# gnn implementation
import networkx as nx
import matplotlib.pyplot as plt
from corerec.vish_graphs import draw_graph

class GNN:
    """
    GNN Class

    This class implements Graph Neural Networks (GNNs) for recommendation systems.
    GNNs are a type of neural network designed to operate on graph-structured data,
    capturing complex relationships between nodes through message passing and aggregation.

    Attributes:
        num_layers (int): Number of layers in the GNN.
        hidden_dim (int): Dimensionality of hidden layers.
        learning_rate (float): Learning rate for training the GNN.
        epochs (int): Number of training epochs.
        graph (Graph): The graph structure representing users and items.

    Methods:
        build_model():
            Constructs the GNN model architecture, defining layers and operations for
            message passing and node aggregation.

        train(data):
            Trains the GNN model on the provided data, optimizing node embeddings for
            recommendation tasks.

        recommend(user_id, top_n=10):
            Generates top-N recommendations for a given user by leveraging learned node
            embeddings and graph structure.

        evaluate(test_data):
            Evaluates the performance of the GNN model on test data, providing metrics
            such as accuracy and precision.
    """
    def __init__(self):
        self.graph = None

    def load_graph(self, file_path):
        """
        Load a graph from a file.
        """
        self.graph = nx.read_adjlist(file_path, delimiter=',', nodetype=int)
        return self.graph

    def visualize_graph(self, recommended_nodes=None, top_nodes=None, node_labels=None):
        """
        Visualize the graph using the draw_graph function.
        """
        if self.graph is not None:
            # Use a simpler layout for visualization
            pos = nx.circular_layout(self.graph)
            draw_graph(self.graph, pos=pos, 
                       top_nodes=top_nodes, 
                       recommended_nodes=recommended_nodes, 
                       node_labels=node_labels)
        else:
            print("Graph not loaded. Please load a graph first.")

# Example usage
# gnn = GNN()
# gnn.load_graph('graph_dataset.csv')
# gnn.visualize_graph()
