did the needful

svm_prefilter_service/mule.py

@@ -0,0 +1,136 @@
+#!/usr/bin/env python3
+import sentry_sdk
+import os
+import requests
+import tempfile
+import numpy
+import json
+import uwsgi
+
+from config import Config
+from pyAudioAnalysis.audioTrainTest import load_model, load_model_knn, classifier_wrapper
+from pyAudioAnalysis import audioBasicIO
+from pyAudioAnalysis import MidTermFeatures
+import numpy
+
+if Config.SENTRY_DSN:
+    sentry_sdk.init(
+        dsn=Config.SENTRY_DSN,
+        send_default_pii=True,
+        release=Config.RELEASE_ID,
+        environment=Config.RELEASEMODE
+    )
+
+
+class ModelMemer:
+
+    def __init__(self):
+        self._loaded_model = None
+
+    def download_model_if_needed(self):
+        models_dir = "/tmp/svm_model"
+        os.makedirs(models_dir, exist_ok=True)
+
+        model_file = os.path.join(models_dir, "model")
+        means_file = os.path.join(models_dir, "modelMEANS")
+
+        if os.path.isfile(model_file) and self._loaded_model:
+            return
+
+        r = requests.get(Config.MODEL_INFO_URL)
+        r.raise_for_status()
+        self.model_details = r.json()
+
+        r = requests.get(self.model_details['files']['model'])
+        r.raise_for_status()
+
+        with open(model_file, 'wb') as f:
+            f.write(r.content)
+
+        r = requests.get(self.model_details['files']['means'])
+        r.raise_for_status()
+
+        with open(means_file, 'wb') as f:
+            f.write(r.content)
+
+        if self.model_details['type'] == 'knn':
+            self.classifier, self.mean, self.std, self.classes, self.mid_window, self.mid_step, self.short_window, \
+            self.short_step, self.compute_beat \
+                = load_model_knn(model_file)
+
+        else:
+            self.classifier, self.mean, self.std, self.classes, self.mid_window, self.mid_step, self.short_window, \
+            self.short_step, self.compute_beat \
+                = load_model(model_file)
+
+        target_class_name = self.model_details['target_class_name']
+        self.target_id = self.classes.index(target_class_name)
+
+
+def run_classification(audio_file_path: str, memer: ModelMemer):
+    memer.download_model_if_needed()
+
+    # run extraction
+    sampling_rate, signal = audioBasicIO.read_audio_file(audio_file_path)
+    signal = audioBasicIO.stereo_to_mono(signal)
+
+    if sampling_rate == 0:
+        raise AssertionError("Could not read the file properly: Sampling rate zero")
+
+    if signal.shape[0] / float(sampling_rate) <= memer.mid_window:
+        raise AssertionError("Could not read the file properly: Signal shape is not good")
+
+    # feature extraction:
+    mid_features, s, _ = \
+        MidTermFeatures.mid_feature_extraction(signal, sampling_rate,
+                                               memer.mid_window * sampling_rate,
+                                               memer.mid_step * sampling_rate,
+                                               round(sampling_rate * memer.short_window),
+                                               round(sampling_rate * memer.short_step))
+
+    # long term averaging of mid-term statistics
+    mid_features = mid_features.mean(axis=1)
+    if memer.compute_beat:
+        beat, beat_conf = MidTermFeatures.beat_extraction(s, memer.short_step)
+        mid_features = numpy.append(mid_features, beat)
+        mid_features = numpy.append(mid_features, beat_conf)
+
+    feature_vector = (mid_features - memer.mean) / memer.std
+    class_id, probability = classifier_wrapper(
+        memer.classifier, memer.model_details['type'].lower(), feature_vector
+    )
+    class_id = int(class_id)  # faszom
+
+    return bool((class_id == memer.target_id) and (probability[class_id] > 0.5))
+
+
+def main():
+    memer = ModelMemer()
+
+    while True:
+        message = uwsgi.mule_get_msg()
+        task = json.loads(message)
+        audio_file_path = task['audio_file_path']
+        description = task['description']
+        try:
+            result = run_classification(audio_file_path, memer)
+            if result:
+                # upload to real input service
+                files = {
+                    "file": (
+                        os.path.basename(audio_file_path),
+                        open(audio_file_path, 'rb').read(),
+                        'audio/wave',
+                        {'Content-length': os.path.getsize(audio_file_path)}
+                    ),
+                    "description": (None, json.dumps(description), "application/json")
+                }
+
+                r = requests.post(Config.INPUT_SERVICE_URL, files=files)
+                r.raise_for_status()
+        finally:
+            os.remove(audio_file_path)
+
+
+if __name__ == '__main__':
+    main()