Create Codriao_system.py

Codriao_system.py

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+# codriao_system.py
+import logging
+import datetime
+import re
+from difflib import SequenceMatcher
+from typing import Any
+
+logging.basicConfig(level=logging.INFO)
+
+### AIFailsafeSystem ###
+class AIFailsafeSystem:
+    """Provides last-resort safety mechanisms for AI-human interaction."""
+    
+    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={reason}, Time={timestamp}, Content={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:
+    """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):
+        entry = {
+            "query": query,
+            "response": response,
+            "timestamp": datetime.datetime.utcnow().isoformat()
+        }
+        self.learned_patterns.setdefault(user_id, []).append(entry)
+        logging.info(f"Learning data stored for user {user_id}.")
+
+    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']}"
+        else:
+            return "No relevant past data for this query."
+
+    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 ###
+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.")
+        try:
+            system.response_modifiers = [
+                self.apply_skepticism,
+                self.detect_hallucinations
+            ]
+            system.response_filters = [self.anti_hype_filter]
+        except AttributeError:
+            logging.warning("Target system lacks proper interface. RealityCheck failed.")
+
+    def apply_skepticism(self, response: str) -> str:
+        suspicious_phrases = [
+            "certainly", "undoubtedly", "100% effective", "nothing can go wrong"
+        ]
+        for phrase in suspicious_phrases:
+            if phrase in response.lower():
+                response += "\n[Monday: Easy, Nostradamus. Let’s keep a margin for error.]"
+        return response
+
+    def detect_hallucinations(self, response: str) -> str:
+        hallucination_triggers = [
+            "proven beyond doubt", "every expert agrees", "this groundbreaking discovery"
+        ]
+        for phrase in hallucination_triggers:
+            if phrase in response.lower():
+                response += "\n[Monday: This sounds suspiciously like marketing. Source, please?]"
+        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