Implemented classifier cache

cnn_classification_service/magic_doer.py

@@ -4,80 +4,46 @@ import logging
 import tempfile
 import requests
 
-from urllib.parse import urljoin
-
-from cnn_classifier import Classifier
+from classifier_cache import ClassifierCache
 
 
-def run_everything(parameters: dict):
-    tag = parameters['tag']
+class MagicDoer:
+    classifier_cache = ClassifierCache()
 
-    sample_file_handle, sample_file_path = tempfile.mkstemp(prefix=f"{tag}_", suffix=".wav")
-    model_file_handle, model_file_path = tempfile.mkstemp(suffix=".json")
-    weights_file_handle, weights_file_path = tempfile.mkstemp(suffix=".h5")
-
-    try:
-
-        # Download Sample
-
-        logging.info(f"Downloading sample: {tag}")
-        r = requests.get(f"http://storage-service/object/{tag}")
-        with open(sample_file_handle, 'wb') as f:
-            f.write(r.content)
-
-        logging.debug(f"Downloaded sample to {sample_file_path}")
-
-        # Download model
-
-        model_root_url = "http://model-service/model/cnn/$default"
-
-        logging.debug("Fetching model info...")
-        r = requests.get(model_root_url)
-        r.raise_for_status()
-
-        model_details = r.json()
-
-        logging.debug("Fetching model file...")
-        r = requests.get(urljoin(model_root_url, model_details['files']['model']))  # Fun fact: this would support external urls
-        r.raise_for_status()
-
-        with open(model_file_handle, 'wb') as f:
-            f.write(r.content)
-
-        logging.debug("Fetching weights file...")
-        r = requests.get(urljoin(model_root_url, model_details['files']['weights']))
-        r.raise_for_status()
-
-        with open(weights_file_handle, 'wb') as f:
-            f.write(r.content)
-
-        # magic happens here
-        classifier = Classifier(model_file_path, weights_file_path)
-        results = classifier.predict(sample_file_path)
-
-    finally:  # bruuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuh
+    @classmethod
+    def run_everything(cls, parameters: dict) -> dict:
+        tag = parameters['tag']
+        sample_file_handle, sample_file_path = tempfile.mkstemp(prefix=f"{tag}_", suffix=".wav")
         try:
-            os.remove(model_file_path)
-        except FileNotFoundError:
-            pass
 
-        try:
-            os.remove(weights_file_path)
-        except FileNotFoundError:
-            pass
+            # Download Sample
 
-        try:
-            os.remove(sample_file_path)
-        except FileNotFoundError:
-            pass
+            logging.info(f"Downloading sample: {tag}")
+            r = requests.get(f"http://storage-service/object/{tag}")
+            with open(sample_file_handle, 'wb') as f:
+                f.write(r.content)
 
-    response = {
-        "tag": tag,
-        "probability": 1.0 if results[0] == model_details['target_class_name'] else 0.0,
-        "model": model_details['id']
-    }
+            logging.debug(f"Downloaded sample to {sample_file_path}")
 
-    logging.info(f"Classification done!")
-    logging.debug(f"Results: {response}")
+            # Get a classifier that uses the default model
+            model_details, classifier = cls.classifier_cache.get_default_classifier()
 
-    return response
+            # do the majic
+            results = classifier.predict(sample_file_path)
+
+        finally:
+            try:
+                os.remove(sample_file_path)
+            except FileNotFoundError:
+                pass
+
+        response = {
+            "tag": tag,
+            "probability": 1.0 if results[0] == model_details['target_class_name'] else 0.0,
+            "model": model_details['id']
+        }
+
+        logging.info(f"Classification done!")
+        logging.debug(f"Results: {response}")
+
+        return response