ailibrary_framework.py

import torch
from drl_upn_networks import DRLNetwork, UPNNetwork
from archetype_blender import ArchetypeBlender
from user_neural_pattern import UserNeuralPattern
from deep_resonance_learning import DeepResonanceLearning

class AILibrary:
    def __init__(self, input_dim=128, pattern_dim=64, num_archetypes=3):
        self.input_dim = input_dim
        self.pattern_dim = pattern_dim
        
        # Initialize all components
        self.drl_network = DRLNetwork(input_dim, pattern_dim * 2, pattern_dim)
        self.upn_network = UPNNetwork(input_dim, pattern_dim)
        self.archetype_blender = ArchetypeBlender(num_archetypes, pattern_dim)
        self.user_pattern = UserNeuralPattern(input_dim, pattern_dim)
        self.deep_resonance = DeepResonanceLearning(input_dim, pattern_dim)
        
        # Initialize training state
        self.is_training = False
        self.training_history = []
    
    def process_user_input(self, user_input):
        """
        Process user input through the entire AI pipeline
        
        Args:
            user_input (torch.Tensor): Input tensor of shape (input_dim,)
            
        Returns:
            dict: Contains processed patterns and resonance outputs
        """
        # Generate user neural pattern
        user_pattern = self.user_pattern(user_input)
        
        # Get resonance pattern
        resonance = self.deep_resonance(user_input)
        
        # Process through DRL network
        drl_output = self.drl_network(user_input, resonance)
        
        # Generate UPN pattern
        upn_pattern = self.upn_network.generate_pattern(user_input)
        
        # Blend archetypes based on patterns
        archetype_blend = self.archetype_blender.get_harmonic_blend(drl_output)
        
        return {
            "user_pattern": user_pattern,
            "resonance": resonance,
            "drl_output": drl_output,
            "upn_pattern": upn_pattern,
            "archetype_blend": archetype_blend
        }
    
    def train_step(self, input_batch, target_batch):
        """
        Perform a single training step
        
        Args:
            input_batch (torch.Tensor): Batch of input data
            target_batch (torch.Tensor): Batch of target data
        """
        self.is_training = True
        # Implementation of training logic here
        results = self.process_user_input(input_batch)
        self.training_history.append({
            "input": input_batch,
            "target": target_batch,
            "results": results
        })
        self.is_training = False
        return results

    def save_state(self, path):
        """Save the current state of all components"""
        state = {
            "drl_network": self.drl_network.state_dict(),
            "upn_network": self.upn_network.state_dict(),
            "user_pattern": self.user_pattern.state_dict(),
            "deep_resonance": self.deep_resonance.state_dict(),
            "training_history": self.training_history
        }
        torch.save(state, path)
    
    def load_state(self, path):
        """Load a previously saved state"""
        state = torch.load(path)
        self.drl_network.load_state_dict(state["drl_network"])
        self.upn_network.load_state_dict(state["upn_network"])
        self.user_pattern.load_state_dict(state["user_pattern"])
        self.deep_resonance.load_state_dict(state["deep_resonance"])
        self.training_history = state["training_history"]

# Example test function
def test_ailibrary():
    """
    Test the AILibrary framework with sample data
    """
    ai_lib = AILibrary()
    sample_input = torch.randn(128)  # Create sample input
    results = ai_lib.process_user_input(sample_input)
    return results