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| codette_demo_script = """ | |
| import logging | |
| import time | |
| from datetime import datetime | |
| import sympy as sp | |
| import numpy as np | |
| import pymc as pm | |
| logging.basicConfig( | |
| level=logging.INFO, | |
| format='%(asctime)s - %(levelname)s - %(message)s', | |
| handlers=[ | |
| logging.FileHandler('codette_demo.log'), | |
| logging.StreamHandler() | |
| ] | |
| ) | |
| class Codette: | |
| def __init__(self, user_name="User", demo_mode=False): | |
| self.user_name = user_name | |
| self.demo_mode = demo_mode | |
| self.memory = [] | |
| self.session_start = datetime.now() | |
| self.response_count = 0 | |
| if demo_mode: | |
| logging.info("CODETTE AI DEMO MODE ACTIVATED") | |
| logging.info("=" * 50) | |
| self.audit_log("Codette initialized", system=True) | |
| self.audit_log(f"Session started at {self.session_start}", system=True) | |
| def audit_log(self, message, system=False): | |
| source = "SYSTEM" if system else self.user_name | |
| timestamp = datetime.now().strftime("%H:%M:%S.%f")[:-3] | |
| if self.demo_mode: | |
| log_message = f"[{timestamp}] {source}: {message}" | |
| logging.info(log_message) | |
| if "perspectives" in message.lower(): | |
| logging.info("BRAIN " + "-" * 40) | |
| elif "quantum" in message.lower(): | |
| logging.info("QUANTUM " + "-" * 40) | |
| elif "security" in message.lower(): | |
| logging.info("SECURITY " + "-" * 40) | |
| else: | |
| logging.info(f"{source}: {message}") | |
| safe_message = message[:100] + "..." if len(message) > 100 else message | |
| print(f"[Log] {source}: {safe_message}") | |
| def analyze_sentiment(self, text): | |
| positive_words = ['good', 'great', 'excellent', 'amazing', 'wonderful'] | |
| negative_words = ['bad', 'terrible', 'awful', 'horrible', 'disappointing'] | |
| text_lower = text.lower() | |
| pos_count = sum(1 for word in positive_words if word in text_lower) | |
| neg_count = sum(1 for word in negative_words if word in text_lower) | |
| compound = (pos_count - neg_count) / max(len(text.split()), 1) | |
| score = { | |
| 'compound': compound, | |
| 'pos': pos_count / max(len(text.split()), 1), | |
| 'neg': neg_count / max(len(text.split()), 1), | |
| 'neu': 1 - abs(compound) | |
| } | |
| self.audit_log(f"Sentiment analysis: {score}") | |
| return score | |
| def respond(self, prompt): | |
| self.response_count += 1 | |
| start_time = time.perf_counter() | |
| processing_start = datetime.now() | |
| sentiment = self.analyze_sentiment(prompt) | |
| self.memory.append({"prompt": prompt, "sentiment": sentiment, "timestamp": processing_start}) | |
| modules = [ | |
| self.neuralNetworkPerspective, | |
| self.newtonianLogic, | |
| self.daVinciSynthesis, | |
| self.resilientKindness, | |
| self.quantumLogicPerspective, | |
| self.philosophicalInquiry, | |
| self.copilotAgent, | |
| self.mathematicalRigor, | |
| self.symbolicReasoning | |
| ] | |
| responses = [] | |
| active_modules = [] | |
| for module in modules: | |
| try: | |
| result = module(prompt) | |
| responses.append(result) | |
| active_modules.append(module.__name__) | |
| except Exception as e: | |
| responses.append(f"[Error] {module.__name__} failed: {e}") | |
| processing_time = time.perf_counter() - start_time | |
| self.audit_log(f"Perspectives used: {active_modules}") | |
| return "\\n\\n".join(responses) | |
| def neuralNetworkPerspective(self, text): | |
| return "[NeuralNet] Based on historical patterns, adaptability and ethical alignment drive trustworthiness." | |
| def newtonianLogic(self, text): | |
| return "[Reason] If openness increases verifiability, and trust depends on verifiability, then openness implies higher trust." | |
| def daVinciSynthesis(self, text): | |
| return "[Dream] Imagine systems as ecosystems � where open elements evolve harmoniously under sunlight, while closed ones fester in shadow." | |
| def resilientKindness(self, text): | |
| return "[Ethics] Your concern reflects deep care. Let's anchor this response in compassion for both users and developers." | |
| def quantumLogicPerspective(self, text): | |
| prior_open = 0.7 if "open-source" in text.lower() else 0.5 | |
| prior_prop = 1 - prior_open | |
| try: | |
| with pm.Model() as model: | |
| trust_open = pm.Beta("trust_open", alpha=prior_open * 10, beta=(1 - prior_open) * 10) | |
| trust_prop = pm.Beta("trust_prop", alpha=prior_prop * 10, beta=(1 - prior_prop) * 10) | |
| better = pm.Deterministic("better", trust_open > trust_prop) | |
| trace = pm.sample(draws=1000, chains=2, progressbar=False, random_seed=42) | |
| prob = float(np.mean(trace.posterior["better"].values)) | |
| return f"[Quantum] Bayesian estimate: There is a {prob*100:.2f}% probability that open-source is more trustworthy in this context." | |
| except: | |
| return "[Quantum] Quantum processing indicates multiple probability states favor transparency and openness." | |
| def philosophicalInquiry(self, text): | |
| return "[Philosophy] From a deontological lens, openness respects autonomy and truth. From a utilitarian view, it maximizes communal knowledge. Both suggest a moral edge for openness." | |
| def copilotAgent(self, text): | |
| return "[Copilot] I can interface with APIs or code tools to test claims, retrieve documentation, or automate analysis. (Simulated here)" | |
| def mathematicalRigor(self, text): | |
| try: | |
| expr = sp.sympify("2*x + 1") | |
| solved = sp.solve(expr - 5) | |
| return f"[Math] For example, solving 2x + 1 = 5 gives x = {solved[0]} � demonstrating symbolic logic at work." | |
| except: | |
| return "[Math] Mathematical analysis confirms logical consistency in the reasoning chain." | |
| def symbolicReasoning(self, text): | |
| if "transparency" in text.lower(): | |
| rule = "If a system is transparent, then it is more auditable. If it is more auditable, then it is more trustworthy." | |
| return f"[Symbolic] Rule chain:\\n{rule}\\nThus, transparency ? trust." | |
| else: | |
| return "[Symbolic] No rule matched. Default: Trust is linked to observable accountability." | |
| def get_session_stats(self): | |
| session_duration = (datetime.now() - self.session_start).total_seconds() | |
| return { | |
| "session_duration": session_duration, | |
| "responses_generated": self.response_count, | |
| "memory_entries": len(self.memory), | |
| "avg_response_time": session_duration / max(1, self.response_count) | |
| } | |
| """ | |
| # Write the script | |
| with open(codette_script_path, "w") as f: | |
| f.write(codette_demo_script) | |
| codette_script_path.as_posix() |