removing the animation when computing som on slice

app.py

@@ -151,23 +151,9 @@ def get_som_mp4(file, slice_select, reducer=reducer10d, cluster=cluster_som):
     #compute the 10 dimensional embeding vector
     embedding10d = reducer.transform(data)
 
-    # retrieve the prediction and get the animation
-    prediction = cluster_som.predict(embedding10d)
-    processed_data = process_som_data(data,prediction)
-
-    # Write the processed data to a CSV file
-    # header = ['Gait', 'TS', 'State', 'Condition', 'Shape1', 'Shape2', 'Shape3', 'Shape4', 'Color1', 'Color2', 'Color3', 'Color4', 'Danger1', 'Danger2', 'Danger3', 'Danger4']
-    # with open('animation_table.csv', 'w', newline='') as csvfile:
-    #     csv_writer = csv.writer(csvfile)
-    #     csv_writer.writerow(header)
-    #     csv_writer.writerows(processed_data)
-    
-    # os.system('curl -X POST -F "csv_file=@animation_table.csv" https://metric-space.ngrok.io/generate --output animation.mp4')
-
-    # prediction = cluster_som.predict(embedding10d)
     fig = cluster.plot_activation_v2(embedding10d, slice_select)
 
-    return fig, 'animation.mp4'
+    return fig
 
 def attach_label_to_json(json_file, label_text):
     # Read the JSON file
@@ -242,7 +228,7 @@ with gr.Blocks(title='Cabasus') as cabasus_sensor:
         slice_slider.change(plot_sensor_data_from_json, inputs=[json_file_box, leg_dropdown, slice_slider], 
                             outputs=[plot_box_leg, plot_slice_leg, get_all_slice, slice_json_box, plot_box_overlay])
         
-        som_create.click(get_som_mp4, inputs=[json_file_box, slice_slider], outputs=[som_figures, animation])
+        som_create.click(get_som_mp4, inputs=[json_file_box, slice_slider], outputs=[som_figures])
 
         #redoing the whole calculation with the file loading
         csv_file_box.change(get_som_mp4_v2, inputs=[csv_file_box, slice_size_slider, sample_rate, window_size_slider],