Use list of features as direct input

classification/classifier.py

@@ -41,7 +41,7 @@ class Classifier:
         model_path = os.path.join(parent_dir, "models", "model.pkl")
         model = joblib.load(model_path)
 
-        features = np.array(data["features"])
+        features = np.array(data)
 
         if features.shape[-1] != 4:
             raise ValueError("Expected 4 features per input.")

main.py

@@ -9,11 +9,11 @@ if __name__ == "__main__":
     cls.train_and_save()
 
     # Testing
-    data = { "features": [
-        [6.5, 3.0, 5.8, 2.2],
-        [6.1, 2.8, 4.7, 1.2]
-    ]}
-    results = cls.load_and_test(data)
+    data = {
+        "features": [[6.5, 3.0, 5.8, 2.2],[6.1, 2.8, 4.7, 1.2]]
+    }
+    features = data["features"]
+    results = cls.load_and_test(features)
     print("results:", results)
 
     # Response similar to REST API call

tests/test_classifier.py

@@ -21,4 +21,4 @@ def response():
     return ["virginica", "versicolor"]
 
 def test_response(setup_pipeline, requests, response):
-    assert response == setup_pipeline.load_and_test(requests)["predictions"]
+    assert response == setup_pipeline.load_and_test(requests["features"])["predictions"]