Update app.py

app.py

@@ -669,18 +669,18 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
 # =============================================================================
 # === START: S1-D · R1a · LNP Corona =========================================
 # =============================================================================
-        # with gr.TabItem("S1-D · R1a · LNP Corona"):
-        #     gr.HTML(proj_badge("S1-D · R1a", "LNP Protein Corona (Serum)", "AUC = 0.791"))
-        #     with gr.Row():
-        #         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,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")
-        #     o6 = gr.Markdown()
-        #     gr.Examples([[100,-5,1.5,"Ionizable"],[80,5,0.5,"Cationic"]], inputs=[sz,zt,pg,lp])
-        #     b6.click(predict_corona, [sz,zt,pg,lp], o6)
+        with gr.TabItem("S1-D · R1a · LNP Corona"):
+            gr.HTML(proj_badge("S1-D · R1a", "LNP Protein Corona (Serum)", "AUC = 0.791"))
+            with gr.Row():
+                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,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")
+            o6 = gr.Markdown()
+            gr.Examples([[100,-5,1.5,"Ionizable"],[80,5,0.5,"Cationic"]], inputs=[sz,zt,pg,lp])
+            b6.click(predict_corona, [sz,zt,pg,lp], o6)
 # =============================================================================
 # === END: S1-D · R1a =========================================================
 # =============================================================================
@@ -688,25 +688,25 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
 # =============================================================================
 # === START: S1-D · R2a · Flow Corona ========================================
 # =============================================================================
-        # with gr.TabItem("S1-D · R2a · Flow Corona"):
-        #     gr.HTML(proj_badge("S1-D · R2a", "Flow Corona — Vroman Effect"))
-        #     with gr.Row():
-        #         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")
-        #         fl8 = gr.Slider(0,40,value=20,step=1,label="Flow cm/s")
-        #     b8 = gr.Button("Model Vroman Effect", variant="primary")
-        #     o8t = gr.Markdown(); o8p = gr.Image(label="Kinetics")
-        #     gr.Examples([[100,-5,1.5,"Ionizable",40],[150,5,0.5,"Cationic",10]], inputs=[s8,z8,pg8,ch8,fl8])
-        #     b8.click(predict_flow, [s8,z8,pg8,ch8,fl8], [o8t,o8p])
+        with gr.TabItem("S1-D · R2a · Flow Corona"):
+            gr.HTML(proj_badge("S1-D · R2a", "Flow Corona — Vroman Effect"))
+            with gr.Row():
+                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")
+                fl8 = gr.Slider(0,40,value=20,step=1,label="Flow cm/s")
+            b8 = gr.Button("Model Vroman Effect", variant="primary")
+            o8t = gr.Markdown(); o8p = gr.Image(label="Kinetics")
+            gr.Examples([[100,-5,1.5,"Ionizable",40],[150,5,0.5,"Cationic",10]], inputs=[s8,z8,pg8,ch8,fl8])
+            b8.click(predict_flow, [s8,z8,pg8,ch8,fl8], [o8t,o8p])
 # =============================================================================
 # === END: S1-D · R2a =========================================================
 # =============================================================================
 
 # =============================================================================
-# === START: S1-D · R3a · LNP Brain ==========================================
+# === START: S1-D · R3a · LNP Brain ========================================== оце
 # =============================================================================
         # with gr.TabItem("S1-D · R3a · LNP Brain"):
         #     gr.HTML(proj_badge("S1-D · R3a", "LNP Brain Delivery — ApoE% + BBB probability"))
@@ -755,25 +755,25 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
 # =============================================================================
 # === START: S1-E · R1a · Liquid Biopsy ======================================
 # =============================================================================
-        # 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 =========================================================
 # =============================================================================
@@ -781,9 +781,9 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
 # =============================================================================
 # === START: S1-E · R1b · Validator 🔶 =======================================
 # =============================================================================
-        # with gr.TabItem("S1-E · R1b · Validator 🔶"):
-        #     gr.HTML(proj_badge("S1-E · R1b", "Protein Panel Validator", "🔶 In progress"))
-        #     gr.Markdown("> Coming next.")
+        with gr.TabItem("S1-E · R1b · Validator 🔶"):
+            gr.HTML(proj_badge("S1-E · R1b", "Protein Panel Validator", "🔶 In progress"))
+            gr.Markdown("> Coming next.")
 # =============================================================================
 # === END: S1-E · R1b =========================================================
 # =============================================================================
@@ -791,20 +791,20 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
 # =============================================================================
 # === START: S1-F · R1a · DIPG Toolkit =======================================
 # =============================================================================
-        # with gr.TabItem("S1-F · R1a · DIPG Toolkit"):
-        #     gr.HTML(proj_badge("S1-F · R1a", "DIPG: H3K27M + CSF LNP + Circadian", "PBTA"))
-        #     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")
-        #     b_dv.click(dipg_variants, [sort_d], o_dv)
-        #     gr.Markdown("---")
-        #     with gr.Row():
-        #         d_peg  = gr.Slider(0.5, 3.0, value=1.5, step=0.1, label="PEG mol%")
-        #         d_size = gr.Slider(60, 150, value=90, step=5, label="Target size (nm)")
-        #     b_dc  = gr.Button("Rank CSF Formulations", variant="primary")
-        #     o_dct = gr.Dataframe(label="CSF LNP ranking")
-        #     o_dcp = gr.Image(label="ApoE% in CSF corona")
-        #     b_dc.click(dipg_csf, [d_peg, d_size], [o_dct, o_dcp])
+        with gr.TabItem("S1-F · R1a · DIPG Toolkit"):
+            gr.HTML(proj_badge("S1-F · R1a", "DIPG: H3K27M + CSF LNP + Circadian", "PBTA"))
+            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")
+            b_dv.click(dipg_variants, [sort_d], o_dv)
+            gr.Markdown("---")
+            with gr.Row():
+                d_peg  = gr.Slider(0.5, 3.0, value=1.5, step=0.1, label="PEG mol%")
+                d_size = gr.Slider(60, 150, value=90, step=5, label="Target size (nm)")
+            b_dc  = gr.Button("Rank CSF Formulations", variant="primary")
+            o_dct = gr.Dataframe(label="CSF LNP ranking")
+            o_dcp = gr.Image(label="ApoE% in CSF corona")
+            b_dc.click(dipg_csf, [d_peg, d_size], [o_dct, o_dcp])
 # =============================================================================
 # === END: S1-F · R1a =========================================================
 # =============================================================================
@@ -812,15 +812,15 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
 # =============================================================================
 # === START: S1-F · R2a · UVM Toolkit ========================================
 # =============================================================================
-        # with gr.TabItem("S1-F · R2a · UVM Toolkit"):
-        #     gr.HTML(proj_badge("S1-F · R2a", "UVM: GNAQ/GNA11 + vitreous + m6A", "TCGA-UVM"))
-        #     b_uv = gr.Button("Load UVM Variants", variant="primary")
-        #     o_uv = gr.Dataframe(label="GNAQ/GNA11 map")
-        #     b_uv.click(uvm_variants, [], o_uv)
-        #     b_uw  = gr.Button("Rank Vitreous Formulations", variant="primary")
-        #     o_uwt = gr.Dataframe(label="Vitreous LNP retention ranking")
-        #     o_uwp = gr.Image(label="Retention (hours)")
-        #     b_uw.click(uvm_vitreous, [], [o_uwt, o_uwp])
+        with gr.TabItem("S1-F · R2a · UVM Toolkit"):
+            gr.HTML(proj_badge("S1-F · R2a", "UVM: GNAQ/GNA11 + vitreous + m6A", "TCGA-UVM"))
+            b_uv = gr.Button("Load UVM Variants", variant="primary")
+            o_uv = gr.Dataframe(label="GNAQ/GNA11 map")
+            b_uv.click(uvm_variants, [], o_uv)
+            b_uw  = gr.Button("Rank Vitreous Formulations", variant="primary")
+            o_uwt = gr.Dataframe(label="Vitreous LNP retention ranking")
+            o_uwp = gr.Image(label="Retention (hours)")
+            b_uw.click(uvm_vitreous, [], [o_uwt, o_uwp])
 # =============================================================================
 # === END: S1-F · R2a =========================================================
 # =============================================================================
@@ -828,17 +828,17 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
 # =============================================================================
 # === START: S1-F · R3a · pAML Toolkit =======================================
 # =============================================================================
-        # with gr.TabItem("S1-F · R3a · pAML Toolkit"):
-        #     gr.HTML(proj_badge("S1-F · R3a", "pAML: FLT3-ITD + BM niche + ferroptosis", "TARGET-AML"))
-        #     var_sel = gr.Dropdown(
-        #         ["FLT3-ITD", "NPM1 c.860_863dupTCAG", "DNMT3A p.R882H",
-        #          "CEBPA biallelic", "IDH1/2 mutation"],
-        #         value="FLT3-ITD", label="Select variant"
-        #     )
-        #     b_pf  = gr.Button("Analyze Ferroptosis Profile", variant="primary")
-        #     o_pft = gr.HTML()
-        #     o_pfp = gr.Image(label="Target radar")
-        #     b_pf.click(paml_ferroptosis, var_sel, [o_pft, o_pfp])
+        with gr.TabItem("S1-F · R3a · pAML Toolkit"):
+            gr.HTML(proj_badge("S1-F · R3a", "pAML: FLT3-ITD + BM niche + ferroptosis", "TARGET-AML"))
+            var_sel = gr.Dropdown(
+                ["FLT3-ITD", "NPM1 c.860_863dupTCAG", "DNMT3A p.R882H",
+                 "CEBPA biallelic", "IDH1/2 mutation"],
+                value="FLT3-ITD", label="Select variant"
+            )
+            b_pf  = gr.Button("Analyze Ferroptosis Profile", variant="primary")
+            o_pft = gr.HTML()
+            o_pfp = gr.Image(label="Target radar")
+            b_pf.click(paml_ferroptosis, var_sel, [o_pft, o_pfp])
 # =============================================================================
 # === END: S1-F · R3a =========================================================
 # =============================================================================