refractor(inference): dependency injection for unit testing

packages/mlopspython-inference/mlopspython_inference/inference_pillow.py

@@ -1,3 +1,4 @@
+import logging
 from io import BytesIO
 
 import numpy as np
@@ -22,11 +23,25 @@ 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):
+        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)
+        self.model = model #Déleguer à une autre classe | créer LoadModel
 
     def execute(self, filepath:str|BytesIO):
         img = load_image(filepath)

packages/mlopspython-inference/tests/test_inference.py

@@ -2,21 +2,22 @@
 from pathlib import Path
 import pytest
 
-from mlopspython_inference.inference_pillow import Inference
+from mlopspython_inference.inference_pillow import Inference, ModelPillow, ModelMock
 
 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 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"}

production/api/Dockerfile

@@ -12,6 +12,7 @@ COPY packages/ packages/
 COPY production/api/pyproject.toml production/api/pyproject.toml
 COPY production/api/uv.lock production/api/uv.lock
 
+
 WORKDIR /app/production/api
 RUN uv venv && source .venv/bin/activate && uv sync --no-dev --frozen --no-editable