#!/usr/bin/env python3
import sentry_sdk
import os
import requests
import tempfile
import numpy
import json
import pika
import uwsgi

from pyAudioAnalysis.audioTrainTest import load_model, load_model_knn, classifier_wrapper

SENTRY_DSN = os.environ.get("SENTRY_DSN")
if SENTRY_DSN:
    sentry_sdk.init(
        dsn=SENTRY_DSN,
        send_default_pii=True,
        release=os.environ.get('RELEASE_ID', 'test'),
        environment=os.environ.get('RELEASEMODE', 'dev')
    )


def run_classification(task, target_class_name: str):
    _, temp_model_name = tempfile.mkstemp()
    temp_means_name = temp_model_name + "MEANS"

    r = requests.get(f"http://model-service/model/{task['model']}/details")
    r.raise_for_status()

    model_details = r.json()

    try:

        r = requests.get(f"http://model-service/model/{task['model']}")
        r.raise_for_status()

        with open(temp_model_name, 'wb') as f:
            f.write(r.content)

        r = requests.get(f"http://model-service/model/{task['model']}?means")
        r.raise_for_status()

        with open(temp_means_name, 'wb') as f:
            f.write(r.content)

        if model_details['type'] == 'knn':
            classifier, mean, std, classes, mid_window, mid_step, short_window, short_step, compute_beat \
                = load_model_knn(temp_model_name)

        else:
            classifier, mean, std, classes, mid_window, mid_step, short_window, short_step, compute_beat \
                = load_model(temp_model_name)

        target_id = classes.index(target_class_name)  # Might raise ValueError

        feature_vector = (numpy.array(task['features']) - mean) / std
        class_id, probability = classifier_wrapper(classifier, model_details['type'], feature_vector)

    finally:  # bruuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuh
        try:
            os.remove(temp_model_name)
        except FileNotFoundError:
            pass

        try:
            os.remove(temp_means_name)
        except FileNotFoundError:
            pass

    results = {
        "tag": task['tag'],
        "model": task['model'],
        "is_target": bool(class_id == target_id),
        "probability": probability[target_id]
    }

    return results


def main():
    connection = pika.BlockingConnection(pika.connection.URLParameters(os.environ['PIKA_URL']))
    channel = connection.channel()
    channel.exchange_declare(exchange=os.environ['PIKA_EXCHANGE'], exchange_type='fanout')

    while True:
        message = uwsgi.mule_get_msg()
        task = json.loads(message)
        results = run_classification(task, os.environ['TARGET_CLASS_NAME'])
        channel.basic_publish(exchange=os.environ['PIKA_EXCHANGE'], routing_key='classification-result',
                              body=json.dumps(results).encode("utf-8"))


if __name__ == '__main__':
    main()