feat(canvas): Add NX model introspection API endpoints

Phase 4 of Canvas Professional Upgrade:
- Add /studies/{id}/nx/introspect endpoint for full model introspection
- Add /studies/{id}/nx/expressions endpoint for expression list
- Add caching to avoid re-running NX journal on each request
- Add frontend API client methods: introspectNxModel, getNxExpressions
- Use existing introspect_part.py extractor

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

atomizer-dashboard/backend/api/routes/optimization.py

@@ -3907,3 +3907,167 @@ async def export_study_data(study_id: str, format: str):
         raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Failed to export data: {str(e)}")
+
+
+# ============================================================================
+# NX Model Introspection Endpoints
+# ============================================================================
+
+@router.get("/studies/{study_id}/nx/introspect")
+async def introspect_nx_model(study_id: str, force: bool = False):
+    """
+    Introspect NX model to extract expressions and properties.
+
+    Uses the optimization_engine.extractors.introspect_part module to run
+    NX journal that extracts expressions, mass properties, materials, etc.
+
+    Args:
+        study_id: Study identifier
+        force: Force re-introspection even if cached results exist
+
+    Returns:
+        JSON with introspection results including expressions list
+    """
+    try:
+        study_dir = resolve_study_path(study_id)
+
+        if not study_dir.exists():
+            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
+
+        # Find model directory
+        model_dir = study_dir / "1_model"
+        if not model_dir.exists():
+            model_dir = study_dir / "0_model"
+        if not model_dir.exists():
+            raise HTTPException(status_code=404, detail=f"Model directory not found for {study_id}")
+
+        # Find .prt file
+        prt_files = list(model_dir.glob("*.prt"))
+        # Exclude idealized parts
+        prt_files = [f for f in prt_files if '_i.prt' not in f.name.lower()]
+
+        if not prt_files:
+            raise HTTPException(status_code=404, detail=f"No .prt files found in {model_dir}")
+
+        prt_file = prt_files[0]  # Use first non-idealized part
+
+        # Check for cached results
+        cache_file = model_dir / "_introspection_cache.json"
+        if cache_file.exists() and not force:
+            try:
+                with open(cache_file, 'r') as f:
+                    cached = json.load(f)
+                # Check if cache is for same file
+                if cached.get('part_file') == str(prt_file):
+                    return {
+                        "study_id": study_id,
+                        "cached": True,
+                        "introspection": cached
+                    }
+            except:
+                pass  # Invalid cache, re-run
+
+        # Run introspection
+        try:
+            from optimization_engine.extractors.introspect_part import introspect_part
+            result = introspect_part(str(prt_file), str(model_dir), verbose=False)
+
+            # Cache results
+            with open(cache_file, 'w') as f:
+                json.dump(result, f, indent=2)
+
+            return {
+                "study_id": study_id,
+                "cached": False,
+                "introspection": result
+            }
+
+        except ImportError:
+            raise HTTPException(
+                status_code=500,
+                detail="introspect_part module not available"
+            )
+        except Exception as e:
+            raise HTTPException(
+                status_code=500,
+                detail=f"Introspection failed: {str(e)}"
+            )
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to introspect: {str(e)}")
+
+
+@router.get("/studies/{study_id}/nx/expressions")
+async def get_nx_expressions(study_id: str):
+    """
+    Get just the expressions from NX model (simplified endpoint).
+
+    Returns list of user expressions suitable for design variable selection.
+
+    Args:
+        study_id: Study identifier
+
+    Returns:
+        JSON with expressions array containing name, value, units, formula
+    """
+    try:
+        study_dir = resolve_study_path(study_id)
+
+        if not study_dir.exists():
+            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
+
+        # Check cache first
+        model_dir = study_dir / "1_model"
+        if not model_dir.exists():
+            model_dir = study_dir / "0_model"
+
+        cache_file = model_dir / "_introspection_cache.json" if model_dir.exists() else None
+
+        if cache_file and cache_file.exists():
+            with open(cache_file, 'r') as f:
+                cached = json.load(f)
+
+            expressions = cached.get('expressions', {}).get('user', [])
+            return {
+                "study_id": study_id,
+                "expressions": expressions,
+                "count": len(expressions)
+            }
+
+        # No cache, need to run introspection
+        # Find .prt file
+        if not model_dir or not model_dir.exists():
+            raise HTTPException(status_code=404, detail=f"Model directory not found for {study_id}")
+
+        prt_files = list(model_dir.glob("*.prt"))
+        prt_files = [f for f in prt_files if '_i.prt' not in f.name.lower()]
+
+        if not prt_files:
+            raise HTTPException(status_code=404, detail=f"No .prt files found")
+
+        prt_file = prt_files[0]
+
+        try:
+            from optimization_engine.extractors.introspect_part import introspect_part
+            result = introspect_part(str(prt_file), str(model_dir), verbose=False)
+
+            # Cache for future
+            with open(cache_file, 'w') as f:
+                json.dump(result, f, indent=2)
+
+            expressions = result.get('expressions', {}).get('user', [])
+            return {
+                "study_id": study_id,
+                "expressions": expressions,
+                "count": len(expressions)
+            }
+
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"Failed to get expressions: {str(e)}")
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to get expressions: {str(e)}")

atomizer-dashboard/frontend/src/api/client.ts

@@ -348,6 +348,55 @@ class ApiClient {
     if (!response.ok) throw new Error(`Failed to export ${format}`);
     return response.json();
   }
+  // NX Model introspection
+  async introspectNxModel(studyId: string, force: boolean = false): Promise<{
+    study_id: string;
+    cached: boolean;
+    introspection: {
+      success: boolean;
+      part_file: string;
+      expressions: {
+        user: Array<{
+          name: string;
+          value: number;
+          units?: string;
+          formula?: string;
+        }>;
+        internal: any[];
+        user_count: number;
+        total_count: number;
+      };
+      mass_properties: {
+        mass_kg: number;
+        mass_g: number;
+        volume_mm3: number;
+        surface_area_mm2: number;
+        center_of_gravity_mm: [number, number, number];
+      };
+    };
+  }> {
+    const url = force
+      ? `${API_BASE}/optimization/studies/${studyId}/nx/introspect?force=true`
+      : `${API_BASE}/optimization/studies/${studyId}/nx/introspect`;
+    const response = await fetch(url);
+    if (!response.ok) throw new Error('Failed to introspect NX model');
+    return response.json();
+  }
+
+  async getNxExpressions(studyId: string): Promise<{
+    study_id: string;
+    expressions: Array<{
+      name: string;
+      value: number;
+      units?: string;
+      formula?: string;
+    }>;
+    count: number;
+  }> {
+    const response = await fetch(`${API_BASE}/optimization/studies/${studyId}/nx/expressions`);
+    if (!response.ok) throw new Error('Failed to get NX expressions');
+    return response.json();
+  }
 }
 
 export const apiClient = new ApiClient();