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()