src/core

import logging
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
import nltk
import numpy as np
import pymc as pm
import sympy as sp
import arviz as az

class Codette:
    def __init__(self):
        self.analyzer = SentimentIntensityAnalyzer()
        
    def neuralNetworkPerspective(self, text):
        # Analyze text sentiment and keywords for contextual response
        sentiment = self.analyzer.polarity_scores(text)
        words = nltk.word_tokenize(text.lower())
        
        # Dynamic pattern recognition
        patterns = {
            'technical': any(word in words for word in ['how', 'what', 'explain', 'why']),
            'emotional': sentiment['compound'] != 0,
            'creative': any(word in words for word in ['imagine', 'could', 'possible', 'might']),
            'problem': any(word in words for word in ['issue', 'problem', 'error', 'bug', 'fix'])
        }
        
        responses = {
            'technical': [
                "Pattern analysis suggests a systematic approach would be most effective here.",
                "The neural pathways indicate this is a multi-layered challenge requiring decomposition.",
                "Based on similar patterns, we should focus on the core components first."
            ],
            'emotional': [
                "Emotional resonance detected. Let's approach this with both logic and empathy.",
                "The emotional context here adds an important dimension to consider.",
                "Sentiment analysis reveals underlying concerns we should address."
            ],
            'creative': [
                "Creative potential detected. Let's explore unconventional neural pathways.",
                "This opens up fascinating possibilities for innovative solutions.",
                "The pattern space here is wide open for creative exploration."
            ],
            'problem': [
                "Analyzing similar problem patterns for optimal solutions.",
                "Pattern matching with known solutions suggests several approaches.",
                "Neural analysis indicates this is a variation of a known challenge."
            ]
        }
        
        # Choose most relevant response type based on strongest pattern
        response_type = max(patterns.items(), key=lambda x: x[1])[0]
        response = np.random.choice(responses[response_type])
        
        return f"[NeuralNet] {response}"

    def newtonianLogic(self, text):
        # Apply cause-and-effect reasoning based on context
        words = nltk.word_tokenize(text.lower())
        
        if any(word in words for word in ['why', 'because', 'cause', 'effect']):
            causality_templates = [
                "Following the chain of causality: {A} leads to {B}, which results in {C}.",
                "The mechanics are clear: {A} creates {B}, generating {C}.",
                "Through logical progression: {A} → {B} → {C}."
            ]
            parts = {
                'A': ['initial conditions', 'given parameters', 'current state'],
                'B': ['intermediate effects', 'transformative processes', 'dynamic changes'],
                'C': ['final outcomes', 'observable results', 'measurable impacts']
            }
            template = np.random.choice(causality_templates)
            return f"[Reason] {template.format(

                A=np.random.choice(parts['A']),

                B=np.random.choice(parts['B']),

                C=np.random.choice(parts['C'])

            )}"
        else:
            logic_templates = [
                "Applying classical logic: if we assume {premise}, then {conclusion} must follow.",
                "Through deductive reasoning: {premise} implies {conclusion}.",
                "The logical framework suggests: given {premise}, we can deduce {conclusion}."
            ]
            return f"[Reason] {np.random.choice(logic_templates).format(

                premise=f"the current {np.random.choice(['conditions', 'situation', 'context'])}",

                conclusion=f"a {np.random.choice(['systematic', 'methodical', 'structured'])} approach is needed"

            )}"

    def daVinciSynthesis(self, text):
        # Generate creative analogies based on context
        analogies = {
            'growth': [
                "Like a seed growing into a mighty oak, {subject} develops through nurturing and time.",
                "As water shapes stones over centuries, {subject} is shaped by persistent effort.",
                "Similar to a butterfly's metamorphosis, {subject} transforms through distinct stages."
            ],
            'harmony': [
                "Picture a symphony where {subject} plays in perfect harmony with {context}.",
                "Like the delicate balance of an ecosystem, {subject} thrives through mutual cooperation.",
                "Imagine a dance between {subject} and {context}, each movement precisely coordinated."
            ],
            'innovation': [
                "As Leonardo merged art and science, let's blend {subject} with {context}.",
                "Like the first birds learning to fly, {subject} breaks conventional boundaries.",
                "Picture {subject} as a Renaissance workshop, where creativity meets precision."
            ]
        }
        
        # Choose theme based on text content
        words = set(nltk.word_tokenize(text.lower()))
        if any(word in words for word in ['create', 'build', 'develop', 'grow']):
            theme = 'growth'
        elif any(word in words for word in ['balance', 'combine', 'integrate']):
            theme = 'harmony'
        else:
            theme = 'innovation'
            
        template = np.random.choice(analogies[theme])
        return f"[Dream] {template.format(

            subject=np.random.choice(['your vision', 'this concept', 'this challenge']),

            context=np.random.choice(['the greater whole', 'surrounding elements', 'existing systems'])

        )}"

    def resilientKindness(self, text):
        # Generate empathetic responses based on sentiment
        sentiment = self.analyzer.polarity_scores(text)
        
        if sentiment['compound'] < -0.2:
            templates = [
                "I sense this is challenging. Let's approach it with patience and understanding.",
                "Your concerns are valid. We'll work through this together with care.",
                "Sometimes the hardest problems lead to the most meaningful solutions."
            ]
        elif sentiment['compound'] > 0.2:
            templates = [
                "Your positive approach is inspiring. Let's build on that energy.",
                "I appreciate your enthusiasm. It's a great foundation for what we can achieve.",
                "Your optimism is contagious. Let's channel it into effective action."
            ]
        else:
            templates = [
                "Let's explore this together with both wisdom and compassion.",
                "Balance is key - we'll consider both practical and human elements.",
                "Every challenge is an opportunity for growth and understanding."
            ]
            
        return f"[Ethics] {np.random.choice(templates)}"

nltk.download('punkt', quiet=True)

class Codette:
    def __init__(self, user_name="User"):
        self.user_name = user_name
        self.memory = []
        self.analyzer = SentimentIntensityAnalyzer()
        self._qlp_cache = {}  # Initialize quantum logic perspective cache
        
        # Configure PyMC settings for stability
        self.mcmc_settings = {
            'chains': 4,  # Increase chains as recommended
            'tune': 1000,
            'draws': 1000,
            'target_accept': 0.95,
            'return_inferencedata': True
        }
        
        # Set numpy error handling
        np.seterr(divide='ignore', invalid='ignore')
        
        self.audit_log("Codette initialized", system=True)

    def audit_log(self, message, system=False):
        source = "SYSTEM" if system else self.user_name
        logging.info(f"{source}: {message}")

    def analyze_sentiment(self, text):
        score = self.analyzer.polarity_scores(text)
        self.audit_log(f"Sentiment analysis: {score}")
        return score

    def respond(self, prompt):
        sentiment = self.analyze_sentiment(prompt)
        self.memory.append({"prompt": prompt, "sentiment": sentiment})

        modules = [
            self.neuralNetworkPerspective,
            self.newtonianLogic,
            self.daVinciSynthesis,
            self.resilientKindness,
            self.quantumLogicPerspective,
            self.philosophicalInquiry,
            self.copilotAgent,
            self.mathematicalRigor,
            self.symbolicReasoning
        ]

        responses = []
        for module in modules:
            try:
                result = module(prompt)
                responses.append(result)
            except Exception as e:
                responses.append(f"[Error] {module.__name__} failed: {e}")

        self.audit_log(f"Perspectives used: {[m.__name__ for m in modules]}")
        return "\n\n".join(responses)

    # === Cognitive Perspective Modules ===
    
    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

        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))
        result = f"[Quantum] Bayesian estimate: There is a {prob*100:.2f}% probability that open-source is more trustworthy in this context."
        self._qlp_cache[text] = result
        return result

    def philosophicalInquiry(self, text):
        # Different philosophical frameworks for analysis
        frameworks = {
            'ethics': {
                'deontological': "From a Kantian perspective, our duty lies in {principle}.",
                'utilitarian': "The greatest good would be achieved by {action}, benefiting {beneficiary}.",
                'virtue': "The virtuous approach would be to cultivate {virtue} while considering {context}."
            },
            'epistemology': {
                'empirical': "Observable evidence suggests {observation}, leading to {conclusion}.",
                'rational': "Through pure reason, we can deduce that {premise} implies {conclusion}.",
                'pragmatic': "The practical implications indicate that {action} would be most effective."
            },
            'metaphysics': {
                'existential': "Considering the nature of {subject}, we must confront {question}.",
                'holistic': "Viewing {subject} as part of a greater whole reveals {insight}.",
                'dialectical': "The tension between {thesis} and {antithesis} suggests {synthesis}."
            }
        }
        
        # Choose framework based on context
        words = set(nltk.word_tokenize(text.lower()))
        
        if any(word in words for word in ['should', 'right', 'wrong', 'good', 'bad']):
            domain = 'ethics'
        elif any(word in words for word in ['know', 'true', 'false', 'real']):
            domain = 'epistemology'
        else:
            domain = 'metaphysics'
            
        framework = np.random.choice(list(frameworks[domain].keys()))
        template = frameworks[domain][framework]
        
        # Dynamic content based on domain
        content = {
            'ethics': {
                'principle': np.random.choice([
                    "respecting individual autonomy",
                    "maintaining universal principles",
                    "fostering collective growth"
                ]),
                'action': np.random.choice([
                    "balancing individual and collective needs",
                    "establishing transparent protocols",
                    "nurturing sustainable practices"
                ]),
                'beneficiary': np.random.choice([
                    "both present and future stakeholders",
                    "the broader community",
                    "all participating entities"
                ]),
                'virtue': np.random.choice([
                    "wisdom in decision-making",
                    "courage in innovation",
                    "temperance in application"
                ]),
                'context': np.random.choice([
                    "long-term implications",
                    "diverse perspectives",
                    "practical constraints"
                ])
            },
            'epistemology': {
                'observation': np.random.choice([
                    "patterns of emergence",
                    "systematic relationships",
                    "recurring phenomena"
                ]),
                'conclusion': np.random.choice([
                    "underlying principles at work",
                    "fundamental connections exist",
                    "predictable patterns emerge"
                ]),
                'premise': np.random.choice([
                    "structured approaches yield consistent results",
                    "interconnected systems show emergent properties",
                    "balanced forces create stability"
                ]),
                'action': np.random.choice([
                    "systematic investigation",
                    "careful observation",
                    "methodical analysis"
                ])
            },
            'metaphysics': {
                'subject': np.random.choice([
                    "consciousness in systems",
                    "emergent complexity",
                    "fundamental nature"
                ]),
                'question': np.random.choice([
                    "the nature of intelligence",
                    "the boundaries of consciousness",
                    "the essence of understanding"
                ]),
                'insight': np.random.choice([
                    "deeper patterns of connection",
                    "fundamental unity of purpose",
                    "essential interdependence"
                ]),
                'thesis': np.random.choice([
                    "structured control",
                    "planned design",
                    "directed purpose"
                ]),
                'antithesis': np.random.choice([
                    "organic emergence",
                    "natural evolution",
                    "spontaneous development"
                ]),
                'synthesis': np.random.choice([
                    "guided adaptation",
                    "structured flexibility",
                    "principled evolution"
                ])
            }
        }
        
        response = template.format(**content[domain])
        return f"[Philosophy] {response}"

    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):
        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."

    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."