from sys import argv, exit

if len(argv) != 2:
    exit(1)

from model import *
from preprocessing import *
from cc import decode_batch_predictions

import numpy as np
from spellchecker import SpellChecker

sc = SpellChecker()

m = model(input_dim = fft_length // 2 + 1,
          output_dim = char_to_num.vocabulary_size())

m.load_weights('model41-best.keras')

sg, _ = encode_single_sample_selectable_dir(argv[1], "")

seq = m.predict(np.array([sg]))

dc = decode_batch_predictions(seq)[0]
print(f"Decode : {dc}")

cdc = ' '.join([sc.correction(i) if sc.correction(i) else i for i in dc.split()])
print(f"Correct: {cdc}")