Add memory usage metric

__init__.py

@@ -14,7 +14,10 @@
 
 # Create namespaces
 import bridgenlp.adapters as adapters
-import bridgenlp.pipes as pipes
+try:
+    import bridgenlp.pipes as pipes
+except Exception:
+    pipes = None
 
 __all__ = [
     "adapters",

bridgenlp/base.py

@@ -19,6 +19,7 @@
 
 from .config import BridgeConfig
 from .result import BridgeResult
+from .utils import get_model_memory_usage
 
 
 class BridgeBase(ABC):
@@ -41,7 +42,8 @@ def __init__(self, config: Optional[BridgeConfig] = None):
             "num_calls": 0,
             "total_time": 0.0,
             "total_tokens": 0,
-            "errors": 0
+            "errors": 0,
+            "memory_mb": 0.0
         }
         self._metrics_lock = threading.RLock()
 
@@ -160,6 +162,19 @@ def _measure_performance(self):
             elapsed = time.time() - start_time
             with self._metrics_lock:
                 self._metrics["total_time"] += elapsed
+                if self._metrics.get("memory_mb", 0.0) == 0.0:
+                    model = None
+                    # Try to access common model attributes
+                    if hasattr(self, "model"):
+                        try:
+                            model = self.model
+                        except Exception:
+                            model = getattr(self, "_model", None)
+                    elif hasattr(self, "_model"):
+                        model = getattr(self, "_model", None)
+
+                    if model is not None:
+                        self._metrics["memory_mb"] = get_model_memory_usage(model)
 
     def get_metrics(self) -> Dict[str, float]:
         """
@@ -188,7 +203,8 @@ def reset_metrics(self) -> None:
                 "num_calls": 0,
                 "total_time": 0.0,
                 "total_tokens": 0,
-                "errors": 0
+                "errors": 0,
+                "memory_mb": 0.0
             }
 
     def __enter__(self):

bridgenlp/cli.py

@@ -338,14 +338,15 @@ def read_chunks(stream, chunk_size):
 
     # Print summary statistics
     elapsed_time = time.time() - start_time
-    if processed_count > 0 and show_progress:
-        print(f"Processed {processed_count} texts in {elapsed_time:.4f}s "
-              f"({processed_count / elapsed_time:.2f} texts/sec)" if elapsed_time > 0 else
-              f"Processed {processed_count} texts in {elapsed_time:.4f}s", 
-              file=sys.stderr)
-
-        # Print metrics if available
-        if hasattr(bridge, 'get_metrics'):
+    if processed_count > 0:
+        if show_progress:
+            print(f"Processed {processed_count} texts in {elapsed_time:.4f}s "
+                  f"({processed_count / elapsed_time:.2f} texts/sec)" if elapsed_time > 0 else
+                  f"Processed {processed_count} texts in {elapsed_time:.4f}s",
+                  file=sys.stderr)
+
+        # Print metrics if requested
+        if bridge.config.collect_metrics and hasattr(bridge, 'get_metrics'):
             metrics = bridge.get_metrics()
             if metrics:
                 print("Performance metrics:", file=sys.stderr)

tests/test_memory_reporting.py

@@ -0,0 +1,34 @@
+import pytest
+from unittest.mock import patch
+from bridgenlp.base import BridgeBase
+from bridgenlp.config import BridgeConfig
+from bridgenlp.result import BridgeResult
+
+class DummyAdapter(BridgeBase):
+    def __init__(self):
+        super().__init__(BridgeConfig(collect_metrics=True))
+        self._model = None
+
+    def _load_model(self):
+        self._model = object()
+
+    def from_text(self, text: str) -> BridgeResult:
+        with self._measure_performance():
+            if self._model is None:
+                self._load_model()
+            return BridgeResult(tokens=text.split())
+
+    def from_tokens(self, tokens):
+        return BridgeResult(tokens=tokens)
+
+    def from_spacy(self, doc):
+        return BridgeResult(tokens=[t.text for t in doc]).attach_to_spacy(doc)
+
+
+def test_memory_metric_recorded():
+    adapter = DummyAdapter()
+    with patch('bridgenlp.base.get_model_memory_usage', return_value=12.5) as mem:
+        adapter.from_text("hello world")
+        metrics = adapter.get_metrics()
+        assert metrics['memory_mb'] == 12.5
+        assert mem.called