import torch
from sklearn.metrics import roc_auc_score, average_precision_score


def evaluate_gnn(model, graph_data):
    device = torch.device("cpu")

    edge_index = torch.tensor(graph_data["edge_index"], dtype=torch.long).to(device)
    edge_attr = torch.tensor(graph_data["edge_attr"], dtype=torch.float32).to(device)
    x = torch.tensor(graph_data["x"], dtype=torch.float32).to(device)
    y = torch.tensor(graph_data["y"], dtype=torch.float32).to(device)

    src = edge_index[0]
    dst = edge_index[1]

    model.eval()

    with torch.no_grad():
        logits = model(x, edge_index, edge_attr, src, dst)  # ✅ FIXED
        probs = torch.sigmoid(logits).cpu().numpy()

    y_true = y.cpu().numpy()

    roc = roc_auc_score(y_true, probs)
    pr = average_precision_score(y_true, probs)

    return roc, pr