95 lines
2.8 KiB
Python
95 lines
2.8 KiB
Python
#!/usr/bin/env python3
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import sentry_sdk
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import os
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import requests
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import tempfile
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import numpy
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import json
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import pika
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import uwsgi
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from pyAudioAnalysis.audioTrainTest import load_model, load_model_knn, classifier_wrapper
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SENTRY_DSN = os.environ.get("SENTRY_DSN")
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if SENTRY_DSN:
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sentry_sdk.init(
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dsn=SENTRY_DSN,
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send_default_pii=True,
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release=os.environ.get('RELEASE_ID', 'test'),
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environment=os.environ.get('RELEASEMODE', 'dev')
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)
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def run_classification(task, target_class_name: str):
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_, temp_model_name = tempfile.mkstemp()
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temp_means_name = temp_model_name + "MEANS"
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r = requests.get(f"http://model-service/model/{task['model']}/details")
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r.raise_for_status()
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model_details = r.json()
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try:
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r = requests.get(f"http://model-service/model/{task['model']}")
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r.raise_for_status()
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with open(temp_model_name, 'wb') as f:
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f.write(r.content)
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r = requests.get(f"http://model-service/model/{task['model']}?means")
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r.raise_for_status()
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with open(temp_means_name, 'wb') as f:
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f.write(r.content)
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if model_details['type'] == 'knn':
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classifier, mean, std, classes, mid_window, mid_step, short_window, short_step, compute_beat \
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= load_model_knn(temp_model_name)
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else:
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classifier, mean, std, classes, mid_window, mid_step, short_window, short_step, compute_beat \
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= load_model(temp_model_name)
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target_id = classes.index(target_class_name) # Might raise ValueError
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feature_vector = (numpy.array(task['features']) - mean) / std
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class_id, probability = classifier_wrapper(classifier, model_details['type'], feature_vector)
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finally: # bruuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuh
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try:
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os.remove(temp_model_name)
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except FileNotFoundError:
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pass
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try:
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os.remove(temp_means_name)
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except FileNotFoundError:
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pass
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results = {
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"tag": task['tag'],
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"model": task['model'],
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"is_target": class_id == target_id,
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"probability": probability[target_id]
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}
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return results
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def main():
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connection = pika.BlockingConnection(pika.connection.URLParameters(os.environ['PIKA_URL']))
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channel = connection.channel()
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channel.exchange_declare(exchange=os.environ['PIKA_EXCHANGE'], exchange_type='fanout')
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while True:
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message = uwsgi.mule_get_msg()
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task = json.loads(message)
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results = run_classification(task, os.environ['TARGET_CLASS_NAME'])
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channel.basic_publish(exchange=os.environ['PIKA_EXCHANGE'], routing_key='classification-result',
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body=json.dumps(results).encode("utf-8"))
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if __name__ == '__main__':
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main()
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