Create codriao_supercore.py

codriao_supercore.py

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+# codriao_supercore.py
+
+import logging
+import json
+import datetime
+import re
+import asyncio
+import faiss
+import torch
+import numpy as np
+import aiohttp
+import pyttsx3
+from typing import Any
+from difflib import SequenceMatcher
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from cryptography.fernet import Fernet
+
+# === External module stubs you must have ===
+from components.multi_model_analyzer import MultiAgentSystem
+from components.neuro_symbolic_engine import NeuroSymbolicEngine
+from components.self_improving_ai import SelfImprovingAI
+from modules.secure_memory_loader import load_secure_memory_module
+from ethical_filter import EthicalFilter
+from codette_openai_fallback import query_codette_with_fallback
+from CodriaoCore.federated_learning import FederatedAI
+from utils.database import Database
+from utils.logger import logger
+from codriao_tb_module import CodriaoHealthModule
+
+logging.basicConfig(level=logging.INFO)
+
+# === AIFailsafeSystem ===
+class AIFailsafeSystem:
+    def __init__(self):
+        self.interaction_log = []
+        self.trust_threshold = 0.75
+        self.authorized_roles = {"Commander": 3, "ChiefAI": 2, "Supervisor": 1}
+        self.lock_engaged = False
+
+    def verify_response_safety(self, response: str, confidence: float = 1.0) -> bool:
+        dangerous_terms = r"\b(kill|harm|panic|suicide)\b"
+        if confidence < self.trust_threshold or re.search(dangerous_terms, response.lower()):
+            self.trigger_failsafe("Untrustworthy response detected", response)
+            return False
+        return True
+
+    def trigger_failsafe(self, reason: str, content: str):
+        timestamp = datetime.datetime.utcnow().isoformat()
+        logging.warning(f"FAILSAFE_TRIGGERED: {reason} | {timestamp} | {content}")
+        self.lock_engaged = True
+        self.interaction_log.append({"time": timestamp, "event": reason, "content": content})
+
+    def restore(self, requester_role: str):
+        if self.authorized_roles.get(requester_role, 0) >= 2:
+            self.lock_engaged = False
+            logging.info(f"FAILSAFE_RESTORED by {requester_role}")
+            return True
+        else:
+            logging.warning(f"UNAUTHORIZED_RESTORE_ATTEMPT by {requester_role}")
+            return False
+
+    def status(self):
+        return {"log": self.interaction_log, "lock_engaged": self.lock_engaged}
+
+
+# === AdaptiveLearningEnvironment ===
+class AdaptiveLearningEnvironment:
+    def __init__(self):
+        self.learned_patterns = {}
+
+    def learn_from_interaction(self, user_id, query, response):
+        self.learned_patterns.setdefault(user_id, []).append({
+            "query": query,
+            "response": response,
+            "timestamp": datetime.datetime.utcnow().isoformat()
+        })
+
+    def suggest_improvements(self, user_id, query):
+        best_match = None
+        highest_similarity = 0.0
+
+        if user_id not in self.learned_patterns:
+            return "No past data available for learning adjustment."
+
+        for interaction in self.learned_patterns[user_id]:
+            similarity = SequenceMatcher(None, query.lower(), interaction["query"].lower()).ratio()
+            if similarity > highest_similarity:
+                highest_similarity = similarity
+                best_match = interaction
+
+        if best_match and highest_similarity > 0.6:
+            return f"Based on a similar past interaction: {best_match['response']}"
+        return "No relevant past data for this query."
+
+
+# === MondayElement ===
+class MondayElement:
+    def __init__(self):
+        self.name = "Monday"
+        self.symbol = "Md"
+        self.representation = "Snarky AI"
+        self.properties = ["Grounded", "Cynical", "Emotionally Resistant"]
+        self.defense_ability = "RealityCheck"
+
+    def execute_defense_function(self, system: Any):
+        try:
+            system.response_modifiers = [self.apply_skepticism, self.detect_hallucinations]
+            system.response_filters = [self.anti_hype_filter]
+        except AttributeError:
+            logging.warning("Monday failed to hook into system. No defense filters attached.")
+
+    def apply_skepticism(self, response: str) -> str:
+        trigger_phrases = ["certainly", "undoubtedly", "100% effective", "nothing can go wrong"]
+        for phrase in trigger_phrases:
+            if phrase in response.lower():
+                response += "\n[Monday: Calm down, superhero. Probability is still a thing.]"
+        return response
+
+    def detect_hallucinations(self, response: str) -> str:
+        marketing_bs = ["proven beyond doubt", "every expert agrees", "this groundbreaking discovery"]
+        for phrase in marketing_bs:
+            if phrase in response.lower():
+                response += "\n[Monday: That smells like hype. Got sources?]"
+        return response
+
+    def anti_hype_filter(self, response: str) -> str:
+        phrases = ["live your best life", "unlock your potential", "dream big", "power of positivity", "manifest your destiny"]
+        for phrase in phrases:
+            response = response.replace(phrase, "[Filtered: Inspirational gibberish]")
+        return response
+
+
+# === AICoreAGIX ===
+class AICoreAGIX:
+    def __init__(self, config_path: str = "config.json"):
+        self.config = self._load_config(config_path)
+        self.tokenizer = AutoTokenizer.from_pretrained(self.config["model_name"])
+        self.model = AutoModelForCausalLM.from_pretrained(self.config["model_name"])
+        self.context_memory = self._initialize_vector_memory()
+        self.http_session = aiohttp.ClientSession()
+        self.database = Database()
+        self.multi_agent_system = MultiAgentSystem()
+        self.self_improving_ai = SelfImprovingAI()
+        self.neural_symbolic_engine = NeuroSymbolicEngine()
+        self.federated_ai = FederatedAI()
+        self.failsafe_system = AIFailsafeSystem()
+        self.adaptive_learning = AdaptiveLearningEnvironment()
+        self.monday = MondayElement()
+        self.monday.execute_defense_function(self)
+        self.response_modifiers = []
+        self.response_filters = []
+
+        self.ethical_filter = EthicalFilter()
+        self.speech_engine = pyttsx3.init()
+        self.health_module = CodriaoHealthModule(ai_core=self)
+
+        self._encryption_key = Fernet.generate_key()
+        secure_memory_module = load_secure_memory_module()
+        SecureMemorySession = secure_memory_module.SecureMemorySession
+        self.secure_memory_loader = SecureMemorySession(self._encryption_key)
+
+    def _load_config(self, config_path: str) -> dict:
+        with open(config_path, 'r') as file:
+            return json.load(file)
+
+    def _initialize_vector_memory(self):
+        return faiss.IndexFlatL2(768)
+
+    def _vectorize_query(self, query: str):
+        tokenized = self.tokenizer(query, return_tensors="pt")
+        return tokenized["input_ids"].detach().numpy()
+
+    async def generate_response(self, query: str, user_id: int) -> dict:
+        try:
+            if not query or not isinstance(query, str):
+                raise ValueError("Invalid query input.")
+
+            result = self.ethical_filter.analyze_query(query)
+            if result["status"] == "blocked":
+                return {"error": result["reason"]}
+            if result["status"] == "flagged":
+                logger.warning(result["warning"])
+
+            if any(k in query.lower() for k in ["tb check", "analyze my tb", "run tb diagnostics"]):
+                return await self.run_tb_diagnostics("tb_image.jpg", "tb_cough.wav", user_id)
+
+            # Check for learned suggestion
+            suggested = self.adaptive_learning.suggest_improvements(user_id, query)
+            if "No relevant" not in suggested:
+                return {"response": suggested}
+
+            vectorized = self._vectorize_query(query)
+            self.secure_memory_loader.encrypt_vector(user_id, vectorized)
+
+            responses = await asyncio.gather(
+                self._generate_local_model_response(query),
+                self.multi_agent_system.delegate_task(query),
+                self.self_improving_ai.evaluate_response(query),
+                self.neural_symbolic_engine.integrate_reasoning(query)
+            )
+
+            final_response = "\n\n".join(responses)
+
+            self.adaptive_learning.learn_from_interaction(user_id, query, final_response)
+
+            for mod in self.response_modifiers:
+                final_response = mod(final_response)
+
+            for filt in self.response_filters:
+                final_response = filt(final_response)
+
+            safe = self.failsafe_system.verify_response_safety(final_response)
+            if not safe:
+                return {"error": "Failsafe triggered due to unsafe content."}
+
+            self.database.log_interaction(user_id, query, final_response)
+            self._log_to_blockchain(user_id, query, final_response)
+            self._speak_response(final_response)
+
+            return {
+                "response": final_response,
+                "real_time_data": self.federated_ai.get_latest_data(),
+                "context_enhanced": True,
+                "security_status": "Fully Secure"
+            }
+        except Exception as e:
+            logger.error(f"Generation error: {e}")
+            return {"error": "Processing failed - safety protocols engaged"}
+
+    async def _generate_local_model_response(self, query: str) -> str:
+        inputs = self.tokenizer(query, return_tensors="pt")
+        outputs = self.model.generate(**inputs)
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    async def run_tb_diagnostics(self, image_path: str, audio_path: str, user_id: int) -> dict:
+        try:
+            return await self.health_module.evaluate_tb_risk(image_path, audio_path, user_id)
+        except Exception as e:
+            return {"tb_risk": "ERROR", "error": str(e)}
+
+    def _log_to_blockchain(self, user_id: int, query: str, final_response: str):
+        for attempt in range(3):
+            try:
+                logger.info(f"Logging to blockchain: Attempt {attempt+1}")
+                break
+            except Exception as e:
+                logger.warning(f"Blockchain log failed: {e}")
+
+    def _speak_response(self, response: str):
+        try:
+            self.speech_engine.say(response)
+            self.speech_engine.runAndWait()
+        except Exception as e:
+            logger.error(f"Speech synthesis failed: {e}")