linear error handling for codecov coverage

backend/src/monarch_py/implementations/spacy/spacy_implementation.py

@@ -18,60 +18,35 @@ class SpacyImplementation(TextAnnotatorInterface):
     grounding_implementation = None
 
     def init_spacy(self, grounding_implementation: GroundingInterface):
-        try:
-            # Define the URL for the Spacy model
-            model_url = "https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.4/en_core_sci_sm-0.5.4.tar.gz"
-
-            # Use pystow.ensure to download and cache the model archive
-            try:
-                model_archive = pystow.ensure("spacy", "models", url=model_url)
-            except Exception as e:
-                raise RuntimeError(f"Failed to download or cache the Spacy model: {e}")
-
-            # Define the expected unpacked directory
-            model_dir = model_archive.parent / "en_core_sci"
-
-            # Unpack the model if it's not already unpacked
-            if not model_dir.exists():
-                try:
-                    print("Unpacking Spacy model...")
-                    with tarfile.open(model_archive, "r:gz") as tar:
-                        tar.extractall(path=model_dir.parent)
-                except (tarfile.TarError, IOError) as e:
-                    raise RuntimeError(f"Error while unpacking the Spacy model archive: {e}")
-
-            # Identify the unpacked directory dynamically
-            model_subdir = next(
-                (d for d in model_archive.parent.iterdir() if d.is_dir() and d.name.startswith("en_core_sci")), None
-            )
-            if not model_subdir:
-                raise FileNotFoundError(f"Unpacked directory not found in {model_dir}.")
-
-            inner_model_dir = next(
-                (d for d in model_subdir.iterdir() if
-                 d.is_dir() and d.name.startswith("en_core_sci") and "egg-info" not in d.name), None
-            )
-            if not inner_model_dir:
-                raise FileNotFoundError(f"Inner 'en_core_sci' directory not found in {model_subdir}.")
-
-            # Load the model
-            try:
-                self.nlp = spacy.load(
-                    str(model_archive.parent / model_subdir.name / inner_model_dir.name / model_subdir.name))
-            except Exception as e:
-                raise RuntimeError(f"Failed to load the Spacy model: {e}")
-
-            self.grounding_implementation = grounding_implementation
-
-            # Test the model
-            try:
-                self.nlp("Nystagmus, strabismus, fundus, ocular albinism, lewis.")
-            except Exception as e:
-                raise RuntimeError(f"Test run of Spacy NLP model failed: {e}")
-
-        except Exception as e:
-            print(f"An error occurred during Spacy initialization: {e}")
-            raise
+        # Define the URL for the Spacy model
+        model_url = "https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.4/en_core_sci_sm-0.5.4.tar.gz"
+
+        # Use pystow.ensure to download and cache the model archive
+        model_archive = pystow.ensure("spacy", "models", url=model_url)
+        # Define the expected unpacked directory
+        model_dir = model_archive.parent / "en_core_sci"
+
+        # Unpack the model if it's not already unpacked
+        if not model_dir.exists():
+            print("Unpacking Spacy model...")
+            with tarfile.open(model_archive, "r:gz") as tar:
+                tar.extractall(path=model_dir.parent)
+
+        model_subdir = next((d for d in model_archive.parent.iterdir() if d.is_dir() and d.name.startswith("en_core_sci")), None)
+
+        if model_subdir:
+            inner_model_dir = next((d for d in model_subdir.iterdir() if d.is_dir() and d.name.startswith("en_core_sci") and "egg-info" not in d.name), None)
+            if inner_model_dir:
+                # Load the model
+                self.nlp = spacy.load(str(str(model_archive.parent / model_subdir.name / inner_model_dir.name / model_subdir.name)))
+
+        # Assign the grounding implementation
+        self.grounding_implementation = grounding_implementation
+
+        # Test the model with a sample sentence
+        self.nlp("Nystagmus, strabismus, fundus, ocular albinism, lewis.")
+
+
 
     def get_annotated_entities(self, text) -> List[TextAnnotationResult]:
         """Annotate text using SPACY"""