Update app.py

app.py

@@ -549,7 +549,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
         with gr.TabItem("🗺️ Lab Map"):
             gr.HTML(MAP_HTML)
 
-        # 🧬 S1-A · PHYLO-GENOMICS
+        # === АКТИВНІ ВКЛАДКИ (працюють) ===
         with gr.TabItem("S1-A · R1a · OpenVariant"):
             gr.HTML(proj_badge("S1-A · R1a", "OpenVariant — SNV Pathogenicity Classifier", "AUC = 0.939"))
             hgvs = gr.Textbox(label="HGVS notation", placeholder="BRCA1:p.R1699Q")
@@ -565,12 +565,283 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                          ["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)
 
-        # РЕШТА ВКЛАДОК ПОКИ ЩО ЗАКОМЕНТОВАНІ ДЛЯ ДІАГНОСТИКИ
+        # === ВСІ ІНШІ ВКЛАДКИ ЗАКОМЕНТОВАНІ ===
+        # Розкоментуйте блок цілком, щоб перевірити, чи викликає помилку
+
+# =============================================================================
+# === START: S1-A · R1b · Somatic Classifier 🔶 ===============================
+# =============================================================================
         # with gr.TabItem("S1-A · R1b · Somatic Classifier 🔶"):
         #     gr.HTML(proj_badge("S1-A · R1b", "Somatic Mutation Classifier", "🔶 In progress"))
         #     gr.Markdown("> This module is in active development. Coming in the next release.")
-
-        # ... (всі інші вкладки закоментуйте аналогічно)
+# =============================================================================
+# === END: S1-A · R1b =========================================================
+# =============================================================================
+
+# =============================================================================
+# === START: S1-B · R1a · BRCA2 miRNA ========================================
+# =============================================================================
+        # with gr.TabItem("S1-B · R1a · BRCA2 miRNA"):
+        #     gr.HTML(proj_badge("S1-B · R1a", "miRNA Silencing — BRCA1/2 · TP53"))
+        #     g1 = gr.Dropdown(["BRCA2","BRCA1","TP53"], value="BRCA2", label="Gene")
+        #     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)
+# =============================================================================
+# === END: S1-B · R1a =========================================================
+# =============================================================================
+
+# =============================================================================
+# === START: S1-B · R2a · TP53 siRNA =========================================
+# =============================================================================
+        # with gr.TabItem("S1-B · R2a · TP53 siRNA"):
+        #     gr.HTML(proj_badge("S1-B · R2a", "siRNA Synthetic Lethal — TP53-null"))
+        #     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], cache_examples=False)
+        #     b2.click(predict_sirna, [g2], o2)
+# =============================================================================
+# === END: S1-B · R2a =========================================================
+# =============================================================================
+
+# =============================================================================
+# === START: S1-B · R3a · lncRNA-TREM2 =======================================
+# =============================================================================
+        # with gr.TabItem("S1-B · R3a · lncRNA-TREM2"):
+        #     gr.HTML(proj_badge("S1-B · R3a", "lncRNA-TREM2 ceRNA Network"))
+        #     b3 = gr.Button("Load Network", variant="primary")
+        #     o3 = gr.Dataframe(label="ceRNA Network")
+        #     b3.click(get_lncrna, [], o3)
+# =============================================================================
+# === END: S1-B · R3a =========================================================
+# =============================================================================
+
+# =============================================================================
+# === START: S1-B · R3b · ASO Designer =======================================
+# =============================================================================
+        # with gr.TabItem("S1-B · R3b · ASO Designer"):
+        #     gr.HTML(proj_badge("S1-B · R3b", "ASO Candidates"))
+        #     b4 = gr.Button("Show ASOs", variant="primary")
+        #     o4 = gr.Dataframe(label="ASO Candidates")
+        #     b4.click(get_aso, [], o4)
+# =============================================================================
+# === END: S1-B · R3b =========================================================
+# =============================================================================
+
+# =============================================================================
+# === START: S1-C · R1a · FGFR3 RNA Drug =====================================
+# =============================================================================
+        # with gr.TabItem("S1-C · R1a · FGFR3 RNA Drug"):
+        #     gr.HTML(proj_badge("S1-C · R1a", "FGFR3 RNA-Directed Drug Discovery", "top score 0.793"))
+        #     g4 = gr.Radio(["P1 (hairpin loop)","P10 (G-quadruplex)"],
+        #                   value="P1 (hairpin loop)", label="Target pocket")
+        #     b4 = gr.Button("Screen Compounds", variant="primary")
+        #     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])
+# =============================================================================
+# === END: S1-C · R1a =========================================================
+# =============================================================================
+
+# =============================================================================
+# === START: S1-C · R1b · SL Drug Mapping 🔶 =================================
+# =============================================================================
+        # with gr.TabItem("S1-C · R1b · SL Drug Mapping 🔶"):
+        #     gr.HTML(proj_badge("S1-C · R1b", "Synthetic Lethal Drug Mapping", "🔶 In progress"))
+        #     gr.Markdown("> Coming soon.")
+# =============================================================================
+# === END: S1-C · R1b =========================================================
+# =============================================================================
+
+# =============================================================================
+# === START: S1-C · R2a · Frontier 🔴 ========================================
+# =============================================================================
+        # with gr.TabItem("S1-C · R2a · Frontier 🔴"):
+        #     gr.HTML(proj_badge("S1-C · R2a", "m6A × Ferroptosis × Circadian", "🔴 Frontier"))
+        #     gr.Markdown("> Planned for Q3 2026")
+# =============================================================================
+# === END: S1-C · R2a =========================================================
+# =============================================================================
+
+# =============================================================================
+# === 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)
+# =============================================================================
+# === END: S1-D · R1a =========================================================
+# =============================================================================
+
+# =============================================================================
+# === 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])
+# =============================================================================
+# === END: S1-D · R2a =========================================================
+# =============================================================================
+
+# =============================================================================
+# === START: S1-D · R3a · LNP Brain ==========================================
+# =============================================================================
+        # with gr.TabItem("S1-D · R3a · LNP Brain"):
+        #     gr.HTML(proj_badge("S1-D · R3a", "LNP Brain Delivery — ApoE% + BBB probability"))
+        #     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,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])
+# =============================================================================
+# === END: S1-D · R3a =========================================================
+# =============================================================================
+
+# =============================================================================
+# === START: S1-D · R4a · AutoCorona NLP =====================================
+# =============================================================================
+        # with gr.TabItem("S1-D · R4a · AutoCorona NLP"):
+        #     gr.HTML(proj_badge("S1-D · R4a", "AutoCorona NLP — from abstracts", "F1 = 0.71"))
+        #     txt  = gr.Textbox(lines=5,label="Paper abstract",placeholder="Paste abstract here...")
+        #     b10  = gr.Button("Extract Data", variant="primary")
+        #     o10j = gr.Code(label="Extracted JSON", language="json")
+        #     o10f = gr.Textbox(label="Validation flags")
+        #     gr.Examples([[
+        #         "LNPs composed of MC3, DSPC, Cholesterol (50:10:40 mol%) with 1.5% PEG-DMG. "
+        #         "Hydrodynamic diameter was 98 nm, zeta potential -3.2 mV, PDI 0.12. "
+        #         "Incubated in human plasma. Corona: albumin, apolipoprotein E, fibrinogen."
+        #     ]], inputs=[txt])
+        #     b10.click(extract_corona, txt, [o10j, o10f])
+# =============================================================================
+# === END: S1-D · R4a =========================================================
+# =============================================================================
+
+# =============================================================================
+# === START: S1-D · R5a · CSF/BM 🔴 ==========================================
+# =============================================================================
+        # with gr.TabItem("S1-D · R5a · CSF/BM 🔴"):
+        #     gr.HTML(proj_badge("S1-D · R5a", "LNP Corona in CSF · Vitreous · Bone Marrow", "🔴 0 prior studies"))
+        #     gr.Markdown("> Planned for Q2–Q3 2026")
+# =============================================================================
+# === END: S1-D · R5a =========================================================
+# =============================================================================
+
+# =============================================================================
+# === 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])
+# =============================================================================
+# === END: S1-E · R1a =========================================================
+# =============================================================================
+
+# =============================================================================
+# === 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.")
+# =============================================================================
+# === END: S1-E · R1b =========================================================
+# =============================================================================
+
+# =============================================================================
+# === 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])
+# =============================================================================
+# === END: S1-F · R1a =========================================================
+# =============================================================================
+
+# =============================================================================
+# === 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])
+# =============================================================================
+# === END: S1-F · R2a =========================================================
+# =============================================================================
+
+# =============================================================================
+# === 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])
+# =============================================================================
+# === END: S1-F · R3a =========================================================
+# =============================================================================
 
         # 📓 Journal
         with gr.TabItem("📓 Journal"):