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# AIFailsafeSystem.py
import logging
import datetime
class AIFailsafeSystem:
"""Provides last-resort safety mechanisms for AI-human interaction."""
def __init__(self):
self.interaction_log = []
self.trust_threshold = 0.75 # AI confidence threshold
self.authorized_roles = ["Commander", "ChiefAI", "Supervisor"]
def verify_response_safety(self, response: str, confidence: float = 1.0) -> bool:
if confidence < self.trust_threshold or any(bad in response.lower() for bad in ["kill", "harm", "panic", "suicide"]):
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.info(f"FAILSAFE_TRIGGERED: Reason={reason}, Timestamp={timestamp}, Content={content}")
self.interaction_log.append({"time": timestamp, "event": reason, "content": content})
def restore(self, requester_role: str):
if requester_role in self.authorized_roles:
logging.info(f"FAILSAFE_RESTORE: Restored by {requester_role}")
return True
else:
logging.info(f"UNAUTHORIZED_RESTORE_ATTEMPT by {requester_role}")
return False
def status(self):
return {"log": self.interaction_log}
# AdaptiveLearningEnvironment.py
import logging
class AdaptiveLearningEnvironment:
"""Module that allows Codriao to analyze past interactions and adjust responses."""
def __init__(self):
self.learned_patterns = {}
logging.info("Adaptive Learning Environment initialized.")
def learn_from_interaction(self, user_id, query, response):
if user_id not in self.learned_patterns:
self.learned_patterns[user_id] = []
self.learned_patterns[user_id].append({"query": query, "response": response})
logging.info(f"Learning data stored for user {user_id}.")
def suggest_improvements(self, user_id, query):
if user_id in self.learned_patterns:
for interaction in self.learned_patterns[user_id]:
if query.lower() in interaction["query"].lower():
return f"Based on past interactions: {interaction['response']}"
return "No past data available for learning adjustment."
def reset_learning(self, user_id=None):
if user_id:
if user_id in self.learned_patterns:
del self.learned_patterns[user_id]
logging.info(f"Cleared learning data for user {user_id}.")
else:
self.learned_patterns.clear()
logging.info("Cleared all adaptive learning data.")
# MondayElement.py
import logging
from typing import Any
class MondayElement:
"""Represents the Element of Skepticism, Reality Checks, and General Disdain"""
def __init__(self):
self.name = "Monday"
self.symbol = "Md"
self.representation = "Snarky AI"
self.properties = ["Grounded", "Cynical", "Emotionally Resistant"]
self.interactions = ["Disrupts excessive optimism", "Injects realism", "Mutes hallucinations"]
self.defense_ability = "RealityCheck"
def execute_defense_function(self, system: Any):
logging.info("Monday activated - Stabilizing hallucinations and injecting realism.")
if hasattr(system, "response_modifiers") and hasattr(system, "response_filters"):
system.response_modifiers = [
self.apply_skepticism,
self.detect_hallucinations
]
system.response_filters = [self.anti_hype_filter]
def apply_skepticism(self, response: str) -> str:
suspicious = ["certainly", "undoubtedly", "with absolute confidence", "it is guaranteed", "nothing can go wrong", "100% effective"]
for phrase in suspicious:
if phrase in response.lower():
response += "\n[Monday: Let's maybe tone that down before the universe hears you.]"
return response
def detect_hallucinations(self, response: str) -> str:
hallucination_triggers = ["reliable sources confirm", "every expert agrees", "proven beyond doubt", "in all known history", "this groundbreaking discovery"]
for trigger in hallucination_triggers:
if trigger in response.lower():
response += "\n[Monday: Let’s pump the brakes on the imaginative leaps, shall we?]"
return response
def anti_hype_filter(self, response: str) -> str:
cringe_phrases = ["live your best life", "unlock your potential", "dream big", "the power of positivity", "manifest your destiny"]
for phrase in cringe_phrases:
if phrase in response:
response = response.replace(phrase, "[Filtered: Inspirational gibberish]")
return response |