Update app.py
Browse files
app.py
CHANGED
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@@ -221,7 +221,6 @@ def safe_img_from_fig(fig):
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return img
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except Exception:
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plt.close(fig)
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# Return a blank image as fallback
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return Image.new('RGB', (100, 100), color=CARD)
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def predict_mirna(gene):
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@@ -517,7 +516,7 @@ def proj_badge(code, title, metric=""):
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f"</div>"
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)
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# ========== 3D моделі
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def plot_nanoparticle(r, peg):
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theta = np.linspace(0, 2*np.pi, 30)
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phi = np.linspace(0, np.pi, 30)
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@@ -594,8 +593,8 @@ def plot_corona():
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css = f"""
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body, .gradio-container {{ background: {BG} !important; color: {TXT} !important; }}
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/*
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.
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color: {TXT} !important;
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background: {CARD} !important;
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font-size: 13px !important;
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@@ -603,14 +602,14 @@ body, .gradio-container {{ background: {BG} !important; color: {TXT} !important;
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padding: 8px 16px !important;
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border-radius: 6px 6px 0 0 !important;
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}}
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.
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border-bottom: 3px solid {ACC} !important;
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color: {ACC} !important;
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background: {BG} !important;
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}}
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/*
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color: {DIM} !important;
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background: {BG} !important;
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font-size: 12px !important;
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@@ -621,32 +620,58 @@ body, .gradio-container {{ background: {BG} !important; color: {TXT} !important;
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border-bottom: none !important;
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margin-right: 3px !important;
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}}
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.
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color: {ACC2} !important;
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background: {CARD} !important;
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border-color: {ACC2} !important;
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border-bottom: none !important;
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}}
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.
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background: {CARD} !important;
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border: 1px solid {BORDER} !important;
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border-radius: 0 6px 6px 6px !important;
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padding: 14px !important;
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}}
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/*
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}}
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color: {
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}}
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h1, h2, h3 {{ color: {ACC} !important; }}
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.gr-button-primary {{ background: {ACC} !important; border: none !important; }}
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footer {{ display: none !important; }}
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@@ -691,7 +716,7 @@ MAP_HTML = f"""
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</div>
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"""
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# ==========
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with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
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gr.Markdown(
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"# 🔬 K R&D Lab · Science Sphere — S1 Biomedical\n"
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@@ -699,384 +724,372 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
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"*Research only. Not clinical advice.*"
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)
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with gr.
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#
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with gr.
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gr.
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gr.HTML(section_header(
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"S1-A", "PHYLO-GENOMICS", "— What breaks in DNA",
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"R1a OpenVariant ✅ · R1b Somatic classifier 🔶"
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))
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with gr.Tabs(elem_classes="inner-tabs"):
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with gr.TabItem("R1 · Variant classification"):
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with gr.Tabs(elem_classes="sub-sub-tabs"):
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# R1a · OpenVariant
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with gr.TabItem("R1a · OpenVariant"):
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gr.HTML(proj_badge("S1-A · R1a", "OpenVariant — SNV Pathogenicity Classifier", "AUC=0.939"))
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hgvs = gr.Textbox(label="HGVS notation", placeholder="BRCA1:p.R1699Q")
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gr.Markdown("**Or enter functional scores manually:**")
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with gr.Row():
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sift = gr.Slider(0,1,value=0.5,step=0.01,label="SIFT (0=damaging)")
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pp = gr.Slider(0,1,value=0.5,step=0.01,label="PolyPhen-2")
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gn = gr.Slider(0,0.01,value=0.001,step=0.0001,label="gnomAD AF")
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b_v = gr.Button("Predict Pathogenicity", variant="primary")
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o_v = gr.HTML()
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gr.Examples([["BRCA1:p.R1699Q",0.82,0.05,0.0012],
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["TP53:p.R248W",0.00,1.00,0.0],
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["BRCA2:p.D2723A",0.01,0.98,0.0]], inputs=[hgvs,sift,pp,gn], cache_examples=False)
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b_v.click(predict_variant, [hgvs,sift,pp,gn], o_v)
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# R1b · Somatic Classifier (в розробці)
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with gr.TabItem("R1b · Somatic Classifier 🔶"):
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gr.HTML(proj_badge("S1-A · R1b", "Somatic Mutation Classifier", "🔶 In progress"))
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gr.Markdown("> This module is in active development. Coming in the next release.")
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# === S1-B · PHYLO-RNA ===
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with gr.TabItem("🔬 S1-B PHYLO-RNA"):
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gr.HTML(section_header(
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"S1-B", "PHYLO-RNA", "— How to silence it via RNA",
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"R1a miRNA ✅ · R2a siRNA ✅ · R3a lncRNA ✅ · R3b ASO ✅"
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))
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with gr.Tabs(elem_classes="inner-tabs"):
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# R1 · miRNA silencing
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with gr.TabItem("R1 · miRNA silencing"):
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with gr.Tabs(elem_classes="sub-sub-tabs"):
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with gr.TabItem("R1a · BRCA2 miRNA"):
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gr.HTML(proj_badge("S1-B · R1a", "miRNA Silencing — BRCA1/2 · TP53"))
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g1 = gr.Dropdown(["BRCA2","BRCA1","TP53"], value="BRCA2", label="Gene")
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b1 = gr.Button("Find miRNAs", variant="primary")
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o1 = gr.Dataframe(label="Top 5 downregulated miRNAs")
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gr.Examples([["BRCA2"],["BRCA1"],["TP53"]], inputs=[g1])
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b1.click(predict_mirna, [g1], o1)
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# R2 · siRNA SL
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with gr.TabItem("R2 · siRNA SL"):
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with gr.Tabs(elem_classes="sub-sub-tabs"):
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with gr.TabItem("R2a · TP53 siRNA"):
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gr.HTML(proj_badge("S1-B · R2a", "siRNA Synthetic Lethal — TP53-null"))
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g2 = gr.Dropdown(["LUAD","BRCA","COAD"], value="LUAD", label="Cancer type")
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b2 = gr.Button("Find Targets", variant="primary")
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o2 = gr.Dataframe(label="Top 5 synthetic lethal targets")
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gr.Examples([["LUAD"],["BRCA"],["COAD"]], inputs=[g2], cache_examples=False)
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b2.click(predict_sirna, [g2], o2)
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# R3 · lncRNA + ASO
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with gr.TabItem("R3 · lncRNA + ASO"):
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with gr.Tabs(elem_classes="sub-sub-tabs"):
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with gr.TabItem("R3a · lncRNA-TREM2"):
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gr.HTML(proj_badge("S1-B · R3a", "lncRNA-TREM2 ceRNA Network"))
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b3a = gr.Button("Load ceRNA", variant="primary")
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o3a = gr.Dataframe(label="ceRNA Network (R3a)")
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b3a.click(lambda: pd.DataFrame(CERNA), [], o3a)
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with gr.TabItem("R3b · ASO Designer"):
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gr.HTML(proj_badge("S1-B · R3b", "ASO Designer"))
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b3b = gr.Button("Load ASO Candidates", variant="primary")
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o3b = gr.Dataframe(label="ASO Candidates (R3b)")
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b3b.click(lambda: pd.DataFrame(ASO), [], o3b)
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"> **Planned datasets:** TCGA-PAAD · GEO m6A atlases · Circadian gene panels\n\n"
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"> **Expected timeline:** Q3 2026"
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with gr.TabItem("R3 · Brain BBB"):
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with gr.Tabs(elem_classes="sub-sub-tabs"):
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with gr.TabItem("R3a · LNP Brain"):
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gr.HTML(proj_badge("S1-D · R3a", "LNP Brain Delivery"))
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smi = gr.Textbox(label="Ionizable lipid SMILES",
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value="CC(C)CC(=O)OCC(COC(=O)CC(C)C)OC(=O)CC(C)C")
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with gr.Row():
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pk = gr.Slider(4,8,value=6.5,step=0.1,label="pKa")
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zt9 = gr.Slider(-20,10,value=-3,step=1,label="Zeta (mV)")
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b9 = gr.Button("Predict BBB Crossing", variant="primary")
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o9t = gr.Markdown()
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o9p = gr.Image(label="Radar profile")
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gr.Examples([["CC(C)CC(=O)OCC(COC(=O)CC(C)C)OC(=O)CC(C)C",6.5,-3]], inputs=[smi,pk,zt9])
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b9.click(predict_bbb, [smi,pk,zt9], [o9t,o9p])
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# R4 · NLP
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with gr.TabItem("R4 · NLP"):
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with gr.Tabs(elem_classes="sub-sub-tabs"):
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with gr.TabItem("R4a · AutoCorona NLP"):
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gr.HTML(proj_badge("S1-D · R4a", "AutoCorona NLP", "F1=0.71"))
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txt = gr.Textbox(lines=5,label="Paper abstract",placeholder="Paste abstract here...")
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b10 = gr.Button("Extract Data", variant="primary")
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o10j = gr.Code(label="Extracted JSON", language="json")
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o10f = gr.Textbox(label="Validation flags")
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gr.Examples([[
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"LNPs composed of MC3, DSPC, Cholesterol (50:10:40 mol%) with 1.5% PEG-DMG. "
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"Hydrodynamic diameter was 98 nm, zeta potential -3.2 mV, PDI 0.12. "
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"Incubated in human plasma. Corona: albumin, apolipoprotein E, fibrinogen."
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]], inputs=[txt])
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b10.click(extract_corona, txt, [o10j, o10f])
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# R5 · Exotic fluids
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with gr.TabItem("R5 · Exotic fluids 🔴⭐"):
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with gr.Tabs(elem_classes="sub-sub-tabs"):
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with gr.TabItem("R5a · CSF/Vitreous/BM"):
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gr.HTML(proj_badge("S1-D · R5a", "LNP Corona in CSF · Vitreous · Bone Marrow", "🔴 0 prior studies"))
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gr.Markdown(
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"> **Research gap:** Protein corona has only been characterized in serum/plasma. "
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"CSF, vitreous humor, and bone marrow interstitial fluid remain completely unstudied.\n\n"
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"> **Target cancers:** DIPG (CSF) · UVM (vitreous) · pAML (bone marrow)\n\n"
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"> **Expected timeline:** Q2–Q3 2026"
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with gr.Tabs():
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with gr.TabItem("
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b_dv.click(dipg_variants, [sort_d], o_dv)
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with gr.TabItem("CSF LNP"):
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with gr.Row():
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gr.Markdown(
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"| Delivery | CED convection | LNP corona **in CSF** |\n"
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|
|
|
|
| 977 |
with gr.Tabs():
|
| 978 |
-
with gr.TabItem("
|
| 979 |
-
|
| 980 |
-
o_uv = gr.Dataframe(label="GNAQ/GNA11 map · TCGA-UVM")
|
| 981 |
-
b_uv.click(uvm_variants, [], o_uv)
|
| 982 |
-
with gr.TabItem("Vitreous LNP"):
|
| 983 |
-
b_uw = gr.Button("Rank Vitreous Formulations", variant="primary")
|
| 984 |
-
o_uwt = gr.Dataframe(label="Vitreous LNP retention ranking")
|
| 985 |
-
o_uwp = gr.Image(label="Retention (hours)")
|
| 986 |
-
b_uw.click(uvm_vitreous, [], [o_uwt, o_uwp])
|
| 987 |
-
with gr.TabItem("Research Gap"):
|
| 988 |
gr.Markdown(
|
| 989 |
-
"**
|
| 990 |
-
"
|
| 991 |
-
"
|
| 992 |
-
"
|
| 993 |
-
"| Delivery | Intravitreal injection | LNP corona **in vitreous humor** |\n"
|
| 994 |
-
"| Biology | PLCβ→PKC→MAPK | GNAQ × METTL3 × YTHDF2 axis |"
|
| 995 |
)
|
| 996 |
-
|
| 997 |
-
|
| 998 |
-
|
| 999 |
-
|
| 1000 |
-
|
| 1001 |
-
|
| 1002 |
-
|
| 1003 |
-
|
| 1004 |
-
|
| 1005 |
-
|
| 1006 |
-
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1007 |
with gr.Tabs():
|
| 1008 |
-
with gr.TabItem("
|
| 1009 |
-
|
| 1010 |
-
|
| 1011 |
-
|
| 1012 |
-
|
| 1013 |
-
|
| 1014 |
-
|
| 1015 |
-
o_pft = gr.HTML()
|
| 1016 |
-
o_pfp = gr.Image(label="Target radar")
|
| 1017 |
-
b_pf.click(paml_ferroptosis, var_sel, [o_pft, o_pfp])
|
| 1018 |
-
with gr.TabItem("BM Niche LNP"):
|
| 1019 |
-
gr.Dataframe(
|
| 1020 |
-
value=pd.DataFrame(PAML_BM_LNP),
|
| 1021 |
-
label="Bone marrow niche LNP candidates · TARGET-AML context"
|
| 1022 |
)
|
| 1023 |
-
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
| 1024 |
gr.Markdown(
|
| 1025 |
-
"**
|
| 1026 |
-
"
|
| 1027 |
-
"
|
| 1028 |
-
"
|
| 1029 |
-
"| Delivery | Liposomal daunorubicin | LNP corona **in bone marrow** |\n"
|
| 1030 |
-
"| Biology | Midostaurin inhibits FLT3 | Ferroptosis SL + FLT3i |"
|
| 1031 |
)
|
|
|
|
|
|
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|
|
|
|
|
| 1032 |
|
| 1033 |
-
|
| 1034 |
-
|
| 1035 |
-
|
| 1036 |
-
|
| 1037 |
-
|
| 1038 |
-
|
| 1039 |
-
|
| 1040 |
-
|
| 1041 |
-
|
| 1042 |
-
|
| 1043 |
-
|
| 1044 |
-
|
| 1045 |
-
|
| 1046 |
-
|
| 1047 |
-
|
| 1048 |
-
|
| 1049 |
-
with gr.TabItem("DNA Helix"):
|
| 1050 |
-
gr.Markdown("### 3D Model of a DNA Double Helix")
|
| 1051 |
-
dna_btn = gr.Button("Generate DNA", variant="primary")
|
| 1052 |
-
dna_plot = gr.Plot()
|
| 1053 |
-
dna_btn.click(plot_dna, [], dna_plot)
|
| 1054 |
|
| 1055 |
-
|
| 1056 |
-
|
| 1057 |
-
|
| 1058 |
-
|
| 1059 |
-
|
| 1060 |
|
| 1061 |
-
|
| 1062 |
-
|
| 1063 |
-
|
| 1064 |
-
|
| 1065 |
-
|
| 1066 |
-
note_tab = gr.Textbox(label="Project code (e.g. S1-A·R1a)", value="General")
|
| 1067 |
-
with gr.Row():
|
| 1068 |
-
save_btn = gr.Button("💾 Save", variant="primary")
|
| 1069 |
-
refresh_btn = gr.Button("🔄 Refresh")
|
| 1070 |
-
clear_btn = gr.Button("🗑️ Clear Journal", variant="secondary")
|
| 1071 |
-
journal_display = gr.Markdown(value=load_journal())
|
| 1072 |
-
|
| 1073 |
-
save_btn.click(save_note, [note_text, note_tab], journal_display)
|
| 1074 |
-
refresh_btn.click(load_journal, [], journal_display)
|
| 1075 |
-
clear_btn.click(clear_journal, [], journal_display)
|
| 1076 |
|
| 1077 |
-
|
| 1078 |
-
|
| 1079 |
-
|
| 1080 |
## 🧪 Guided Investigations
|
| 1081 |
> 🟢 Beginner → 🟡 Intermediate → 🔴 Advanced
|
| 1082 |
|
|
@@ -1086,7 +1099,23 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
|
|
| 1086 |
**S1-B · R2a** siRNA – count "Novel" targets across cancer types
|
| 1087 |
**S1-E · R1a** Liquid Biopsy – find minimal signal for CANCER
|
| 1088 |
**S1-G · 3D Lab** – explore nanoparticle, DNA, and protein corona models
|
| 1089 |
-
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
| 1090 |
|
| 1091 |
gr.Markdown(
|
| 1092 |
"---\n**K R&D Lab** · MIT License · "
|
|
|
|
| 221 |
return img
|
| 222 |
except Exception:
|
| 223 |
plt.close(fig)
|
|
|
|
| 224 |
return Image.new('RGB', (100, 100), color=CARD)
|
| 225 |
|
| 226 |
def predict_mirna(gene):
|
|
|
|
| 516 |
f"</div>"
|
| 517 |
)
|
| 518 |
|
| 519 |
+
# ========== 3D моделі ==========
|
| 520 |
def plot_nanoparticle(r, peg):
|
| 521 |
theta = np.linspace(0, 2*np.pi, 30)
|
| 522 |
phi = np.linspace(0, np.pi, 30)
|
|
|
|
| 593 |
css = f"""
|
| 594 |
body, .gradio-container {{ background: {BG} !important; color: {TXT} !important; }}
|
| 595 |
|
| 596 |
+
/* Вкладки верхнього рівня (категорії) */
|
| 597 |
+
.tabs-outer .tab-nav button {{
|
| 598 |
color: {TXT} !important;
|
| 599 |
background: {CARD} !important;
|
| 600 |
font-size: 13px !important;
|
|
|
|
| 602 |
padding: 8px 16px !important;
|
| 603 |
border-radius: 6px 6px 0 0 !important;
|
| 604 |
}}
|
| 605 |
+
.tabs-outer .tab-nav button.selected {{
|
| 606 |
border-bottom: 3px solid {ACC} !important;
|
| 607 |
color: {ACC} !important;
|
| 608 |
background: {BG} !important;
|
| 609 |
}}
|
| 610 |
|
| 611 |
+
/* Контейнер вкладок всередині основної колонки */
|
| 612 |
+
.main-tabs .tab-nav button {{
|
| 613 |
color: {DIM} !important;
|
| 614 |
background: {BG} !important;
|
| 615 |
font-size: 12px !important;
|
|
|
|
| 620 |
border-bottom: none !important;
|
| 621 |
margin-right: 3px !important;
|
| 622 |
}}
|
| 623 |
+
.main-tabs .tab-nav button.selected {{
|
| 624 |
color: {ACC2} !important;
|
| 625 |
background: {CARD} !important;
|
| 626 |
border-color: {ACC2} !important;
|
| 627 |
border-bottom: none !important;
|
| 628 |
}}
|
| 629 |
+
.main-tabs > .tabitem {{
|
| 630 |
background: {CARD} !important;
|
| 631 |
border: 1px solid {BORDER} !important;
|
| 632 |
border-radius: 0 6px 6px 6px !important;
|
| 633 |
padding: 14px !important;
|
| 634 |
}}
|
| 635 |
|
| 636 |
+
/* Стиль для badges */
|
| 637 |
+
.proj-badge {{
|
| 638 |
+
background: {CARD};
|
| 639 |
+
border-left: 3px solid {ACC};
|
| 640 |
+
padding: 8px 12px;
|
| 641 |
+
border-radius: 0 6px 6px 0;
|
| 642 |
+
margin-bottom: 8px;
|
| 643 |
}}
|
| 644 |
+
.proj-code {{
|
| 645 |
+
color: {DIM};
|
| 646 |
+
font-size: 11px;
|
| 647 |
+
}}
|
| 648 |
+
.proj-title {{
|
| 649 |
+
color: {TXT};
|
| 650 |
+
font-size: 14px;
|
| 651 |
+
font-weight: 600;
|
| 652 |
+
}}
|
| 653 |
+
.proj-metric {{
|
| 654 |
+
background: #0f2a3f;
|
| 655 |
+
color: {ACC2};
|
| 656 |
+
padding: 1px 7px;
|
| 657 |
+
border-radius: 3px;
|
| 658 |
+
font-size: 10px;
|
| 659 |
+
margin-left: 6px;
|
| 660 |
}}
|
| 661 |
|
| 662 |
+
/* Журнал */
|
| 663 |
+
.journal {{
|
| 664 |
+
background: {CARD};
|
| 665 |
+
border: 1px solid {BORDER};
|
| 666 |
+
border-radius: 8px;
|
| 667 |
+
padding: 14px;
|
| 668 |
+
}}
|
| 669 |
+
.journal h3 {{
|
| 670 |
+
color: {ACC};
|
| 671 |
+
margin-top: 0;
|
| 672 |
+
}}
|
| 673 |
+
|
| 674 |
+
/* Загальні */
|
| 675 |
h1, h2, h3 {{ color: {ACC} !important; }}
|
| 676 |
.gr-button-primary {{ background: {ACC} !important; border: none !important; }}
|
| 677 |
footer {{ display: none !important; }}
|
|
|
|
| 716 |
</div>
|
| 717 |
"""
|
| 718 |
|
| 719 |
+
# ========== UI З ДВОМА КОЛОНКАМИ ==========
|
| 720 |
with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
|
| 721 |
gr.Markdown(
|
| 722 |
"# 🔬 K R&D Lab · Science Sphere — S1 Biomedical\n"
|
|
|
|
| 724 |
"*Research only. Not clinical advice.*"
|
| 725 |
)
|
| 726 |
|
| 727 |
+
with gr.Row():
|
| 728 |
+
# Основна колонка з вкладками (ширша)
|
| 729 |
+
with gr.Column(scale=4):
|
| 730 |
+
with gr.Tabs(elem_classes="tabs-outer") as outer_tabs:
|
| 731 |
+
# 🗺️ Lab Map
|
| 732 |
+
with gr.TabItem("🗺️ Lab Map"):
|
| 733 |
+
gr.HTML(MAP_HTML)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 734 |
|
| 735 |
+
# === S1-A · PHYLO-GENOMICS ===
|
| 736 |
+
with gr.TabItem("🧬 S1-A · PHYLO-GENOMICS"):
|
| 737 |
+
gr.HTML(section_header(
|
| 738 |
+
"S1-A", "PHYLO-GENOMICS", "— What breaks in DNA",
|
| 739 |
+
"R1a OpenVariant ✅ · R1b Somatic classifier 🔶"
|
| 740 |
+
))
|
| 741 |
+
with gr.Tabs(elem_classes="main-tabs"):
|
| 742 |
+
# R1 · Variant classification
|
| 743 |
+
with gr.TabItem("R1 · Variant classification"):
|
| 744 |
+
with gr.Tabs():
|
| 745 |
+
# R1a · OpenVariant
|
| 746 |
+
with gr.TabItem("R1a · OpenVariant"):
|
| 747 |
+
gr.HTML(proj_badge("S1-A · R1a", "OpenVariant — SNV Pathogenicity Classifier", "AUC=0.939"))
|
| 748 |
+
hgvs = gr.Textbox(label="HGVS notation", placeholder="BRCA1:p.R1699Q")
|
| 749 |
+
gr.Markdown("**Or enter functional scores manually:**")
|
| 750 |
+
with gr.Row():
|
| 751 |
+
sift = gr.Slider(0,1,value=0.5,step=0.01,label="SIFT (0=damaging)")
|
| 752 |
+
pp = gr.Slider(0,1,value=0.5,step=0.01,label="PolyPhen-2")
|
| 753 |
+
gn = gr.Slider(0,0.01,value=0.001,step=0.0001,label="gnomAD AF")
|
| 754 |
+
b_v = gr.Button("Predict Pathogenicity", variant="primary")
|
| 755 |
+
o_v = gr.HTML()
|
| 756 |
+
gr.Examples([["BRCA1:p.R1699Q",0.82,0.05,0.0012],
|
| 757 |
+
["TP53:p.R248W",0.00,1.00,0.0],
|
| 758 |
+
["BRCA2:p.D2723A",0.01,0.98,0.0]], inputs=[hgvs,sift,pp,gn], cache_examples=False)
|
| 759 |
+
b_v.click(predict_variant, [hgvs,sift,pp,gn], o_v)
|
| 760 |
+
# R1b · Somatic Classifier (в розробці)
|
| 761 |
+
with gr.TabItem("R1b · Somatic Classifier 🔶"):
|
| 762 |
+
gr.HTML(proj_badge("S1-A · R1b", "Somatic Mutation Classifier", "🔶 In progress"))
|
| 763 |
+
gr.Markdown("> This module is in active development. Coming in the next release.")
|
|
|
|
|
|
|
|
|
|
| 764 |
|
| 765 |
+
# === S1-B · PHYLO-RNA ===
|
| 766 |
+
with gr.TabItem("🔬 S1-B · PHYLO-RNA"):
|
| 767 |
+
gr.HTML(section_header(
|
| 768 |
+
"S1-B", "PHYLO-RNA", "— How to silence it via RNA",
|
| 769 |
+
"R1a miRNA ✅ · R2a siRNA ✅ · R3a lncRNA ✅ · R3b ASO ✅"
|
| 770 |
+
))
|
| 771 |
+
with gr.Tabs(elem_classes="main-tabs"):
|
| 772 |
+
# R1 · miRNA silencing
|
| 773 |
+
with gr.TabItem("R1 · miRNA silencing"):
|
| 774 |
+
with gr.Tabs():
|
| 775 |
+
with gr.TabItem("R1a · BRCA2 miRNA"):
|
| 776 |
+
gr.HTML(proj_badge("S1-B · R1a", "miRNA Silencing — BRCA1/2 · TP53"))
|
| 777 |
+
g1 = gr.Dropdown(["BRCA2","BRCA1","TP53"], value="BRCA2", label="Gene")
|
| 778 |
+
b1 = gr.Button("Find miRNAs", variant="primary")
|
| 779 |
+
o1 = gr.Dataframe(label="Top 5 downregulated miRNAs")
|
| 780 |
+
gr.Examples([["BRCA2"],["BRCA1"],["TP53"]], inputs=[g1])
|
| 781 |
+
b1.click(predict_mirna, [g1], o1)
|
| 782 |
+
# R2 · siRNA SL
|
| 783 |
+
with gr.TabItem("R2 · siRNA SL"):
|
| 784 |
+
with gr.Tabs():
|
| 785 |
+
with gr.TabItem("R2a · TP53 siRNA"):
|
| 786 |
+
gr.HTML(proj_badge("S1-B · R2a", "siRNA Synthetic Lethal — TP53-null"))
|
| 787 |
+
g2 = gr.Dropdown(["LUAD","BRCA","COAD"], value="LUAD", label="Cancer type")
|
| 788 |
+
b2 = gr.Button("Find Targets", variant="primary")
|
| 789 |
+
o2 = gr.Dataframe(label="Top 5 synthetic lethal targets")
|
| 790 |
+
gr.Examples([["LUAD"],["BRCA"],["COAD"]], inputs=[g2], cache_examples=False)
|
| 791 |
+
b2.click(predict_sirna, [g2], o2)
|
| 792 |
+
# R3 · lncRNA + ASO
|
| 793 |
+
with gr.TabItem("R3 · lncRNA + ASO"):
|
| 794 |
+
with gr.Tabs():
|
| 795 |
+
with gr.TabItem("R3a · lncRNA-TREM2"):
|
| 796 |
+
gr.HTML(proj_badge("S1-B · R3a", "lncRNA-TREM2 ceRNA Network"))
|
| 797 |
+
b3a = gr.Button("Load ceRNA", variant="primary")
|
| 798 |
+
o3a = gr.Dataframe(label="ceRNA Network (R3a)")
|
| 799 |
+
b3a.click(lambda: pd.DataFrame(CERNA), [], o3a)
|
| 800 |
+
with gr.TabItem("R3b · ASO Designer"):
|
| 801 |
+
gr.HTML(proj_badge("S1-B · R3b", "ASO Designer"))
|
| 802 |
+
b3b = gr.Button("Load ASO Candidates", variant="primary")
|
| 803 |
+
o3b = gr.Dataframe(label="ASO Candidates (R3b)")
|
| 804 |
+
b3b.click(lambda: pd.DataFrame(ASO), [], o3b)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 805 |
|
| 806 |
+
# === S1-C · PHYLO-DRUG ===
|
| 807 |
+
with gr.TabItem("💊 S1-C · PHYLO-DRUG"):
|
| 808 |
+
gr.HTML(section_header(
|
| 809 |
+
"S1-C", "PHYLO-DRUG", "— Which molecule treats it",
|
| 810 |
+
"R1a FGFR3 ✅ · R1b SL drug mapping 🔶 · R2a Frontier 🔴⭐"
|
| 811 |
+
))
|
| 812 |
+
with gr.Tabs(elem_classes="main-tabs"):
|
| 813 |
+
# R1 · RNA-directed drug
|
| 814 |
+
with gr.TabItem("R1 · RNA-directed drug"):
|
| 815 |
+
with gr.Tabs():
|
| 816 |
+
with gr.TabItem("R1a · FGFR3 RNA Drug"):
|
| 817 |
+
gr.HTML(proj_badge("S1-C · R1a", "FGFR3 RNA-Directed Drug Discovery", "top score 0.793"))
|
| 818 |
+
g4 = gr.Radio(["P1 (hairpin loop)","P10 (G-quadruplex)"],
|
| 819 |
+
value="P1 (hairpin loop)", label="Target pocket")
|
| 820 |
+
b4_drug = gr.Button("Screen Compounds", variant="primary")
|
| 821 |
+
o4t = gr.Dataframe(label="Top 5 candidates")
|
| 822 |
+
o4p = gr.Image(label="Binding scores")
|
| 823 |
+
gr.Examples([["P1 (hairpin loop)"],["P10 (G-quadruplex)"]], inputs=[g4])
|
| 824 |
+
b4_drug.click(predict_drug, [g4], [o4t, o4p])
|
| 825 |
+
with gr.TabItem("R1b · SL Drug Mapping 🔶"):
|
| 826 |
+
gr.HTML(proj_badge("S1-C · R1b", "Synthetic Lethal Drug Mapping", "🔶 In progress"))
|
| 827 |
+
gr.Markdown("> In development. Coming soon.")
|
| 828 |
+
# R2 · Frontier
|
| 829 |
+
with gr.TabItem("R2 · Frontier"):
|
| 830 |
+
with gr.Tabs():
|
| 831 |
+
with gr.TabItem("R2a · m6A×Ferroptosis×Circadian 🔴⭐"):
|
| 832 |
+
gr.HTML(proj_badge("S1-C · R2a", "m6A × Ferroptosis × Circadian", "🔴 Frontier"))
|
| 833 |
+
gr.Markdown(
|
| 834 |
+
"> **Research gap:** This triple intersection has never been studied as an integrated system.\n\n"
|
| 835 |
+
"> **Planned datasets:** TCGA-PAAD · GEO m6A atlases · Circadian gene panels\n\n"
|
| 836 |
+
"> **Expected timeline:** Q3 2026"
|
| 837 |
+
)
|
| 838 |
|
| 839 |
+
# === S1-D · PHYLO-LNP ===
|
| 840 |
+
with gr.TabItem("🧪 S1-D · PHYLO-LNP"):
|
| 841 |
+
gr.HTML(section_header(
|
| 842 |
+
"S1-D", "PHYLO-LNP", "— How to deliver the drug",
|
| 843 |
+
"R1a Corona ✅ · R2a Flow ✅ · R3a Brain ✅ · R4a NLP ✅ · R5a CSF/BM 🔴⭐"
|
| 844 |
+
))
|
| 845 |
+
with gr.Tabs(elem_classes="main-tabs"):
|
| 846 |
+
# R1 · Serum corona
|
| 847 |
+
with gr.TabItem("R1 · Serum corona"):
|
| 848 |
+
with gr.Tabs():
|
| 849 |
+
with gr.TabItem("R1a · LNP Corona ML"):
|
| 850 |
+
gr.HTML(proj_badge("S1-D · R1a", "LNP Protein Corona (Serum)", "AUC=0.791"))
|
| 851 |
+
with gr.Row():
|
| 852 |
+
sz = gr.Slider(50,300,value=100,step=1,label="Size (nm)")
|
| 853 |
+
zt = gr.Slider(-40,10,value=-5,step=1,label="Zeta (mV)")
|
| 854 |
+
with gr.Row():
|
| 855 |
+
pg = gr.Slider(0,5,value=1.5,step=0.1,label="PEG mol%")
|
| 856 |
+
lp = gr.Dropdown(["Ionizable","Cationic","Anionic","Neutral"],value="Ionizable",label="Lipid type")
|
| 857 |
+
b6 = gr.Button("Predict", variant="primary")
|
| 858 |
+
o6 = gr.Markdown()
|
| 859 |
+
gr.Examples([[100,-5,1.5,"Ionizable"],[80,5,0.5,"Cationic"]], inputs=[sz,zt,pg,lp])
|
| 860 |
+
b6.click(predict_corona, [sz,zt,pg,lp], o6)
|
| 861 |
+
# R2 · Flow corona
|
| 862 |
+
with gr.TabItem("R2 · Flow corona"):
|
| 863 |
+
with gr.Tabs():
|
| 864 |
+
with gr.TabItem("R2a · Flow Corona"):
|
| 865 |
+
gr.HTML(proj_badge("S1-D · R2a", "Flow Corona — Vroman Effect"))
|
| 866 |
+
with gr.Row():
|
| 867 |
+
s8 = gr.Slider(50,300,value=100,step=1,label="Size (nm)")
|
| 868 |
+
z8 = gr.Slider(-40,10,value=-5,step=1,label="Zeta (mV)")
|
| 869 |
+
pg8 = gr.Slider(0,5,value=1.5,step=0.1,label="PEG mol%")
|
| 870 |
+
with gr.Row():
|
| 871 |
+
ch8 = gr.Dropdown(["Ionizable","Cationic","Anionic","Neutral"],value="Ionizable",label="Charge")
|
| 872 |
+
fl8 = gr.Slider(0,40,value=20,step=1,label="Flow cm/s (aorta=40)")
|
| 873 |
+
b8 = gr.Button("Model Vroman Effect", variant="primary")
|
| 874 |
+
o8t = gr.Markdown()
|
| 875 |
+
o8p = gr.Image(label="Kinetics")
|
| 876 |
+
gr.Examples([[100,-5,1.5,"Ionizable",40],[150,5,0.5,"Cationic",10]], inputs=[s8,z8,pg8,ch8,fl8])
|
| 877 |
+
b8.click(predict_flow, [s8,z8,pg8,ch8,fl8], [o8t,o8p])
|
| 878 |
+
# R3 · Brain BBB
|
| 879 |
+
with gr.TabItem("R3 · Brain BBB"):
|
| 880 |
with gr.Tabs():
|
| 881 |
+
with gr.TabItem("R3a · LNP Brain"):
|
| 882 |
+
gr.HTML(proj_badge("S1-D · R3a", "LNP Brain Delivery"))
|
| 883 |
+
smi = gr.Textbox(label="Ionizable lipid SMILES",
|
| 884 |
+
value="CC(C)CC(=O)OCC(COC(=O)CC(C)C)OC(=O)CC(C)C")
|
|
|
|
|
|
|
| 885 |
with gr.Row():
|
| 886 |
+
pk = gr.Slider(4,8,value=6.5,step=0.1,label="pKa")
|
| 887 |
+
zt9 = gr.Slider(-20,10,value=-3,step=1,label="Zeta (mV)")
|
| 888 |
+
b9 = gr.Button("Predict BBB Crossing", variant="primary")
|
| 889 |
+
o9t = gr.Markdown()
|
| 890 |
+
o9p = gr.Image(label="Radar profile")
|
| 891 |
+
gr.Examples([["CC(C)CC(=O)OCC(COC(=O)CC(C)C)OC(=O)CC(C)C",6.5,-3]], inputs=[smi,pk,zt9])
|
| 892 |
+
b9.click(predict_bbb, [smi,pk,zt9], [o9t,o9p])
|
| 893 |
+
# R4 · NLP
|
| 894 |
+
with gr.TabItem("R4 · NLP"):
|
| 895 |
+
with gr.Tabs():
|
| 896 |
+
with gr.TabItem("R4a · AutoCorona NLP"):
|
| 897 |
+
gr.HTML(proj_badge("S1-D · R4a", "AutoCorona NLP", "F1=0.71"))
|
| 898 |
+
txt = gr.Textbox(lines=5,label="Paper abstract",placeholder="Paste abstract here...")
|
| 899 |
+
b10 = gr.Button("Extract Data", variant="primary")
|
| 900 |
+
o10j = gr.Code(label="Extracted JSON", language="json")
|
| 901 |
+
o10f = gr.Textbox(label="Validation flags")
|
| 902 |
+
gr.Examples([[
|
| 903 |
+
"LNPs composed of MC3, DSPC, Cholesterol (50:10:40 mol%) with 1.5% PEG-DMG. "
|
| 904 |
+
"Hydrodynamic diameter was 98 nm, zeta potential -3.2 mV, PDI 0.12. "
|
| 905 |
+
"Incubated in human plasma. Corona: albumin, apolipoprotein E, fibrinogen."
|
| 906 |
+
]], inputs=[txt])
|
| 907 |
+
b10.click(extract_corona, txt, [o10j, o10f])
|
| 908 |
+
# R5 · Exotic fluids
|
| 909 |
+
with gr.TabItem("R5 · Exotic fluids 🔴⭐"):
|
| 910 |
+
with gr.Tabs():
|
| 911 |
+
with gr.TabItem("R5a · CSF/Vitreous/BM"):
|
| 912 |
+
gr.HTML(proj_badge("S1-D · R5a", "LNP Corona in CSF · Vitreous · Bone Marrow", "🔴 0 prior studies"))
|
| 913 |
gr.Markdown(
|
| 914 |
+
"> **Research gap:** Protein corona has only been characterized in serum/plasma. "
|
| 915 |
+
"CSF, vitreous humor, and bone marrow interstitial fluid remain completely unstudied.\n\n"
|
| 916 |
+
"> **Target cancers:** DIPG (CSF) · UVM (vitreous) · pAML (bone marrow)\n\n"
|
| 917 |
+
"> **Expected timeline:** Q2–Q3 2026"
|
|
|
|
|
|
|
| 918 |
)
|
| 919 |
+
|
| 920 |
+
# === S1-E · PHYLO-BIOMARKERS ===
|
| 921 |
+
with gr.TabItem("🩸 S1-E · PHYLO-BIOMARKERS"):
|
| 922 |
+
gr.HTML(section_header(
|
| 923 |
+
"S1-E", "PHYLO-BIOMARKERS", "— Detect without biopsy",
|
| 924 |
+
"R1a Liquid Biopsy ✅ · R1b Protein validator 🔶"
|
| 925 |
+
))
|
| 926 |
+
with gr.Tabs(elem_classes="main-tabs"):
|
| 927 |
+
with gr.TabItem("R1 · Liquid biopsy"):
|
| 928 |
+
with gr.Tabs():
|
| 929 |
+
with gr.TabItem("R1a · Liquid Biopsy"):
|
| 930 |
+
gr.HTML(proj_badge("S1-E · R1a", "Liquid Biopsy Classifier", "AUC=0.992*"))
|
| 931 |
+
with gr.Row():
|
| 932 |
+
p1=gr.Slider(-3,3,value=0,step=0.1,label="CTHRC1")
|
| 933 |
+
p2=gr.Slider(-3,3,value=0,step=0.1,label="FHL2")
|
| 934 |
+
p3=gr.Slider(-3,3,value=0,step=0.1,label="LDHA")
|
| 935 |
+
p4=gr.Slider(-3,3,value=0,step=0.1,label="P4HA1")
|
| 936 |
+
p5=gr.Slider(-3,3,value=0,step=0.1,label="SERPINH1")
|
| 937 |
+
with gr.Row():
|
| 938 |
+
p6=gr.Slider(-3,3,value=0,step=0.1,label="ABCA8")
|
| 939 |
+
p7=gr.Slider(-3,3,value=0,step=0.1,label="CA4")
|
| 940 |
+
p8=gr.Slider(-3,3,value=0,step=0.1,label="CKB")
|
| 941 |
+
p9=gr.Slider(-3,3,value=0,step=0.1,label="NNMT")
|
| 942 |
+
p10=gr.Slider(-3,3,value=0,step=0.1,label="CACNA2D2")
|
| 943 |
+
b7=gr.Button("Classify", variant="primary")
|
| 944 |
+
o7t=gr.HTML()
|
| 945 |
+
o7p=gr.Image(label="Feature contributions")
|
| 946 |
+
gr.Examples([[2,2,1.5,1.8,1.6,-1,-1.2,-0.8,1.4,-1.1],[0]*10],
|
| 947 |
+
inputs=[p1,p2,p3,p4,p5,p6,p7,p8,p9,p10])
|
| 948 |
+
b7.click(predict_cancer, [p1,p2,p3,p4,p5,p6,p7,p8,p9,p10], [o7t,o7p])
|
| 949 |
+
with gr.TabItem("R1b · Protein Validator 🔶"):
|
| 950 |
+
gr.HTML(proj_badge("S1-E · R1b", "Protein Panel Validator", "🔶 In progress"))
|
| 951 |
+
gr.Markdown("> Coming next — validates R1a results against GEO plasma proteomics datasets.")
|
| 952 |
+
|
| 953 |
+
# === S1-F · PHYLO-RARE ===
|
| 954 |
+
with gr.TabItem("🧠 S1-F · PHYLO-RARE"):
|
| 955 |
+
gr.HTML(section_header(
|
| 956 |
+
"S1-F", "PHYLO-RARE", "�� Where almost nobody has looked yet",
|
| 957 |
+
"<b style='color:#ef4444'>⚠️ <300 cases/yr · <5% survival · 0–1 prior studies per gap</b><br>"
|
| 958 |
+
"R1a DIPG 🔶 · R2a UVM 🔶 · R3a pAML 🔶"
|
| 959 |
+
))
|
| 960 |
+
with gr.Tabs(elem_classes="main-tabs"):
|
| 961 |
+
# R1 · DIPG
|
| 962 |
+
with gr.TabItem("R1 · DIPG"):
|
| 963 |
with gr.Tabs():
|
| 964 |
+
with gr.TabItem("R1a · DIPG Toolkit"):
|
| 965 |
+
gr.HTML(proj_badge("S1-F · R1a", "DIPG Toolkit", "PBTA · GSE126319"))
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 966 |
gr.Markdown(
|
| 967 |
+
"> **Why DIPG?** Diffuse Intrinsic Pontine Glioma — median survival 9–11 months. "
|
| 968 |
+
"H3K27M oncohistone in **78%** cases. "
|
| 969 |
+
"CSF delivery is the only viable route past the brainstem BBB. "
|
| 970 |
+
"Circadian disruption (BMAL1 suppression) newly linked — **0 prior LNP studies**."
|
|
|
|
|
|
|
| 971 |
)
|
| 972 |
+
with gr.Tabs():
|
| 973 |
+
with gr.TabItem("Variants"):
|
| 974 |
+
sort_d = gr.Radio(["Frequency", "Drug status"], value="Frequency", label="Sort by")
|
| 975 |
+
b_dv = gr.Button("Load DIPG Variants", variant="primary")
|
| 976 |
+
o_dv = gr.Dataframe(label="H3K27M co-mutations · PBTA/GSE126319")
|
| 977 |
+
b_dv.click(dipg_variants, [sort_d], o_dv)
|
| 978 |
+
with gr.TabItem("CSF LNP"):
|
| 979 |
+
with gr.Row():
|
| 980 |
+
d_peg = gr.Slider(0.5, 3.0, value=1.5, step=0.1, label="PEG mol%")
|
| 981 |
+
d_size = gr.Slider(60, 150, value=90, step=5, label="Target size (nm)")
|
| 982 |
+
b_dc = gr.Button("Rank CSF Formulations", variant="primary")
|
| 983 |
+
o_dct = gr.Dataframe(label="CSF LNP ranking")
|
| 984 |
+
o_dcp = gr.Image(label="ApoE% in CSF corona")
|
| 985 |
+
b_dc.click(dipg_csf, [d_peg, d_size], [o_dct, o_dcp])
|
| 986 |
+
with gr.TabItem("Research Gap"):
|
| 987 |
+
gr.Markdown(
|
| 988 |
+
"**Data:** PBTA (n=240) · GSE126319 (n=28) · GTEx circadian genes\n\n"
|
| 989 |
+
"| Layer | Known | This study gap |\n"
|
| 990 |
+
"|-------|-------|----------------|\n"
|
| 991 |
+
"| Genomics | H3K27M freq=78% | H3K27M × BMAL1/CLOCK |\n"
|
| 992 |
+
"| Delivery | CED convection | LNP corona **in CSF** |\n"
|
| 993 |
+
"| Biology | PRC2 inhibition | Ferroptosis in H3K27M+ DIPG |"
|
| 994 |
+
)
|
| 995 |
+
# R2 · UVM
|
| 996 |
+
with gr.TabItem("R2 · UVM"):
|
| 997 |
with gr.Tabs():
|
| 998 |
+
with gr.TabItem("R2a · UVM Toolkit"):
|
| 999 |
+
gr.HTML(proj_badge("S1-F · R2a", "UVM Toolkit", "TCGA-UVM n=80"))
|
| 1000 |
+
gr.Markdown(
|
| 1001 |
+
"> **Why UVM?** Uveal Melanoma — metastatic 5-yr survival **15%**. "
|
| 1002 |
+
"GNAQ/GNA11 mutations in 78% cases. "
|
| 1003 |
+
"Vitreous humor protein corona has **never been profiled**. "
|
| 1004 |
+
"METTL3/WTAP upregulated in GNAQ+ tumors — 0 therapeutic studies."
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1005 |
)
|
| 1006 |
+
with gr.Tabs():
|
| 1007 |
+
with gr.TabItem("Variants + m6A"):
|
| 1008 |
+
b_uv = gr.Button("Load UVM Variants", variant="primary")
|
| 1009 |
+
o_uv = gr.Dataframe(label="GNAQ/GNA11 map · TCGA-UVM")
|
| 1010 |
+
b_uv.click(uvm_variants, [], o_uv)
|
| 1011 |
+
with gr.TabItem("Vitreous LNP"):
|
| 1012 |
+
b_uw = gr.Button("Rank Vitreous Formulations", variant="primary")
|
| 1013 |
+
o_uwt = gr.Dataframe(label="Vitreous LNP retention ranking")
|
| 1014 |
+
o_uwp = gr.Image(label="Retention (hours)")
|
| 1015 |
+
b_uw.click(uvm_vitreous, [], [o_uwt, o_uwp])
|
| 1016 |
+
with gr.TabItem("Research Gap"):
|
| 1017 |
+
gr.Markdown(
|
| 1018 |
+
"**Data:** TCGA-UVM (n=80) · GEO m6A atlases · Vitreous proteomics\n\n"
|
| 1019 |
+
"| Layer | Known | This study gap |\n"
|
| 1020 |
+
"|-------|-------|----------------|\n"
|
| 1021 |
+
"| Genomics | GNAQ/GNA11 mutations | m6A landscape GNAQ+ vs GNA11+ |\n"
|
| 1022 |
+
"| Delivery | Intravitreal injection | LNP corona **in vitreous humor** |\n"
|
| 1023 |
+
"| Biology | PLCβ→PKC→MAPK | GNAQ × METTL3 × YTHDF2 axis |"
|
| 1024 |
+
)
|
| 1025 |
+
# R3 · pAML
|
| 1026 |
+
with gr.TabItem("R3 · pAML"):
|
| 1027 |
+
with gr.Tabs():
|
| 1028 |
+
with gr.TabItem("R3a · pAML Toolkit"):
|
| 1029 |
+
gr.HTML(proj_badge("S1-F · R3a", "pAML Toolkit", "TARGET-AML n≈197"))
|
| 1030 |
gr.Markdown(
|
| 1031 |
+
"> **Why pAML?** Pediatric AML — relapse OS **<30%**. "
|
| 1032 |
+
"FLT3-ITD in 25% cases. "
|
| 1033 |
+
"Bone marrow niche LNP corona: **never studied**. "
|
| 1034 |
+
"Ferroptosis–FLT3 intersection: 0 prior studies (FerrDb v2 confirmed)."
|
|
|
|
|
|
|
| 1035 |
)
|
| 1036 |
+
with gr.Tabs():
|
| 1037 |
+
with gr.TabItem("Ferroptosis Explorer"):
|
| 1038 |
+
var_sel = gr.Dropdown(
|
| 1039 |
+
["FLT3-ITD", "NPM1 c.860_863dupTCAG", "DNMT3A p.R882H",
|
| 1040 |
+
"CEBPA biallelic", "IDH1/2 mutation"],
|
| 1041 |
+
value="FLT3-ITD", label="Select variant"
|
| 1042 |
+
)
|
| 1043 |
+
b_pf = gr.Button("Analyze Ferroptosis Profile", variant="primary")
|
| 1044 |
+
o_pft = gr.HTML()
|
| 1045 |
+
o_pfp = gr.Image(label="Target radar")
|
| 1046 |
+
b_pf.click(paml_ferroptosis, var_sel, [o_pft, o_pfp])
|
| 1047 |
+
with gr.TabItem("BM Niche LNP"):
|
| 1048 |
+
gr.Dataframe(
|
| 1049 |
+
value=pd.DataFrame(PAML_BM_LNP),
|
| 1050 |
+
label="Bone marrow niche LNP candidates · TARGET-AML context"
|
| 1051 |
+
)
|
| 1052 |
+
with gr.TabItem("Research Gap"):
|
| 1053 |
+
gr.Markdown(
|
| 1054 |
+
"**Data:** TARGET-AML (n=197) · BeatAML · FerrDb v2\n\n"
|
| 1055 |
+
"| Layer | Known | This study gap |\n"
|
| 1056 |
+
"|-------|-------|----------------|\n"
|
| 1057 |
+
"| Genomics | FLT3-ITD → Midostaurin | FLT3-ITD × GPX4/SLC7A11 |\n"
|
| 1058 |
+
"| Delivery | Liposomal daunorubicin | LNP corona **in bone marrow** |\n"
|
| 1059 |
+
"| Biology | Midostaurin inhibits FLT3 | Ferroptosis SL + FLT3i |"
|
| 1060 |
+
)
|
| 1061 |
|
| 1062 |
+
# === S1-G · 3D Lab ===
|
| 1063 |
+
with gr.TabItem("🧊 S1-G · 3D Lab"):
|
| 1064 |
+
gr.HTML(section_header(
|
| 1065 |
+
"S1-G", "PHYLO-SIM", "— 3D Models & Simulations",
|
| 1066 |
+
"Interactive visualizations for learning"
|
| 1067 |
+
))
|
| 1068 |
+
with gr.Tabs(elem_classes="main-tabs"):
|
| 1069 |
+
with gr.TabItem("Nanoparticle"):
|
| 1070 |
+
gr.Markdown("### 3D Model of a Lipid Nanoparticle")
|
| 1071 |
+
with gr.Row():
|
| 1072 |
+
np_radius = gr.Slider(2, 20, value=5, step=1, label="Radius (nm)")
|
| 1073 |
+
np_peg = gr.Slider(0, 1, value=0.3, step=0.05, label="PEG density")
|
| 1074 |
+
np_btn = gr.Button("Generate", variant="primary")
|
| 1075 |
+
np_plot = gr.Plot(label="Nanoparticle")
|
| 1076 |
+
np_btn.click(plot_nanoparticle, [np_radius, np_peg], np_plot)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1077 |
|
| 1078 |
+
with gr.TabItem("DNA Helix"):
|
| 1079 |
+
gr.Markdown("### 3D Model of a DNA Double Helix")
|
| 1080 |
+
dna_btn = gr.Button("Generate DNA", variant="primary")
|
| 1081 |
+
dna_plot = gr.Plot()
|
| 1082 |
+
dna_btn.click(plot_dna, [], dna_plot)
|
| 1083 |
|
| 1084 |
+
with gr.TabItem("Protein Corona"):
|
| 1085 |
+
gr.Markdown("### Schematic of Protein Corona on Nanoparticle")
|
| 1086 |
+
corona_btn = gr.Button("Show Corona", variant="primary")
|
| 1087 |
+
corona_plot = gr.Plot()
|
| 1088 |
+
corona_btn.click(plot_corona, [], corona_plot)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1089 |
|
| 1090 |
+
# === Learning ===
|
| 1091 |
+
with gr.TabItem("📚 Learning"):
|
| 1092 |
+
gr.Markdown("""
|
| 1093 |
## 🧪 Guided Investigations
|
| 1094 |
> 🟢 Beginner → 🟡 Intermediate → 🔴 Advanced
|
| 1095 |
|
|
|
|
| 1099 |
**S1-B · R2a** siRNA – count "Novel" targets across cancer types
|
| 1100 |
**S1-E · R1a** Liquid Biopsy – find minimal signal for CANCER
|
| 1101 |
**S1-G · 3D Lab** – explore nanoparticle, DNA, and protein corona models
|
| 1102 |
+
""")
|
| 1103 |
+
|
| 1104 |
+
# Права колонка з журналом
|
| 1105 |
+
with gr.Column(scale=1, min_width=300):
|
| 1106 |
+
with gr.Group(elem_classes="journal"):
|
| 1107 |
+
gr.Markdown("## 📓 Lab Journal")
|
| 1108 |
+
note_input = gr.Textbox(label="📝 Observation", placeholder="Your observation...", lines=2)
|
| 1109 |
+
note_tab = gr.Textbox(label="Project code (e.g. S1-A·R1a)", value="General", visible=False)
|
| 1110 |
+
with gr.Row():
|
| 1111 |
+
save_btn = gr.Button("💾 Save", size="sm", variant="primary")
|
| 1112 |
+
refresh_btn = gr.Button("🔄 Refresh", size="sm")
|
| 1113 |
+
clear_btn = gr.Button("🗑️ Clear", size="sm")
|
| 1114 |
+
journal_display = gr.Markdown(value=load_journal())
|
| 1115 |
+
|
| 1116 |
+
save_btn.click(save_note, [note_input, note_tab], journal_display)
|
| 1117 |
+
refresh_btn.click(load_journal, [], journal_display)
|
| 1118 |
+
clear_btn.click(clear_journal, [], journal_display)
|
| 1119 |
|
| 1120 |
gr.Markdown(
|
| 1121 |
"---\n**K R&D Lab** · MIT License · "
|