from typing import Tuple
import tempfile
import os
import os.path
import shutil

import librosa
import librosa.display
import numpy
import matplotlib.pyplot
from keras.models import model_from_json
from keras import optimizers
from keras_preprocessing.image import ImageDataGenerator


class Classifier(object):

    def __init__(self, model_filename: str, weights_filename: str):
        with open(model_filename, 'r') as f:
            self.loaded_model = model_from_json(f.read())

        self.loaded_model.load_weights(weights_filename)
        self.datagen = ImageDataGenerator(rescale=1. / 255., validation_split=0.25)
        self.loaded_model.compile(optimizers.rmsprop(lr=0.0005, decay=1e-6), loss="categorical_crossentropy",
                                  metrics=["accuracy"])
        self.loaded_model.summary()

    @staticmethod
    def create_spectrogram(wav_filename: str) -> Tuple[str, str]:
        matplotlib.pyplot.interactive(False)
        clip, sample_rate = librosa.load(wav_filename, sr=None)
        fig = matplotlib.pyplot.figure(figsize=[0.72, 0.72])
        ax = fig.add_subplot(111)
        ax.axes.get_xaxis().set_visible(False)
        ax.axes.get_yaxis().set_visible(False)
        ax.set_frame_on(False)
        spectogram = librosa.feature.melspectrogram(y=clip, sr=sample_rate)
        librosa.display.specshow(librosa.power_to_db(spectogram, ref=numpy.max))

        target_dir = tempfile.mkdtemp()

        # Change extension to jpg... mert 110% biztos vagyok benne hogy a keras nem bírná beolvasni máshogy
        file_name = os.path.join(target_dir, "unknown", f"{wav_filename[:-4]}.jpg")

        matplotlib.pyplot.savefig(file_name, dpi=400, bbox_inches='tight', pad_inches=0)
        matplotlib.pyplot.close()
        fig.clf()
        matplotlib.pyplot.close(fig)
        matplotlib.pyplot.close('all')

        return target_dir, file_name

    def _run_predictor(self, directory: str) -> list:
        predict_generator = self.datagen.flow_from_directory(
            directory=directory,
            batch_size=128,
            seed=42,
            shuffle=False,
            class_mode="categorical",
            target_size=(64, 64))

        prediction = self.loaded_model.predict_generator(predict_generator, steps=1)

        predicted_class_indices = numpy.argmax(prediction, axis=1)

        labels = {
            'anser': 0,
            'columba': 1,
            'hirundo': 2,
            'passer': 3,
            'sturnus': 4,
            'turdus': 5,
            'upupa': 6
        }
        labels = dict((v, k) for k, v in labels.items())

        predictions = [labels[k] for k in predicted_class_indices]

        return predictions

    def predict(self, wav_filename: str) -> list:
        directory, _ = self.create_spectrogram(wav_filename)

        result = self._run_predictor(directory)
        shutil.rmtree(directory)  # The image is no longer needed

        return result