refactor(inference): dependancy injection

packages/mlopspython-inference/mlopspython_inference/inference_pillow.py

@@ -22,9 +22,24 @@ def load_image(filename: str|BytesIO):
 
 BASE_PATH = Path(__file__).resolve().parent
 
+class IModel():
+    def predict(self, img)->np.ndarray:
+        pass
+
+class ModelPillow(IModel):
+    def __init__(self, model_path: str):
+        self.model = load_model(model_path)
+
+    def predict(self, img)->np.ndarray:
+        return self.model.predict(img)
+
+class ModelMock(IModel):
+    def predict(self, img)->np.ndarray:
+        return np.array([[1, 0, 0]])
+
 
 class Inference:
-    def __init__(self, logging, model_path: str):
+    def __init__(self, logging, model: IModel):
         self.logger = logging.getLogger(__name__)
         self.model = load_model(model_path)
 

packages/mlopspython-inference/tests/test_inference.py

@@ -1,22 +1,25 @@
 import logging
 from pathlib import Path
 import pytest
+from .mocks import ModelMock
+
 
 from mlopspython_inference.inference_pillow import Inference
 
 BASE_PATH = Path(__file__).resolve().parent
 input_directory = BASE_PATH / "input"
 
 
-@pytest.mark.skip(reason="Modèle lourd / GPU non requis sur CI. Enlever ce skip si nécessaire.")
+#@pytest.mark.skip(reason="Modèle lourd / GPU non requis sur CI. Enlever ce skip si nécessaire.")
 def test_inference_runs_with_sample_model_and_image():
-    model_path = input_directory / "model" / "final_model.h5"
+    model_path = input_directory / "model" / "final_model.keras"
     image_path = input_directory / "images" / "cat.png"
 
     assert model_path.is_file(), "Modèle de test manquant"
     assert image_path.is_file(), "Image de test manquante"
 
-    inference = Inference(logging, str(model_path))
+    #model = ModelMock()
+    inference = Inference(logging, model)
     result = inference.execute(str(image_path))
 
     assert result["prediction"] in {"Cat", "Dog", "Other"}