Finished main stuff

extractor_service/extraction.py

@@ -5,7 +5,6 @@ import logging
 
 import json
 import tempfile
-from json import JSONEncoder
 
 import requests
 from pyAudioAnalysis import audioBasicIO
@@ -13,20 +12,14 @@ from pyAudioAnalysis import MidTermFeatures
 import numpy
 
 
-class NumpyArrayEncoder(JSONEncoder):
+class NumpyArrayEncoder(json.JSONEncoder):
     def default(self, obj):
         if isinstance(obj, numpy.ndarray):
             return obj.tolist()
-        return JSONEncoder.default(self, obj)
+        return json.JSONEncoder.default(self, obj)
 
 
-def do_extraction(file_path: str):
-    logging.info("Getting default model details...")
-    r = requests.get("http://model-service/model/$default/details")
-    r.raise_for_status()
-
-    model_details = r.json()
-
+def do_extraction(model_details: dict, file_path: str):
     logging.info("Running extraction...")
 
     sampling_rate, signal = audioBasicIO.read_audio_file(file_path)
@@ -41,10 +34,10 @@ def do_extraction(file_path: str):
     # feature extraction:
     mid_features, s, _ = \
         MidTermFeatures.mid_feature_extraction(signal, sampling_rate,
-                                  model_details['mid_window'] * sampling_rate,
-                                  model_details['mid_step'] * sampling_rate,
-                                  round(sampling_rate * model_details['short_window']),
-                                  round(sampling_rate * model_details['short_step']))
+                                               model_details['mid_window'] * sampling_rate,
+                                               model_details['mid_step'] * sampling_rate,
+                                               round(sampling_rate * model_details['short_window']),
+                                               round(sampling_rate * model_details['short_step']))
 
     # long term averaging of mid-term statistics
     mid_features = mid_features.mean(axis=1)
@@ -53,7 +46,7 @@ def do_extraction(file_path: str):
         mid_features = numpy.append(mid_features, beat)
         mid_features = numpy.append(mid_features, beat_conf)
 
-    #feature_vector = (mid_features - mean) / std    # normalization
+    # feature_vector = (mid_features - mean) / std    # normalization
 
     return mid_features
 
@@ -67,9 +60,19 @@ def run_everything(parameters: dict):
     with open(file_path, 'wb') as f:
         f.write(r.content)
 
+    logging.debug(f"Downloaded sample to {file_path}")
+
+    logging.info("Getting default model details...")
+    r = requests.get("http://model-service/model/$default/details")
+    r.raise_for_status()
+
+    model_details = r.json()
+
+    logging.debug(f"Using model {model_details['id']}")
+
     # download done. Do extraction magic
     try:
-        results = do_extraction(file_path)
+        results = do_extraction(model_details, file_path)
     finally:
         os.remove(file_path)
 
@@ -77,11 +80,17 @@ def run_everything(parameters: dict):
 
     response = {
         "tag": tag,
-        "results": results
+        "results": results,
+        "model": model_details['id']
     }
 
     logging.debug(f"Data being pushed: {str(response)}")
 
-    r = requests.post('http://classification-service/classify', data=json.dumps(results, cls=NumpyArrayEncoder), headers={'Content-Type': 'application/json'})
-    #r.raise_for_status() # An error in a service should not kill other services
+    r = requests.post(
+        'http://classification-service/classify',
+        data=json.dumps(response, cls=NumpyArrayEncoder),
+        headers={'Content-Type': 'application/json'}
+    )
+
+    # r.raise_for_status() # An error in a service should not kill other services
     logging.info(f"Classification service response: {r.status_code}")