added

app.py

@@ -19,7 +19,7 @@ cluster_som = ClusterSOM()
 cluster_som.load("models/cluster_som2.pkl")
 
 # ml inference
-def get_som_mp4(file, slice_select=1, reducer=reducer10d, cluster=cluster_som):
+def get_som_mp4(file, slice_select, reducer=reducer10d, cluster=cluster_som):
 
     try:
         train_x, train_y  = read_json_files(file)
@@ -77,7 +77,7 @@ with gr.Blocks(title='Cabasus') as cabasus_sensor:
             repeat_process = gr.Button('Restart process', visible=False)  
         with gr.Row():
             leg_dropdown = gr.Dropdown(choices=['GZ1', 'GZ2', 'GZ3', 'GZ4'], label='select leg', value='GZ1')
-            slice_slider = gr.Slider(minimum=1, maximum=300, label='Current slice', step=1)
+            
         with gr.Row():
             plot_box_leg = gr.Plot(label="Filtered Signal Plot")
             plot_box_overlay = gr.Plot(label="Overlay Signal Plot")
@@ -85,8 +85,9 @@ with gr.Blocks(title='Cabasus') as cabasus_sensor:
         with gr.Row():
             plot_slice_leg = gr.Plot(label="Sliced Signal Plot")
             get_all_slice = gr.Plot(label="Real Signal Plot")
-        
-        som_create = gr.Button('generate som')
+        with gr.Row():
+            som_create = gr.Button('generate som')
+            slice_slider = gr.Slider(minimum=1, maximum=300, label='Current slice', step=1)
         som_figures = gr.Plot(label="som activations")
         with gr.Row():
             slice_json_box = gr.File(label='Slice json file')

funcs/ml_inference.py

@@ -1,7 +1,7 @@
 import torch
 from funcs.dataloader import BaseDataset2, read_json_files
 
-def get_som_mp4(file, reducer10d, cluster_som, slice_select=1):
+def get_som_mp4(file, reducer10d, cluster_som, slice_select):
 
     try:
         train_x, train_y  = read_json_files(file)

funcs/som.py

@@ -428,7 +428,7 @@ class ClusterSOM:
             self.label_centroids, self.label_encodings = model_data[5:]
 
 
-    def plot_activation_v2(self, data, slice_select=1):
+    def plot_activation_v2(self, data, slice_select):
         """
         Generate a GIF visualization of the prediction output using the activation maps of individual SOMs.
         """
@@ -465,14 +465,6 @@ class ClusterSOM:
             ax.set_yticks(range(activation_map.shape[0]))
             ax.grid(True, linestyle='-', linewidth=0.5)
 
-        # Create a colorbar for each frame
-        # fig.subplots_adjust(right=0.8)
-        # cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
-
         plt.tight_layout()
-        # try:
-        #     fig.colorbar(im_active, cax=cbar_ax)
-        # except:
-        #     pass
 
         return fig