src/core/emotional_intelligence.py

import torch
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Optional

@dataclass
class EmotionalState:
    vector: torch.Tensor  # 128-dimensional emotional state
    intensity: float
    valence: float
    arousal: float
    dominance: float

class EmotionalProcessor:
    def __init__(self):
        self.emotional_memory = EmotionalMemory()
        self.state_analyzer = EmotionalStateAnalyzer()
        self.response_generator = EmotionalResponseGenerator()
        
    def process_emotional_context(self, input_data: Dict[str, Any]) -> EmotionalState:
        context_vector = self._extract_emotional_context(input_data)
        current_state = self.state_analyzer.analyze(context_vector)
        self.emotional_memory.update(current_state)
        return self._generate_emotional_response(current_state)
        
    def _extract_emotional_context(self, input_data: Dict[str, Any]) -> torch.Tensor:
        return torch.cat([
            self._process_linguistic_affect(input_data.get('text')),
            self._process_social_context(input_data.get('social')),
            self._process_environmental_factors(input_data.get('environment'))
        ])