🔧 refactor: reformat load_model function for readability

- Adjusted parameter alignment in `load_model` definition
- Refactored `load_large_model` logic for clarity
- Enhanced fallback logic for strict and non-strict modes

src/fast_langdetect/ft_detect/infer.py

@@ -47,7 +47,8 @@ class DetectError(Exception):
     pass
 
 
-def load_model(low_memory: bool = False, download_proxy: Optional[str] = None,
+def load_model(low_memory: bool = False,
+               download_proxy: Optional[str] = None,
                use_strict_mode: bool = False) -> "fasttext.FastText._FastText":
     """
     Load the FastText model based on memory preference.
@@ -75,57 +76,55 @@ def load_local_small_model():
             logger.error(f"Failed to load the local small model '{LOCAL_SMALL_MODEL_PATH}': {e}")
             raise DetectError("Unable to load low-memory model from local resources.")
 
+    def load_large_model():
+        """Try to load the large model."""
+        try:
+            loaded_model = fasttext.load_model(str(model_path))
+            _model_cache.set_model(ModelType.HIGH_MEMORY, loaded_model)
+            return loaded_model
+        except Exception as e:
+            logger.error(f"Failed to load the large model '{model_path}': {e}")
+        return None
+
     if low_memory:
         # Attempt to load the local small model
         return load_local_small_model()
 
     # Path for the large model
     model_path = Path(CACHE_DIRECTORY) / "lid.176.bin"
 
-    def load_large_model():
-        if model_path.exists():
-            try:
-                loaded_model = fasttext.load_model(str(model_path))
-                _model_cache.set_model(ModelType.HIGH_MEMORY, loaded_model)
-                return loaded_model
-            except Exception as e:
-                logger.error(f"Failed to load the large model '{model_path}': {e}")
-                if use_strict_mode:
-                    raise DetectError("Strict mode enabled: Unable to load the large model.")
-                else:
-                    logger.info("Attempting to fall back to local small model.")
-        return None
-
-    # Attempt to load large model
-    loaded_model = load_large_model()
-    if loaded_model:
-        return loaded_model
-
-    if not use_strict_mode:
-        # If not strict or download fails, attempt to download
-        model_url = "https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin"
-        try:
-            logger.info(f"Downloading large model from {model_url} to {model_path}")
-            download(
-                url=model_url,
-                folder=CACHE_DIRECTORY,
-                filename="lid.176.bin",
-                proxy=download_proxy,
-                retry_max=3,
-                timeout=20
-            )
-            # Try loading the model again after download
-            return load_large_model()
-        except Exception as e:
-            logger.error(f"Failed to download the large model: {e}")
-            logger.info("Attempting to fall back to local small model.")
-
-    # Fallback to the small model if download fails and not in strict mode
-    if not use_strict_mode:
+    if model_path.exists():
+        # Attempt to load large model
+        loaded_model = load_large_model()
+        if loaded_model:
+            return loaded_model
+
+    # If the large model is not present, attempt to download (only if necessary)
+    model_url = "https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin"
+    try:
+        logger.info(f"Downloading large model from {model_url} to {model_path}")
+        download(
+            url=model_url,
+            folder=CACHE_DIRECTORY,
+            filename="lid.176.bin",
+            proxy=download_proxy,
+            retry_max=3,
+            timeout=20
+        )
+        # Try loading the model again after download
+        loaded_model = load_large_model()
+        if loaded_model:
+            return loaded_model
+    except Exception as e:
+        logger.error(f"Failed to download the large model: {e}")
+
+    # Handle fallback logic for strict and non-strict modes
+    if use_strict_mode:
+        raise DetectError("Strict mode enabled: Unable to download or load the large model.")
+    else:
+        logger.info("Attempting to fall back to local small model.")
         return load_local_small_model()
 
-    raise DetectError("Strict mode enabled: Unable to download or load the large model.")
-
 
 def detect(text: str, *,
            low_memory: bool = True,