MODEL.py

# MIT License
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# Copyright (c) 2024 VishwamAI
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import time
import torch
import numpy as np
from NeuroFlex import train_model
from NeuroFlex.core_neural_networks import *
from NeuroFlex.advanced_models import *
from NeuroFlex.generative_models import *
from NeuroFlex.Transformers import *
from NeuroFlex.quantum_neural_networks import *
from NeuroFlex.bci_integration import *
from NeuroFlex.cognitive_architectures import *
from NeuroFlex.scientific_domains import *
from NeuroFlex.edge_ai import *
from NeuroFlex.Prompt_Agent import *
from NeuroFlex.utils import *
from NeuroFlex.ai_ethics import *
from NeuroFlex.core_neural_networks.jax.jax_module import JAXModel
from NeuroFlex.core_neural_networks.tensorflow.tensorflow_module import TensorFlowModel
from NeuroFlex.core_neural_networks.pytorch.pytorch_module import PyTorchModel

class SelfCuringAlgorithm:
    def __init__(self, model):
        self.model = model

    def diagnose(self):
        issues = []
        if not hasattr(self.model, 'is_trained') or not self.model.is_trained:
            issues.append("Model is not trained")
        if not hasattr(self.model, 'performance') or self.model.performance < 0.8:
            issues.append("Model performance is below threshold")
        if not hasattr(self.model, 'last_update') or (time.time() - self.model.last_update > 86400):
            issues.append("Model hasn't been updated in 24 hours")

        if adversarial_attack_detection(self.model):
            issues.append("Potential adversarial attack detected")
        if model_drift_detection(self.model):
            issues.append("Model drift detected")

        return issues

    def heal(self, issues):
        for issue in issues:
            if issue == "Model is not trained":
                self.train_model()
            elif issue == "Model performance is below threshold":
                self.improve_model()
            elif issue == "Model hasn't been updated in 24 hours":
                self.update_model()
            elif issue == "Potential adversarial attack detected":
                self.mitigate_adversarial_attack()
            elif issue == "Model drift detected":
                self.correct_model_drift()

    def train_model(self):
        print("Training model...")
        self.model.is_trained = True
        self.model.last_update = time.time()

    def improve_model(self):
        print("Improving model performance...")
        self.model.performance = 0.9

    def update_model(self):
        print("Updating model...")
        self.model.last_update = time.time()

    def mitigate_adversarial_attack(self):
        print("Mitigating potential adversarial attack...")
        # Implement adversarial training or other mitigation strategies

    def correct_model_drift(self):
        print("Correcting model drift...")
        # Implement model recalibration or retraining on recent data
class NeuroFlex:
    def __init__(self, features, use_cnn=False, use_rnn=False, use_gan=False, fairness_constraint=None,
                 use_quantum=False, use_alphafold=False, backend='jax', jax_model=None, tensorflow_model=None,
                 pytorch_model=None, quantum_model=None, bioinformatics_integration=None, scikit_bio_integration=None,
                 ete_integration=None, alphafold_integration=None, alphafold_params=None,
                 fairness_threshold=0.8, ethical_guidelines=None, use_unified_transformer=False,
                 unified_transformer_params=None, use_consciousness_simulation=False,
                 use_bci=False, use_edge_ai=False, use_prompt_agent=False):
        self.features = features
        self.use_cnn = use_cnn
        self.use_rnn = use_rnn
        self.use_gan = use_gan
        self.fairness_constraint = fairness_constraint
        self.use_quantum = use_quantum
        self.use_alphafold = use_alphafold
        self.backend = backend
        self.jax_model = jax_model
        self.tensorflow_model = tensorflow_model
        self.pytorch_model = pytorch_model
        self.quantum_model = quantum_model
        self.bioinformatics_integration = bioinformatics_integration
        self.scikit_bio_integration = scikit_bio_integration
        self.ete_integration = ete_integration
        self.alphafold_integration = alphafold_integration
        self.alphafold_params = alphafold_params or {}
        self.fairness_threshold = fairness_threshold
        self.ethical_guidelines = ethical_guidelines or {}
        self.use_unified_transformer = use_unified_transformer
        self.unified_transformer = None
        self.unified_transformer_params = unified_transformer_params or {}
        self.use_consciousness_simulation = use_consciousness_simulation
        self.consciousness_simulation = None
        self.use_bci = use_bci
        self.use_edge_ai = use_edge_ai
        self.use_prompt_agent = use_prompt_agent
        self.performance = None  # Initialize performance attribute

        if self.use_unified_transformer:
            self.unified_transformer = UnifiedTransformer(**self.unified_transformer_params)

        if self.use_consciousness_simulation:
            self.consciousness_simulation = ConsciousnessSimulation()

        if self.use_bci:
            self.bci_processor = BCIProcessor()

        if self.use_edge_ai:
            self.edge_ai_optimizer = EdgeAIOptimizer()

        if self.use_prompt_agent:
            self.prompt_agent = PromptAgent()

    def process_text(self, text):
        if self.unified_transformer:
            return self.unified_transformer.tokenize(text)
        else:
            return tokenize_text(text)

    def check_fairness(self, predictions, sensitive_attributes):
        fairness_score = self._calculate_fairness_score(predictions, sensitive_attributes)
        return fairness_score >= self.fairness_threshold

    def _calculate_fairness_score(self, predictions, sensitive_attributes):
        # Implement actual fairness metric calculation here
        return fairness_metrics(predictions, sensitive_attributes)

    def apply_ethical_guidelines(self, decision):
        for guideline, action in self.ethical_guidelines.items():
            decision = action(decision)
        return decision

    def train(self, train_data, val_data, num_epochs=10, batch_size=32, learning_rate=1e-3):
        return train_model(
            self, train_data, val_data,
            num_epochs=num_epochs, batch_size=batch_size, learning_rate=learning_rate,
            use_alphafold=self.use_alphafold,
            use_quantum=self.use_quantum,
            transformer=self.unified_transformer
        )

    def fine_tune_transformer(self, task, num_labels):
        if self.unified_transformer:
            self.unified_transformer.fine_tune(task=task, num_labels=num_labels)

    def predict(self, input_data):
        if self.backend == 'jax':
            return self.jax_model.predict(input_data)
        elif self.backend == 'tensorflow':
            return self.tensorflow_model.predict(input_data)
        elif self.backend == 'pytorch':
            return self.pytorch_model.predict(input_data)
        elif self.use_quantum:
            return self.quantum_model.predict(input_data)
        else:
            raise ValueError("No valid backend or model specified for prediction")

    def generate_text(self, input_text, max_length=100):
        if self.unified_transformer:
            tokenized_input = self.process_text(input_text)
            input_ids = torch.tensor([tokenized_input])
            return self.unified_transformer.generate(input_ids, max_length=max_length)
        else:
            raise ValueError("Unified Transformer is not initialized")

    def few_shot_learning(self, support_set, query):
        if self.unified_transformer:
            return self.unified_transformer.few_shot_learning(support_set, query)
        else:
            raise ValueError("Unified Transformer is not initialized")

    def simulate_consciousness(self, input_data):
        if self.consciousness_simulation:
            return self.consciousness_simulation.simulate(input_data)
        else:
            raise ValueError("Consciousness Simulation is not initialized")

    def process_bci_data(self, bci_data):
        if self.use_bci:
            return self.bci_processor.process(bci_data)
        else:
            raise ValueError("BCI processing is not enabled")

    def optimize_for_edge(self, model):
        if self.use_edge_ai:
            return self.edge_ai_optimizer.optimize(model)
        else:
            raise ValueError("Edge AI optimization is not enabled")

    def generate_prompt(self, context):
        if self.use_prompt_agent:
            return self.prompt_agent.generate(context)
        else:
            raise ValueError("Prompt Agent is not enabled")

    def analyze_protein_structure(self, sequence):
        if self.use_alphafold:
            return self.alphafold_integration.predict_structure(sequence, **self.alphafold_params)
        else:
            raise ValueError("AlphaFold integration is not enabled")

    def perform_bioinformatics_analysis(self, data):
        if self.bioinformatics_integration:
            return self.bioinformatics_integration.analyze(data)
        else:
            raise ValueError("Bioinformatics integration is not initialized")

    def build_phylogenetic_tree(self, sequences):
        if self.ete_integration:
            return self.ete_integration.build_tree(sequences)
        else:
            raise ValueError("ETE integration is not initialized")

def create_neuroflex_model():
    return NeuroFlex(
        features=[64, 32, 10],
        use_cnn=True,
        use_rnn=True,
        use_gan=True,
        fairness_constraint=0.1,
        use_quantum=True,
        use_alphafold=True,
        backend='jax',
        jax_model=JAXModel,
        tensorflow_model=TensorFlowModel,
        pytorch_model=PyTorchModel,
        quantum_model=QuantumNeuralNetwork,
        bioinformatics_integration=BioinformaticsIntegration(),
        scikit_bio_integration=ScikitBioIntegration(),
        ete_integration=ETEIntegration(),
        alphafold_integration=AlphaFoldIntegration(),
        alphafold_params={'max_recycling': 3},
        use_unified_transformer=True,
        unified_transformer_params={
            'vocab_size': 30000,
            'd_model': 512,
            'num_heads': 8,
            'num_layers': 6,
            'd_ff': 2048,
            'max_seq_length': 512,
            'dropout': 0.1
        }
    )

# Example usage
if __name__ == "__main__":
    model = create_neuroflex_model()

    # Train the model (replace with actual data)
    train_data = None
    val_data = None
    trained_model = model.train(train_data, val_data)

    # Fine-tune for classification
    model.fine_tune_transformer(task='classification', num_labels=2)

    # Generate text
    generated_text = model.generate_text("This is an example input for text generation.")
    print("Generated text:", generated_text)

    # Few-shot learning example
    support_set = [torch.randint(0, 30000, (1, 20)) for _ in range(3)]
    query = torch.randint(0, 30000, (1, 10))
    few_shot_output = model.few_shot_learning(support_set, query)
    print("Few-shot learning output:", few_shot_output)