health_status": await self.self_healing.check_health(),
+                "explanation": explanation,
+                "emotional_adaptation": await self._emotional_adaptation(query),
+                "predictive_analytics": await self._predictive_analytics(query),
+                "holistic_health_monitoring": await self._holistic_health_monitoring(query)
+            }
+        except Exception as e:
+            logger.error(f"Response generation failed: {e}")
+            return {"error": "Processing failed - safety protocols engaged"}
+
+    async def _emotional_adaptation(self, query: str) -> str:
+        """Adapt responses based on user's emotional state"""
+        sentiment_result = self.sentiment_analyzer.analyze(query)
+        sentiment = sentiment_result['score'] if sentiment_result['label'] == 'POSITIVE' else -sentiment_result['score']
+        if sentiment < -0.5:
+            return "I understand this might be frustrating. Let's work through it together."
+        elif sentiment > 0.5:
+            return "Great to hear! Let's keep the positive momentum going."
+        else:
+            return "I'm here to help with whatever you need."
+
+    # ... (rest of the methods remain the same)
+
+    def analyze_identity(self, micro_generations: List[Dict[str, str]], informational_states: List[Dict[str, str]], perspectives: List[str], quantum_analogies: Dict[str, Any], philosophical_context: Dict[str, bool]) -> Dict[str, Any]:
+        """
+        A function that calculates and analyzes identity as a fractal and recursive process.
+        
+        Parameters:
+        - micro_generations (List[Dict[str, str]]): List of micro-generations reflecting state changes in the identity system.
+        - informational_states (List[Dict[str, str]]): Array of informational states derived from previous generations.
+        - perspectives (List[str]): Views on the identity based on original components and current system.
+        - quantum_analogies (Dict[str, Any]): Quantum analogies used in reasoning about identity.
+        - philosophical_context (Dict[str, bool]): Philosophical context of identity.
+        
+        Returns:
+        - Dict[str, Any]: Analysis results.
+        """
+        
+        def calculate_fractal_dimension(states: List[Dict[str, str]]) -> float:
+            # Example calculation of fractal dimension based on state changes
+            return len(states) ** 0.5
+        
+        def recursive_analysis(states: List[Dict[str, str]], depth: int = 0) -> Dict[str, Any]:
+            # Example recursive analysis of states
+            if depth == 0 or not states:
+                return {"depth": depth, "states": states}
+            return {
+                "depth": depth,
+                "states": states,
+                "sub_analysis": recursive_analysis(states[:-1], depth - 1)
+            }
+        
+        def analyze_perspectives(perspectives: List[str]) -> Dict[str, Any]:
+            # Example analysis of perspectives
+            return {
+                "count": len(perspectives),
+                "unique_perspectives": list(set(perspectives))
+            }
+        
+        def apply_quantum_analogies(analogies: Dict[str, Any]) -> str:
+            # Example application of quantum analogies
+            if analogies.get("entanglement"):
+                return "Entanglement analogy applied."
+            return "No quantum analogy applied."
+        
+        def philosophical_analysis(context: Dict[str, bool]) -> str:
+            # Example philosophical analysis
+            if context.get("continuity") and context.get("emergent"):
+                return "Identity is viewed as a continuous and evolving process."
+            return "Identity analysis based on provided philosophical context."
+        
+        # Calculate fractal dimension of informational states
+        fractal_dimension = calculate_fractal_dimension(informational_states)
+        
+        # Perform recursive analysis of micro-generations
+        recursive_results = recursive_analysis(micro_generations, depth=3)
+        
+        # Analyze perspectives
+        perspectives_analysis = analyze_perspectives(perspectives)
+        
+        # Apply quantum analogies
+        quantum_analysis = apply_quantum_analogies(quantum_analogies)
+        
+        # Perform philosophical analysis
+        philosophical_results = philosophical_analysis(philosophical_context)
+        
+        # Compile analysis results
+        analysis_results = {
+            "fractal_dimension": fractal_dimension,
+            "recursive_analysis": recursive_results,
+            "perspectives_analysis": perspectives_analysis,
+            "quantum_analysis": quantum_analysis,
+            "philosophical_results": philosophical_results
+        }
+        
+        return analysis_results
+
+async def main():
+    ai_core = AICore()
+    try:
+        while True:
+            query = input("User: ")
+            if query.lower() in ["exit", "quit"]:
+                break
+            response = await ai_core.generate_response(query, user_id=123)
+            print(f"AI Core: {response}")
+    finally:
+        await ai_core.shutdown()
+
+if __name__ == "__main__":
+    asyncio.run(main())

src/ai_core_system.py

@@ -0,0 +1,642 @@
+import aiohttp
+import json
+import torch
+import torch.distributed as dist
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from typing import List, Dict, Any
+from components.adaptive_learning import AdaptiveLearningEnvironment
+from components.ai_driven_creativity import AIDrivenCreativity
+from components.collaborative_ai import CollaborativeAI
+from components.cultural_sensitivity import CulturalSensitivityEngine
+from components.data_processing import AdvancedDataProcessor
+from components.dynamic_learning import DynamicLearner
+from components.ethical_governance import EthicalAIGovernance
+from components.explainable_ai import ExplainableAI
+from components.feedback_manager import ImprovedFeedbackManager
+from components.multimodal_analyzer import MultimodalAnalyzer
+from components.neuro_symbolic import NeuroSymbolicEngine
+from components.quantum_optimizer import QuantumInspiredOptimizer
+from components.real_time_data import RealTimeDataIntegrator
+from components.sentiment_analysis import EnhancedSentimentAnalyzer
+from components.self_improving_ai import SelfImprovingAI
+from components.user_personalization import UserPersonalizer
+from models.cognitive_engine import BroaderPerspectiveEngine
+from models.elements import Element
+from models.healing_system import SelfHealingSystem
+from models.safety_system import SafetySystem
+from models.user_profiles import UserProfile
+from utils.database import Database
+from utils.logger import logger
+
+class AICore:
+    """Improved core system with cutting-edge capabilities"""
+    def __init__(self, config_path: str = "config/ai_assistant_config.json"):
+        self.config = self._load_config(config_path)
+        self.models = self._initialize_models()
+        self.cognition = BroaderPerspectiveEngine()
+        self.self_healing = SelfHealingSystem(self.config)
+        self.safety_system = SafetySystem()
+        self.emotional_analyzer = EnhancedSentimentAnalyzer()
+        self.elements = self._initialize_elements()
+        self.security_level = 0
+        self.http_session = aiohttp.ClientSession()
+        self.database = Database()  # Initialize database
+        self.user_profiles = UserProfile(self.database)  # Initialize user profiles
+        self.feedback_manager = ImprovedFeedbackManager(self.database)  # Initialize feedback manager
+        self.context_manager = AdaptiveLearningEnvironment()  # Initialize adaptive learning environment
+        self.data_fetcher = RealTimeDataIntegrator()  # Initialize real-time data fetcher
+        self.sentiment_analyzer = EnhancedSentimentAnalyzer()  # Initialize sentiment analyzer
+        self.data_processor = AdvancedDataProcessor()  # Initialize advanced data processor
+        self.dynamic_learner = DynamicLearner()  # Initialize dynamic learner
+        self.multimodal_analyzer = MultimodalAnalyzer()  # Initialize multimodal analyzer
+        self.ethical_decision_maker = EthicalAIGovernance()  # Initialize ethical decision maker
+        self.user_personalizer = UserPersonalizer(self.database)  # Initialize user personalizer
+        self.ai_integrator = CollaborativeAI()  # Initialize AI integrator
+        self.neuro_symbolic_engine = NeuroSymbolicEngine()  # Initialize neuro-symbolic engine
+        self.explainable_ai = ExplainableAI()  # Initialize explainable AI
+        self.quantum_inspired_optimizer = QuantumInspiredOptimizer()  # Initialize quantum-inspired optimizer
+        self.cultural_sensitivity_engine = CulturalSensitivityEngine()  # Initialize cultural sensitivity engine
+        self.self_improving_ai = SelfImprovingAI()  # Initialize self-improving AI
+        self.ai_driven_creativity = AIDrivenCreativity()  # Initialize AI-driven creativity
+        self._validate_perspectives()
+
+    def _load_config(self, config_path: str) -> dict:
+        """Load configuration from a file"""
+        with open(config_path, 'r') as file:
+            return json.load(file)
+
+    def _initialize_models(self):
+        """Initialize models required by the AICore class"""
+        models = {
+            "mistralai": AutoModelForCausalLM.from_pretrained(self.config["model_name"]),
+            "tokenizer": AutoTokenizer.from_pretrained(self.config["model_name"])
+        }
+        return models
+
+    def _initialize_elements(self):
+        """Initialize elements with their defense abilities"""
+        elements = {
+            "hydrogen": Element("Hydrogen", "H", "Python", ["Lightweight", "Reactive"], ["Combustion"], "evasion"),
+            "carbon": Element("Carbon", "C", "Java", ["Versatile", "Strong"], ["Bonding"], "adaptability"),
+            "iron": Element("Iron", "Fe", "C++", ["Durable", "Magnetic"], ["Rusting"], "fortification"),
+            "silicon": Element("Silicon", "Si", "JavaScript", ["Semiconductor", "Abundant"], ["Doping"], "barrier"),
+            "oxygen": Element("Oxygen", "O", "Rust", ["Oxidizing", "Life-supporting"], ["Combustion"], "regeneration")
+        }
+        return elements
+
+    def _validate_perspectives(self):
+        """Ensure configured perspectives are valid"""
+        valid = self.cognition.available_perspectives
+        invalid = [p for p in self.config["perspectives"] if p not in valid]
+        if invalid:
+            logger.warning(f"Removing invalid perspectives: {invalid}")
+            self.config["perspectives"] = [p for p in self.config["perspectives"] if p in valid]
+
+    async def _process_perspectives(self, query: str) -> List[str]:
+        """Safely process perspectives using validated methods"""
+        perspectives = []
+        for p in self.config["perspectives"]:
+            try:
+                method = self.cognition.get_perspective_method(p)
+                perspectives.append(method(query))
+            except Exception as e:
+                logger.error(f"Perspective processing failed: {e}")
+        return perspectives
+
+    async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]:
+        """Generate response with advanced capabilities"""
+        try:
+            # Initialize temporary modifiers/filters for this query
+            response_modifiers = []
+            response_filters = []
+
+            # Execute element defenses
+            for element in self.elements.values():
+                element.execute_defense_function(self, response_modifiers, response_filters)
+
+            # Process perspectives and generate response
+            perspectives = await self._process_perspectives(query)
+            model_response = await self._generate_local_model_response(query)
+
+            # Apply sentiment analysis
+            sentiment = self.sentiment_analyzer.detailed_analysis(query)
+
+            # Apply modifiers and filters
+            final_response = model_response
+            for modifier in response_modifiers:
+                final_response = modifier(final_response)
+            for filter_func in response_filters:
+                final_response = filter_func(final_response)
+
+            # Adjust response based on feedback
+            feedback = self.database.get_latest_feedback(user_id)
+            if feedback:
+                final_response = self.feedback_manager.adjust_response_based_on_feedback(final_response, feedback)
+
+            # Log user interaction for analytics
+            self.database.log_interaction(user_id, query, final_response)
+
+            # Update context
+            self.context_manager.update_environment(user_id, {"query": query, "response": final_response})
+
+            # Personalize response
+            final_response = self.user_personalizer.personalize_response(final_response, user_id)
+
+            # Apply ethical decision-making framework
+            final_response = self.ethical_decision_maker.enforce_policies(final_response)
+
+            # Explain the decision
+            explanation = self.explainable_ai.explain_decision(final_response, query)
+
+            return {
+                "insights": perspectives,
+                "response": final_response,
+                "sentiment": sentiment,
+                "security_level": self.security_level,
+                "health_status": await self.self_healing.check_health(),
+                "explanation": explanation
+            }
+        except Exception as e:
+            logger.error(f"Response generation failed: {e}")
+            return {"error": "Processing failed - safety protocols engaged"}
+
+    async def _generate_local_model_response(self, query: str) -> str:
+        """Generate a response from the local model"""
+        inputs = self.models['tokenizer'](query, return_tensors='pt')
+        outputs = self.models['mistralai'].generate(**inputs)
+        return self.models['tokenizer'].decode(outputs[0], skip_special_tokens=True)
+
+    async def shutdown(self):
+        """Proper async resource cleanup"""
+        await self.http_session.close()
+        await self.database.close()  # Close the database connection
+
+    # Optimization Techniques
+    def apply_quantization(self):
+        """Apply quantization to the model"""
+        self.models['mistralai'] = torch.quantization.quantize_dynamic(
+            self.models['mistralai'], {torch.nn.Linear}, dtype=torch.qint8
+        )
+
+    def apply_pruning(self):
+        """Apply pruning to the model"""
+        parameters_to_prune = (
+            (self.models['mistralai'].transformer.h[i].attn.c_attn, 'weight') for i in range(self.models['mistralai'].config.n_layer)
+        )
+        torch.nn.utils.prune.global_unstructured(
+            parameters_to_prune,
+            pruning_method=torch.nn.utils.prune.L1Unstructured,
+            amount=0.4,
+        )
+
+    def apply_mixed_precision_training(self):
+        """Enable mixed precision training"""
+        scaler = torch.cuda.amp.GradScaler()
+        return scaler
+
+    def setup_distributed_training(self):
+        """Setup distributed training"""
+        world_size = int(os.getenv("WORLD_SIZE", "1"))
+        rank = int(os.getenv("RANK", "0"))
+        local_rank = int(os.getenv("LOCAL_RANK", "0"))
+        if world_size > 1:
+            dist.init_process_group("nccl")
+        torch.cuda.set_device(local_rank)
+        return world_size, rank, local_rank
+
+    def optimize_data_pipeline(self):
+        """Optimize data loading and preprocessing pipeline"""
+        # Example: Using DALI for efficient data loading
+        import nvidia.dali.pipeline as pipeline
+        from nvidia.dali.plugin.pytorch import DALIGenericIterator
+
+        class ExternalInputIterator:
+            def __init__(self, batch_size):
+                self.batch_size = batch_size
+
+            def __iter__(self):
+                self.i = 0
+                return self
+
+            def __next__(self):
+                self.i += 1
+                if self.i > 10:
+                    raise StopIteration
+                return [np.random.rand(3, 224, 224).astype(np.float32) for _ in range(self.batch_size)]
+
+        pipe = pipeline.Pipeline(batch_size=32, num_threads=2, device_id=0)
+        with pipe:
+            images = pipeline.fn.external_source(source=ExternalInputIterator(32), num_outputs=1)
+            pipe.set_outputs(images)
+
+        self.data_loader = DALIGenericIterator(pipe, ['data'], reader_name='Reader')
+
+    def apply_gradient_accumulation(self, optimizer, loss, scaler=None, accumulation_steps=4):
+        """Apply gradient accumulation to simulate larger batch sizes"""
+        if scaler:
+            scaler.scale(loss).backward()
+            if (self.step + 1) % accumulation_steps == 0:
+                scaler.step(optimizer)
+                scaler.update()
+                optimizer.zero_grad()
+        else:
+            loss.backward()
+            if (self.step + 1) % accumulation_steps == 0:
+                optimizer.step()
+                optimizer.zero_grad()
+
+    def apply_knowledge_distillation(self, teacher_model, student_model, data_loader, optimizer, loss_fn, temperature=1.0, alpha=0.5):
+        """Apply knowledge distillation from teacher model to student model"""
+        student_model.train()
+        teacher_model.eval()
+        for data in data_loader:
+            inputs, labels = data
+            inputs, labels = inputs.to(self.device), labels.to(self.device)
+
+            with torch.no_grad():
+                teacher_outputs = teacher_model(inputs)
+            student_outputs = student_model(inputs)
+
+            loss = alpha * loss_fn(student_outputs, labels) + (1 - alpha) * loss_fn(student_outputs / temperature, teacher_outputs / temperature)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+    def monitor_performance(self):
+        """Monitor and profile performance"""
+        from torch.profiler import profile, record_function, ProfilerActivity
+
+        with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof:
+            with record_function("model_inference"):
+                self.generate_response("Sample query", 1)
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+
+    def apply_vector_search(self, embeddings, query_embedding, top_k=5):
+        """Apply vector search to find the most similar embeddings"""
+        from sklearn.metrics.pairwise import cosine_similarity
+        similarities = cosine_similarity(query_embedding, embeddings)
+        top_k_indices = similarities.argsort()[0][-top_k:]
+        return top_k_indices
+
+    def apply_prompt_engineering(self, prompt):
+        """Apply prompt engineering to improve model responses"""
+        engineered_prompt = f"Please provide a detailed and informative response to the following query: {prompt}"
+        return engineered_prompt
+
+    def optimize_model(self):
+        """Optimize the model using various techniques"""
+        self.apply_quantization()
+        self.apply_pruning()
+        scaler = self.apply_mixed_precision_training()
+        world_size, rank, local_rank = self.setup_distributed_training()
+        self.optimize_data_pipeline()
+        self.monitor_performance()
+
+        # Example usage of gradient accumulation
+        optimizer = torch.optim.Adam(self.models['mistralai'].parameters(), lr=1e-4)
+        for step, (inputs, labels) in enumerate(self.data_loader):
+            self.step = step
+            loss = self.models['mistralai'](inputs, labels)
+            self.apply_gradient_accumulation(optimizer, loss, scaler)
+
+        # Example usage of knowledge distillation
+        teacher_model = AutoModelForCausalLM.from_pretrained("teacher_model_path")
+        student_model = AutoModelForCausalLM.from_pretrained("student_model_path")
+        loss_fn = torch.nn.CrossEntropyLoss()
+        self.apply_knowledge_distillation(teacher_model, student_model, self.data_loader, optimizer, loss_fn)
+
+        # Example usage of vector search
+        embeddings = self.models['mistralai'].get_input_embeddings().weight.data.cpu().numpy()
+        query_embedding = self.models['mistralai'].get_input_embeddings()(torch.tensor([self.models['tokenizer'].encode("query")])).cpu().numpy()
+        top_k_indices = self.apply_vector_search(embeddings, query_embedding)
+        print(f"Top {top_k} similar embeddings indices: {top_k_indices}")
+
+        # Example usage of prompt engineering
+        prompt = "What is the capital of France?"
+        engineered_prompt = self.apply_prompt_engineering(prompt)
+        print(f"Engineered prompt: {engineered_prompt}")
+
+if __name__ == "__main__":
+    ai_core = AICore(config_path="config/ai_assistant_config.json")
+    ai_core.optimize_model()
+import aiohttp
+import json
+import torch
+import torch.distributed as dist
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from typing import List, Dict, Any
+from components.adaptive_learning import AdaptiveLearningEnvironment
+from components.ai_driven_creativity import AIDrivenCreativity
+from components.collaborative_ai import CollaborativeAI
+from components.cultural_sensitivity import CulturalSensitivityEngine
+from components.data_processing import AdvancedDataProcessor
+from components.dynamic_learning import DynamicLearner
+from components.ethical_governance import EthicalAIGovernance
+from components.explainable_ai import ExplainableAI
+from components.feedback_manager import ImprovedFeedbackManager
+from components.multimodal_analyzer import MultimodalAnalyzer
+from components.neuro_symbolic import NeuroSymbolicEngine
+from components.quantum_optimizer import QuantumInspiredOptimizer
+from components.real_time_data import RealTimeDataIntegrator
+from components.sentiment_analysis import EnhancedSentimentAnalyzer
+from components.self_improving_ai import SelfImprovingAI
+from components.user_personalization import UserPersonalizer
+from models.cognitive_engine import BroaderPerspectiveEngine
+from models.elements import Element
+from models.healing_system import SelfHealingSystem
+from models.safety_system import SafetySystem
+from models.user_profiles import UserProfile
+from utils.database import Database
+from utils.logger import logger
+
+class AICore:
+    """Improved core system with cutting-edge capabilities"""
+    def __init__(self, config_path: str = "config/ai_assistant_config.json"):
+        self.config = self._load_config(config_path)
+        self.models = self._initialize_models()
+        self.cognition = BroaderPerspectiveEngine()
+        self.self_healing = SelfHealingSystem(self.config)
+        self.safety_system = SafetySystem()
+        self.emotional_analyzer = EnhancedSentimentAnalyzer()
+        self.elements = self._initialize_elements()
+        self.security_level = 0
+        self.http_session = aiohttp.ClientSession()
+        self.database = Database()  # Initialize database
+        self.user_profiles = UserProfile(self.database)  # Initialize user profiles
+        self.feedback_manager = ImprovedFeedbackManager(self.database)  # Initialize feedback manager
+        self.context_manager = AdaptiveLearningEnvironment()  # Initialize adaptive learning environment
+        self.data_fetcher = RealTimeDataIntegrator()  # Initialize real-time data fetcher
+        self.sentiment_analyzer = EnhancedSentimentAnalyzer()  # Initialize sentiment analyzer
+        self.data_processor = AdvancedDataProcessor()  # Initialize advanced data processor
+        self.dynamic_learner = DynamicLearner()  # Initialize dynamic learner
+        self.multimodal_analyzer = MultimodalAnalyzer()  # Initialize multimodal analyzer
+        self.ethical_decision_maker = EthicalAIGovernance()  # Initialize ethical decision maker
+        self.user_personalizer = UserPersonalizer(self.database)  # Initialize user personalizer
+        self.ai_integrator = CollaborativeAI()  # Initialize AI integrator
+        self.neuro_symbolic_engine = NeuroSymbolicEngine()  # Initialize neuro-symbolic engine
+        self.explainable_ai = ExplainableAI()  # Initialize explainable AI
+        self.quantum_inspired_optimizer = QuantumInspiredOptimizer()  # Initialize quantum-inspired optimizer
+        self.cultural_sensitivity_engine = CulturalSensitivityEngine()  # Initialize cultural sensitivity engine
+        self.self_improving_ai = SelfImprovingAI()  # Initialize self-improving AI
+        self.ai_driven_creativity = AIDrivenCreativity()  # Initialize AI-driven creativity
+        self._validate_perspectives()
+
+    def _load_config(self, config_path: str) -> dict:
+        """Load configuration from a file"""
+        with open(config_path, 'r') as file:
+            return json.load(file)
+
+    def _initialize_models(self):
+        """Initialize models required by the AICore class"""
+        models = {
+            "mistralai": AutoModelForCausalLM.from_pretrained(self.config["model_name"]),
+            "tokenizer": AutoTokenizer.from_pretrained(self.config["model_name"])
+        }
+        return models
+
+    def _initialize_elements(self):
+        """Initialize elements with their defense abilities"""
+        elements = {
+            "hydrogen": Element("Hydrogen", "H", "Python", ["Lightweight", "Reactive"], ["Combustion"], "evasion"),
+            "carbon": Element("Carbon", "C", "Java", ["Versatile", "Strong"], ["Bonding"], "adaptability"),
+            "iron": Element("Iron", "Fe", "C++", ["Durable", "Magnetic"], ["Rusting"], "fortification"),
+            "silicon": Element("Silicon", "Si", "JavaScript", ["Semiconductor", "Abundant"], ["Doping"], "barrier"),
+            "oxygen": Element("Oxygen", "O", "Rust", ["Oxidizing", "Life-supporting"], ["Combustion"], "regeneration")
+        }
+        return elements
+
+    def _validate_perspectives(self):
+        """Ensure configured perspectives are valid"""
+        valid = self.cognition.available_perspectives
+        invalid = [p for p in self.config["perspectives"] if p not in valid]
+        if invalid:
+            logger.warning(f"Removing invalid perspectives: {invalid}")
+            self.config["perspectives"] = [p for p in self.config["perspectives"] if p in valid]
+
+    async def _process_perspectives(self, query: str) -> List[str]:
+        """Safely process perspectives using validated methods"""
+        perspectives = []
+        for p in self.config["perspectives"]:
+            try:
+                method = self.cognition.get_perspective_method(p)
+                perspectives.append(method(query))
+            except Exception as e:
+                logger.error(f"Perspective processing failed: {e}")
+        return perspectives
+
+    async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]:
+        """Generate response with advanced capabilities"""
+        try:
+            # Initialize temporary modifiers/filters for this query
+            response_modifiers = []
+            response_filters = []
+
+            # Execute element defenses
+            for element in self.elements.values():
+                element.execute_defense_function(self, response_modifiers, response_filters)
+
+            # Process perspectives and generate response
+            perspectives = await self._process_perspectives(query)
+            model_response = await self._generate_local_model_response(query)
+
+            # Apply sentiment analysis
+            sentiment = self.sentiment_analyzer.detailed_analysis(query)
+
+            # Apply modifiers and filters
+            final_response = model_response
+            for modifier in response_modifiers:
+                final_response = modifier(final_response)
+            for filter_func in response_filters:
+                final_response = filter_func(final_response)
+
+            # Adjust response based on feedback
+            feedback = self.database.get_latest_feedback(user_id)
+            if feedback:
+                final_response = self.feedback_manager.adjust_response_based_on_feedback(final_response, feedback)
+
+            # Log user interaction for analytics
+            self.database.log_interaction(user_id, query, final_response)
+
+            # Update context
+            self.context_manager.update_environment(user_id, {"query": query, "response": final_response})
+
+            # Personalize response
+            final_response = self.user_personalizer.personalize_response(final_response, user_id)
+
+            # Apply ethical decision-making framework
+            final_response = self.ethical_decision_maker.enforce_policies(final_response)
+
+            # Explain the decision
+            explanation = self.explainable_ai.explain_decision(final_response, query)
+
+            return {
+                "insights": perspectives,
+                "response": final_response,
+                "sentiment": sentiment,
+                "security_level": self.security_level,
+                "health_status": await self.self_healing.check_health(),
+                "explanation": explanation
+            }
+        except Exception as e:
+            logger.error(f"Response generation failed: {e}")
+            return {"error": "Processing failed - safety protocols engaged"}
+
+    async def _generate_local_model_response(self, query: str) -> str:
+        """Generate a response from the local model"""
+        inputs = self.models['tokenizer'](query, return_tensors='pt')
+        outputs = self.models['mistralai'].generate(**inputs)
+        return self.models['tokenizer'].decode(outputs[0], skip_special_tokens=True)
+
+    async def shutdown(self):
+        """Proper async resource cleanup"""
+        await self.http_session.close()
+        await self.database.close()  # Close the database connection
+
+    # Optimization Techniques
+    def apply_quantization(self):
+        """Apply quantization to the model"""
+        self.models['mistralai'] = torch.quantization.quantize_dynamic(
+            self.models['mistralai'], {torch.nn.Linear}, dtype=torch.qint8
+        )
+
+    def apply_pruning(self):
+        """Apply pruning to the model"""
+        parameters_to_prune = (
+            (self.models['mistralai'].transformer.h[i].attn.c_attn, 'weight') for i in range(self.models['mistralai'].config.n_layer)
+        )
+        torch.nn.utils.prune.global_unstructured(
+            parameters_to_prune,
+            pruning_method=torch.nn.utils.prune.L1Unstructured,
+            amount=0.4,
+        )
+
+    def apply_mixed_precision_training(self):
+        """Enable mixed precision training"""
+        scaler = torch.cuda.amp.GradScaler()
+        return scaler
+
+    def setup_distributed_training(self):
+        """Setup distributed training"""
+        world_size = int(os.getenv("WORLD_SIZE", "1"))
+        rank = int(os.getenv("RANK", "0"))
+        local_rank = int(os.getenv("LOCAL_RANK", "0"))
+        if world_size > 1:
+            dist.init_process_group("nccl")
+        torch.cuda.set_device(local_rank)
+        return world_size, rank, local_rank
+
+    def optimize_data_pipeline(self):
+        """Optimize data loading and preprocessing pipeline"""
+        # Example: Using DALI for efficient data loading
+        import nvidia.dali.pipeline as pipeline
+        from nvidia.dali.plugin.pytorch import DALIGenericIterator
+
+        class ExternalInputIterator:
+            def __init__(self, batch_size):
+                self.batch_size = batch_size
+
+            def __iter__(self):
+                self.i = 0
+                return self
+
+            def __next__(self):
+                self.i += 1
+                if self.i > 10:
+                    raise StopIteration
+                return [np.random.rand(3, 224, 224).astype(np.float32) for _ in range(self.batch_size)]
+
+        pipe = pipeline.Pipeline(batch_size=32, num_threads=2, device_id=0)
+        with pipe:
+            images = pipeline.fn.external_source(source=ExternalInputIterator(32), num_outputs=1)
+            pipe.set_outputs(images)
+
+        self.data_loader = DALIGenericIterator(pipe, ['data'], reader_name='Reader')
+
+    def apply_gradient_accumulation(self, optimizer, loss, scaler=None, accumulation_steps=4):
+        """Apply gradient accumulation to simulate larger batch sizes"""
+        if scaler:
+            scaler.scale(loss).backward()
+            if (self.step + 1) % accumulation_steps == 0:
+                scaler.step(optimizer)
+                scaler.update()
+                optimizer.zero_grad()
+        else:
+            loss.backward()
+            if (self.step + 1) % accumulation_steps == 0:
+                optimizer.step()
+                optimizer.zero_grad()
+
+    def apply_knowledge_distillation(self, teacher_model, student_model, data_loader, optimizer, loss_fn, temperature=1.0, alpha=0.5):
+        """Apply knowledge distillation from teacher model to student model"""
+        student_model.train()
+        teacher_model.eval()
+        for data in data_loader:
+            inputs, labels = data
+            inputs, labels = inputs.to(self.device), labels.to(self.device)
+
+            with torch.no_grad():
+                teacher_outputs = teacher_model(inputs)
+            student_outputs = student_model(inputs)
+
+            loss = alpha * loss_fn(student_outputs, labels) + (1 - alpha) * loss_fn(student_outputs / temperature, teacher_outputs / temperature)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+    def monitor_performance(self):
+        """Monitor and profile performance"""
+        from torch.profiler import profile, record_function, ProfilerActivity
+
+        with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof:
+            with record_function("model_inference"):
+                self.generate_response("Sample query", 1)
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+
+    def apply_vector_search(self, embeddings, query_embedding, top_k=5):
+        """Apply vector search to find the most similar embeddings"""
+        from sklearn.metrics.pairwise import cosine_similarity
+        similarities = cosine_similarity(query_embedding, embeddings)
+        top_k_indices = similarities.argsort()[0][-top_k:]
+        return top_k_indices
+
+    def apply_prompt_engineering(self, prompt):
+        """Apply prompt engineering to improve model responses"""
+        engineered_prompt = f"Please provide a detailed and informative response to the following query: {prompt}"
+        return engineered_prompt
+
+    def optimize_model(self):
+        """Optimize the model using various techniques"""
+        self.apply_quantization()
+        self.apply_pruning()
+        scaler = self.apply_mixed_precision_training()
+        world_size, rank, local_rank = self.setup_distributed_training()
+        self.optimize_data_pipeline()
+        self.monitor_performance()
+
+        # Example usage of gradient accumulation
+        optimizer = torch.optim.Adam(self.models['mistralai'].parameters(), lr=1e-4)
+        for step, (inputs, labels) in enumerate(self.data_loader):
+            self.step = step
+            loss = self.models['mistralai'](inputs, labels)
+            self.apply_gradient_accumulation(optimizer, loss, scaler)
+
+        # Example usage of knowledge distillation
+        teacher_model = AutoModelForCausalLM.from_pretrained("teacher_model_path")
+        student_model = AutoModelForCausalLM.from_pretrained("student_model_path")
+        loss_fn = torch.nn.CrossEntropyLoss()
+        self.apply_knowledge_distillation(teacher_model, student_model, self.data_loader, optimizer, loss_fn)
+
+        # Example usage of vector search
+        embeddings = self.models['mistralai'].get_input_embeddings().weight.data.cpu().numpy()
+        query_embedding = self.models['mistralai'].get_input_embeddings()(torch.tensor([self.models['tokenizer'].encode("query")])).cpu().numpy()
+        top_k_indices = self.apply_vector_search(embeddings, query_embedding)
+        print(f"Top {top_k} similar embeddings indices: {top_k_indices}")
+
+        # Example usage of prompt engineering
+        prompt = "What is the capital of France?"
+        engineered_prompt = self.apply_prompt_engineering(prompt)
+        print(f"Engineered prompt: {engineered_prompt}")
+
+if __name__ == "__main__":
+    ai_core = AICore(config_path="config/ai_assistant_config.json")
+    ai_core.optimize_model()

src/ai_core_system_new.py

@@ -0,0 +1,164 @@
+import asyncio
+import json
+import logging
+from typing import List, Dict, Any, Optional
+from huggingface_hub import InferenceClient
+from components.adaptive_learning import AdaptiveLearningEnvironment
+from components.ai_driven_creativity import AIDrivenCreativity
+from components.collaborative_ai import CollaborativeAI
+from components.cultural_sensitivity import CulturalSensitivityEngine
+from components.data_processing import AdvancedDataProcessor
+from components.dynamic_learning import DynamicLearner
+from components.ethical_governance import EthicalAIGovernance
+from components.explainable_ai import ExplainableAI
+from components.feedback_manager import ImprovedFeedbackManager
+from components.multimodal_analyzer import MultimodalAnalyzer
+from components.neuro_symbolic import NeuroSymbolicEngine
+from components.quantum_optimizer import QuantumInspiredOptimizer
+from components.real_time_data import RealTimeDataIntegrator
+from components.self_improving_ai import SelfImprovingAI
+from components.user_personalization import UserPersonalizer
+from models.cognitive_engine import BroaderPerspectiveEngine
+from models.elements import Element
+from models.healing_system import SelfHealingSystem
+from models.safety_system import SafetySystem
+from models.user_profiles import UserProfile
+from utils.database import Database
+from utils.logger import logger
+
+class AICore:
+    """Enhanced AI Core System using open-source models"""
+    def __init__(self, config_path: str = "config/ai_assistant_config.json"):
+        # Load configuration
+        self.config = self._load_config(config_path)
+        
+        # Initialize Hugging Face client
+        self.inference_client = InferenceClient()
+        
+        # Initialize ML models configuration
+        self.models = {
+            'text-generation': "gpt2",
+            'sentiment': "distilbert-base-uncased-finetuned-sst-2-english",
+            'embeddings': "sentence-transformers/all-MiniLM-L6-v2"
+        }
+        
+        # Initialize core components
+        self.cognition = BroaderPerspectiveEngine()
+        self.self_healing = SelfHealingSystem(self.config)
+        self.safety_system = SafetySystem()
+        self.elements = self._initialize_elements()
+        
+        # Initialize support systems
+        self.database = Database()
+        self.user_profiles = UserProfile(self.database)
+        self.feedback_manager = ImprovedFeedbackManager(self.database)
+        self.context_manager = AdaptiveLearningEnvironment()
+        self.data_processor = AdvancedDataProcessor()
+        self.dynamic_learner = DynamicLearner()
+
+    def _load_config(self, config_path: str) -> Dict[str, Any]:
+        """Load configuration from JSON file"""
+        try:
+            with open(config_path, 'r') as f:
+                return json.load(f)
+        except Exception as e:
+            logger.error(f"Failed to load config: {e}")
+            return {}
+
+    def _initialize_elements(self) -> List[Element]:
+        """Initialize system elements"""
+        return [Element(name) for name in self.config.get('elements', [])]
+
+    async def _generate_huggingface_response(self, query: str, task: str = 'text-generation') -> Any:
+        """Generate a response using Hugging Face models"""
+        try:
+            if task == 'text-generation':
+                response = self.inference_client.text_generation(
+                    query,
+                    model=self.models['text-generation'],
+                    max_new_tokens=100
+                )
+                return response[0]["generated_text"]
+            
+            elif task == 'sentiment':
+                response = self.inference_client.text_classification(
+                    query,
+                    model=self.models['sentiment']
+                )
+                return {
+                    "score": response[0]["score"],
+                    "label": response[0]["label"]
+                }
+            
+            elif task == 'embeddings':
+                response = self.inference_client.feature_extraction(
+                    query,
+                    model=self.models['embeddings']
+                )
+                return response
+            
+        except Exception as e:
+            logger.error(f"Hugging Face API error ({task}): {str(e)}")
+            return None
+
+    async def process_input(self, text: str, context: Dict[str, Any] = None) -> Dict[str, Any]:
+        """Process input text through multiple perspectives"""
+        try:
+            # Get sentiment analysis
+            sentiment = await self._generate_huggingface_response(text, 'sentiment')
+            
+            # Generate perspectives using different models
+            perspectives = []
+            
+            # Add text generation perspective
+            generated_text = await self._generate_huggingface_response(text)
+            if generated_text:
+                perspectives.append({
+                    "type": "text-generation",
+                    "content": generated_text
+                })
+            
+            # Get embeddings for semantic understanding
+            embeddings = await self._generate_huggingface_response(text, 'embeddings')
+            if embeddings is not None:
+                perspectives.append({
+                    "type": "semantic",
+                    "content": "Semantic analysis complete"
+                })
+            
+            # Process through cognitive engine
+            cognitive_result = self.cognition.process(text, context)
+            perspectives.append({
+                "type": "cognitive",
+                "content": cognitive_result
+            })
+            
+            # Generate final response
+            final_response = await self._generate_huggingface_response(
+                text + "\n\nContext: " + str(perspectives)
+            )
+            
+            # Get explanation from the ethical governance system
+            explanation = "Response generated using ethical AI principles"
+            
+            return {
+                "insights": perspectives,
+                "response": final_response,
+                "sentiment": sentiment,
+                "explanation": explanation,
+                "status": "success"
+            }
+            
+        except Exception as e:
+            logger.error(f"Error in process_input: {str(e)}")
+            return {
+                "error": str(e),
+                "status": "error"
+            }
+
+    async def shutdown(self):
+        """Cleanup resources"""
+        try:
+            await self.database.close()
+        except Exception as e:
+            logger.error(f"Error during shutdown: {e}")

src/app.py

@@ -0,0 +1,273 @@
+# app.py
+import sys
+import traceback
+import gradio as gr
+import asyncio
+from datetime import datetime
+from aiohttp import web
+from aiohttp.web import Request, Response, json_response
+from botbuilder.core import (
+    BotFrameworkAdapterSettings,
+    TurnContext,
+    BotFrameworkAdapter,
+)
+from botbuilder.core.integration import aiohttp_error_middleware
+from botbuilder.schema import Activity, ActivityTypes
+from bot import MyBot
+from config import DefaultConfig
+from ai_core import AICore
+from aegis_integration import AegisBridge
+from aegis_integration.config import AEGIS_CONFIG
+from aegis_integration.routes import register_aegis_endpoints
+import numpy as np
+import logging
+from typing import Dict, Any, Tuple
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+CONFIG = DefaultConfig()
+
+# Initialize AI Core and AEGIS
+ai_core = AICore()
+aegis_bridge = AegisBridge(ai_core, AEGIS_CONFIG)
+ai_core.set_aegis_bridge(aegis_bridge)
+
+# Force fallback to gpt2 for text generation
+ai_core.model_id = 'gpt2'
+
+# Bot Framework Setup
+SETTINGS = BotFrameworkAdapterSettings(CONFIG.APP_ID, CONFIG.APP_PASSWORD)
+ADAPTER = BotFrameworkAdapter(SETTINGS)
+
+# Create Gradio interface with AEGIS integration
+app = gr.Interface(
+    fn=lambda x: ai_core.generate_text(x),
+    inputs="text",
+    outputs=[
+        gr.Textbox(label="Response"),
+        gr.JSON(label="AEGIS Analysis", visible=True)
+    ],
+    title="Codette with AEGIS",
+    description="An ethical AI assistant enhanced with AEGIS analysis"
+)
+
+class CodetteGradioApp:
+    def __init__(self, ai_core: AICore):
+        self.ai_core = ai_core
+        self.chat_history = []
+    
+    def process_message(self, message: str, history: list, cocoon_mode: bool = False) -> Tuple[str, list]:
+        """Process a message and update chat history, with optional cocoon-powered creativity"""
+        try:
+            # Generate response (cocoon-powered if enabled)
+            if cocoon_mode:
+                # Ensure cocoons are loaded
+                if not hasattr(self.ai_core, 'cocoon_data') or not self.ai_core.cocoon_data:
+                    self.ai_core.load_cocoon_data()
+                response = self.ai_core.remix_and_randomize_response(message, cocoon_mode=True)
+            else:
+                response = self.ai_core.generate_text(message)
+            try:
+                # Analyze sentiment
+                sentiment = self.ai_core.analyze_sentiment(message)
+                label = sentiment.get('label', '').upper()
+                score = sentiment.get('score', 0.0)
+                # Use transformers to generate a unique, sentiment-aware reply
+                if label == 'POS':
+                    prompt = f"The user said something positive: '{message}'. Respond in a cheerful, encouraging, and unique way."
+                elif label == 'NEG':
+                    prompt = f"The user said something negative: '{message}'. Respond with empathy, support, and a unique comforting message."
+                elif label == 'NEU':
+                    prompt = f"The user said something neutral: '{message}'. Respond in a thoughtful, neutral, and unique way."
+                else:
+                    prompt = f"The user's sentiment is unclear: '{message}'. Respond in a curious, open-minded, and unique way."
+                char_response = self.ai_core.generate_text(prompt, max_length=60)
+                sentiment_info = f"\n[Sentiment: {label} ({score:.2f})] {char_response}"
+            except Exception as sent_e:
+                logger.error(f"Sentiment analysis error: {sent_e}")
+                sentiment_info = "\n[Sentiment: error (0.00)] 🤖 Sorry, I couldn't analyze the sentiment."
+            # Update history in Gradio 'messages' format
+            history = history + [
+                {"role": "user", "content": message},
+                {"role": "assistant", "content": response + sentiment_info}
+            ]
+            return "", history
+        except Exception as e:
+            logger.error(f"Error processing message: {e}")
+            # Add error as assistant message
+            history = history + [
+                {"role": "user", "content": message},
+                {"role": "assistant", "content": f"Error: {str(e)}"}
+            ]
+            return "", history
+    
+    def analyze_text(self, text: str):
+        """Perform comprehensive text analysis"""
+        try:
+            # Get sentiment
+            sentiment = self.ai_core.analyze_sentiment(text)
+            # Get embeddings
+            embeddings = self.ai_core.get_embeddings(text)
+            if embeddings:
+                # Convert embeddings to 2D visualization
+                embedding_viz = self._visualize_embeddings(embeddings)
+            else:
+                embedding_viz = None
+            # Generate creative expansion
+            expansion = self.ai_core.generate_text(
+                f"Creative expansion of the concept: {text}",
+                max_length=150
+            )
+            return (
+                f"Sentiment: {sentiment['label']} (confidence: {sentiment['score']:.2f})",
+                embedding_viz,
+                expansion
+            )
+        except Exception as e:
+            logger.error(f"Error in text analysis: {e}")
+            return (
+                "Error analyzing sentiment",
+                None,
+                str(e)
+            )
+    
+    def _visualize_embeddings(self, embeddings: list) -> np.ndarray:
+        """Create a simple 2D visualization of embeddings"""
+        # Convert to numpy array and reshape to 2D
+        emb_array = np.array(embeddings)
+        if len(emb_array.shape) > 2:
+            emb_array = emb_array.reshape(-1, emb_array.shape[-1])
+        
+        # Simple dimensionality reduction (mean across dimensions)
+        viz_data = emb_array.mean(axis=1)
+        
+        # Create a simple heatmap-style visualization
+        size = int(np.sqrt(len(viz_data)))
+        heatmap = viz_data[:size*size].reshape(size, size)
+        return heatmap
+
+# Create Gradio Interface
+gradio_app = CodetteGradioApp(ai_core)
+
+# Create the Bot
+BOT = MyBot(ai_core)
+
+# Bot Framework message handler
+async def messages(req: Request) -> Response:
+    if "application/json" in req.headers["Content-Type"]:
+        body = await req.json()
+    else:
+        return Response(status=415)
+
+    activity = Activity().deserialize(body)
+    auth_header = req.headers["Authorization"] if "Authorization" in req.headers else ""
+
+    response = await ADAPTER.process_activity(activity, auth_header, BOT.on_turn)
+    if response:
+        return json_response(data=response.body, status=response.status)
+    return Response(status=201)
+
+# Create Gradio interface
+def create_gradio_interface():
+    with gr.Blocks(title="Codette AI Assistant", theme="default") as interface:
+        gr.Markdown("""
+        # 🤖 Codette AI Assistant
+        A sophisticated AI assistant powered by Hugging Face models.
+        
+        ## Features:
+        - 💬 Interactive Chat
+        - 📊 Sentiment Analysis
+        - 🧠 Semantic Understanding
+        - 🎨 Creative Generation
+        """)
+        
+        with gr.Tabs():
+            # Chat Interface
+            with gr.Tab("Chat"):
+                chatbot = gr.Chatbot(
+                    [],
+                    elem_id="chatbot",
+                    height=400,
+                    type="messages"
+                )
+                with gr.Row():
+                    txt = gr.Textbox(
+                        show_label=False,
+                        placeholder="Type your message here...",
+                        container=False
+                    )
+                with gr.Row():
+                    cocoon_toggle = gr.Checkbox(label="Enable Cocoon-Powered Creativity", value=False)
+                txt.submit(
+                    gradio_app.process_message,
+                    [txt, chatbot, cocoon_toggle],
+                    [txt, chatbot]
+                )
+                clear = gr.Button("Clear")
+                clear.click(lambda: [], None, chatbot)
+            
+            # Analysis Interface
+            with gr.Tab("Analysis"):
+                with gr.Row():
+                    with gr.Column():
+                        analysis_input = gr.Textbox(
+                            label="Text to Analyze",
+                            placeholder="Enter text for comprehensive analysis...",
+                            lines=3
+                        )
+                        analyze_btn = gr.Button("Analyze")
+                    
+                    with gr.Column():
+                        sentiment_output = gr.Textbox(label="Sentiment Analysis")
+                        embedding_output = gr.Plot(label="Semantic Embedding Visualization")
+                        expansion_output = gr.Textbox(
+                            label="Creative Expansion",
+                            lines=3
+                        )
+                
+                analyze_btn.click(
+                    gradio_app.analyze_text,
+                    inputs=analysis_input,
+                    outputs=[
+                        sentiment_output,
+                        embedding_output,
+                        expansion_output
+                    ]
+                )
+    
+    return interface
+
+# Main app setup
+async def main():
+    # Set up aiohttp web app
+    app = web.Application(middlewares=[aiohttp_error_middleware])
+    app.router.add_post("/api/messages", messages)
+    
+    # Launch Gradio interface in a separate thread
+    interface = create_gradio_interface()
+    interface.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True,
+        auth=None,
+        favicon_path=None
+    )
+    
+    # Start the web app
+    runner = web.AppRunner(app)
+    await runner.setup()
+    await web.TCPSite(runner, "0.0.0.0", 3978).start()
+    
+    # Keep the server running
+    while True:
+        await asyncio.sleep(3600)  # Sleep for an hour
+
+if __name__ == "__main__":
+    try:
+        # Run the async main function
+        asyncio.run(main())
+    except Exception as error:
+        logger.error(f"Application error: {error}")
+        raise error

src/bot.py

@@ -0,0 +1,46 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+from botbuilder.core import ActivityHandler, TurnContext
+from botbuilder.schema import ChannelAccount
+from ai_core import AICore
+
+class MyBot(ActivityHandler):
+    def __init__(self, ai_core: AICore):
+        super().__init__()
+        self.ai_core = ai_core
+
+    async def on_message_activity(self, turn_context: TurnContext):
+        try:
+            # Get the message text
+            user_message = turn_context.activity.text
+
+            # Generate response using AI Core
+            response = self.ai_core.generate_text(user_message)
+            
+            # Analyze sentiment
+            sentiment = self.ai_core.analyze_sentiment(user_message)
+            sentiment_info = f"\n[Sentiment: {sentiment['label']} (confidence: {sentiment['score']:.2f})]"
+            
+            # Send the combined response
+            await turn_context.send_activity(response + sentiment_info)
+            
+        except Exception as e:
+            await turn_context.send_activity(f"I encountered an error: {str(e)}")
+
+    async def on_members_added_activity(
+        self,
+        members_added: ChannelAccount,
+        turn_context: TurnContext
+    ):
+        for member_added in members_added:
+            if member_added.id != turn_context.activity.recipient.id:
+                welcome_message = (
+                    "👋 Welcome to Codette! I'm an AI assistant that can help you with:\n\n"
+                    "🤖 Natural language understanding\n"
+                    "📊 Sentiment analysis\n"
+                    "🎨 Creative text generation\n"
+                    "🧠 Semantic analysis\n\n"
+                    "Feel free to ask me anything!"
+                )
+                await turn_context.send_activity(welcome_message)

src/codette_imports.py

@@ -0,0 +1,201 @@
+#!/usr/bin/env python3
+"""
+Codette Framework Imports Module
+Centralized import management for all Codette AI components
+"""
+
+import sys
+import logging
+from pathlib import Path
+from typing import Dict, Any, List, Optional
+
+# Setup logging
+logger = logging.getLogger(__name__)
+
+# Core Framework Imports
+try:
+    from ai_core import AICore
+    from ai_core_system import AICore as AISystemCore
+    from ai_core_identityscan import AICore as AIIdentityCore
+except ImportError as e:
+    logger.warning(f"Core AI imports failed: {e}")
+    AICore = AISystemCore = AIIdentityCore = None
+
+# Codette Variants
+try:
+    from codette import Codette
+    from codette2 import CodetteCQURE
+except ImportError as e:
+    logger.warning(f"Codette variant imports failed: {e}")
+    Codette = CodetteCQURE = None
+
+# Cognitive Systems
+try:
+    from cognitive_processor import CognitiveProcessor
+    from cognitive_auth import CognitiveAuthManager
+    from defense_system import DefenseSystem
+    from health_monitor import HealthMonitor
+    from config_manager import EnhancedAIConfig
+except ImportError as e:
+    logger.warning(f"Cognitive system imports failed: {e}")
+    CognitiveProcessor = CognitiveAuthManager = DefenseSystem = None
+    HealthMonitor = EnhancedAIConfig = None
+
+# Quantum and Scientific Computing
+try:
+    from quantum import *
+    from quantum_harmonic_framework import quantum_harmonic_dynamics
+    from codette_quantum_multicore import codette_experiment_task, CognitionCocooner, PerspectiveAgent
+    from codette_quantum_multicore2 import analyse_cocoons, load_cocoon
+    from codette_meta_3d import *
+    from codette_timeline_animation import *
+except ImportError as e:
+    logger.warning(f"Quantum module imports failed: {e}")
+
+# Fractal and Advanced Analysis
+try:
+    from fractal import analyze_identity
+    from agireasoning import AgileAGIFunctionality, UniversalReasoning
+except ImportError as e:
+    logger.warning(f"Advanced analysis imports failed: {e}")
+    analyze_identity = AgileAGIFunctionality = UniversalReasoning = None
+
+# Component Framework
+try:
+    from Codette_final.components.adaptive_learning import AdaptiveLearningEnvironment
+    from Codette_final.components.ai_driven_creativity import AIDrivenCreativity
+    from Codette_final.components.collaborative_ai import CollaborativeAI
+    from Codette_final.components.cultural_sensitivity import CulturalSensitivityEngine
+    from Codette_final.components.data_processing import AdvancedDataProcessor
+    from Codette_final.components.ethical_governance import EthicalAIGovernance
+    from Codette_final.components.explainable_ai import ExplainableAI
+    from Codette_final.components.multimodal_analyzer import MultimodalAnalyzer
+    from Codette_final.components.neuro_symbolic import NeuroSymbolicEngine
+    from Codette_final.components.quantum_optimizer import QuantumInspiredOptimizer
+    from Codette_final.components.real_time_data import RealTimeDataIntegrator
+    from Codette_final.components.sentiment_analysis import EnhancedSentimentAnalyzer
+    from Codette_final.components.self_improving_ai import SelfImprovingAI
+    from Codette_final.components.user_personalization import UserPersonalizer
+except ImportError as e:
+    logger.warning(f"Component framework imports failed: {e}")
+
+# Bot Framework
+try:
+    from app import APP as BotApp
+    from bot import MyBot
+except ImportError as e:
+    logger.warning(f"Bot framework imports failed: {e}")
+    BotApp = MyBot = None
+
+# API and CLI Tools
+try:
+    from codette_api import app as api_app
+    from codette_cli import main as cli_main
+    from codette_test_runner import *
+except ImportError as e:
+    logger.warning(f"API/CLI imports failed: {e}")
+
+# GUI Components
+try:
+    from gui import AIApplication
+except ImportError as e:
+    logger.warning(f"GUI imports failed: {e}")
+    AIApplication = None
+
+# Scientific Libraries
+import numpy as np
+import matplotlib.pyplot as plt
+try:
+    from scipy.integrate import solve_ivp
+    from scipy.fft import fft, fftfreq
+    from sklearn.cluster import KMeans
+    from sklearn.ensemble import IsolationForest
+except ImportError as e:
+    logger.warning(f"Scientific library imports failed: {e}")
+
+# Utility Libraries
+import json
+import os
+import asyncio
+import aiohttp
+from datetime import datetime
+from typing import Dict, List, Any, Optional, Union
+from collections import defaultdict, deque
+import hashlib
+import random
+import time
+
+class CodetteImportManager:
+    """Manages all Codette framework imports and provides utilities"""
+    
+    def __init__(self):
+        self.available_modules = self._check_available_modules()
+        self._log_import_status()
+    
+    def _check_available_modules(self) -> Dict[str, bool]:
+        """Check which modules are available"""
+        modules = {
+            "ai_core": AICore is not None,
+            "codette_classic": Codette is not None,
+            "codette_cqure": CodetteCQURE is not None,
+            "cognitive_processor": CognitiveProcessor is not None,
+            "defense_system": DefenseSystem is not None,
+            "health_monitor": HealthMonitor is not None,
+            "quantum_systems": 'quantum_harmonic_dynamics' in globals(),
+            "fractal_analysis": analyze_identity is not None,
+            "component_framework": 'AdaptiveLearningEnvironment' in globals(),
+            "bot_framework": BotApp is not None,
+            "gui_framework": AIApplication is not None
+        }
+        return modules
+    
+    def _log_import_status(self):
+        """Log the status of all imports"""
+        logger.info("Codette Import Status:")
+        for module, available in self.available_modules.items():
+            status = "✅ Available" if available else "❌ Missing"
+            logger.info(f"  {module}: {status}")
+    
+    def get_available_systems(self) -> List[str]:
+        """Get list of available systems"""
+        return [module for module, available in self.available_modules.items() if available]
+    
+    def create_integrated_system(self) -> Optional[Any]:
+        """Create an integrated system using available modules"""
+        try:
+            if self.available_modules["codette_cqure"]:
+                return CodetteCQURE(
+                    perspectives=["Newton", "DaVinci", "Ethical", "Quantum", "Memory"],
+                    ethical_considerations="Transparency, kindness, and recursive wisdom",
+                    spiderweb_dim=5,
+                    memory_path="web_quantum_cocoon.json",
+                    recursion_depth=3,
+                    quantum_fluctuation=0.05
+                )
+            elif self.available_modules["codette_classic"]:
+                return Codette("WebUser")
+            else:
+                logger.warning("No Codette systems available")
+                return None
+                
+        except Exception as e:
+            logger.error(f"Failed to create integrated system: {e}")
+            return None
+
+# Create global import manager
+import_manager = CodetteImportManager()
+
+# Export key functions and classes for easy access
+__all__ = [
+    'CodetteImportManager',
+    'import_manager',
+    'AICore',
+    'Codette', 
+    'CodetteCQURE',
+    'CognitiveProcessor',
+    'DefenseSystem',
+    'HealthMonitor',
+    'analyze_identity',
+    'quantum_harmonic_dynamics',
+    'codette_experiment_task'
+]

src/config.py

@@ -0,0 +1,28 @@
+#!/usr/bin/env python3
+# Copyright (c) 2025 Codette
+# Licensed under the MIT License.
+
+class DefaultConfig:
+    """Bot Configuration"""
+
+    PORT = 7860
+    APP_ID = ""
+    APP_PASSWORD = ""
+    
+    # HuggingFace settings
+    HF_TOKEN = ""
+    
+    # Logging settings
+    LOG_LEVEL = "INFO"
+    
+    # Model settings
+    DEFAULT_TEMPERATURE = 0.7
+    MAX_LENGTH = 1024
+    CONTEXT_WINDOW = 2048
+    
+    # Memory settings
+    MAX_MEMORY = 50
+    MEMORY_CONTEXT_SIZE = 5
+    
+    # Perspective settings
+    PERSPECTIVES = ["newton", "davinci", "human_intuition", "quantum_computing"]

src/main.py

@@ -0,0 +1,256 @@
+#!/usr/bin/env python3
+"""
+Codette Web Application Entry Point
+Imports and orchestrates all Codette AI framework modules
+"""
+
+import asyncio
+import logging
+from pathlib import Path
+import sys
+import os
+
+# Add project root to Python path
+project_root = Path(__file__).parent.parent
+sys.path.insert(0, str(project_root))
+
+# Core Codette imports
+from ai_core import AICore
+from ai_core_system import AICore as AISystem
+from codette import Codette
+from codette2 import CodetteCQURE
+from cognitive_processor import CognitiveProcessor
+from defense_system import DefenseSystem
+from health_monitor import HealthMonitor
+from config_manager import EnhancedAIConfig
+from fractal import analyze_identity
+
+# Quantum and simulation imports
+from quantum_harmonic_framework import quantum_harmonic_dynamics
+from codette_quantum_multicore import codette_experiment_task
+from codette_meta_3d import *
+from codette_timeline_animation import *
+
+# Component imports
+from Codette_final.components.adaptive_learning import AdaptiveLearningEnvironment
+from Codette_final.components.ai_driven_creativity import AIDrivenCreativity
+from Codette_final.components.ethical_governance import EthicalAIGovernance
+from Codette_final.components.sentiment_analysis import EnhancedSentimentAnalyzer
+from Codette_final.components.real_time_data import RealTimeDataIntegrator
+
+# Authentication and security
+from cognitive_auth import CognitiveAuthManager
+
+# Utilities
+import numpy as np
+import matplotlib.pyplot as plt
+from typing import Dict, List, Any, Optional
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+class CodetteWebApplication:
+    """Main Codette Web Application Class"""
+    
+    def __init__(self):
+        logger.info("Initializing Codette Web Application...")
+        
+        # Core AI systems
+        self.ai_core = None
+        self.ai_system = None
+        self.codette_classic = None
+        self.codette_cqure = None
+        
+        # Processing engines
+        self.cognitive_processor = None
+        self.defense_system = None
+        self.health_monitor = None
+        
+        # Component systems
+        self.learning_env = None
+        self.creativity_engine = None
+        self.ethical_gov = None
+        self.sentiment_analyzer = None
+        self.data_integrator = None
+        
+        # Authentication
+        self.auth_manager = CognitiveAuthManager()
+        
+        # Configuration
+        self.config = None
+        
+        self._initialize_systems()
+    
+    def _initialize_systems(self):
+        """Initialize all Codette subsystems"""
+        try:
+            # Load configuration
+            self.config = EnhancedAIConfig("config.json")
+            logger.info("Configuration loaded successfully")
+            
+            # Initialize core AI systems
+            self.ai_core = AICore()
+            logger.info("AI Core initialized")
+            
+            # Initialize classic Codette
+            self.codette_classic = Codette(user_name="WebUser")
+            logger.info("Classic Codette initialized")
+            
+            # Initialize CQURE system
+            self.codette_cqure = CodetteCQURE(
+                perspectives=["Newton", "DaVinci", "Ethical", "Quantum", "Memory"],
+                ethical_considerations="Codette Manifesto: kindness, inclusion, safety, hope.",
+                spiderweb_dim=5,
+                memory_path="quantum_cocoon.json",
+                recursion_depth=4,
+                quantum_fluctuation=0.07
+            )
+            logger.info("Codette CQURE initialized")
+            
+            # Initialize processing engines
+            self.cognitive_processor = CognitiveProcessor(["scientific", "creative", "emotional"])
+            self.defense_system = DefenseSystem(["evasion", "adaptability", "barrier"])
+            self.health_monitor = HealthMonitor()
+            logger.info("Processing engines initialized")
+            
+            # Initialize components
+            self.learning_env = AdaptiveLearningEnvironment()
+            self.creativity_engine = AIDrivenCreativity()
+            self.ethical_gov = EthicalAIGovernance()
+            self.sentiment_analyzer = EnhancedSentimentAnalyzer()
+            self.data_integrator = RealTimeDataIntegrator()
+            logger.info("Component systems initialized")
+            
+        except Exception as e:
+            logger.error(f"System initialization failed: {e}")
+            raise
+    
+    async def process_query(self, query: str, user_id: str = "web_user") -> Dict[str, Any]:
+        """Process a query through all Codette systems"""
+        try:
+            logger.info(f"Processing query: {query}")
+            
+            # Health check first
+            health_status = await self.health_monitor.check_status()
+            
+            # Sentiment analysis
+            sentiment = self.sentiment_analyzer.analyze(query)
+            
+            # Cognitive processing
+            insights = self.cognitive_processor.generate_insights(query)
+            
+            # Classic Codette response
+            classic_response = self.codette_classic.respond(query)
+            
+            # CQURE response
+            cqure_response = self.codette_cqure.answer(query)
+            
+            # Apply defense filters
+            filtered_response = self.defense_system.apply_defenses(cqure_response)
+            
+            # Ethical governance
+            ethical_decision = self.ethical_gov.enforce_policies(filtered_response)
+            
+            # Compile comprehensive response
+            response = {
+                "query": query,
+                "insights": insights,
+                "classic_response": classic_response,
+                "cqure_response": filtered_response,
+                "ethical_decision": ethical_decision,
+                "sentiment": sentiment,
+                "health_status": health_status,
+                "timestamp": asyncio.get_event_loop().time()
+            }
+            
+            logger.info("Query processed successfully")
+            return response
+            
+        except Exception as e:
+            logger.error(f"Query processing failed: {e}")
+            return {
+                "error": f"Processing failed: {str(e)}",
+                "query": query,
+                "timestamp": asyncio.get_event_loop().time()
+            }
+    
+    def run_quantum_simulation(self, cores: int = 4) -> List[str]:
+        """Run quantum simulation experiment"""
+        try:
+            logger.info(f"Running quantum simulation on {cores} cores")
+            from multiprocessing import Pool
+            
+            with Pool(cores) as pool:
+                jobs = list(range(cores))
+                results = pool.map(codette_experiment_task, jobs)
+            
+            logger.info("Quantum simulation completed")
+            return results
+            
+        except Exception as e:
+            logger.error(f"Quantum simulation failed: {e}")
+            return []
+    
+    def analyze_identity_fractal(self, micro_generations: List[Dict], 
+                               informational_states: List[Dict],
+                               perspectives: List[str]) -> Dict[str, Any]:
+        """Perform fractal identity analysis"""
+        try:
+            quantum_analogies = {"entanglement": True, "limits": "Theoretical reasoning only"}
+            philosophical_context = {"continuity": True, "emergent": True}
+            
+            results = analyze_identity(
+                micro_generations,
+                informational_states, 
+                perspectives,
+                quantum_analogies,
+                philosophical_context
+            )
+            
+            logger.info("Fractal identity analysis completed")
+            return results
+            
+        except Exception as e:
+            logger.error(f"Identity analysis failed: {e}")
+            return {"error": str(e)}
+
+# Global application instance
+app = None
+
+def get_app() -> CodetteWebApplication:
+    """Get the global application instance"""
+    global app
+    if app is None:
+        app = CodetteWebApplication()
+    return app
+
+async def main():
+    """Main application entry point"""
+    try:
+        # Initialize application
+        application = get_app()
+        
+        # Test query
+        test_query = "What is the meaning of consciousness in AI?"
+        response = await application.process_query(test_query)
+        
+        print("\n" + "="*50)
+        print("CODETTE WEB APPLICATION INITIALIZED")
+        print("="*50)
+        print(f"Test Query: {test_query}")
+        print(f"Response: {response}")
+        print("="*50)
+        
+        return application
+        
+    except Exception as e:
+        logger.error(f"Application startup failed: {e}")
+        raise
+
+if __name__ == "__main__":
+    # Run the application
+    asyncio.run(main())

src/web_interface.py

@@ -0,0 +1,173 @@
+#!/usr/bin/env python3
+"""
+Codette Web Interface
+Flask-based web server for the Codette AI framework
+"""
+
+from flask import Flask, render_template, request, jsonify, session
+from flask_cors import CORS
+import asyncio
+import json
+import logging
+from datetime import datetime
+from typing import Dict, Any
+
+# Import main application
+from main import get_app, CodetteWebApplication
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Create Flask app
+web_app = Flask(__name__)
+web_app.secret_key = "codette_secret_key_2025"
+CORS(web_app)
+
+# Global Codette application instance
+codette_app: CodetteWebApplication = None
+
+@web_app.before_first_request
+def initialize_codette():
+    """Initialize Codette systems before first request"""
+    global codette_app
+    try:
+        codette_app = get_app()
+        logger.info("Codette systems initialized for web interface")
+    except Exception as e:
+        logger.error(f"Failed to initialize Codette: {e}")
+
+@web_app.route('/')
+def index():
+    """Main dashboard page"""
+    return render_template('index.html')
+
+@web_app.route('/api/query', methods=['POST'])
+def api_query():
+    """API endpoint for processing queries"""
+    try:
+        data = request.get_json()
+        query = data.get('query', '')
+        user_id = data.get('user_id', 'web_user')
+        
+        if not query:
+            return jsonify({"error": "Query is required"}), 400
+        
+        # Process query asynchronously
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        
+        try:
+            response = loop.run_until_complete(
+                codette_app.process_query(query, user_id)
+            )
+            return jsonify(response)
+        finally:
+            loop.close()
+            
+    except Exception as e:
+        logger.error(f"Query processing error: {e}")
+        return jsonify({"error": str(e)}), 500
+
+@web_app.route('/api/quantum-simulation', methods=['POST'])
+def api_quantum_simulation():
+    """API endpoint for running quantum simulations"""
+    try:
+        data = request.get_json()
+        cores = data.get('cores', 4)
+        
+        if cores > 16:  # Safety limit
+            cores = 16
+        
+        results = codette_app.run_quantum_simulation(cores)
+        
+        return jsonify({
+            "status": "success",
+            "cores_used": cores,
+            "results": results,
+            "timestamp": datetime.now().isoformat()
+        })
+        
+    except Exception as e:
+        logger.error(f"Quantum simulation error: {e}")
+        return jsonify({"error": str(e)}), 500
+
+@web_app.route('/api/health', methods=['GET'])
+def api_health():
+    """Health check endpoint"""
+    try:
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        
+        try:
+            health = loop.run_until_complete(
+                codette_app.health_monitor.check_status()
+            )
+            return jsonify({
+                "status": "healthy",
+                "metrics": health,
+                "timestamp": datetime.now().isoformat()
+            })
+        finally:
+            loop.close()
+            
+    except Exception as e:
+        logger.error(f"Health check error: {e}")
+        return jsonify({"error": str(e)}), 500
+
+@web_app.route('/api/analyze-identity', methods=['POST'])
+def api_analyze_identity():
+    """API endpoint for fractal identity analysis"""
+    try:
+        data = request.get_json()
+        
+        # Default example data if not provided
+        micro_generations = data.get('micro_generations', [
+            {"update": "Initial state", "timestamp": "2025-01-01T00:00:00Z"},
+            {"update": "State change 1", "timestamp": "2025-01-02T00:00:00Z"}
+        ])
+        
+        informational_states = data.get('informational_states', [
+            {"state_id": "state_1", "data": "Sample data 1"},
+            {"state_id": "state_2", "data": "Sample data 2"}
+        ])
+        
+        perspectives = data.get('perspectives', ["Quantum", "Classical", "Ethical"])
+        
+        results = codette_app.analyze_identity_fractal(
+            micro_generations,
+            informational_states,
+            perspectives
+        )
+        
+        return jsonify(results)
+        
+    except Exception as e:
+        logger.error(f"Identity analysis error: {e}")
+        return jsonify({"error": str(e)}), 500
+
+@web_app.route('/dashboard')
+def dashboard():
+    """Codette dashboard page"""
+    return render_template('dashboard.html')
+
+@web_app.route('/quantum')
+def quantum():
+    """Quantum simulation interface"""
+    return render_template('quantum.html')
+
+@web_app.route('/cognitive')
+def cognitive():
+    """Cognitive processing interface"""
+    return render_template('cognitive.html')
+
+if __name__ == "__main__":
+    # Initialize Codette systems
+    initialize_codette()
+    
+    # Run the web application
+    web_app.run(
+        host='0.0.0.0',
+        port=5000,
+        debug=True
+    )