from speech_siamese import siamese_model
from record_mic_speech import record_spectrogram
# from importlib import reload
# import speech_data
# reload(speech_data)
import numpy as np
import os
import pickle
import tensorflow as tf
import csv
from speech_data import padd_zeros

def predict_recording_with(m,sample_size=15):
    spec1 = record_spectrogram(n_sec=1.4)
    spec2 = record_spectrogram(n_sec=1.4)
    inp = create_test_pair(spec1,spec2,sample_size)
    return m.predict([inp[:, 0], inp[:, 1]])

# while(True):
#     print(predict_recording_with(model))


def test_with(audio_group):
    X,Y = speech_data(audio_group)
    print(np.argmax(model.predict([X[:, 0], X[:, 1]]),axis=1))
    print(Y.astype(np.int8))

def evaluate_siamese(audio_group='audio',model_file = 'siamese_speech_model-46-epoch-0.29-acc.h5'):
    records_file  = os.path.join('./outputs',audio_group+'.train.tfrecords')
    const_file = os.path.join('./outputs',audio_group+'.constants')
    model_weights_path =os.path.join('./models/story_words/',model_file)
    (n_spec,n_features,n_records) = pickle.load(open(const_file,'rb'))
    print('evaluating tfrecords({}-train)...'.format(audio_group))

    model = siamese_model((n_spec, n_features))
    model.load_weights(model_weights_path)
    record_iterator = tf.python_io.tf_record_iterator(path=records_file)
    #tqdm(enumerate(record_iterator),total=n_records)
    with open('./outputs/' + audio_group + '.results.csv','w') as result_csv:
        result_csv_w = csv.writer(result_csv, quoting=csv.QUOTE_MINIMAL)
        for (i,string_record) in enumerate(record_iterator):
            # string_record = next(record_iterator)
            example = tf.train.Example()
            example.ParseFromString(string_record)
            spec_n1 = example.features.feature['spec_n1'].int64_list.value[0]
            spec_n2 = example.features.feature['spec_n2'].int64_list.value[0]
            spec_w1 = example.features.feature['spec_w1'].int64_list.value[0]
            spec_w2 = example.features.feature['spec_w2'].int64_list.value[0]
            spec1 = np.array(example.features.feature['spec1'].float_list.value).reshape(spec_n1,spec_w1)
            spec2 = np.array(example.features.feature['spec2'].float_list.value).reshape(spec_n2,spec_w2)
            p_spec1,p_spec2 = padd_zeros(spec1,n_spec),padd_zeros(spec2,n_spec)
            input_arr = np.asarray([[p_spec1,p_spec2]])
            output_arr = np.asarray([example.features.feature['output'].int64_list.value])
            y_pred = model.predict([input_arr[:, 0], input_arr[:, 1]])
            predicted = np.asarray(y_pred[0]>0.5).astype(output_arr.dtype)
            expected = output_arr[0]
            if np.all(predicted == expected):
                continue
            word = example.features.feature['word'].bytes_list.value[0].decode()
            phoneme1 = example.features.feature['phoneme1'].bytes_list.value[0].decode()
            phoneme2 = example.features.feature['phoneme2'].bytes_list.value[0].decode()
            voice1 = example.features.feature['voice1'].bytes_list.value[0].decode()
            voice2 = example.features.feature['voice2'].bytes_list.value[0].decode()
            language = example.features.feature['language'].bytes_list.value[0].decode()
            rate1 = example.features.feature['rate1'].int64_list.value[0]
            rate2 = example.features.feature['rate2'].int64_list.value[0]
            variant1 = example.features.feature['variant1'].bytes_list.value[0].decode()
            variant2 = example.features.feature['variant2'].bytes_list.value[0].decode()
            file1 = example.features.feature['file1'].bytes_list.value[0].decode()
            file2 = example.features.feature['file2'].bytes_list.value[0].decode()
            print(phoneme1,phoneme2,voice1,voice2,rate1,rate2,variant1,variant2,file1,file2)
            result_csv_w.writerow([phoneme1,phoneme2,voice1,voice2,rate1,rate2,variant1,variant2,file1,file2])


evaluate_siamese('story_words',model_file='siamese_speech_model-92-epoch-0.20-acc.h5')
# test_with('rand_edu')
# sunflower_data,sunflower_result = get_word_pairs_data('sweater',15)
# print(np.argmax(model.predict([sunflower_data[:, 0], sunflower_data[:, 1]]),axis=1))
# print(sunflower_result)