import numpy as np
from read_data import prepare_all_leads
from postprocessing import make_predictions_indi, labels_map
from scipy import stats as st
import warnings

warnings.filterwarnings("ignore")

def make_prediction(path):
    leads = prepare_all_leads(path)
    # print(leads[0][0].shape)
    # print(leads[1][0].shape)
    # print(leads[2][0].shape)
    # visualize_sig([leads[0][0], leads[1][0], leads[2][0]])
    x = make_predictions_indi(*leads)
    # print(x.mean(axis=0))
    # print(np.argmax(x, axis=1))
    index = st.mode(np.argmax(x, axis=1))[0][0]
    confidence = x.mean(axis=0)[index]
    return labels_map[index], float(confidence)

if __name__ == "__main__":
    prediction = make_prediction("data/faizan_r8.txt")
    # prediction = make_prediction("data/a_fib.npy")
    print(prediction)