wire it all together

src/preprocessor/soundpreprocessor.py

@@ -1,4 +1,8 @@
 #!/usr/bin/env python3
+import requests
+from pyAudioAnalysis import audioTrainTest as aT
+from pyAudioAnalysis.audioTrainTest import load_model, load_model_knn
+from utils import config
 from .abcpreprocessor import AbcPreProcessor
 
 """
@@ -15,9 +19,28 @@ class SoundPreProcessor(AbcPreProcessor):
     """
     SoundPreProcessor class, responsible for detecting birb chirps in sound sample.
     """
-    def preprocesssignal(self, signal):
+
+    def preprocesssignal(self, signal: str) -> bool:
         """
         Classify a sound sample.
+        :param signal: Access path of the sound sample up for processing.
         :return:
         """
-        pass
+        # TODO: Dynamic model injection?
+        r = requests.get(f"http://model-service/model/{config.MODEL_ID}/details")
+        r.raise_for_status()
+
+        model_details = r.json()
+
+        if model_details['type'] == 'knn':
+            classifier, mean, std, classes, mid_window, mid_step, short_window, short_step, compute_beat \
+                = load_model_knn(config.MODEL_ID + "MEANS")
+
+        else:
+            classifier, mean, std, classes, mid_window, mid_step, short_window, short_step, compute_beat \
+                = load_model(config.MODEL_ID + "MEANS")
+
+        target_id = classes.index(config.TARGET_NAME)
+
+        class_id, probability = aT.file_classification(signal, config.MODEL_ID, "svm")
+        return class_id == target_id