Update app.py

app.py

@@ -456,25 +456,25 @@ with gr.Blocks(css=css, title="K R&D Lab") as demo:
             hgvs = gr.Textbox(label="HGVS notation", placeholder="BRCA1:p.R1699Q")
             gr.Markdown("**Or enter scores manually:**")
             with gr.Row():
-                sift = gr.Slider(0,1,0.5, label="SIFT (0=damaging)")
-                pp   = gr.Slider(0,1,0.5, label="PolyPhen-2")
-                gn   = gr.Slider(0,0.01,0.001, label="gnomAD AF", step=0.0001)
+                sift = gr.Slider(0, 1, value=0.5,    step=0.01,   label="SIFT (0=damaging)")
+                pp   = gr.Slider(0, 1, value=0.5,    step=0.01,   label="PolyPhen-2")
+                gn   = gr.Slider(0, 0.01, value=0.001, step=0.0001, label="gnomAD AF")
             b5 = gr.Button("Predict Pathogenicity", variant="primary")
             o5 = gr.HTML(label="Result")
             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])
-            b5.click(predict_variant, [hgvs,sift,pp,gn], o5)
+                [["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])
+            b5.click(predict_variant, [hgvs, sift, pp, gn], o5)
 
         with gr.TabItem("🧪 LNP Corona"):
             gr.Markdown("### LNP Protein Corona Prediction")
             with gr.Row():
-                sz = gr.Slider(50,300,100, label="Size (nm)")
-                zt = gr.Slider(-40,10,-5,  label="Zeta (mV)")
+                sz = gr.Slider(50, 300, value=100, step=1,   label="Size (nm)")
+                zt = gr.Slider(-40, 10, value=-5,  step=1,   label="Zeta (mV)")
             with gr.Row():
-                pg = gr.Slider(0,5,1.5, label="PEG mol%")
+                pg = gr.Slider(0, 5, value=1.5,    step=0.1, label="PEG mol%")
                 lp = gr.Dropdown(["Ionizable","Cationic","Anionic","Neutral"],
                                  value="Ionizable", label="Lipid type")
             b6 = gr.Button("Predict", variant="primary")
@@ -485,17 +485,17 @@ with gr.Blocks(css=css, title="K R&D Lab") as demo:
         with gr.TabItem("🩸 Liquid Biopsy"):
             gr.Markdown("### Protein Corona Cancer Diagnostics\nClassify cancer vs healthy.")
             with gr.Row():
-                p1=gr.Slider(-3,3,0,label="CTHRC1")
-                p2=gr.Slider(-3,3,0,label="FHL2")
-                p3=gr.Slider(-3,3,0,label="LDHA")
-                p4=gr.Slider(-3,3,0,label="P4HA1")
-                p5=gr.Slider(-3,3,0,label="SERPINH1")
+                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(-3,3,0,label="ABCA8")
-                p7=gr.Slider(-3,3,0,label="CA4")
-                p8=gr.Slider(-3,3,0,label="CKB")
-                p9=gr.Slider(-3,3,0,label="NNMT")
-                p10=gr.Slider(-3,3,0,label="CACNA2D2")
+                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")
@@ -508,13 +508,13 @@ with gr.Blocks(css=css, title="K R&D Lab") as demo:
         with gr.TabItem("🌊 Flow Corona"):
             gr.Markdown("### Corona Remodeling Under Blood Flow")
             with gr.Row():
-                s8  = gr.Slider(50,300,100, label="Size (nm)")
-                z8  = gr.Slider(-40,10,-5,  label="Zeta (mV)")
-                pg8 = gr.Slider(0,5,1.5,    label="PEG mol%")
+                s8  = gr.Slider(50, 300, value=100, step=1,   label="Size (nm)")
+                z8  = gr.Slider(-40, 10, value=-5,  step=1,   label="Zeta (mV)")
+                pg8 = gr.Slider(0, 5,    value=1.5, step=0.1, label="PEG mol%")
             with gr.Row():
                 ch8 = gr.Dropdown(["Ionizable","Cationic","Anionic","Neutral"],
                                   value="Ionizable", label="Charge type")
-                fl8 = gr.Slider(0,40,20, label="Flow rate cm/s (aorta=40)")
+                fl8 = gr.Slider(0, 40, value=20, step=1, label="Flow rate cm/s (aorta=40)")
             b8  = gr.Button("Model Vroman Effect", variant="primary")
             o8t = gr.Markdown()
             o8p = gr.Image(label="Kinetics plot")
@@ -527,14 +527,14 @@ with gr.Blocks(css=css, title="K R&D Lab") as demo:
             smi = gr.Textbox(label="Ionizable lipid SMILES",
                              value="CC(C)CC(=O)OCC(COC(=O)CC(C)C)OC(=O)CC(C)C")
             with gr.Row():
-                pk  = gr.Slider(4,8,6.5, step=0.1, label="pKa")
-                zt9 = gr.Slider(-20,10,-3,          label="Zeta (mV)")
+                pk  = gr.Slider(4, 8,   value=6.5, step=0.1, label="pKa")
+                zt9 = gr.Slider(-20, 10, value=-3,  step=1,   label="Zeta (mV)")
             b9  = gr.Button("Predict BBB Crossing", variant="primary")
             o9t = gr.Markdown()
             o9p = gr.Image(label="Radar profile")
             gr.Examples([["CC(C)CC(=O)OCC(COC(=O)CC(C)C)OC(=O)CC(C)C", 6.5, -3]],
-                        inputs=[smi,pk,zt9])
-            b9.click(predict_bbb, [smi,pk,zt9], [o9t,o9p])
+                        inputs=[smi, pk, zt9])
+            b9.click(predict_bbb, [smi, pk, zt9], [o9t, o9p])
 
         with gr.TabItem("📄 AutoCorona NLP"):
             gr.Markdown("### AutoCorona NLP Extraction\nPaste any paper abstract.")