removed unneeded adapter derivatives and made estimator adapter abstract base class to normal class

image_prediction/estimator/adapter/adapter.py

@@ -1,10 +1,9 @@
-import abc
-
-
-class EstimatorAdapter(abc.ABC):
+class EstimatorAdapter:
     def __init__(self, estimator):
         self.estimator = estimator
 
-    @abc.abstractmethod
     def predict(self, batch):
-        pass
+        return self.estimator.predict(batch)
+
+    def predict_proba(self, batch):
+        return self.estimator.predict_proba(batch)

image_prediction/estimator/adapter/adapters/keras.py

@@ -1,11 +0,0 @@
-import numpy as np
-
-from image_prediction.estimator.adapter.adapter import EstimatorAdapter
-
-
-class KerasEstimatorAdapter(EstimatorAdapter):
-    def __init__(self, estimator):
-        super().__init__(estimator)
-
-    def predict(self, batch: np.array):
-        return self.estimator.predict(batch)

image_prediction/estimator/adapter/adapters/mock.py

@@ -1,15 +0,0 @@
-from image_prediction.estimator.adapter.adapter import EstimatorAdapter
-
-
-class EstimatorMock:
-    @staticmethod
-    def predict(batch):
-        return [None for _ in batch]
-
-
-class EstimatorAdapterMock(EstimatorAdapter):
-    def __init__(self, estimator):
-        super().__init__(estimator=estimator)
-
-    def predict(self, batch):
-        return self.estimator.predict(batch)

image_prediction/pipeline.py

@@ -0,0 +1,16 @@
+from image_prediction.classifier.classifier import Classifier
+from image_prediction.config import CONFIG
+from image_prediction.estimator.adapter.adapter import EstimatorAdapter
+from image_prediction.locations import MLRUNS_DIR
+from image_prediction.model_loader.loader import ModelLoader
+from image_prediction.model_loader.loaders.mlflow import MlflowConnector
+from image_prediction.redai_adapter.mlflow import MlflowModelReader
+
+
+class Pipeline:
+
+    def __init__(self):
+        model_loader = ModelLoader(MlflowConnector(MlflowModelReader(MLRUNS_DIR)))
+        model = model_loader.load_model(CONFIG.service.run_id)
+        classes = model_loader.load_classes(CONFIG.service.run_id)
+        classifier = Classifier(EstimatorAdapter(model), classes)

test/unit_tests/conftest.py

@@ -11,8 +11,7 @@ from PIL import Image
 
 from image_prediction.classifier.classifier import Classifier
 from image_prediction.classifier.image_classifier import ImageClassifier
-from image_prediction.estimator.adapter.adapters.keras import KerasEstimatorAdapter
-from image_prediction.estimator.adapter.adapters.mock import EstimatorMock, EstimatorAdapterMock
+from image_prediction.estimator.adapter.adapter import EstimatorAdapter
 from image_prediction.estimator.preprocessor.preprocessors.basic import BasicPreprocessor
 from image_prediction.exceptions import UnknownEstimatorAdapter, UnknownImageExtractor, UnknownDatabaseType
 from image_prediction.image_extractor.extractor import ImageMetadataPair
@@ -48,12 +47,18 @@ def classifier(estimator_adapter, classes):
     return classifier
 
 
+class EstimatorMock:
+    @staticmethod
+    def predict(batch):
+        return [None for _ in batch]
+
+
 @pytest.fixture
 def estimator_adapter(estimator_type, keras_model, output_batch_generator, monkeypatch):
     if estimator_type == "mock":
-        estimator_adapter = EstimatorAdapterMock(EstimatorMock())
+        estimator_adapter = EstimatorAdapter(EstimatorMock())
     elif estimator_type == "keras":
-        estimator_adapter = KerasEstimatorAdapter(keras_model)
+        estimator_adapter = EstimatorAdapter(keras_model)
     else:
         raise UnknownEstimatorAdapter(f"No adapter for estimator type {estimator_type} was specified.")