Refactor logging statements and improve error handling in operators

- Updated logging statements in Llama3VILAInferenceOperator, MonaiBundleInferenceOperator, and VLMResultsWriterOperator to use more consistent formatting with repr for better clarity.
- Enhanced error handling in the pipeline generator by raising exceptions with context to improve debugging.

Signed-off-by: Victor Chang <[email protected]>

Author: mocsharp

monai/deploy/operators/llama3_vila_inference_operator.py

@@ -159,10 +159,10 @@ def _generate_response(self, image_tensor: torch.Tensor, prompt: str, generation
         if self._mock_mode:
             # Mock response based on common medical VQA patterns
             mock_responses = {
-                "what is this image showing": "This medical image shows anatomical structures with various tissue densities and contrast patterns.",
-                "summarize key findings": "Key findings include: 1) Normal anatomical structures visible, 2) No obvious pathological changes detected, 3) Image quality is adequate for assessment.",
-                "is there a focal lesion": "No focal lesion is identified in the visible field of view.",
-                "describe the image": "This appears to be a medical imaging study showing cross-sectional anatomy with good tissue contrast.",
+                "what is this image showing": "This medical image shows anatomical structures with various tissue densities and contrast patterns.",  # noqa: B950
+                "summarize key findings": "Key findings include: 1) Normal anatomical structures visible, 2) No obvious pathological changes detected, 3) Image quality is adequate for assessment.",  # noqa: B950
+                "is there a focal lesion": "No focal lesion is identified in the visible field of view.",  # noqa: B950
+                "describe the image": "This appears to be a medical imaging study showing cross-sectional anatomy with good tissue contrast.",  # noqa: B950
             }
 
             # Find best matching response
@@ -172,7 +172,7 @@ def _generate_response(self, image_tensor: torch.Tensor, prompt: str, generation
                     return response
 
             # Default response
-            return f"Analysis of the medical image based on the prompt: '{prompt}'. [Mock response - actual model not loaded]"
+            return f"Analysis of the medical image based on the prompt: {prompt!r}. [Mock response - actual model not loaded]"
 
         # In a real implementation, you would:
         # 1. Tokenize the prompt
@@ -272,7 +272,7 @@ def compute(self, op_input, op_output, context):
         request_id = op_input.receive("request_id")
         generation_params = op_input.receive("generation_params")
 
-        self._logger.info(f"Processing request {request_id} with output type '{output_type}'")
+        self._logger.info(f"Processing request {request_id} with output type {output_type!r}")
 
         try:
             # Preprocess image
@@ -317,3 +317,4 @@ def compute(self, op_input, op_output, context):
             op_output.emit(error_result, "result")
             op_output.emit(output_type, "output_type")
             op_output.emit(request_id, "request_id")
+            raise e from None

monai/deploy/operators/monai_bundle_inference_operator.py

@@ -836,7 +836,7 @@ def _receive_input(self, name: str, op_input, context):
                 # No channel present (W, H, D)
                 if expected_channels is not None and expected_channels > 1:
                     raise ValueError(
-                        f"Input for '{name}' has no channel dimension but bundle expects {expected_channels} channels. "
+                        f"Input for '{name!r}' has no channel dimension but bundle expects {expected_channels} channels. "
                         "Provide multi-channel input or add a transform to stack channels before inference."
                     )
                 # else expected 1 or unknown -> proceed without channel
@@ -854,12 +854,12 @@ def _receive_input(self, name: str, op_input, context):
                         ndims = 3
                     else:
                         raise ValueError(
-                            f"Input for '{name}' has {actual_channels} channels but bundle expects {expected_channels}."
+                            f"Input for '{name!r}' has {actual_channels} channels but bundle expects {expected_channels}."
                         )
                 # else exact match or unknown -> keep as-is
             else:
                 # Unsupported rank for medical image input
-                raise ValueError(f"Unsupported input rank {ndims} for '{name}'. Expected 3D (W,H,D) or 4D (W,H,D,C).")
+                raise ValueError(f"Unsupported input rank {ndims} for '{name!r}'. Expected 3D (W,H,D) or 4D (W,H,D,C).")
             value = torch.from_numpy(value).to(self._device)
             if metadata is None:
                 metadata = {}

monai/deploy/operators/vlm_results_writer_operator.py

@@ -117,7 +117,7 @@ def compute(self, op_input, op_output, context):
         output_type = op_input.receive("output_type")
         request_id = op_input.receive("request_id")
 
-        self._logger.info(f"Writing result for request {request_id} with output type '{output_type}'")
+        self._logger.info(f"Writing result for request {request_id} with output type {output_type!r}")
 
         try:
             if output_type == "json":

tools/pipeline-generator/pipeline_generator/cli/run.py

@@ -134,7 +134,7 @@ def run(
                 progress.update(task, description="[green]Virtual environment created")
             except subprocess.CalledProcessError as e:
                 console.print(f"[red]Error creating virtual environment: {e.stderr}[/red]")
-                raise click.Abort()
+                raise click.Abort() from e
     else:
         console.print(f"[dim]Using existing virtual environment: {venv_name}[/dim]")
 
@@ -251,7 +251,7 @@ def run(
                 progress.update(task, description="[green]Dependencies installed")
             except subprocess.CalledProcessError as e:
                 console.print(f"[red]Error installing dependencies: {e.stderr}[/red]")
-                raise click.Abort()
+                raise click.Abort() from e
 
     # Step 3: Run the application
     console.print("\n[green]Starting application...[/green]\n")
@@ -308,7 +308,7 @@ def run(
         raise click.Abort()
     except Exception as e:
         console.print(f"[red]Error running application: {e}[/red]")
-        raise click.Abort()
+        raise click.Abort() from e
 
 
 if __name__ == "__main__":

tools/pipeline-generator/tests/test_security.py

@@ -53,7 +53,7 @@ def test_model_id_validation(self):
                 generator.generate_app(model_id, output_dir)
             except ValueError as e:
                 if "Invalid model_id" in str(e):
-                    pytest.fail(f"Valid model_id '{model_id}' was rejected: {e}")
+                    pytest.fail(f"Valid model_id '{model_id!r}' was rejected: {e}")
                 # Other errors are fine (e.g., download failures)
 
         # Test invalid IDs (should raise ValueError)
@@ -82,7 +82,7 @@ def test_app_name_sanitization(self):
             result_with_app = f"{sanitized}App"
             assert (
                 result_with_app == expected_class_name
-            ), f"Failed for '{input_name}': got '{result_with_app}', expected '{expected_class_name}'"
+            ), f"Failed for '{input_name!r}': got '{result_with_app!r}', expected '{expected_class_name!r}'"
 
     def test_sanitize_for_python_identifier(self):
         """Test the Python identifier sanitization method."""