from src.dataloading import load_run_data
from lmsim.metrics import Kappa_p
import random


def compute_similarity(selected_model_a, selected_model_b, selected_dataset):
    """
    probs_a, gt_a = load_run_data(selected_model_a, selected_dataset)
    probs_b, gt_b = load_run_data(selected_model_b, selected_dataset)

    assert len(probs_a) == len(probs_b), f"Models must have the same number of responses: {len(probs_a)} != {len(probs_b)}"
    
    # Only keep responses where the ground truth is the same
    output_a = []
    output_b = []
    gt = []
    for i in range(len(probs_a)):
        if gt_a == gt_b:
            output_a.append(probs_a[i])
            output_b.append(probs_b[i])
            gt.append(gt_a[i])

    # Placeholder similarity value
    kappa_p = Kappa_p()
    similarity = kappa_p.compute_k(output_a, output_b, gt)
    """
    
    similarity = random.random()
    
    return similarity