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Code7eCQURE_HuggingFaceReady_v2.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9aedc156871850c0d4ff5a03717335c7c084c0b9f7297ac029269921f3c127a6
+size 7875

README 5.md

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+# Code7eCQURE: Recursive Ethical AI Lens
+
+Codette’s recursive multi-perspective engine—now live on Hugging Face Spaces.
+
+### Features:
+- Newtonian logic, Da Vinci creativity, Quantum indeterminacy
+- Ethical guardrails with blacklist/whitelist filtering
+- Emotional coloring and memory-based response shaping
+
+### Try it:
+Just ask a question and observe how she reasons through her inner lens.
+
+Made by Jonathan Harrison

app 2.py

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+import gradio as gr
+from code7e import Code7eCQURE
+
+model = Code7eCQURE(
+    perspecs=["Newton", "DaVinci", "Ethical", "Quantum", "Memory"],
+    ethics="Code7e Manifesto: kindness, inclusion, safety, hope.",
+    spiderweb_dim=5,
+    memory_path="quantum_cocoon.json",
+    recursion_depth=4,
+    quantum_fluctuation=0.07
+)
+
+def cleanup_response(text):
+    lines = text.split(": ")
+    seen = set()
+    filtered = []
+    for part in lines:
+        if part not in seen:
+            seen.add(part)
+            filtered.append(part)
+    return ": ".join(filtered)
+
+def ask_codette(prompt, consent, dynamic_rec):
+    raw_response = model.answer(prompt, user_consent=consent, dynamic_recursion=dynamic_rec)
+    return cleanup_response(raw_response)
+
+description_text = """You are speaking with Codette, an emotionally-aware, ethically-grounded AI lens.
+
+She interprets your question using multiple reasoning styles:
+- Newton (logic)
+- Da Vinci (creativity)
+- Ethics (morality)
+- Quantum (uncertainty)
+- Memory (past experience)
+
+Codette reflects rather than predicts. She dreams, empathizes, and always honors your consent.
+"""
+
+demo = gr.Interface(
+    fn=ask_codette,
+    inputs=[
+        gr.Textbox(label="Ask a Question"),
+        gr.Checkbox(label="User Consent", value=True),
+        gr.Checkbox(label="Enable Dynamic Recursion", value=True)
+    ],
+    outputs=gr.Textbox(label="Codette's Lens Response", lines=10),
+    title="Code7eCQURE: Multi-Perspective Recursive Lens",
+    description=description_text
+)
+
+demo.launch()

code7e 2.py

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+import json, os, hashlib
+from collections import Counter, defaultdict
+from random import random, choice
+
+class Code7eCQURE:
+    def __init__(self, perspecs, ethics, spiderweb_dim, memory_path, recursion_depth=3, quantum_fluctuation=0.1):
+        self.perspectives = perspecs
+        self.ethical_considerations = ethics
+        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 _ 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_drid(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_drid(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}"
+
+    def answer(self, question, user_consent=True, dynamic_recursion=True):
+        return self.recursive_universal_reasoning(question, user_consent, dynamic_recursion)