Update app.py

app.py

@@ -1,10 +1,14 @@
 import os
 import pycaret
 from pycaret.datasets import get_data
-# import pycaret clustering and init setup
+# import pycaret clustering
 from pycaret.clustering import *
-# import ClusteringExperiment and init the class
+# import pycaret anomaly
+from pycaret.anomaly import *
+# import ClusteringExperiment
 from pycaret.clustering import ClusteringExperiment
+# import AnomalyExperiment
+from pycaret.anomaly import AnomalyExperiment
 
 import matplotlib.pyplot as plt
 import matplotlib as mpl
@@ -20,26 +24,27 @@ def main():
     data = get_data('jewellery')
     s = setup(data, session_id = 123)
 
-    exp = ClusteringExperiment()
+    exp_clustering = ClusteringExperiment()
+    exp_anomaly = AnomalyExperiment()
 
     # init setup on exp
-    exp.setup(data, session_id = 123)
+    exp_clustering.setup(data, session_id = 123)
+    exp_anomaly.setup(data, session_id = 123)
 
     # train kmeans model
     kmeans = create_model('kmeans')
+    iforest = create_model('iforest')
 
-    kmeans_cluster = assign_model(kmeans)
-    kmeans_cluster
+    # kmeans_cluster = assign_model(kmeans)
+    # kmeans_cluster
+
+    iforest_anomalies = assign_model(iforest)
+    iforest_anomalies
 
     if st.button("Prediction"):
         # plot pca cluster plot 
-        plot_model(kmeans, plot = 'cluster', display_format = 'streamlit')
-        
-        # Convert the plot to a compatible format for Streamlit
-        # compatible_plot = go.Figure(data=plot['data'], layout=plot['layout'])
-    
-        # Display the plot in Streamlit
-        # st.plotly_chart(compatible_plot)
-        
+        # plot_model(kmeans, plot = 'cluster', display_format = 'streamlit')
+        plot_model(iforest, plot = 'tsne', display_format = 'streamlit')
+               
 if __name__ == '__main__':
     main()