3 class

app.py

@@ -2,43 +2,19 @@ import gradio as gr
 import pandas as pd
 import numpy as np
 
-num_labels = 6
-headers = ["Benign","GG1", "GG2", "GG3", "GG4", "GG5"]
-default_weights = [[0,3,5,5,5,5],
-                    [1,0,1,3,5,5],
-                    [3,1,0,1,5,5],
-                    [3,3,1,0,5,5],
-                    [5,5,5,5,0,1],
-                    [5,5,5,5,1,0]]
+num_labels = 3
+headers = ["Benign","C1","C2"]
+default_weights = pd.DataFrame([[0,1,2],[1,0,1],[2,1,0]],columns=headers)
 example_conf_mats = [
-    pd.DataFrame([
-                [80,0,0,0,0,0],
-                [20,80,0,0,0,0],
-                [0,20,80,20,0,0],
-                [0,0,20,80,0,0],
-                [0,0,0,0,80,20],
-                [0,0,0,0,20,80]],columns=headers),
-    pd.DataFrame([
-                [80,0,0,0,0,0],
-                [15,80,10,0,0,0],
-                [5,15,80,15,0,0],
-                [0,5,10,80,10,10],
-                [0,0,0,5,80,10],
-                [0,0,0,0,10,80]],columns=headers),
-    pd.DataFrame([
-                [80,10, 3, 0, 0, 0],
-                [10,80, 7, 5, 3, 0],
-                [ 7, 7,80,10, 5, 3],
-                [ 3, 3, 7,80, 7, 7],
-                [ 0, 0, 3, 5,80,10],
-                [ 0, 0, 0, 0, 5,80]],columns=headers),
-    pd.DataFrame([
-                [80,0,0,0,10,10],
-                [0,80,0,0,10,10],
-                [0,0,80,0,0,0],
-                [0,0,0,80,0,0],
-                [10,10,10,10,80,0],
-                [10,10,10,10,0,80]],columns=headers)                        
+    pd.DataFrame([[80,10,0],
+                  [20,80,20],
+                  [0,10,80]],columns=headers),
+    pd.DataFrame([[80,10,10],
+                  [10,80,10],
+                  [10,10,80]],columns=headers),
+    pd.DataFrame([[80,10,20],
+                  [0,80,0],
+                  [20,10,80]],columns=headers),
 ]
 
 def submit_vals(*argv):
@@ -71,11 +47,11 @@ with gr.Blocks() as demo:
                 for i in range(num_labels):
                     for j in range(num_labels):
                         if i != j:
-                            sliders.append(gr.Slider(1, 5, value=default_weights[i][j], step=1, label="Impact of misclassifying "+ headers[j] + " as " + headers[i]))          
+                            sliders.append(gr.Slider(1, 5, value=np.abs(i-j), step=1, label="Impact of misclassifying "+ headers[j] + " as " + headers[i]))
+                submit_btn = gr.Button("Submit")            
             with gr.Column():
                 output_err_mat = gr.Dataframe(value = default_weights, datatype = "number", row_count = (num_labels, "fixed"), col_count=(num_labels,"fixed"), label="Error Severity Matrix", interactive=0, headers=headers)
-                refresh_btn = gr.Button("Refresh")  
-            refresh_btn.click(submit_vals, inputs=sliders, outputs=output_err_mat)
+            submit_btn.click(submit_vals, inputs=sliders, outputs=output_err_mat)
     with gr.Tab("Calculate accuracy and Error Severity"):
         with gr.Row():
             with gr.Column():

app_3class.py → app_6class.py

@@ -2,19 +2,43 @@ import gradio as gr
 import pandas as pd
 import numpy as np
 
-num_labels = 3
-headers = ["Benign","C1","C2"]
-default_weights = pd.DataFrame([[0,1,2],[1,0,1],[2,1,0]],columns=headers)
+num_labels = 6
+headers = ["Benign","GG1", "GG2", "GG3", "GG4", "GG5"]
+default_weights = [[0,3,5,5,5,5],
+                    [1,0,1,3,5,5],
+                    [3,1,0,1,5,5],
+                    [3,3,1,0,5,5],
+                    [5,5,5,5,0,1],
+                    [5,5,5,5,1,0]]
 example_conf_mats = [
-    pd.DataFrame([[80,10,0],
-                  [20,80,20],
-                  [0,10,80]],columns=headers),
-    pd.DataFrame([[80,10,10],
-                  [10,80,10],
-                  [10,10,80]],columns=headers),
-    pd.DataFrame([[80,10,20],
-                  [0,80,0],
-                  [20,10,80]],columns=headers),
+    pd.DataFrame([
+                [80,0,0,0,0,0],
+                [20,80,0,0,0,0],
+                [0,20,80,20,0,0],
+                [0,0,20,80,0,0],
+                [0,0,0,0,80,20],
+                [0,0,0,0,20,80]],columns=headers),
+    pd.DataFrame([
+                [80,0,0,0,0,0],
+                [15,80,10,0,0,0],
+                [5,15,80,15,0,0],
+                [0,5,10,80,10,10],
+                [0,0,0,5,80,10],
+                [0,0,0,0,10,80]],columns=headers),
+    pd.DataFrame([
+                [80,10, 3, 0, 0, 0],
+                [10,80, 7, 5, 3, 0],
+                [ 7, 7,80,10, 5, 3],
+                [ 3, 3, 7,80, 7, 7],
+                [ 0, 0, 3, 5,80,10],
+                [ 0, 0, 0, 0, 5,80]],columns=headers),
+    pd.DataFrame([
+                [80,0,0,0,10,10],
+                [0,80,0,0,10,10],
+                [0,0,80,0,0,0],
+                [0,0,0,80,0,0],
+                [10,10,10,10,80,0],
+                [10,10,10,10,0,80]],columns=headers)                        
 ]
 
 def submit_vals(*argv):
@@ -47,11 +71,11 @@ with gr.Blocks() as demo:
                 for i in range(num_labels):
                     for j in range(num_labels):
                         if i != j:
-                            sliders.append(gr.Slider(1, 5, value=np.abs(i-j), step=1, label="Impact of misclassifying "+ headers[j] + " as " + headers[i]))
-                submit_btn = gr.Button("Submit")            
+                            sliders.append(gr.Slider(1, 5, value=default_weights[i][j], step=1, label="Impact of misclassifying "+ headers[j] + " as " + headers[i]))          
             with gr.Column():
                 output_err_mat = gr.Dataframe(value = default_weights, datatype = "number", row_count = (num_labels, "fixed"), col_count=(num_labels,"fixed"), label="Error Severity Matrix", interactive=0, headers=headers)
-            submit_btn.click(submit_vals, inputs=sliders, outputs=output_err_mat)
+                refresh_btn = gr.Button("Refresh")  
+            refresh_btn.click(submit_vals, inputs=sliders, outputs=output_err_mat)
     with gr.Tab("Calculate accuracy and Error Severity"):
         with gr.Row():
             with gr.Column():