labels = sc.auto_label_units(
    sorting_analyzer=sorting_analyzer,
    model_folder=model_path,
    export_to_phy=True,
    trusted=['numpy.dtype']
)

print(labels.head())
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_24256\272560651.py in ?()
----> 1 labels = sc.auto_label_units(
      2     sorting_analyzer=sorting_analyzer,
      3     model_folder=model_path,
      4     export_to_phy=True,

~\Miniforge\envs\cura\Lib\site-packages\spikeinterface\curation\model_based_curation.py in ?(sorting_analyzer, model_folder, model_name, repo_id, label_conversion, trust_model, trusted, export_to_phy, enforce_metric_params)
    273         raise ValueError("The model must be an instance of sklearn.pipeline.Pipeline")
    274 
    275     model_based_classification = ModelBasedClassification(sorting_analyzer, model)
    276 
--> 277     classified_units = model_based_classification.predict_labels(
    278         label_conversion=label_conversion,
    279         export_to_phy=export_to_phy,
    280         model_info=model_info,

~\Miniforge\envs\cura\Lib\site-packages\spikeinterface\curation\model_based_curation.py in ?(self, label_conversion, input_data, export_to_phy, model_info, enforce_metric_params)
    122         self.sorting_analyzer.sorting.set_property("classifier_label", predictions)
    123         self.sorting_analyzer.sorting.set_property("classifier_probability", probabilities)
    124 
    125         if export_to_phy:
--> 126             self._export_to_phy(classified_units)
    127 
    128         return classified_units

~\Miniforge\envs\cura\Lib\site-packages\spikeinterface\curation\model_based_curation.py in ?(self, classified_units)
    193 
    194         import pandas as pd
    195 
    196         # Create a new DataFrame with unit_id, prediction, and probability columns from dict {unit_id: (prediction, probability)}
--> 197         classified_df = pd.DataFrame.from_dict(classified_units, orient="index", columns=["prediction", "probability"])
    198 
    199         # Export to Phy format
    200         try:

~\Miniforge\envs\cura\Lib\site-packages\pandas\core\frame.py in ?(cls, data, orient, dtype, columns)
   1905         orient = orient.lower()  # type: ignore[assignment]
   1906         if orient == "index":
   1907             if len(data) > 0:
   1908                 # TODO speed up Series case
-> 1909                 if isinstance(next(iter(data.values())), (Series, dict)):
   1910                     data = _from_nested_dict(data)
   1911                 else:
   1912                     index = list(data.keys())

TypeError: 'numpy.ndarray' object is not callable

labels = sc.auto_label_units(
    sorting_analyzer=sorting_analyzer,
    model_folder=model_path,
    export_to_phy=False,
    trusted=['numpy.dtype']
)

labels.to_csv(Path(path_phy) / "auto_label.csv", index_label="unit_id")

unit_ids = sorting_analyzer.sorting.unit_ids

for unit_id, (_, row) in zip(unit_ids, labels.iterrows()):
   sorting_analyzer.sorting.set_property('auto_label', [row['prediction']], ids=[unit_id])
   sorting_analyzer.sorting.set_property('auto_label_confidence', [row['probability']], ids=[unit_id])

si.export_to_phy(
    sorting_analyzer=sorting_analyzer,
    output_folder=output_phy_path,
    remove_if_exists=True, copy_binary=False
)