Update src/aibom_generator/generator.py

src/aibom_generator/generator.py

@@ -119,14 +119,20 @@ class AIBOMGenerator:
         metadata["ai:task"] = metadata.get("pipeline_tag", "Text Generation")
         metadata["ai:framework"] = "PyTorch" if "transformers" in metadata.get("library_name", "") else "Unknown"
 
-
         return {k: v for k, v in metadata.items() if v is not None}
 
     def _extract_unstructured_metadata(self, model_card: ModelCard) -> Dict[str, Any]:
         return {}
 
     def _create_metadata_section(self, model_id: str, metadata: Dict[str, Any]) -> Dict[str, Any]:
-        aibom_stub = {"metadata": metadata}  # Build stub for scoring
+        aibom_stub = {
+            "bomFormat": "CycloneDX",
+            "specVersion": "1.6",
+            "serialNumber": f"urn:uuid:{str(uuid.uuid4())}",
+            "version": 1,
+            "metadata": {},
+            "components": [self._create_component_section(model_id, metadata)]
+        }
         score_report = calculate_completeness_score(aibom_stub)
 
         timestamp = datetime.datetime.utcnow().isoformat() + "Z"
@@ -156,10 +162,8 @@ class AIBOMGenerator:
                     value = json.dumps(value)
                 properties.append({"name": key, "value": str(value)})
 
-        # Add quality scoring results
         properties.append({"name": "aibom:quality-score", "value": str(score_report["total_score"])})
         properties.append({"name": "aibom:quality-breakdown", "value": json.dumps(score_report["section_scores"])})
-        properties.append({"name": "aibom:field-checklist", "value": json.dumps(score_report["field_checklist"])})
 
         metadata_section = {
             "timestamp": timestamp,
@@ -222,14 +226,4 @@ class AIBOMGenerator:
         if considerations:
             model_card_section["considerations"] = considerations
 
-        properties = []
-        for key, value in metadata.items():
-            if key not in ["name", "author", "license", "base_model", "library_name", "pipeline_tag", "eval_results", "limitations", "ethical_considerations", "bias", "risks"]:
-                if isinstance(value, (list, dict)):
-                    value = json.dumps(value)
-                properties.append({"name": key, "value": str(value)})
-
-        if properties:
-            model_card_section["properties"] = properties
-
         return model_card_section