#!/usr/bin/env python3
import os
import os.path
import logging

import json
import tempfile

import requests
from pyAudioAnalysis import audioBasicIO
from pyAudioAnalysis import MidTermFeatures
import numpy


class NumpyArrayEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, numpy.ndarray):
            return obj.tolist()
        return json.JSONEncoder.default(self, obj)


def do_extraction(model_details: dict, file_path: str):
    logging.info("Running extraction...")

    sampling_rate, signal = audioBasicIO.read_audio_file(file_path)
    signal = audioBasicIO.stereo_to_mono(signal)

    if sampling_rate == 0:
        raise Exception("Could not read the file properly: Sampling rate zero")

    if signal.shape[0] / float(sampling_rate) <= model_details['mid_window']:
        raise Exception("Could not read the file properly: Signal shape is not good")

    # feature extraction:
    mid_features, s, _ = \
        MidTermFeatures.mid_feature_extraction(signal, sampling_rate,
                                               model_details['mid_window'] * sampling_rate,
                                               model_details['mid_step'] * sampling_rate,
                                               round(sampling_rate * model_details['short_window']),
                                               round(sampling_rate * model_details['short_step']))

    # long term averaging of mid-term statistics
    mid_features = mid_features.mean(axis=1)
    if model_details['compute_beat']:
        beat, beat_conf = MidTermFeatures.beat_extraction(s, model_details['short_step'])
        mid_features = numpy.append(mid_features, beat)
        mid_features = numpy.append(mid_features, beat_conf)

    # feature_vector = (mid_features - mean) / std    # normalization

    return mid_features


def run_everything(parameters: dict):
    tag = parameters['tag']
    logging.info(f"Downloading sample: {tag}")

    _, file_path = tempfile.mkstemp(prefix=f"{tag}_", suffix=".wav")
    r = requests.get(f"http://storage-service/object/{tag}")
    with open(file_path, 'wb') as f:
        f.write(r.content)

    logging.debug(f"Downloaded sample to {file_path}")

    logging.info("Getting default model details...")
    r = requests.get("http://model-service/model/$default/details")
    r.raise_for_status()

    model_details = r.json()

    logging.debug(f"Using model {model_details['id']}")

    # download done. Do extraction magic
    try:
        results = do_extraction(model_details, file_path)
    finally:
        os.remove(file_path)

    logging.info(f"Pushing results to Classifier service...")

    response = {
        "tag": tag,
        "features": results,
        "model": model_details['id']
    }

    logging.debug(f"Data being pushed: {str(response)}")

    r = requests.post(
        'http://classification-service/classify',
        data=json.dumps(response, cls=NumpyArrayEncoder),
        headers={'Content-Type': 'application/json'}
    )

    # r.raise_for_status() # An error in a service should not kill other services
    logging.info(f"Classification service response: {r.status_code}")