Update app.py

app.py

@@ -755,25 +755,25 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
 # =============================================================================
 # === START: S1-E · R1a · Liquid Biopsy ====================================== оце
 # =============================================================================
-        with gr.TabItem("S1-E · R1a · Liquid Biopsy"):
-            gr.HTML(proj_badge("S1-E · R1a", "Liquid Biopsy Classifier", "AUC = 0.992*"))
-            with gr.Row():
-                p1=gr.Slider(-3,3,value=0,step=0.1,label="CTHRC1")
-                p2=gr.Slider(-3,3,value=0,step=0.1,label="FHL2")
-                p3=gr.Slider(-3,3,value=0,step=0.1,label="LDHA")
-                p4=gr.Slider(-3,3,value=0,step=0.1,label="P4HA1")
-                p5=gr.Slider(-3,3,value=0,step=0.1,label="SERPINH1")
-            with gr.Row():
-                p6=gr.Slider(-3,3,value=0,step=0.1,label="ABCA8")
-                p7=gr.Slider(-3,3,value=0,step=0.1,label="CA4")
-                p8=gr.Slider(-3,3,value=0,step=0.1,label="CKB")
-                p9=gr.Slider(-3,3,value=0,step=0.1,label="NNMT")
-                p10=gr.Slider(-3,3,value=0,step=0.1,label="CACNA2D2")
-            b7=gr.Button("Classify", variant="primary")
-            o7t=gr.HTML(); o7p=gr.Image(label="Feature contributions")
-            gr.Examples([[2,2,1.5,1.8,1.6,-1,-1.2,-0.8,1.4,-1.1],[0]*10],
-                        inputs=[p1,p2,p3,p4,p5,p6,p7,p8,p9,p10])
-            b7.click(predict_cancer, [p1,p2,p3,p4,p5,p6,p7,p8,p9,p10], [o7t,o7p])
+        # with gr.TabItem("S1-E · R1a · Liquid Biopsy"):
+        #     gr.HTML(proj_badge("S1-E · R1a", "Liquid Biopsy Classifier", "AUC = 0.992*"))
+        #     with gr.Row():
+        #         p1=gr.Slider(-3,3,value=0,step=0.1,label="CTHRC1")
+        #         p2=gr.Slider(-3,3,value=0,step=0.1,label="FHL2")
+        #         p3=gr.Slider(-3,3,value=0,step=0.1,label="LDHA")
+        #         p4=gr.Slider(-3,3,value=0,step=0.1,label="P4HA1")
+        #         p5=gr.Slider(-3,3,value=0,step=0.1,label="SERPINH1")
+        #     with gr.Row():
+        #         p6=gr.Slider(-3,3,value=0,step=0.1,label="ABCA8")
+        #         p7=gr.Slider(-3,3,value=0,step=0.1,label="CA4")
+        #         p8=gr.Slider(-3,3,value=0,step=0.1,label="CKB")
+        #         p9=gr.Slider(-3,3,value=0,step=0.1,label="NNMT")
+        #         p10=gr.Slider(-3,3,value=0,step=0.1,label="CACNA2D2")
+        #     b7=gr.Button("Classify", variant="primary")
+        #     o7t=gr.HTML(); o7p=gr.Image(label="Feature contributions")
+        #     gr.Examples([[2,2,1.5,1.8,1.6,-1,-1.2,-0.8,1.4,-1.1],[0]*10],
+        #                 inputs=[p1,p2,p3,p4,p5,p6,p7,p8,p9,p10])
+        #     b7.click(predict_cancer, [p1,p2,p3,p4,p5,p6,p7,p8,p9,p10], [o7t,o7p])
 # =============================================================================
 # === END: S1-E · R1a =========================================================
 # =============================================================================