from typing import List, Dict, Tuple

class PredictedGraphWeights:
    """
    Optimizing Multi-Goal Path Finding with Predicted Graph Weights class that aims to improve pathfinding efficiency 
    by predicting edge weights of a graph using machine learning techniques.
    """
    def __init__(self, weights_model):
        # weights_model represents the machine learning model used to predict the weights of the graph
        self.weights_model = weights_model 

    def predict_weights(self, graph: Dict[Tuple[int, int], float]) -> Dict[Tuple[int, int], float]:
        """
        Predicts and updates the weights of edges in the graph

        :param graph: A dictionary representing a weighted graph where the keys represent the edges as tuples
                      (node1, node2) and the values represent the current weights of the edges.
                      
        :return: A dictionary representing the updated graph with predicted weights.
        """
        # For each edge in the graph
        for edge in graph.keys():
            # Edge attributes or other relevant data for model
            edge_data = self.get_edge_data(edge)  # Implement this according to the data model requires

            # Get the predicted weight
            predicted_weight = self.weights_model.predict(edge_data)

            # Update weight in the graph
            graph[edge] = predicted_weight
        
        return graph

    def get_edge_data(self, edge: Tuple[int, int]):
        """
        To be implemented. This method should extract attributes/data from each edge to feed into the 
        weights_model for prediction.

        :param edge: An edge represented as a tuple (node1, node2).

        :return: Extracted edge attributes/data.
        """
        pass

    def optimize_path_finding(self, graph: Dict[Tuple[int, int], float], start_node: int, goal_nodes: List[int]):
        """
        Uses the predicted graph weights to find an optimal path from a start node to multiple goal nodes.

        :param graph: A dictionary representing a graph where the keys represent the edges as tuples
                      (node1, node2) and the values represent the weights of the edges.
                      
        :param start_node: Start node identifier from where the path finding will start.
        
        :param goal_nodes: List of one or more goal node identifiers where the path finding will aim to reach.

        :return: List of node identifiers representing the optimal path.
        """
        # Implement the logic to find an optimal path from start_node to all goal_nodes using the graph with predicted weights
        # This could be any suitable path finding algorithm like A*, Dijkstra's, etc that can handle multiple goals
        pass

# Examples of usage:
# pgw = PredictedGraphWeights(weights_model)
# graph = {(0, 1): 10, (1, 2): 20, (2, 3): 30, (0, 2): 40, (0, 3): 50}  # example graph
# predicted_graph = pgw.predict_weights(graph)
# optimized_path = pgw.optimize_path_finding(predicted_graph, start_node=0, goal_nodes=[2, 3])
