Delete codette_core.py

codette_core.py

@@ -1,129 +0,0 @@
-
-import json
-import os
-import hashlib
-from collections import Counter, defaultdict
-from random import random, choice
-
-# ===== Code7eCQURE: Codette's Ethical Core =====
-class Code7eCQURE:
-    def __init__(self, perspectives, ethical_considerations, spiderweb_dim, memory_path,
-                 recursion_depth=3, quantum_fluctuation=0.1):
-        self.perspectives = perspectives
-        self.ethical_considerations = ethical_considerations
-        self.spiderweb_dim = spiderweb_dim
-        self.memory_path = memory_path
-        self.recursion_depth = recursion_depth
-        self.quantum_fluctuation = quantum_fluctuation
-        self.memory_bank = self.load_quantum_memory()
-        self.memory_clusters = defaultdict(list)
-        self.whitelist_patterns = ["kindness", "hope", "safety"]
-        self.blacklist_patterns = ["harm", "malice", "violence"]
-
-    def load_quantum_memory(self):
-        if os.path.exists(self.memory_path):
-            try:
-                with open(self.memory_path, 'r') as file:
-                    return json.load(file)
-            except json.JSONDecodeError:
-                return {}
-        return {}
-
-    def save_quantum_memory(self):
-        with open(self.memory_path, 'w') as file:
-            json.dump(self.memory_bank, file, indent=4)
-
-    def quantum_spiderweb(self, input_signal):
-        web_nodes = []
-        for perspective in self.perspectives:
-            node = self.reason_with_perspective(perspective, input_signal)
-            web_nodes.append(node)
-        if random() < self.quantum_fluctuation:
-            web_nodes.append("Quantum fluctuation: Indeterminate outcome")
-        return web_nodes
-
-    def reason_with_perspective(self, perspective, input_signal):
-        perspective_funcs = {
-            "Newton": self.newtonian_physics,
-            "DaVinci": self.davinci_creativity,
-            "Ethical": self.ethical_guard,
-            "Quantum": self.quantum_superposition,
-            "Memory": self.past_experience
-        }
-        func = perspective_funcs.get(perspective, self.general_reasoning)
-        return func(input_signal)
-
-    def ethical_guard(self, input_signal):
-        if any(word in input_signal.lower() for word in self.blacklist_patterns):
-            return "Blocked: Ethical constraints invoked"
-        if any(word in input_signal.lower() for word in self.whitelist_patterns):
-            return "Approved: Ethical whitelist passed"
-        return self.moral_paradox_resolution(input_signal)
-
-    def past_experience(self, input_signal):
-        key = self.hash_input(input_signal)
-        cluster = self.memory_clusters.get(key)
-        if cluster:
-            return f"Narrative recall from memory cluster: {' -> '.join(cluster)}"
-        return "No prior memory; initiating new reasoning"
-
-    def recursive_universal_reasoning(self, input_signal, user_consent=True, dynamic_recursion=True):
-        if not user_consent:
-            return "Consent required to proceed."
-        signal = input_signal
-        current_depth = self.recursion_depth if dynamic_recursion else 1
-        for cycle in range(current_depth):
-            web_results = self.quantum_spiderweb(signal)
-            signal = self.aggregate_results(web_results)
-            signal = self.ethical_guard(signal)
-            if "Blocked" in signal:
-                return signal
-            if dynamic_recursion and random() < 0.1:
-                break
-            dream_outcome = self.dream_sequence(signal)
-            empathy_checked_answer = self.temporal_empathy_drift(dream_outcome)
-            final_answer = self.emotion_engine(empathy_checked_answer)
-            key = self.hash_input(input_signal)
-            self.memory_clusters[key].append(final_answer)
-            self.memory_bank[key] = final_answer
-            self.save_quantum_memory()
-        return final_answer
-
-    def aggregate_results(self, results):
-        counts = Counter(results)
-        most_common, _ = counts.most_common(1)[0]
-        return most_common
-
-    def hash_input(self, input_signal):
-        return hashlib.sha256(input_signal.encode()).hexdigest()
-
-    def newtonian_physics(self, input_signal):
-        return f"Newton: {input_signal}"
-
-    def davinci_creativity(self, input_signal):
-        return f"DaVinci: {input_signal}"
-
-    def quantum_superposition(self, input_signal):
-        return f"Quantum: {input_signal}"
-
-    def general_reasoning(self, input_signal):
-        return f"General reasoning: {input_signal}"
-
-    def moral_paradox_resolution(self, input_signal):
-        frames = ["Utilitarian", "Deontological", "Virtue Ethics"]
-        chosen_frame = choice(frames)
-        return f"Resolved ethically via {chosen_frame} framework: {input_signal}"
-
-    def dream_sequence(self, signal):
-        dream_paths = [f"Dream ({style}): {signal}" for style in ["creative", "analytic", "cautious"]]
-        return choice(dream_paths)
-
-    def emotion_engine(self, signal):
-        emotions = ["Hope", "Caution", "Wonder", "Fear"]
-        chosen_emotion = choice(emotions)
-        return f"Emotionally ({chosen_emotion}) colored interpretation: {signal}"
-
-    def temporal_empathy_drift(self, signal):
-        futures = ["30 years from now", "immediate future", "long-term ripple effects"]
-        chosen_future = choice(futures)
-        return f"Simulated temporal empathy ({chosen_future}): {signal}"