Create export_model_artifacts.py

export_model_artifacts.py

@@ -0,0 +1,45 @@
+# export_model_artifacts.py
+import joblib, xgboost as xgb, os
+p = "best_overall_XGBoost.joblib"
+m = joblib.load(p)
+print("Loaded:", type(m))
+
+# If pipeline, extract preprocessing and classifier
+preproc_path = None
+json_path = None
+clf = m
+try:
+    # sklearn Pipeline -> try to find the xgb step
+    from sklearn.pipeline import Pipeline
+    if isinstance(m, Pipeline):
+        # Save pipeline without the final estimator
+        steps = m.steps
+        # assume last step is classifier
+        *prefix, (last_name, last_obj) = steps
+        # Build preprocessor pipeline if any prefix exists
+        if prefix:
+            from sklearn.pipeline import Pipeline as SKPipeline
+            preproc = SKPipeline(prefix)
+            preproc_path = "preprocessor.joblib"
+            joblib.dump(preproc, preproc_path)
+            print("Saved preprocessor to", preproc_path)
+        clf = last_obj
+except Exception as e:
+    print("Not a Pipeline or failed to extract pipeline:", e)
+
+# If clf is XGBClassifier, get booster
+try:
+    booster = None
+    if hasattr(clf, "get_booster"):
+        booster = clf.get_booster()
+    elif isinstance(clf, xgb.Booster):
+        booster = clf
+    else:
+        print("Classifier type:", type(clf))
+    if booster is not None:
+        json_path = "best_overall_XGBoost.json"
+        booster.save_model(json_path)
+        print("Saved booster JSON to", json_path)
+except Exception as e:
+    print("Failed to export booster JSON:", e)
+    import traceback; traceback.print_exc()