diff --git a/examples/tutorials/curation/plot_1_automated_curation.py b/examples/tutorials/curation/plot_1_automated_curation.py
index 8e447dd12a..f355a48032 100644
--- a/examples/tutorials/curation/plot_1_automated_curation.py
+++ b/examples/tutorials/curation/plot_1_automated_curation.py
@@ -7,7 +7,6 @@
 
 import warnings
 warnings.filterwarnings("ignore")
-from pathlib import Path
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -97,7 +96,6 @@
 # human_labels = sorting_analyzer.sorting.get_property('quality')
 
 from sklearn.metrics import confusion_matrix, balanced_accuracy_score
-import seaborn as sns
 
 label_conversion = model_info['label_conversion']
 predictions = [ labels[a][0] for a in range(10) ]
@@ -107,7 +105,9 @@
 # Calculate balanced accuracy for the confusion matrix
 balanced_accuracy = balanced_accuracy_score(human_labels, predictions)
 
-sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='viridis')
+plt.imshow(conf_matrix)
+for (index, value) in np.ndenumerate(conf_matrix):
+    plt.annotate( str(value), xy=index, color="white", fontsize="15")
 plt.xlabel('Predicted Label')
 plt.ylabel('Human Label')
 plt.xticks(ticks = [0.5, 1.5], labels = list(label_conversion.values()))
diff --git a/pyproject.toml b/pyproject.toml
index e51fcb2a4d..6d724a32c6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -200,6 +200,8 @@ docs = [
     "numba", # For many postprocessing functions
     "xarray", # For use of SortingAnalyzer zarr format
     "networkx",
+    "skops", # For auotmated curation
+    "sklearn", # For auotmated curation
     # Download data
     "pooch>=1.8.2",
     "datalad>=1.0.2",