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Dagster/Dagster_Op/app/engine/dagster_builder.py

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+from dagster import op, job, In
+import os, json
+from app.features.pysera_node import run_pysera
+
+DATA_DIR = os.path.join("data", "images")
+MASK_DIR = os.path.join("data", "masks")
+OUTPUT_DIR = os.path.join("app", "storage", "artifacts")
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+@op(ins={"filename": In(str)})
+def load_image(filename: str) -> str:
+    # Return normalized image file path (do not load arrays/objects)
+    return os.path.normpath(os.path.join(DATA_DIR, filename))
+
+@op(ins={"filename": In(str)})
+def load_mask(filename: str) -> str:
+    # Return normalized mask file path (do not load arrays/objects)
+    return os.path.normpath(os.path.join(MASK_DIR, filename))
+
+@op
+def extract_features(img_path: str, mask_path: str) -> str:
+    # Validate file paths and call PySERA
+    if not os.path.exists(img_path):
+        raise FileNotFoundError(f"Image path not found: {img_path}")
+    if not os.path.exists(mask_path):
+        raise FileNotFoundError(f"Mask path not found: {mask_path}")
+
+    features = run_pysera(img_path, mask_path)
+    out_path = os.path.join(OUTPUT_DIR, "CT_pitch_features.json")
+    with open(out_path, "w", encoding="utf-8") as f:
+        json.dump(features, f, indent=2)
+    return out_path
+
+@job
+def radiuma_job():
+    # Compose ops: produce file paths, then extract features
+    img = load_image()
+    mask = load_mask()
+    extract_features(img, mask)