from __future__ import annotations from dataclasses import dataclass from typing import Any, Callable, Dict, List @dataclass class CognitiveModel: """Represents a cognitive model (human or machine). Attributes ---------- name : str Unique identifier for the model. process : Callable[[Any], Any] A function that transforms input data into an output. description : str Human-readable explanation of what the model does. """ name: str process: Callable[[Any], Any] description: str = "" class CognitionIntegrator: """ Integrates multiple cognitive models by aggregating their outputs. The integrator stores a list of cognitive models and can invoke each model's `process` function to produce a combined result. """ def __init__(self) -> None: self.models: List[CognitiveModel] = [] def register(self, model: CognitiveModel) -> None: """Register a new cognitive model for integration.""" self.models.append(model) def integrate(self, input_data: Any) -> Dict[str, Any]: """ Run all registered models on the input data. Parameters ---------- input_data : Any The input value to provide to each model. Returns ------- Dict[str, Any] A mapping of model names to their respective outputs. """ outputs: Dict[str, Any] = {} for model in self.models: outputs[model.name] = model.process(input_data) return outputs if __name__ == "__main__": # Demonstrate integrating two simple cognitive models def to_upper(text: str) -> str: return text.upper() def count_chars(text: str) -> int: return len(text) integrator = CognitionIntegrator() integrator.register(CognitiveModel("upper_case", to_upper, "Convert text to uppercase")) integrator.register(CognitiveModel("char_count", count_chars, "Count characters in text")) result = integrator.integrate("Lucidia") print(result)