from corerec.vish_graphs import run_optimal_path

class SemanticModels:
    """
    SemanticModels Class

    This class implements semantic models for graph-based recommendation systems.
    Semantic models enhance traditional graph-based methods by incorporating semantic
    information, such as item content or user profiles, into the recommendation process.

    Attributes:
        semantic_graph (Graph): The graph structure enriched with semantic information.
        embedding_dim (int): Dimensionality of semantic embeddings.
        similarity_threshold (float): Threshold for determining similarity between nodes.
        max_iterations (int): Maximum number of iterations for convergence in semantic algorithms.

    Methods:
        build_semantic_graph(data, semantic_info):
            Constructs a semantic graph by integrating semantic information into the
            existing graph structure, enhancing node representations.

        compute_semantic_similarity():
            Computes similarity scores between nodes using semantic information, improving
            recommendation accuracy.

        recommend(user_id, top_n=10):
            Generates top-N recommendations for a given user by analyzing the semantic
            graph structure and node similarities.

        update_semantic_info(new_semantic_data):
            Updates the semantic information in the graph, allowing for dynamic and
            context-aware recommendations.
    """
    def __init__(self):
        self.graph = []

    def set_graph(self, graph):
        """
        Set the graph for semantic models.
        """
        self.graph = graph

    def find_optimal_path(self, start_city):
        """
        Find the optimal path using the run_optimal_path function.
        """
        run_optimal_path(self.graph, start_city)

# Define the graph variable
# graph = [
#     # Example adjacency list or matrix
#     [0, 1, 0],
#     [1, 0, 1],
#     [0, 1, 0]
# ]

# # Example usage
# semantic_model = SemanticModels()
# semantic_model.set_graph(graph)
# semantic_model.find_optimal_path(0)
