Update app.py

app.py

@@ -620,7 +620,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                     o_v = gr.HTML()
                     gr.Examples([["BRCA1:p.R1699Q",0.82,0.05,0.0012],
                                  ["TP53:p.R248W",0.00,1.00,0.0],
-                                 ["BRCA2:p.D2723A",0.01,0.98,0.0]], inputs=[hgvs,sift,pp,gn])
+                                 ["BRCA2:p.D2723A",0.01,0.98,0.0]], inputs=[hgvs,sift,pp,gn], cache_examples=False)
                     b_v.click(predict_variant, [hgvs,sift,pp,gn], o_v)
 
                 with gr.TabItem("S1-R1b · Somatic 🔶"):
@@ -643,7 +643,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                     b1 = gr.Button("Find miRNAs", variant="primary")
                     o1 = gr.Dataframe(label="Top 5 downregulated miRNAs")
                     gr.Examples([["BRCA2"],["BRCA1"],["TP53"]], inputs=[g1])
-                    b1.click(predict_mirna, g1, o1)
+                    b1.click(predict_mirna, [g1], o1)
 
                 with gr.TabItem("S1-R3 · siRNA"):
                     gr.HTML(proj_badge("S1-B · PHYLO-RNA · S1-R3",
@@ -651,8 +651,8 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                     g2 = gr.Dropdown(["LUAD","BRCA","COAD"], value="LUAD", label="Cancer type")
                     b2 = gr.Button("Find Targets", variant="primary")
                     o2 = gr.Dataframe(label="Top 5 synthetic lethal targets")
-                    gr.Examples([["LUAD"],["BRCA"],["COAD"]], inputs=[g2])
-                    b2.click(predict_sirna, g2, o2)
+                    gr.Examples([["LUAD"],["BRCA"],["COAD"]], inputs=[g2], cache_examples=False)
+                    b2.click(predict_sirna, [g2], o2)
 
                 with gr.TabItem("S1-R4 · lncRNA + ASO"):
                     gr.HTML(proj_badge("S1-B · PHYLO-RNA · S1-R4 + S1-R4b",
@@ -680,7 +680,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                     o4t = gr.Dataframe(label="Top 5 candidates")
                     o4p = gr.Image(label="Binding scores")
                     gr.Examples([["P1 (hairpin loop)"],["P10 (G-quadruplex)"]], inputs=[g4])
-                    b4.click(predict_drug, g4, [o4t, o4p])
+                    b4.click(predict_drug, [g4], [o4t, o4p])
 
                 with gr.TabItem("S1-R13 · Frontier 🔴⭐"):
                     gr.HTML(proj_badge("S1-C · PHYLO-DRUG · S1-R13",
@@ -828,7 +828,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                             sort_d = gr.Radio(["Frequency", "Drug status"], value="Frequency", label="Sort by")
                             b_dv   = gr.Button("Load DIPG Variants", variant="primary")
                             o_dv   = gr.Dataframe(label="H3K27M co-mutations · PBTA/GSE126319")
-                            b_dv.click(dipg_variants, sort_d, o_dv)
+                            b_dv.click(dipg_variants, [sort_d], o_dv)
                         with gr.TabItem("CSF LNP Formulations"):
                             with gr.Row():
                                 d_peg  = gr.Slider(0.5, 3.0, value=1.5, step=0.1, label="PEG mol%")