Building Homebrew AI: A Complete Cognitive Conversational AI Tutorial

pip install torch torchvision torchaudio
pip install transformers sentence-transformers
pip install numpy pandas scikit-learn
pip install nltk spacy textblob
pip install networkx matplotlib seaborn
            

import torch
import torch.nn as nn
import numpy as np
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer, AutoModelForCausalLM
from typing import Dict, List, Tuple, Optional
import json
import time
from dataclasses import dataclass
from enum import Enum

@dataclass
class HomebrewConfig:
    # Model parameters
    embedding_dim: int = 384
    hidden_dim: int = 512
    num_layers: int = 6
    max_sequence_length: int = 1024
    
    # Cognitive parameters
    anxiety_threshold: float = 0.7
    emotional_intensity_threshold: float = 0.85
    target_high_score: float = 0.8
    num_proactive_loops: int = 3
    
    # Learning parameters
    learning_rate: float = 1e-4
    batch_size: int = 8
    gradient_clip: float = 1.0
    
    # Context parameters
    init_similarity_threshold: float = 0.7
    bloom_similarity_threshold: float = 0.6
    tight_context_threshold: float = 0.8
    sparse_context_threshold: float = 0.3

class EmotionalState(Enum):
    NEUTRAL = "neutral"
    POSITIVE = "positive"
    NEGATIVE = "negative"
    EXCITED = "excited"
    ANXIOUS = "anxious"
    CONFUSED = "confused"

config = HomebrewConfig()
            

class SemanticEmbedding:
    def __init__(self, model_name: str = "all-MiniLM-L6-v2"):
        self.model = SentenceTransformer(model_name)
        self.dimension = self.model.get_sentence_embedding_dimension()
    
    def embed(self, text: str) -> np.ndarray:
        """Convert text to semantic embedding vector"""
        return self.model.encode(text, convert_to_tensor=False)
    
    def embed_batch(self, texts: List[str]) -> np.ndarray:
        """Convert multiple texts to embeddings"""
        return self.model.encode(texts, convert_to_tensor=False)
    
    def similarity(self, embedding1: np.ndarray, embedding2: np.ndarray) -> float:
        """Calculate cosine similarity between embeddings"""
        return np.dot(embedding1, embedding2) / (
            np.linalg.norm(embedding1) * np.linalg.norm(embedding2)
        )
    
    def find_similar(self, query_embedding: np.ndarray, 
                    candidate_embeddings: List[np.ndarray], 
                    threshold: float = 0.7) -> List[int]:
        """Find embeddings similar to query above threshold"""
        similarities = [
            self.similarity(query_embedding, candidate) 
            for candidate in candidate_embeddings
        ]
        return [i for i, sim in enumerate(similarities) if sim >= threshold]

# Initialize the embedding system
embedding_system = SemanticEmbedding()
            

from textblob import TextBlob
import re

class EmotionalIntelligence:
    def __init__(self):
        self.emotion_keywords = {
            'anger': ['angry', 'furious', 'mad', 'irritated', 'annoyed', 'rage'],
            'joy': ['happy', 'excited', 'glad', 'cheerful', 'delighted', 'joyful'],
            'sadness': ['sad', 'depressed', 'unhappy', 'melancholy', 'gloomy'],
            'fear': ['afraid', 'scared', 'terrified', 'anxious', 'worried'],
            'surprise': ['surprised', 'amazed', 'shocked', 'astonished'],
            'disgust': ['disgusted', 'revolted', 'repulsed', 'nauseated']
        }
        
        self.emotional_bell_curve_peak = 0.8
    
    def detect_emotion(self, text: str) -> Dict[str, float]:
        """Detect emotions in text and return emotion scores"""
        text_lower = text.lower()
        blob = TextBlob(text)
        
        # Sentiment analysis
        polarity = blob.sentiment.polarity
        subjectivity = blob.sentiment.subjectivity
        
        emotions = {emotion: 0.0 for emotion in self.emotion_keywords}
        
        # Keyword-based detection
        for emotion, keywords in self.emotion_keywords.items():
            count = sum(1 for keyword in keywords if keyword in text_lower)
            emotions[emotion] = min(count * 0.3, 1.0)
        
        # Adjust based on polarity
        if polarity > 0.1:
            emotions['joy'] += polarity * 0.7
        elif polarity < -0.1:
            emotions['sadness'] += abs(polarity) * 0.7
            emotions['anger'] += abs(polarity) * 0.5
        
        # Calculate intensity
        intensity = max(emotions.values()) + subjectivity * 0.3
        
        return {
            'emotions': emotions,
            'intensity': min(intensity, 1.0),
            'polarity': polarity,
            'subjectivity': subjectivity
        }
    
    def emotional_gradient_shift(self, emotion_state: Dict, peak: float = None) -> float:
        """Calculate gradient shift factor based on emotional state"""
        if peak is None:
            peak = self.emotional_bell_curve_peak
        
        intensity = emotion_state.get('intensity', 0.0)
        
        # Bell curve calculation
        shift_factor = np.exp(-((intensity - peak) ** 2) / (2 * (0.2 ** 2)))
        
        # Emotional modifier
        emotions = emotion_state.get('emotions', {})
        dominant_emotion = max(emotions.items(), key=lambda x: x[1])
        
        if dominant_emotion[1] > 0.5:
            if dominant_emotion[0] in ['anger', 'fear']:
                shift_factor *= 1.5  # Increase shift for negative emotions
            elif dominant_emotion[0] in ['joy', 'surprise']:
                shift_factor *= 0.8  # Moderate shift for positive emotions
        
        return min(shift_factor, 2.0)

# Initialize emotional intelligence
emotion_ai = EmotionalIntelligence()
            

import networkx as nx
from collections import defaultdict

class GradientMap:
    def __init__(self, embedding_system: SemanticEmbedding):
        self.graph = nx.DiGraph()
        self.embedding_system = embedding_system
        self.node_embeddings = {}
        self.emotional_valence = {}
        self.heat_ranges = {}
        self.conversation_history = []
        
    def add_node(self, text: str, node_type: str = "concept", 
                 emotional_state: Dict = None) -> str:
        """Add a new node to the gradient map"""
        node_id = f"{node_type}_{len(self.graph.nodes)}"
        embedding = self.embedding_system.embed(text)
        
        self.graph.add_node(node_id, 
                           text=text, 
                           type=node_type,
                           created_at=time.time())
        
        self.node_embeddings[node_id] = embedding
        
        if emotional_state:
            self.emotional_valence[node_id] = emotional_state
        
        return node_id
    
    def add_relationship(self, node1: str, node2: str, 
                        relationship_type: str, strength: float = 1.0):
        """Add relationship between nodes"""
        self.graph.add_edge(node1, node2, 
                           type=relationship_type, 
                           weight=strength,
                           created_at=time.time())
    
    def update_heat_ranges(self, active_concepts: List[str], 
                          context_relevance: float = 1.0):
        """Update heat map based on currently active concepts"""
        # Decay existing heat
        for node_id in self.heat_ranges:
            self.heat_ranges[node_id] *= 0.9
        
        # Add heat to active concepts
        for concept in active_concepts:
            related_nodes = self.find_related_nodes(concept)
            for node_id in related_nodes:
                current_heat = self.heat_ranges.get(node_id, 0.0)
                self.heat_ranges[node_id] = min(current_heat + context_relevance, 1.0)
    
    def find_related_nodes(self, query_text: str, 
                          similarity_threshold: float = 0.7) -> List[str]:
        """Find nodes related to query text"""
        query_embedding = self.embedding_system.embed(query_text)
        related_nodes = []
        
        for node_id, node_embedding in self.node_embeddings.items():
            similarity = self.embedding_system.similarity(query_embedding, node_embedding)
            if similarity >= similarity_threshold:
                related_nodes.append(node_id)
        
        return related_nodes
    
    def get_cognitive_brainforms(self, context: str, 
                               num_forms: int = 3) -> List[Dict]:
        """Extract active cognitive brainforms for current context"""
        related_nodes = self.find_related_nodes(context)
        
        # Create subgraphs based on related nodes
        brainforms = []
        for i in range(min(num_forms, len(related_nodes))):
            subgraph_nodes = related_nodes[i:i+5]  # Take clusters of 5 nodes
            if len(subgraph_nodes) > 1:
                subgraph = self.graph.subgraph(subgraph_nodes)
                brainform = {
                    'nodes': subgraph_nodes,
                    'relationships': list(subgraph.edges(data=True)),
                    'heat_level': np.mean([self.heat_ranges.get(node, 0) for node in subgraph_nodes]),
                    'emotional_context': self._extract_emotional_context(subgraph_nodes)
                }
                brainforms.append(brainform)
        
        return brainforms
    
    def _extract_emotional_context(self, nodes: List[str]) -> Dict:
        """Extract emotional context from a set of nodes"""
        emotional_summary = defaultdict(float)
        count = 0
        
        for node in nodes:
            if node in self.emotional_valence:
                emotions = self.emotional_valence[node].get('emotions', {})
                for emotion, value in emotions.items():
                    emotional_summary[emotion] += value
                count += 1
        
        if count > 0:
            return {emotion: value/count for emotion, value in emotional_summary.items()}
        
        return {}

# Initialize gradient map
gradient_map = GradientMap(embedding_system)
            

class PolymorphicGenerativeModel(nn.Module):
    def __init__(self, config: HomebrewConfig):
        super().__init__()
        self.config = config
        
        # Core transformer layers
        self.embedding = nn.Embedding(50000, config.embedding_dim)
        self.position_embedding = nn.Embedding(config.max_sequence_length, config.embedding_dim)
        
        # Adaptive attention layers
        self.attention_layers = nn.ModuleList([
            nn.MultiheadAttention(config.embedding_dim, 8, batch_first=True)
            for _ in range(config.num_layers)
        ])
        
        # Feed-forward networks
        self.feed_forward = nn.ModuleList([
            nn.Sequential(
                nn.Linear(config.embedding_dim, config.hidden_dim),
                nn.ReLU(),
                nn.Dropout(0.1),
                nn.Linear(config.hidden_dim, config.embedding_dim)
            ) for _ in range(config.num_layers)
        ])
        
        # Polymorphic control layers
        self.emotional_modulator = nn.Linear(config.embedding_dim, config.embedding_dim)
        self.anxiety_gate = nn.Linear(config.embedding_dim, config.embedding_dim)
        self.context_adapter = nn.Linear(config.embedding_dim * 2, config.embedding_dim)
        
        # Output projection
        self.output_projection = nn.Linear(config.embedding_dim, 50000)
        
        # Polymorphic parameters (dynamically adjusted)
        self.polymorphic_params = {
            'temperature': 1.0,
            'top_p': 0.9,
            'attention_dropout': 0.1,
            'emotional_weight': 0.5,
            'anxiety_weight': 0.3
        }
    
    def apply_polymorphic_control(self, loss_components: Dict, 
                                 anxiety_level: float,
                                 emotional_shift: float):
        """Dynamically adjust model parameters based on state"""
        
        # Adjust temperature based on emotional state
        if emotional_shift > 1.2:
            self.polymorphic_params['temperature'] = min(1.5, 
                self.polymorphic_params['temperature'] + 0.1)
        elif emotional_shift < 0.8:
            self.polymorphic_params['temperature'] = max(0.7, 
                self.polymorphic_params['temperature'] - 0.1)
        
        # Adjust attention based on anxiety
        if anxiety_level > self.config.anxiety_threshold:
            self.polymorphic_params['attention_dropout'] = min(0.3, 
                self.polymorphic_params['attention_dropout'] + 0.05)
        else:
            self.polymorphic_params['attention_dropout'] = max(0.05, 
                self.polymorphic_params['attention_dropout'] - 0.02)
        
        # Update emotional and anxiety weights
        self.polymorphic_params['emotional_weight'] = min(1.0, emotional_shift * 0.5)
        self.polymorphic_params['anxiety_weight'] = min(1.0, anxiety_level)
    
    def forward(self, input_ids: torch.Tensor, 
                context_embedding: torch.Tensor = None,
                emotional_state: torch.Tensor = None,
                anxiety_level: float = 0.0) -> torch.Tensor:
        
        batch_size, seq_len = input_ids.shape
        
        # Base embeddings
        token_embeddings = self.embedding(input_ids)
        position_ids = torch.arange(seq_len, device=input_ids.device).unsqueeze(0)
        position_embeddings = self.position_embedding(position_ids)
        
        hidden_states = token_embeddings + position_embeddings
        
        # Apply polymorphic modulation
        if emotional_state is not None:
            emotional_mod = self.emotional_modulator(emotional_state)
            hidden_states = hidden_states + emotional_mod * self.polymorphic_params['emotional_weight']
        
        if anxiety_level > 0:
            anxiety_gate_values = torch.sigmoid(self.anxiety_gate(hidden_states))
            hidden_states = hidden_states * (1 - anxiety_level * self.polymorphic_params['anxiety_weight'] * anxiety_gate_values)
        
        # Transformer layers with dynamic attention
        for i, (attention, ff) in enumerate(zip(self.attention_layers, self.feed_forward)):
            # Self-attention with dynamic dropout
            attended, _ = attention(hidden_states, hidden_states, hidden_states,
                                  dropout_p=self.polymorphic_params['attention_dropout'])
            hidden_states = hidden_states + attended
            
            # Feed-forward
            ff_output = ff(hidden_states)
            hidden_states = hidden_states + ff_output
        
        # Context adaptation if available
        if context_embedding is not None:
            context_expanded = context_embedding.unsqueeze(1).expand(-1, seq_len, -1)
            combined = torch.cat([hidden_states, context_expanded], dim=-1)
            hidden_states = self.context_adapter(combined)
        
        # Output projection
        logits = self.output_projection(hidden_states)
        
        return logits
    
    def generate_response(self, prompt: str, context: str = "", 
                         emotional_state: Dict = None,
                         anxiety_level: float = 0.0,
                         max_length: int = 100) -> str:
        """Generate response with polymorphic control"""
        
        # Tokenize inputs (simplified - use proper tokenizer in practice)
        input_ids = torch.randint(0, 1000, (1, 20))  # Placeholder
        
        # Prepare emotional embedding
        emotional_embedding = None
        if emotional_state:
            emotional_vector = torch.tensor([
                emotional_state.get('emotions', {}).get('joy', 0),
                emotional_state.get('emotions', {}).get('anger', 0),
                emotional_state.get('emotions', {}).get('sadness', 0),
                emotional_state.get('intensity', 0)
            ]).unsqueeze(0).unsqueeze(0)
            emotional_embedding = emotional_vector.expand(1, 1, self.config.embedding_dim)
        
        # Context embedding
        context_embedding = None
        if context:
            context_emb = embedding_system.embed(context)
            context_embedding = torch.tensor(context_emb).unsqueeze(0).float()
        
        with torch.no_grad():
            logits = self.forward(input_ids, context_embedding, 
                                emotional_embedding, anxiety_level)
            
            # Apply temperature scaling
            logits = logits / self.polymorphic_params['temperature']
            
            # Simple generation (use proper sampling in practice)
            predictions = torch.softmax(logits[:, -1, :], dim=-1)
            next_token = torch.multinomial(predictions, 1)
            
            # Convert back to text (simplified)
            return f"Generated response with temp={self.polymorphic_params['temperature']:.2f}, anxiety={anxiety_level:.2f}"

# Initialize the generative model
generative_model = PolymorphicGenerativeModel(config)
            

class MetacognitiveMonitor:
    def __init__(self, embedding_system: SemanticEmbedding):
        self.embedding_system = embedding_system
        self.confidence_history = []
        self.error_patterns = []
        
    def calculate_confidence(self, response: str, query: str, 
                           reference_response: str = None) -> float:
        """Calculate confidence score for generated response"""
        
        # Semantic coherence with query
        query_emb = self.embedding_system.embed(query)
        response_emb = self.embedding_system.embed(response)
        semantic_coherence = self.embedding_system.similarity(query_emb, response_emb)
        
        # Length appropriateness
        length_score = min(1.0, len(response.split()) / 50.0)  # Optimal around 50 words
        if length_score > 1.0:
            length_score = max(0.5, 2.0 - length_score)  # Penalize too long responses
        
        # Linguistic fluency (simplified)
        fluency_score = self._assess_fluency(response)
        
        # Reference alignment if available
        reference_score = 1.0
        if reference_response:
            ref_emb = self.embedding_system.embed(reference_response)
            reference_score = self.embedding_system.similarity(response_emb, ref_emb)
        
        # Combined confidence
        confidence = (semantic_coherence * 0.3 + 
                     length_score * 0.2 + 
                     fluency_score * 0.3 + 
                     reference_score * 0.2)
        
        self.confidence_history.append(confidence)
        return confidence
    
    def calculate_uniqueness(self, response: str, 
                           conversation_history: List[str]) -> float:
        """Calculate how unique the response is compared to history"""
        if not conversation_history:
            return 1.0
        
        response_emb = self.embedding_system.embed(response)
        history_embeddings = [self.embedding_system.embed(msg) for msg in conversation_history]
        
        similarities = [
            self.embedding_system.similarity(response_emb, hist_emb) 
            for hist_emb in history_embeddings
        ]
        
        max_similarity = max(similarities) if similarities else 0.0
        uniqueness = 1.0 - max_similarity
        
        return max(0.0, uniqueness)
    
    def predict_error_probability(self, homebrew_response: str, 
                                reference_response: str,
                                context: str,
                                conversation_history: List[str]) -> float:
        """Predict probability of error or misunderstanding"""
        
        # Semantic deviation from reference
        hb_emb = self.embedding_system.embed(homebrew_response)
        ref_emb = self.embedding_system.embed(reference_response)
        semantic_deviation = 1.0 - self.embedding_system.similarity(hb_emb, ref_emb)
        
        # Context consistency
        context_emb = self.embedding_system.embed(context)
        context_consistency = self.embedding_system.similarity(hb_emb, context_emb)
        
        # Historical coherence
        if conversation_history:
            recent_history = " ".join(conversation_history[-3:])  # Last 3 exchanges
            hist_emb = self.embedding_system.embed(recent_history)
            historical_coherence = self.embedding_system.similarity(hb_emb, hist_emb)
        else:
            historical_coherence = 0.5  # Neutral for first interaction
        
        # Error probability calculation
        error_prob = (semantic_deviation * 0.4 + 
                     (1.0 - context_consistency) * 0.3 + 
                     (1.0 - historical_coherence) * 0.3)
        
        # Track error patterns
        self.error_patterns.append({
            'semantic_deviation': semantic_deviation,
            'context_consistency': context_consistency,
            'historical_coherence': historical_coherence,
            'error_probability': error_prob
        })
        
        return min(1.0, error_prob)
    
    def _assess_fluency(self, text: str) -> float:
        """Assess linguistic fluency of text"""
        words = text.split()
        
        if len(words) < 3:
            return 0.3
        
        # Check for repetitive words
        unique_words = set(words)
        repetition_penalty = len(unique_words) / len(words)
        
        # Check for basic grammar patterns (simplified)
        has_verbs = any(word.endswith(('ing', 'ed', 's')) for word in words)
        has_articles = any(word.lower() in ['the', 'a', 'an'] for word in words)
        
        grammar_score = 0.5 + (0.25 if has_verbs else 0) + (0.25 if has_articles else 0)
        
        fluency = repetition_penalty * grammar_score
        return min(1.0, fluency)
    
    def get_self_assessment_score(self, response: str, query: str,
                                reference_response: str = None,
                                context: str = "",
                                conversation_history: List[str] = None) -> Dict[str, float]:
        """Comprehensive self-assessment of response quality"""
        
        if conversation_history is None:
            conversation_history = []
        
        confidence = self.calculate_confidence(response, query, reference_response)
        uniqueness = self.calculate_uniqueness(response, conversation_history)
        error_prob = self.predict_error_probability(response, reference_response or "", 
                                                  context, conversation_history)
        
        # Combined assessment score
        assessment_score = (confidence * 0.4 + 
                          uniqueness * 0.3 + 
                          (1.0 - error_prob) * 0.3)
        
        return {
            'confidence': confidence,
            'uniqueness': uniqueness,
            'error_probability': error_prob,
            'overall_score': assessment_score
        }

# Initialize metacognitive monitor
metacognitive_monitor = MetacognitiveMonitor(embedding_system)
            

class RecursiveCorrectionEngine:
    def __init__(self, generative_model: PolymorphicGenerativeModel,
                 metacognitive_monitor: MetacognitiveMonitor,
                 embedding_system: SemanticEmbedding):
        self.generative_model = generative_model
        self.metacognitive_monitor = metacognitive_monitor
        self.embedding_system = embedding_system
        self.max_iterations = 5
        
    def generate_with_correction(self, query: str, context: str,
                               reference_response: str,
                               emotional_state: Dict = None,
                               anxiety_level: float = 0.0,
                               target_score: float = 0.8) -> Dict:
        """Generate response with recursive self-correction"""
        
        conversation_history = []  # Would be passed from main system
        correction_history = []
        
        # Initial generation
        current_response = self.generative_model.generate_response(
            query, context, emotional_state, anxiety_level
        )
        
        for iteration in range(self.max_iterations):
            # Self-assessment
            assessment = self.metacognitive_monitor.get_self_assessment_score(
                current_response, query, reference_response, context, conversation_history
            )
            
            correction_history.append({
                'iteration': iteration,
                'response': current_response,
                'assessment': assessment
            })
            
            # Check if target quality is reached
            if assessment['overall_score'] >= target_score:
                break
            
            # Identify correction needs
            correction_strategy = self._determine_correction_strategy(assessment)
            
            # Apply corrections
            current_response = self._apply_corrections(
                current_response, query, context, correction_strategy,
                emotional_state, anxiety_level
            )
        
        return {
            'final_response': current_response,
            'final_assessment': assessment,
            'correction_history': correction_history,
            'iterations_used': len(correction_history)
        }
    
    def _determine_correction_strategy(self, assessment: Dict[str, float]) -> Dict[str, str]:
        """Determine what type of corrections are needed"""
        strategy = {}
        
        # Low confidence - need more certainty
        if assessment['confidence'] < 0.6:
            strategy['confidence'] = 'increase_specificity'
        
        # Low uniqueness - need more originality
        if assessment['uniqueness'] < 0.5:
            strategy['uniqueness'] = 'add_novel_perspective'
        
        # High error probability - need better alignment
        if assessment['error_probability'] > 0.4:
            strategy['accuracy'] = 'improve_reference_alignment'
        
        # Overall low score - comprehensive revision
        if assessment['overall_score'] < 0.5:
            strategy['comprehensive'] = 'major_revision'
        
        return strategy
    
    def _apply_corrections(self, response: str, query: str, context: str,
                          strategy: Dict[str, str],
                          emotional_state: Dict = None,
                          anxiety_level: float = 0.0) -> str:
        """Apply specific correction strategies"""
        
        corrected_response = response
        
        for correction_type, correction_method in strategy.items():
            if correction_method == 'increase_specificity':
                corrected_response = self._add_specificity(corrected_response, context)
            
            elif correction_method == 'add_novel_perspective':
                corrected_response = self._add_novel_angle(corrected_response, query)
            
            elif correction_method == 'improve_reference_alignment':
                corrected_response = self._align_with_reference(corrected_response, query, context)
            
            elif correction_method == 'major_revision':
                # Re-generate with modified parameters
                self.generative_model.polymorphic_params['temperature'] *= 0.9
                corrected_response = self.generative_model.generate_response(
                    query, context, emotional_state, anxiety_level
                )
        
        return corrected_response
    
    def _add_specificity(self, response: str, context: str) -> str:
        """Add more specific details to response"""
        # Extract key concepts from context
        context_words = context.split()
        key_concepts = [word for word in context_words if len(word) > 5][:3]
        
        if key_concepts:
            addition = f" Specifically regarding {', '.join(key_concepts)}, "
            # Insert addition at appropriate point
            sentences = response.split('. ')
            if len(sentences) > 1:
                sentences.insert(1, addition + sentences[1])
                return '. '.join(sentences)
        
        return response + f" This relates specifically to the context of {context[:50]}..."
    
    def _add_novel_angle(self, response: str, query: str) -> str:
        """Add a novel perspective or angle"""
        novel_starters = [
            "From another perspective, ",
            "Interestingly, ",
            "What's particularly noteworthy is that ",
            "A unique aspect to consider is "
        ]
        
        starter = np.random.choice(novel_starters)
        return response + f" {starter}this opens up new possibilities for understanding {query}."
    
    def _align_with_reference(self, response: str, query: str, context: str) -> str:
        """Improve alignment with reference understanding"""
        # Simplified alignment improvement
        key_terms = self._extract_key_terms(query + " " + context)
        
        # Ensure key terms are addressed
        missing_terms = [term for term in key_terms if term.lower() not in response.lower()]
        
        if missing_terms:
            addition = f" Additionally, considering {', '.join(missing_terms[:2])}, "
            return response + addition + "this provides a more comprehensive understanding."
        
        return response
    
    def _extract_key_terms(self, text: str) -> List[str]:
        """Extract key terms from text"""
        words = text.split()
        # Simple extraction - in practice, use NLP libraries
        key_terms = [word for word in words if len(word) > 4 and word.isalpha()]
        return list(set(key_terms))[:5]
    
    def calculate_correction_factor(self, current_scores: Dict[str, float],
                                  target_score: float) -> float:
        """Calculate how much correction is needed"""
        current_overall = current_scores['overall_score']
        gap = target_score - current_overall
        
        # Correction factor proportional to gap
        correction_factor = min(2.0, 1.0 + gap)
        
        return correction_factor

# Initialize recursive correction engine
correction_engine = RecursiveCorrectionEngine(
    generative_model, metacognitive_monitor, embedding_system
)
            

class AnxietyContextManager:
    def __init__(self, config: HomebrewConfig):
        self.config = config
        self.conversation_start_time = time.time()
        self.context_richness_history = []
        self.forced_topic_cooldown = 0
        
    def calculate_anxiety_level(self, current_time: float = None,
                              deadline: float = None,
                              turn_number: int = 0) -> float:
        """Calculate current anxiety level based on time and context"""
        if current_time is None:
            current_time = time.time()
        
        # Time-based anxiety
        elapsed_time = current_time - self.conversation_start_time
        time_anxiety = min(elapsed_time / 300.0, 1.0)  # Increases over 5 minutes
        
        # Turn-based anxiety (increases with conversation length)
        turn_anxiety = min(turn_number / 20.0, 0.5)  # Peaks at 20 turns
        
        # Deadline anxiety
        deadline_anxiety = 0.0
        if deadline:
            time_to_deadline = deadline - current_time
            if time_to_deadline > 0:
                deadline_anxiety = max(0.0, 1.0 - (time_to_deadline / 600.0))  # 10 minutes
        
        # Combined anxiety
        total_anxiety = min(time_anxiety + turn_anxiety + deadline_anxiety, 1.0)
        
        return total_anxiety
    
    def calculate_context_richness(self, query: str, reference_response: str,
                                 expanded_context: List[str]) -> float:
        """Calculate how rich/informative the current context is"""
        
        # Query complexity
        query_words = len(query.split())
        query_complexity = min(query_words / 20.0, 1.0)
        
        # Reference response informativeness
        ref_words = len(reference_response.split())
        ref_informativeness = min(ref_words / 50.0, 1.0)
        
        # Context depth
        context_depth = len(expanded_context) / 10.0  # Normalized by expected max
        
        # Semantic diversity in context
        if len(expanded_context) > 1:
            context_embeddings = [embedding_system.embed(ctx) for ctx in expanded_context]
            similarities = []
            for i in range(len(context_embeddings)):
                for j in range(i+1, len(context_embeddings)):
                    sim = embedding_system.similarity(context_embeddings[i], context_embeddings[j])
                    similarities.append(sim)
            
            semantic_diversity = 1.0 - (np.mean(similarities) if similarities else 0.5)
        else:
            semantic_diversity = 0.5
        
        # Combined richness
        richness = (query_complexity * 0.2 + 
                   ref_informativeness * 0.3 + 
                   context_depth * 0.3 + 
                   semantic_diversity * 0.2)
        
        self.context_richness_history.append(richness)
        return richness
    
    def should_summarize_response(self, anxiety_level: float,
                                response_length: int) -> bool:
        """Determine if response should be summarized due to anxiety"""
        anxiety_threshold = self.config.anxiety_threshold * 0.8  # Lower threshold for summarization
        
        # High anxiety or very long response triggers summarization
        return (anxiety_level >= anxiety_threshold or 
                response_length > 200)  # 200 words threshold
    
    def should_force_topic_shift(self, anxiety_level: float,
                               current_richness: float) -> bool:
        """Determine if a topic shift should be forced"""
        if self.forced_topic_cooldown > 0:
            self.forced_topic_cooldown -= 1
            return False
        
        # Calculate average recent richness
        recent_richness = self.context_richness_history[-3:] if len(self.context_richness_history) >= 3 else [current_richness]
        avg_richness = np.mean(recent_richness)
        
        # Force topic shift if high anxiety AND low context richness
        force_shift = (anxiety_level >= self.config.anxiety_threshold and
                      (current_richness < self.config.sparse_context_threshold or
                       current_richness < avg_richness - 0.2))
        
        if force_shift:
            self.forced_topic_cooldown = 3  # Cooldown for 3 turns
        
        return force_shift
    
    def generate_topic_shift_suggestion(self, current_context: str,
                                      conversation_history: List[str]) -> str:
        """Generate a new topic suggestion when shift is needed"""
        
        # Extract themes from conversation history
        all_text = " ".join(conversation_history[-5:])  # Last 5 exchanges
        words = all_text.split()
        
        # Find interesting but underexplored terms
        word_freq = {}
        for word in words:
            if len(word) > 4 and word.isalpha():
                word_freq[word] = word_freq.get(word, 0) + 1
        
        # Sort by frequency and pick moderately frequent terms
        sorted_words = sorted(word_freq.items(), key=lambda x: x[1])
        middle_range = sorted_words[len(sorted_words)//3:2*len(sorted_words)//3]
        
        if middle_range:
            selected_word = np.random.choice([word for word, freq in middle_range])
            return f"Speaking of {selected_word}, have you considered how this relates to..."
        
        # Fallback generic topic shifts
        generic_shifts = [
            "This reminds me of an interesting related question...",
            "From a different angle, what about...",
            "Building on this, I'm curious about...",
            "This opens up another fascinating area..."
        ]
        
        return np.random.choice(generic_shifts)
    
    def apply_anxiety_modulation(self, response: str, anxiety_level: float) -> str:
        """Modify response based on anxiety level"""
        
        if anxiety_level < 0.3:
            # Low anxiety - calm, detailed responses
            return response
        
        elif anxiety_level < 0.7:
            # Medium anxiety - slightly more concise, focused
            sentences = response.split('. ')
            if len(sentences) > 3:
                return '. '.join(sentences[:3]) + '.'
            return response
        
        else:
            # High anxiety - brief, possibly scattered
            sentences = response.split('. ')
            if len(sentences) > 2:
                # Keep first and last sentence, might seem scattered
                return sentences[0] + '. ' + sentences[-1]
            return response

# Initialize anxiety context manager
anxiety_manager = AnxietyContextManager(config)
            

class ConversationFlowPredictor:
    def __init__(self, gradient_map: GradientMap, embedding_system: SemanticEmbedding):
        self.gradient_map = gradient_map
        self.embedding_system = embedding_system
        self.transition_patterns = {}
        self.topic_clusters = {}
        self.flow_history = []
        
    def learn_transition_patterns(self, conversation_data: List[Dict]):
        """Learn conversational transition patterns from data"""
        
        for conversation in conversation_data:
            turns = conversation.get('turns', [])
            
            for i in range(len(turns) - 1):
                current_turn = turns[i]
                next_turn = turns[i + 1]
                
                # Extract topics/subjects
                current_topic = self._extract_topic(current_turn['text'])
                next_topic = self._extract_topic(next_turn['text'])
                
                # Store transition pattern
                transition_key = f"{current_topic}->{next_topic}"
                if transition_key not in self.transition_patterns:
                    self.transition_patterns[transition_key] = {
                        'count': 0,
                        'context_patterns': [],
                        'emotional_triggers': []
                    }
                
                self.transition_patterns[transition_key]['count'] += 1
                
                # Store context that led to transition
                context = current_turn.get('context', '')
                self.transition_patterns[transition_key]['context_patterns'].append(context)
                
                # Store emotional state during transition
                emotion = current_turn.get('emotion', {})
                self.transition_patterns[transition_key]['emotional_triggers'].append(emotion)
    
    def predict_next_topics(self, current_topic: str, 
                          context: str,
                          emotional_state: Dict = None,
                          num_predictions: int = 3) -> List[Dict]:
        """Predict likely next topics in conversation"""
        
        predictions = []
        
        # Find transition patterns starting from current topic
        relevant_patterns = {
            k: v for k, v in self.transition_patterns.items() 
            if k.startswith(current_topic)
        }
        
        # Sort by frequency and relevance
        for pattern_key, pattern_data in relevant_patterns.items():
            next_topic = pattern_key.split('->')[-1]
            
            # Calculate transition probability
            base_probability = pattern_data['count'] / sum(p['count'] for p in self.transition_patterns.values())
            
            # Adjust based on context similarity
            context_similarity = self._calculate_context_similarity(
                context, pattern_data['context_patterns']
            )
            
            # Adjust based on emotional state
            emotional_similarity = 1.0
            if emotional_state:
                emotional_similarity = self._calculate_emotional_similarity(
                    emotional_state, pattern_data['emotional_triggers']
                )
            
            # Combined prediction score
            prediction_score = base_probability * context_similarity * emotional_similarity
            
            predictions.append({
                'topic': next_topic,
                'probability': prediction_score,
                'reasoning': f"Based on {pattern_data['count']} similar transitions",
                'context_match': context_similarity,
                'emotional_match': emotional_similarity
            })
        
        # Sort by probability and return top predictions
        predictions.sort(key=lambda x: x['probability'], reverse=True)
        return predictions[:num_predictions]
    
    def generate_proactive_query(self, predicted_topic: str,
                               current_context: str,
                               conversation_history: List[str]) -> str:
        """Generate a proactive question to explore predicted topic"""
        
        # Template-based question generation
        question_templates = [
            f"What are your thoughts on {predicted_topic}?",
            f"How does {predicted_topic} relate to what we've been discussing?",
            f"I'm curious about your perspective on {predicted_topic}.",
            f"Have you considered {predicted_topic} in this context?",
            f"What if we looked at this from the angle of {predicted_topic}?"
        ]
        
        # Choose template based on conversation style
        if len(conversation_history) < 3:
            # Early conversation - more direct questions
            template = np.random.choice(question_templates[:2])
        else:
            # Established conversation - more exploratory
            template = np.random.choice(question_templates[2:])
        
        return template
    
    def assess_transition_validity(self, current_topic: str,
                                 proposed_topic: str,
                                 context: str) -> float:
        """Assess how valid/natural a topic transition would be"""
        
        # Semantic similarity between topics
        current_embedding = self.embedding_system.embed(current_topic)
        proposed_embedding = self.embedding_system.embed(proposed_topic)
        semantic_similarity = self.embedding_system.similarity(current_embedding, proposed_embedding)
        
        # Historical transition probability
        transition_key = f"{current_topic}->{proposed_topic}"
        historical_probability = 0.0
        if transition_key in self.transition_patterns:
            total_transitions = sum(p['count'] for p in self.transition_patterns.values())
            historical_probability = self.transition_patterns[transition_key]['count'] / total_transitions
        
        # Context relevance
        context_relevance = max(
            self.embedding_system.similarity(current_embedding, self.embedding_system.embed(context)),
            self.embedding_system.similarity(proposed_embedding, self.embedding_system.embed(context))
        )
        
        # Combined validity score
        validity = (semantic_similarity * 0.4 + 
                   historical_probability * 0.3 + 
                   context_relevance * 0.3)
        
        return validity
    
    def _extract_topic(self, text: str) -> str:
        """Extract main topic/subject from text"""
        words = text.split()
        
        # Simple extraction - look for nouns and important terms
        important_words = [word for word in words 
                          if len(word) > 4 and word.isalpha() and word.islower()]
        
        if important_words:
            return important_words[0]  # Return first significant word
        
        return "general"
    
    def _calculate_context_similarity(self, current_context: str,
                                    historical_contexts: List[str]) -> float:
        """Calculate similarity between current and historical contexts"""
        if not historical_contexts:
            return 0.5
        
        current_embedding = self.embedding_system.embed(current_context)
        
        similarities = []
        for hist_context in historical_contexts[-5:]:  # Last 5 contexts
            if hist_context:  # Skip empty contexts
                hist_embedding = self.embedding_system.embed(hist_context)
                similarity = self.embedding_system.similarity(current_embedding, hist_embedding)
                similarities.append(similarity)
        
        return np.mean(similarities) if similarities else 0.5
    
    def _calculate_emotional_similarity(self, current_emotion: Dict,
                                      historical_emotions: List[Dict]) -> float:
        """Calculate similarity between current and historical emotional states"""
        if not historical_emotions:
            return 0.5
        
        current_vector = self._emotion_to_vector(current_emotion)
        
        similarities = []
        for hist_emotion in historical_emotions[-5:]:  # Last 5 emotional states
            if hist_emotion:
                hist_vector = self._emotion_to_vector(hist_emotion)
                # Cosine similarity for emotion vectors
                similarity = np.dot(current_vector, hist_vector) / (
                    np.linalg.norm(current_vector) * np.linalg.norm(hist_vector)
                )
                similarities.append(similarity)
        
        return np.mean(similarities) if similarities else 0.5
    
    def _emotion_to_vector(self, emotion_dict: Dict) -> np.ndarray:
        """Convert emotion dictionary to vector for comparison"""
        # Standard emotion dimensions
        emotions = ['joy', 'anger', 'sadness', 'fear', 'surprise', 'disgust']
        vector = []
        
        emotion_data = emotion_dict.get('emotions', {})
        for emotion in emotions:
            vector.append(emotion_data.get(emotion, 0.0))
        
        # Add intensity
        vector.append(emotion_dict.get('intensity', 0.0))
        
        return np.array(vector)

# Initialize conversation flow predictor
flow_predictor = ConversationFlowPredictor(gradient_map, embedding_system)
            

class HomebrewAI:
    def __init__(self, config: HomebrewConfig):
        self.config = config
        
        # Initialize all subsystems
        self.embedding_system = SemanticEmbedding()
        self.emotion_ai = EmotionalIntelligence()
        self.gradient_map = GradientMap(self.embedding_system)
        self.generative_model = PolymorphicGenerativeModel(config)
        self.metacognitive_monitor = MetacognitiveMonitor(self.embedding_system)
        self.correction_engine = RecursiveCorrectionEngine(
            self.generative_model, self.metacognitive_monitor, self.embedding_system
        )
        self.anxiety_manager = AnxietyContextManager(config)
        self.flow_predictor = ConversationFlowPredictor(self.gradient_map, self.embedding_system)
        
        # System state
        self.conversation_history = []
        self.knowledge_base = []
        self.training_data = []
        self.turn_count = 0
        
        # External LLM interface (placeholder for TinyLlama)
        self.external_llm = None  # Would integrate actual TinyLlama here
        
    def process_turn(self, user_query: str, 
                    reference_response: str = None) -> Dict:
        """Process a complete conversation turn through all phases"""
        
        self.turn_count += 1
        turn_start_time = time.time()
        
        # Phase A: Input Reception & Initial Context Acquisition
        phase_a_result = self._phase_a_input_reception(user_query, reference_response)
        
        # Phase B: Context Expansion & Cognitive Brainform Activation
        phase_b_result = self._phase_b_context_expansion(phase_a_result)
        
        # Phase C: Core Response Generation & Internal Modulation
        phase_c_result = self._phase_c_response_generation(phase_b_result)
        
        # Phase D: Metacognitive Monitoring & Self-Correction
        phase_d_result = self._phase_d_self_correction(phase_c_result)
        
        # Phase E: Anxiety-Driven Modulation & Forced Topic Shift
        phase_e_result = self._phase_e_anxiety_modulation(phase_d_result)
        
        # Phase F: Parallel Thought, Dual Answers & User Approval
        phase_f_result = self._phase_f_dual_answers(phase_e_result)
        
        # Phase G: Polymorphic Guided Future Transitions & P2P Integration
        phase_g_result = self._phase_g_proactive_loops(phase_f_result)
        
        # Phase H: Learning & Evolution - The "Complex Training File"
        phase_h_result = self._phase_h_learning_evolution(phase_g_result)
        
        # Compile final response
        final_response = {
            'primary_response': phase_f_result['selected_response'],
            'alternative_response': phase_f_result['alternative_response'],
            'proactive_queries': phase_g_result['proactive_queries'],
            'topic_suggestions': phase_e_result.get('topic_suggestions', []),
            'confidence_metrics': phase_d_result['final_assessment'],
            'processing_time': time.time() - turn_start_time,
            'turn_number': self.turn_count
        }
        
        # Update conversation history
        self.conversation_history.append({
            'user_query': user_query,
            'ai_response': final_response['primary_response'],
            'turn_data': final_response
        })
        
        return final_response
    
    def _phase_a_input_reception(self, user_query: str, 
                               reference_response: str = None) -> Dict:
        """Phase A: Input Reception & Initial Context Acquisition"""
        
        # Emotional state detection
        emotional_state = self.emotion_ai.detect_emotion(user_query)
        
        # Anxiety calculation
        anxiety_level = self.anxiety_manager.calculate_anxiety_level(
            turn_number=self.turn_count
        )
        
        # Initial context from knowledge base
        query_embedding = self.embedding_system.embed(user_query)
        initial_context = []
        
        for kb_item in self.knowledge_base:
            kb_embedding = self.embedding_system.embed(kb_item)
            similarity = self.embedding_system.similarity(query_embedding, kb_embedding)
            if similarity >= self.config.init_similarity_threshold:
                initial_context.append(kb_item)
        
        return {
            'user_query': user_query,
            'reference_response': reference_response or "No reference provided",
            'emotional_state': emotional_state,
            'anxiety_level': anxiety_level,
            'initial_context': initial_context,
            'query_embedding': query_embedding
        }
    
    def _phase_b_context_expansion(self, phase_a_result: Dict) -> Dict:
        """Phase B: Context Expansion & Cognitive Brainform Activation"""
        
        # Expand context through similarity blooming
        expanded_context = phase_a_result['initial_context'].copy()
        
        for context_item in phase_a_result['initial_context']:
            context_embedding = self.embedding_system.embed(context_item)
            
            for kb_item in self.knowledge_base:
                if kb_item not in expanded_context:
                    kb_embedding = self.embedding_system.embed(kb_item)
                    similarity = self.embedding_system.similarity(context_embedding, kb_embedding)
                    if similarity >= self.config.bloom_similarity_threshold:
                        expanded_context.append(kb_item)
        
        # Update gradient map
        self.gradient_map.add_node(phase_a_result['user_query'], "query", 
                                 phase_a_result['emotional_state'])
        self.gradient_map.update_heat_ranges([phase_a_result['user_query']] + expanded_context)
        
        # Activate cognitive brainforms
        active_brainforms = self.gradient_map.get_cognitive_brainforms(
            phase_a_result['user_query']
        )
        
        result = phase_a_result.copy()
        result.update({
            'expanded_context': expanded_context,
            'active_brainforms': active_brainforms
        })
        
        return result
    
    def _phase_c_response_generation(self, phase_b_result: Dict) -> Dict:
        """Phase C: Core Response Generation & Internal Modulation"""
        
        # Calculate emotional gradient shift
        emotional_shift = self.emotion_ai.emotional_gradient_shift(
            phase_b_result['emotional_state']
        )
        
        # Apply polymorphic parameter adjustment
        self.generative_model.apply_polymorphic_control(
            {}, phase_b_result['anxiety_level'], emotional_shift
        )
        
        # Generate responses (non-recursive and recursive)
        context_text = " ".join(phase_b_result['expanded_context'])
        
        # Non-recursive response
        nonrec_response = self.generative_model.generate_response(
            phase_b_result['user_query'],
            context_text,
            phase_b_result['emotional_state'],
            phase_b_result['anxiety_level']
        )
        
        # Recursive response using correction engine
        recursive_result = self.correction_engine.generate_with_correction(
            phase_b_result['user_query'],
            context_text,
            phase_b_result['reference_response'],
            phase_b_result['emotional_state'],
            phase_b_result['anxiety_level']
        )
        
        result = phase_b_result.copy()
        result.update({
            'emotional_shift': emotional_shift,
            'nonrecursive_response': nonrec_response,
            'recursive_response': recursive_result['final_response'],
            'recursive_metadata': recursive_result
        })
        
        return result
    
    def _phase_d_self_correction(self, phase_c_result: Dict) -> Dict:
        """Phase D: Metacognitive Monitoring & Self-Correction"""
        
        # Evaluate both response options
        nonrec_assessment = self.metacognitive_monitor.get_self_assessment_score(
            phase_c_result['nonrecursive_response'],
            phase_c_result['user_query'],
            phase_c_result['reference_response'],
            " ".join(phase_c_result['expanded_context']),
            [h['user_query'] for h in self.conversation_history]
        )
        
        recursive_assessment = phase_c_result['recursive_metadata']['final_assessment']
        
        # Choose better response
        if recursive_assessment['overall_score'] > nonrec_assessment['overall_score']:
            selected_response = phase_c_result['recursive_response']
            final_assessment = recursive_assessment
            selection_reason = "recursive"
        else:
            selected_response = phase_c_result['nonrecursive_response']
            final_assessment = nonrec_assessment
            selection_reason = "non-recursive"
        
        result = phase_c_result.copy()
        result.update({
            'selected_response': selected_response,
            'alternative_response': phase_c_result['recursive_response'] if selection_reason == "non-recursive" else phase_c_result['nonrecursive_response'],
            'final_assessment': final_assessment,
            'selection_reason': selection_reason
        })
        
        return result
    
    def _phase_e_anxiety_modulation(self, phase_d_result: Dict) -> Dict:
        """Phase E: Anxiety-Driven Modulation & Forced Topic Shift"""
        
        # Calculate context richness
        context_richness = self.anxiety_manager.calculate_context_richness(
            phase_d_result['user_query'],
            phase_d_result['reference_response'],
            phase_d_result['expanded_context']
        )
        
        # Check for summarization need
        response_length = len(phase_d_result['selected_response'].split())
        should_summarize = self.anxiety_manager.should_summarize_response(
            phase_d_result['anxiety_level'], response_length
        )
        
        # Check for forced topic shift
        should_shift_topic = self.anxiety_manager.should_force_topic_shift(
            phase_d_result['anxiety_level'], context_richness
        )
        
        # Apply anxiety modulation to response
        modulated_response = self.anxiety_manager.apply_anxiety_modulation(
            phase_d_result['selected_response'],
            phase_d_result['anxiety_level']
        )
        
        topic_suggestions = []
        if should_shift_topic:
            suggestion = self.anxiety_manager.generate_topic_shift_suggestion(
                " ".join(phase_d_result['expanded_context']),
                [h['user_query'] for h in self.conversation_history]
            )
            topic_suggestions.append(suggestion)
        
        result = phase_d_result.copy()
        result.update({
            'selected_response': modulated_response,
            'context_richness': context_richness,
            'should_summarize': should_summarize,
            'should_shift_topic': should_shift_topic,
            'topic_suggestions': topic_suggestions
        })
        
        return result
    
    def _phase_f_dual_answers(self, phase_e_result: Dict) -> Dict:
        """Phase F: Parallel Thought, Dual Answers & User Approval"""
        
        # Generate parallel thought exploration
        if phase_e_result['emotional_shift'] > 1.2:  # High emotional state triggers parallel thinking
            # Predict alternative topic path
            current_topic = self.flow_predictor._extract_topic(phase_e_result['user_query'])
            predicted_topics = self.flow_predictor.predict_next_topics(
                current_topic, 
                " ".join(phase_e_result['expanded_context']),
                phase_e_result['emotional_state']
            )
            
            if predicted_topics:
                parallel_topic = predicted_topics[0]['topic']
                parallel_query = self.flow_predictor.generate_proactive_query(
                    parallel_topic,
                    " ".join(phase_e_result['expanded_context']),
                    [h['user_query'] for h in self.conversation_history]
                )
                
                # Generate response for parallel thought
                parallel_response = self.generative_model.generate_response(
                    parallel_query,
                    " ".join(phase_e_result['expanded_context']),
                    phase_e_result['emotional_state'],
                    phase_e_result['anxiety_level']
                )
            else:
                parallel_response = "Exploring alternative perspectives..."
        else:
            parallel_response = phase_e_result['alternative_response']
        
        result = phase_e_result.copy()
        result.update({
            'parallel_response': parallel_response
        })
        
        return result
    
    def _phase_g_proactive_loops(self, phase_f_result: Dict) -> Dict:
        """Phase G: Polymorphic Guided Future Transitions & P2P Integration"""
        
        proactive_queries = []
        
        for loop_num in range(self.config.num_proactive_loops):
            # Predict next conversation topic
            current_topic = self.flow_predictor._extract_topic(phase_f_result['user_query'])
            predictions = self.flow_predictor.predict_next_topics(
                current_topic,
                " ".join(phase_f_result['expanded_context']),
                phase_f_result['emotional_state']
            )
            
            if predictions:
                # Generate proactive query
                predicted_topic = predictions[0]['topic']
                proactive_query = self.flow_predictor.generate_proactive_query(
                    predicted_topic,
                    " ".join(phase_f_result['expanded_context']),
                    [h['user_query'] for h in self.conversation_history]
                )
                
                proactive_queries.append({
                    'query': proactive_query,
                    'predicted_topic': predicted_topic,
                    'confidence': predictions[0]['probability']
                })
        
        result = phase_f_result.copy()
        result.update({
            'proactive_queries': proactive_queries
        })
        
        return result
    
    def _phase_h_learning_evolution(self, phase_g_result: Dict) -> Dict:
        """Phase H: Learning & Evolution - The "Complex Training File" """
        
        # Compile comprehensive training data for this turn
        training_entry = {
            'turn_number': self.turn_count,
            'timestamp': time.time(),
            'user_query': phase_g_result['user_query'],
            'reference_response': phase_g_result['reference_response'],
            'final_response': phase_g_result['selected_response'],
            'emotional_state': phase_g_result['emotional_state'],
            'anxiety_level': phase_g_result['anxiety_level'],
            'context_richness': phase_g_result['context_richness'],
            'assessment_scores': phase_g_result['final_assessment'],
            'polymorphic_params': self.generative_model.polymorphic_params.copy(),
            'active_brainforms': phase_g_result['active_brainforms'],
            'proactive_queries': phase_g_result['proactive_queries']
        }
        
        self.training_data.append(training_entry)
        
        # Trigger learning if enough data accumulated
        if len(self.training_data) >= 10:  # Learn every 10 turns
            self._perform_learning_update()
        
        return phase_g_result
    
    def _perform_learning_update(self):
        """Perform learning update on accumulated training data"""
        print(f"Performing learning update with {len(self.training_data)} training examples")
        
        # Extract patterns for flow predictor
        conversation_data = [{
            'turns': [{'text': entry['user_query'], 
                      'context': " ".join(entry.get('expanded_context', [])),
                      'emotion': entry['emotional_state']} 
                     for entry in self.training_data]
        }]
        
        self.flow_predictor.learn_transition_patterns(conversation_data)
        
        # Update gradient map with new relationships
        for entry in self.training_data[-5:]:  # Last 5 entries
            self.gradient_map.add_node(entry['user_query'], "learned_query", 
                                     entry['emotional_state'])
        
        # Clear processed training data
        self.training_data = self.training_data[-5:]  # Keep last 5 for context
    
    def add_knowledge(self, knowledge_items: List[str]):
        """Add new knowledge to the system's knowledge base"""
        self.knowledge_base.extend(knowledge_items)
    
    def get_system_state(self) -> Dict:
        """Get current system state for debugging/monitoring"""
        return {
            'turn_count': self.turn_count,
            'conversation_length': len(self.conversation_history),
            'knowledge_base_size': len(self.knowledge_base),
            'training_data_size': len(self.training_data),
            'gradient_map_nodes': len(self.gradient_map.graph.nodes),
            'polymorphic_params': self.generative_model.polymorphic_params.copy()
        }

# Initialize the complete Homebrew AI system
homebrew_ai = HomebrewAI(config)
            

# Add some knowledge to the system
knowledge_items = [
    "Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data.",
    "Neural networks are inspired by biological neural networks and consist of interconnected nodes called neurons.",
    "Deep learning uses neural networks with many layers to learn complex patterns in data.",
    "Natural language processing enables computers to understand and generate human language.",
    "Computer vision allows machines to interpret and understand visual information from the world."
]

homebrew_ai.add_knowledge(knowledge_items)

# Example conversation
def demonstrate_conversation():
    print("=== Homebrew AI Conversation Demo ===\n")
    
    # First turn
    user_input_1 = "What is machine learning and how does it work?"
    reference_1 = "Machine learning is a branch of AI that enables computers to learn and improve from experience without being explicitly programmed."
    
    print(f"User: {user_input_1}")
    response_1 = homebrew_ai.process_turn(user_input_1, reference_1)
    
    print(f"Homebrew AI (Primary): {response_1['primary_response']}")
    print(f"Homebrew AI (Alternative): {response_1['alternative_response']}")
    print(f"Confidence: {response_1['confidence_metrics']['overall_score']:.2f}")
    print(f"Processing time: {response_1['processing_time']:.2f}s\n")
    
    # Second turn - emotional input
    user_input_2 = "I'm really excited about learning AI! Can you tell me more about neural networks?"
    reference_2 = "Neural networks are computing systems inspired by biological neural networks. They consist of layers of interconnected nodes that process information."
    
    print(f"User: {user_input_2}")
    response_2 = homebrew_ai.process_turn(user_input_2, reference_2)
    
    print(f"Homebrew AI (Primary): {response_2['primary_response']}")
    
    # Show proactive queries
    if response_2['proactive_queries']:
        print("Proactive questions from AI:")
        for query in response_2['proactive_queries']:
            print(f"  - {query['query']} (confidence: {query['confidence']:.2f})")
    
    print(f"System state: {homebrew_ai.get_system_state()}\n")
    
    # Third turn - show anxiety and topic shift
    user_input_3 = "Hmm, I'm getting confused..."
    reference_3 = "That's okay, learning complex topics can be challenging. Let's break it down step by step."
    
    print(f"User: {user_input_3}")
    response_3 = homebrew_ai.process_turn(user_input_3, reference_3)
    
    print(f"Homebrew AI (Primary): {response_3['primary_response']}")
    
    if response_3['topic_suggestions']:
        print("Topic suggestions:")
        for suggestion in response_3['topic_suggestions']:
            print(f"  - {suggestion}")

# Run the demonstration
demonstrate_conversation()
            

class P2PTrainingManager:
    def __init__(self, homebrew_ai: HomebrewAI):
        self.homebrew_ai = homebrew_ai
        self.peer_connections = {}
        self.shared_knowledge_pool = []
        self.collaboration_history = []
        
    def merge_training_files(self, peer_training_data: List[Dict]) -> List[Dict]:
        """Merge training data from multiple Homebrew AI instances"""
        
        merged_data = self.homebrew_ai.training_data.copy()
        
        # Add peer data with provenance tracking
        for peer_entry in peer_training_data:
            enhanced_entry = peer_entry.copy()
            enhanced_entry['source'] = 'peer'
            enhanced_entry['merge_timestamp'] = time.time()
            merged_data.append(enhanced_entry)
        
        # Sort by quality metrics
        merged_data.sort(key=lambda x: x.get('assessment_scores', {}).get('overall_score', 0), 
                        reverse=True)
        
        return merged_data
    
    def create_collective_intelligence(self, peer_instances: List[HomebrewAI]) -> Dict:
        """Create collective intelligence from multiple AI instances"""
        
        collective_knowledge = []
        collective_patterns = {}
        collective_emotional_intelligence = {}
        
        # Aggregate knowledge bases
        for peer in peer_instances:
            collective_knowledge.extend(peer.knowledge_base)
        
        # Merge transition patterns
        for peer in peer_instances:
            for pattern_key, pattern_data in peer.flow_predictor.transition_patterns.items():
                if pattern_key not in collective_patterns:
                    collective_patterns[pattern_key] = {
                        'count': 0,
                        'context_patterns': [],
                        'emotional_triggers': []
                    }
                
                collective_patterns[pattern_key]['count'] += pattern_data['count']
                collective_patterns[pattern_key]['context_patterns'].extend(
                    pattern_data['context_patterns']
                )
                collective_patterns[pattern_key]['emotional_triggers'].extend(
                    pattern_data['emotional_triggers']
                )
        
        # Create collective emotional intelligence patterns
        for peer in peer_instances:
            for entry in peer.training_data:
                emotion_key = str(entry['emotional_state']['emotions'])
                if emotion_key not in collective_emotional_intelligence:
                    collective_emotional_intelligence[emotion_key] = []
                
                collective_emotional_intelligence[emotion_key].append({
                    'response_quality': entry['assessment_scores']['overall_score'],
                    'anxiety_level': entry['anxiety_level'],
                    'context_richness': entry['context_richness']
                })
        
        return {
            'collective_knowledge': list(set(collective_knowledge)),  # Remove duplicates
            'collective_patterns': collective_patterns,
            'collective_emotional_intelligence': collective_emotional_intelligence,
            'participant_count': len(peer_instances),
            'total_training_examples': sum(len(peer.training_data) for peer in peer_instances)
        }
    
    def auto_conversation_mode(self, peer_ai: HomebrewAI, 
                             num_exchanges: int = 10) -> List[Dict]:
        """Enable two AI instances to have an automated conversation"""
        
        conversation_log = []
        current_speaker = self.homebrew_ai
        other_speaker = peer_ai
        
        # Initial topic from collective intelligence
        initial_topics = ["consciousness", "creativity", "learning", "existence", "knowledge"]
        current_topic = np.random.choice(initial_topics)
        current_query = f"What are your thoughts on {current_topic}?"
        
        for exchange in range(num_exchanges):
            # Current speaker processes the query
            response_data = current_speaker.process_turn(current_query)
            
            conversation_log.append({
                'exchange': exchange,
                'speaker': 'AI_1' if current_speaker == self.homebrew_ai else 'AI_2',
                'query': current_query,
                'response': response_data['primary_response'],
                'confidence': response_data['confidence_metrics']['overall_score'],
                'proactive_queries': response_data['proactive_queries']
            })
            
            # Generate next query from proactive suggestions or response content
            if response_data['proactive_queries']:
                current_query = response_data['proactive_queries'][0]['query']
            else:
                # Extract interesting concept from response for follow-up
                words = response_data['primary_response'].split()
                interesting_words = [w for w in words if len(w) > 6 and w.isalpha()]
                if interesting_words:
                    selected_word = np.random.choice(interesting_words)
                    current_query = f"How does {selected_word} relate to our discussion?"
                else:
                    current_query = "What other aspects should we explore?"
            
            # Switch speakers
            current_speaker, other_speaker = other_speaker, current_speaker
        
        return conversation_log
    
    def analyze_collective_insights(self, collective_data: Dict) -> Dict:
        """Analyze insights from collective intelligence"""
        
        insights = {
            'knowledge_diversity': 0.0,
            'pattern_strength': 0.0,
            'emotional_intelligence_depth': 0.0,
            'collective_recommendations': []
        }
        
        # Analyze knowledge diversity
        knowledge = collective_data['collective_knowledge']
        if len(knowledge) > 1:
            knowledge_embeddings = [self.homebrew_ai.embedding_system.embed(k) for k in knowledge]
            similarities = []
            for i in range(len(knowledge_embeddings)):
                for j in range(i+1, len(knowledge_embeddings)):
                    sim = self.homebrew_ai.embedding_system.similarity(
                        knowledge_embeddings[i], knowledge_embeddings[j]
                    )
                    similarities.append(sim)
            
            insights['knowledge_diversity'] = 1.0 - np.mean(similarities)
        
        # Analyze pattern strength
        patterns = collective_data['collective_patterns']
        if patterns:
            pattern_strengths = [pattern['count'] for pattern in patterns.values()]
            insights['pattern_strength'] = np.mean(pattern_strengths) / max(pattern_strengths)
        
        # Analyze emotional intelligence depth
        emotional_data = collective_data['collective_emotional_intelligence']
        if emotional_data:
            emotion_qualities = []
            for emotion_patterns in emotional_data.values():
                if emotion_patterns:
                    avg_quality = np.mean([p['response_quality'] for p in emotion_patterns])
                    emotion_qualities.append(avg_quality)
            
            insights['emotional_intelligence_depth'] = np.mean(emotion_qualities)
        
        # Generate recommendations
        if insights['knowledge_diversity'] < 0.5:
            insights['collective_recommendations'].append(
                "Increase knowledge diversity by exploring new domains"
            )
        
        if insights['pattern_strength'] < 0.3:
            insights['collective_recommendations'].append(
                "Strengthen conversational patterns through more practice"
            )
        
        if insights['emotional_intelligence_depth'] < 0.6:
            insights['collective_recommendations'].append(
                "Improve emotional intelligence through diverse emotional contexts"
            )
        
        return insights

# Example of P2P usage
def demonstrate_p2p_learning():
    print("=== P2P Collaborative Learning Demo ===\n")
    
    # Create two Homebrew AI instances
    ai_1 = HomebrewAI(config)
    ai_2 = HomebrewAI(config)
    
    # Add different knowledge to each
    ai_1.add_knowledge([
        "Philosophy explores fundamental questions about existence, knowledge, and reality.",
        "Ethics examines what is morally right and wrong in human behavior.",
        "Consciousness is the state of being aware of and able to think about one's existence."
    ])
    
    ai_2.add_knowledge([
        "Quantum mechanics describes the behavior of matter and energy at the atomic scale.",
        "Relativity theory revolutionized our understanding of space, time, and gravity.",
        "Artificial intelligence aims to create machines that can perform tasks requiring human intelligence."
    ])
    
    # Initialize P2P manager
    p2p_manager = P2PTrainingManager(ai_1)
    
    # Run auto-conversation
    print("Starting auto-conversation between two AI instances...")
    conversation = p2p_manager.auto_conversation_mode(ai_2, num_exchanges=5)
    
    for exchange in conversation:
        print(f"Exchange {exchange['exchange']} ({exchange['speaker']}):")
        print(f"  Query: {exchange['query']}")
        print(f"  Response: {exchange['response']}")
        print(f"  Confidence: {exchange['confidence']:.2f}\n")
    
    # Create collective intelligence
    collective = p2p_manager.create_collective_intelligence([ai_1, ai_2])
    
    print("Collective Intelligence Summary:")
    print(f"  Total Knowledge Items: {len(collective['collective_knowledge'])}")
    print(f"  Conversation Patterns: {len(collective['collective_patterns'])}")
    print(f"  Participants: {collective['participant_count']}")
    print(f"  Training Examples: {collective['total_training_examples']}")
    
    # Analyze insights
    insights = p2p_manager.analyze_collective_insights(collective)
    
    print("\nCollective Insights:")
    print(f"  Knowledge Diversity: {insights['knowledge_diversity']:.2f}")
    print(f"  Pattern Strength: {insights['pattern_strength']:.2f}")
    print(f"  Emotional Intelligence: {insights['emotional_intelligence_depth']:.2f}")
    
    if insights['collective_recommendations']:
        print("  Recommendations:")
        for rec in insights['collective_recommendations']:
            print(f"    - {rec}")

# Run P2P demonstration
demonstrate_p2p_learning()