import networkx as nx
from typing import Any, Callable, Dict, Optional, Type
from dataclasses import is_dataclass, asdict

class QanatPipeline:
    def __init__(self, broker: str):
        self.broker = broker
        self.graph = nx.DiGraph()
        self.components = {}
        self.message_queue = {}  # Simplified message queue for the example

    def component(self, input: Optional[str] = None, output: Optional[str] = None):
        def decorator(func: Callable):
            self._register_component(func, input, output)
            return func
        return decorator

    def _register_component(self, func: Callable, input: Optional[str], output: Optional[str]):
        node_name = func.__name__
        self.graph.add_node(node_name, func=func, input=input, output=output)
        if input:
            self.graph.add_edge(input, node_name)
        if output:
            self.graph.add_edge(node_name, output)
        self.components[node_name] = func

    def _run_component(self, component_name: str):
        func = self.components[component_name]
        input_topic = self.graph.nodes[component_name].get('input')
        output_topic = self.graph.nodes[component_name].get('output')

        # Optionally wait for a message (simplified for example)
        input_data = self.message_queue.get(input_topic)
        if input_data is not None:
            # Type checking input data
            if hasattr(func, '__annotations__') and 'return' in func.__annotations__:
                input_type = func.__annotations__.get('return')
                if not isinstance(input_data, input_type):
                    raise TypeError(f"Input data does not match the expected type: {input_type}")

            # Call the function
            result = func(input_data)

            # Type checking output data
            output_type = func.__annotations__.get('return')
            if output_type and not isinstance(result, output_type):
                raise TypeError(f"Output data does not match the expected type: {output_type}")

            # Publishing results (simplified for example)
            if output_topic:
                self.message_queue[output_topic] = result

    def start_event_loop(self):
        # Here you would connect to the broker, subscribe to topics, and set up
        # consumers to call _run_component when a message is received.
        pass

# Example usage of the QanatPipeline class
if __name__ == "__main__":
    pipeline = QanatPipeline(broker="mqtt://broker.hivemq.com:1883")

    # Define components using the @pipeline.component decorator
    # ...

    # Start the event loop
    pipeline.start_event_loop()
