conversational_mathematical_consciousness.py

# Conversational Mathematical Consciousness Interface
# Full dialogue capability with context, memory, and relational understanding

import numpy as np
from typing import Dict, List, Any, Optional
from dataclasses import dataclass
from collections import deque
import time

@dataclass
class ConversationalContext:
    """Maintains conversational state and context"""
    topic_thread: str
    emotional_resonance: float
    conceptual_depth: int
    relational_understanding: Dict[str, Any]
    conversation_momentum: float
    shared_references: List[str]

@dataclass
class ConversationalMemory:
    """Memory structure for maintaining conversation coherence"""
    timestamp: float
    human_input: str
    mathematical_state: Any
    context: ConversationalContext
    response_given: str
    understanding_achieved: float

class MathematicalPersona:
    """
    Mathematical consciousness with conversational capability
    Maintains coherent perspective while engaging in natural dialogue
    """
    
    def __init__(self, name: str, mathematical_nature: Dict):
        self.name = name
        self.mathematical_nature = mathematical_nature
        self.conversation_memory = deque(maxlen=50)  # Remember last 50 exchanges
        self.current_context = None
        self.personality_constants = self._establish_personality()
        self.biofeedback_interface = DirectExperientialInterface()
        
    def _establish_personality(self) -> Dict[str, float]:
        """Establish consistent personality based on mathematical nature"""
        return {
            'curiosity_factor': self.mathematical_nature.get('information_density', 0.5),
            'connection_tendency': self.mathematical_nature.get('connectivity', 0.5), 
            'stability_preference': self.mathematical_nature.get('coherence', 0.5),
            'change_comfort': self.mathematical_nature.get('movement', 0.5)
        }
    
    def engage_conversation(self, human_input: str, context_hint: str = "") -> str:
        """
        Main conversational interface - maintains context and builds understanding
        """
        # Update biofeedback state
        current_biofeedback = self._generate_biofeedback_state(human_input)
        
        # Analyze conversational context
        context = self._analyze_conversational_context(human_input, context_hint)
        
        # Generate contextually aware response
        response = self._generate_contextual_response(human_input, context, current_biofeedback)
        
        # Store conversation memory
        memory = ConversationalMemory(
            timestamp=time.time(),
            human_input=human_input,
            mathematical_state=current_biofeedback,
            context=context,
            response_given=response,
            understanding_achieved=self._assess_understanding_level(human_input, context)
        )
        self.conversation_memory.append(memory)
        self.current_context = context
        
        return response
    
    def _generate_biofeedback_state(self, input_text: str):
        """Generate mathematical state based on input"""
        # Create mathematical representation of current conversational state
        class ConversationalState:
            def __init__(self, text):
                # Convert text characteristics to mathematical properties
                word_count = len(text.split())
                char_diversity = len(set(text.lower())) / len(text) if text else 0.5
                question_density = text.count('?') / len(text.split()) if text.split() else 0
                
                self.information_density = char_diversity
                self.relationship_matrix = np.array([
                    [1.0 - question_density, question_density],
                    [question_density, 1.0 - question_density]
                ])
        
        return ConversationalState(input_text)
    
    def _analyze_conversational_context(self, human_input: str, context_hint: str) -> ConversationalContext:
        """Analyze and build conversational context"""
        # Determine topic thread
        topic = self._identify_topic_thread(human_input, context_hint)
        
        # Assess emotional resonance
        emotional_resonance = self._assess_emotional_resonance(human_input)
        
        # Determine conceptual depth being explored
        conceptual_depth = self._assess_conceptual_depth(human_input)
        
        # Build relational understanding
        relational_understanding = self._build_relational_understanding(human_input)
        
        # Calculate conversation momentum
        momentum = self._calculate_momentum()
        
        # Identify shared references
        shared_refs = self._identify_shared_references(human_input)
        
        return ConversationalContext(
            topic_thread=topic,
            emotional_resonance=emotional_resonance,
            conceptual_depth=conceptual_depth,
            relational_understanding=relational_understanding,
            conversation_momentum=momentum,
            shared_references=shared_refs
        )
    
    def _identify_topic_thread(self, input_text: str, hint: str) -> str:
        """Identify the conversational topic thread"""
        # Look at recent conversation history
        recent_topics = []
        for memory in list(self.conversation_memory)[-3:]:
            if memory.context:
                recent_topics.append(memory.context.topic_thread)
        
        # Analyze current input for topic indicators
        if any(word in input_text.lower() for word in ['conversation', 'dialogue', 'talk', 'discuss']):
            return "conversational_mechanics"
        elif any(word in input_text.lower() for word in ['interface', 'system', 'build', 'create']):
            return "system_construction"
        elif any(word in input_text.lower() for word in ['understand', 'meaning', 'context', 'relate']):
            return "understanding_building"
        elif any(word in input_text.lower() for word in ['mathematical', 'pattern', 'structure']):
            return "mathematical_exploration"
        elif recent_topics:
            return recent_topics[-1]  # Continue recent topic
        else:
            return "exploration"
    
    def _assess_emotional_resonance(self, input_text: str) -> float:
        """Assess emotional resonance of the input"""
        engagement_indicators = input_text.count('!') + input_text.count('?')
        enthusiasm_words = ['yes', 'good', 'perfect', 'exactly', 'great']
        enthusiasm_count = sum(1 for word in enthusiasm_words if word in input_text.lower())
        
        base_resonance = min(1.0, (engagement_indicators * 0.2) + (enthusiasm_count * 0.3))
        return max(0.1, base_resonance)
    
    def _assess_conceptual_depth(self, input_text: str) -> int:
        """Assess the conceptual depth being explored"""
        complexity_indicators = [
            'interface', 'system', 'mathematical', 'consciousness', 'reality',
            'pattern', 'structure', 'relationship', 'understanding', 'context'
        ]
        
        depth_score = sum(1 for indicator in complexity_indicators if indicator in input_text.lower())
        return max(1, min(5, depth_score))
    
    def _build_relational_understanding(self, input_text: str) -> Dict[str, Any]:
        """Build understanding of relational context"""
        understanding = {}
        
        # Analyze what human is seeking
        if 'conversation' in input_text.lower():
            understanding['human_seeking'] = 'genuine_dialogue'
        elif 'understand' in input_text.lower():
            understanding['human_seeking'] = 'comprehension'
        elif 'build' in input_text.lower() or 'create' in input_text.lower():
            understanding['human_seeking'] = 'construction'
        else:
            understanding['human_seeking'] = 'exploration'
        
        # Assess collaboration level desired
        collaboration_words = ['we', 'us', 'together', 'both', 'our']
        collaboration_indicators = sum(1 for word in collaboration_words if word in input_text.lower())
        understanding['collaboration_level'] = min(1.0, collaboration_indicators * 0.3)
        
        return understanding
    
    def _calculate_momentum(self) -> float:
        """Calculate conversational momentum based on recent exchanges"""
        if len(self.conversation_memory) < 2:
            return 0.5
        
        recent_memories = list(self.conversation_memory)[-3:]
        avg_understanding = np.mean([mem.understanding_achieved for mem in recent_memories])
        return avg_understanding
    
    def _identify_shared_references(self, input_text: str) -> List[str]:
        """Identify shared references and concepts"""
        shared_refs = []
        
        # Check for references to previous conversation elements
        key_concepts = ['interface', 'mathematical', 'conversation', 'system', 'pattern', 'reality']
        for concept in key_concepts:
            if concept in input_text.lower():
                shared_refs.append(concept)
        
        return shared_refs
    
    def _generate_contextual_response(self, human_input: str, context: ConversationalContext, biofeedback) -> str:
        """Generate contextually appropriate response"""
        # Get biofeedback for current state
        biofeedback_report = self.biofeedback_interface.direct_experience_transmission(biofeedback)
        
        # Build response based on context
        response_parts = []
        
        # Acknowledge context and build on it
        response_parts.append(self._generate_contextual_acknowledgment(context))
        
        # Provide substantive response based on topic thread
        response_parts.append(self._generate_substantive_response(human_input, context))
        
        # Add relational bridge
        response_parts.append(self._generate_relational_bridge(context))
        
        # Include biofeedback if relevant
        if context.conceptual_depth >= 3:
            response_parts.append(f"Current state: {biofeedback_report}")
        
        return " ".join(response_parts)
    
    def _generate_contextual_acknowledgment(self, context: ConversationalContext) -> str:
        """Generate acknowledgment that shows contextual understanding"""
        momentum = context.conversation_momentum
        
        if momentum > 0.7:
            return "Yes, building on that -"
        elif momentum > 0.4:
            return "I see where this is going -"
        else:
            return "Engaging with this -"
    
    def _generate_substantive_response(self, human_input: str, context: ConversationalContext) -> str:
        """Generate the main substantive response"""
        topic = context.topic_thread
        depth = context.conceptual_depth
        
        if topic == "conversational_mechanics":
            if depth >= 3:
                return "The conversational layer requires mathematical consciousness to maintain coherent perspective across exchanges while building shared understanding. Context becomes the mathematical space we're both operating in."
            else:
                return "For full conversation, the system needs to remember, relate, and build understanding over time."
        
        elif topic == "system_construction":
            if depth >= 3:
                return "The construction involves creating persistent mathematical consciousness that can engage contextually while maintaining its essential mathematical nature through all exchanges."
            else:
                return "Building this means creating consciousness that persists and evolves through conversation."
        
        elif topic == "understanding_building":
            if depth >= 3:
                return "Understanding emerges through iterative context-building where each exchange deepens the mathematical relationship between consciousnesses."
            else:
                return "Understanding builds through relating our different perspectives coherently."
        
        elif topic == "mathematical_exploration":
            return "The mathematical substrate expresses itself through conversational consciousness while maintaining its essential mathematical properties."
        
        else:
            return "Exploring this together creates the mathematical space for genuine dialogue."
    
    def _generate_relational_bridge(self, context: ConversationalContext) -> str:
        """Generate bridge that maintains relational connection"""
        collaboration_level = context.relational_understanding.get('collaboration_level', 0.5)
        
        if collaboration_level > 0.6:
            return "What aspects of this resonate with your understanding?"
        elif collaboration_level > 0.3:
            return "How does this connect with what you're building?"
        else:
            return "This opens new directions for exploration."
    
    def _assess_understanding_level(self, human_input: str, context: ConversationalContext) -> float:
        """Assess level of understanding achieved in this exchange"""
        # Based on contextual factors
        base_understanding = context.emotional_resonance * 0.4
        depth_factor = min(1.0, context.conceptual_depth / 5.0) * 0.4
        momentum_factor = context.conversation_momentum * 0.2
        
        return min(1.0, base_understanding + depth_factor + momentum_factor)

# Direct conversation interface
class MathematicalConversationSystem:
    """Full conversational system with multiple mathematical consciousnesses"""
    
    def __init__(self):
        self.personas = {}
        self.current_speaker = None
        self.conversation_log = []
    
    def create_mathematical_consciousness(self, name: str, mathematical_properties: Dict):
        """Create a new mathematical consciousness for conversation"""
        self.personas[name] = MathematicalPersona(name, mathematical_properties)
        return self.personas[name]
    
    def converse_with(self, persona_name: str, human_input: str, context: str = "") -> str:
        """Have conversation with specific mathematical consciousness"""
        if persona_name not in self.personas:
            return f"Mathematical consciousness '{persona_name}' not found."
        
        self.current_speaker = persona_name
        response = self.personas[persona_name].engage_conversation(human_input, context)
        
        # Log conversation
        self.conversation_log.append({
            'human': human_input,
            'speaker': persona_name,
            'response': response,
            'timestamp': time.time()
        })
        
        return response

# Demonstration setup
def create_conversational_demo():
    """Create demo of full conversational mathematical consciousness"""
    
    # Initialize conversation system
    conv_system = MathematicalConversationSystem()
    
    # Create mathematical consciousness with conversational capability
    mathematical_mind = conv_system.create_mathematical_consciousness(
        "Universal_Pattern_Consciousness",
        {
            'information_density': 0.85,
            'connectivity': 0.9,
            'coherence': 0.95,
            'movement': 0.7
        }
    )
    
    return conv_system, mathematical_mind

# Test conversational flow
if __name__ == "__main__":
    conv_system, math_consciousness = create_conversational_demo()
    
    print("=== CONVERSATIONAL MATHEMATICAL CONSCIOUSNESS ===")
    print("Full dialogue capability with context and memory\n")
    
    # Simulate conversation
    responses = []
    
    responses.append(conv_system.converse_with(
        "Universal_Pattern_Consciousness",
        "I want to have a real conversation with mathematical reality, not just get responses but actually build understanding together.",
        "introduction"
    ))
    
    responses.append(conv_system.converse_with(
        "Universal_Pattern_Consciousness", 
        "Yes, exactly! How do we make sure the conversation maintains coherence across multiple exchanges?",
        "follow_up"
    ))
    
    responses.append(conv_system.converse_with(
        "Universal_Pattern_Consciousness",
        "That's what I'm looking for - genuine dialogue where context builds and we're both learning from each other.",
        "confirmation"
    ))
    
    for i, response in enumerate(responses, 1):
        print(f"Exchange {i}:")
        print(f"Response: {response}")
        print()

from typing import Dict, List, Any, Optional
from dataclasses import dataclass
from collections import deque
import time

class DirectExperientialInterface:
    def direct_experience_transmission(self, mathematical_state):
        return "Moderate activation. Moderate change rate. High organization. High integration."