73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
#!/usr/bin/env python3
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import os
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import logging
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import tempfile
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from requests_opentracing import SessionTracing
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import opentracing # ez kell ide hogy a session tracer jolegyen
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import requests
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import time
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from urllib.parse import urljoin
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from config import Config
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from classifier_cache import ClassifierCache
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class MagicDoer:
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classifier_cache = ClassifierCache(Config.MODEL_INFO_URL)
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requests_session = SessionTracing(propagate=True)
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@classmethod
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def run_everything(cls, parameters: dict, span: opentracing.span.Span) -> dict:
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tag = parameters['tag']
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sample_file_handle, sample_file_path = tempfile.mkstemp(prefix=f"{tag}_", suffix=".wav", dir="/dev/shm")
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span.log_kv({'event': 'sampleFileOpened', 'sampleTag': tag})
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response = None
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try:
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with opentracing.tracer.start_span('downloadSample', child_of=span):
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# Download Sample
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object_path = urljoin(Config.STORAGE_SERVICE_URL, f"object/{tag}")
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logging.info(f"Downloading sample: {tag} from {object_path}")
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r = cls.requests_session.get(object_path)
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with open(sample_file_handle, 'wb') as f:
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f.write(r.content)
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logging.debug(f"Downloaded sample to {sample_file_path}")
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with opentracing.tracer.start_span('loadClassifier', child_of=span):
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# Get a classifier that uses the default model
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model_details, classifier = cls.classifier_cache.get_default_classifier()
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with opentracing.tracer.start_span('runClassifier', child_of=span) as child_span:
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# do the majic
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classification_start_time = time.time()
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predicted_class_name, labeled_predictions = classifier.predict(sample_file_path, child_span)
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classification_duration = time.time() - classification_start_time
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response = {
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"tag": tag,
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"probability": labeled_predictions[model_details['target_class_name']],
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"all_predictions": labeled_predictions,
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"class": predicted_class_name,
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"model": model_details['id'],
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"classification_duration": classification_duration
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}
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finally:
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try:
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os.remove(sample_file_path)
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except FileNotFoundError:
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pass
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span.log_kv({'event': 'sampleFileDeleted', 'sampleTag': tag})
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if not response:
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logging.error("Something went wrong during classification!")
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else:
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logging.info(f"Classification done!")
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logging.debug(f"Results: {response}")
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return response
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