Update app.py

app.py

@@ -18,6 +18,7 @@ TXT  = "#f1f5f9"
 GRN  = "#22c55e"
 RED  = "#ef4444"
 DIM  = "#8e9bae"
+BORDER = "#334155"
 
 LOG_PATH = Path("/tmp/lab_journal.csv")
 
@@ -26,13 +27,11 @@ def log_entry(tab, inputs, result, note=""):
         write_header = not LOG_PATH.exists()
         with open(LOG_PATH, "a", newline="", encoding="utf-8") as f:
             w = csv.DictWriter(f, fieldnames=["timestamp","tab","inputs","result","note"])
-            if write_header:
-                w.writeheader()
+            if write_header: w.writeheader()
             w.writerow({"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M"),
                         "tab": tab, "inputs": str(inputs),
                         "result": str(result)[:200], "note": note})
-    except Exception:
-        pass
+    except Exception: pass
 
 def load_journal():
     try:
@@ -167,9 +166,8 @@ def predict_drug(pocket):
     fig, ax = plt.subplots(figsize=(6, 4), facecolor=CARD)
     ax.set_facecolor(CARD)
     ax.barh(df["Compound"], df["Final_score"], color=ACC)
-    ax.set_xlabel("Final Score", color=TXT)
-    ax.tick_params(colors=TXT)
-    for sp in ax.spines.values(): sp.set_edgecolor("#334155")
+    ax.set_xlabel("Final Score", color=TXT); ax.tick_params(colors=TXT)
+    for sp in ax.spines.values(): sp.set_edgecolor(BORDER)
     ax.set_title(f"Top compounds — {pocket}", color=TXT, fontsize=10)
     plt.tight_layout()
     buf = BytesIO(); plt.savefig(buf, format="png", dpi=120, facecolor=CARD); plt.close(); buf.seek(0)
@@ -179,8 +177,7 @@ def predict_drug(pocket):
 def predict_variant(hgvs, sift, polyphen, gnomad):
     hgvs = hgvs.strip()
     if hgvs in VARIANT_DB:
-        r = VARIANT_DB[hgvs]
-        cls, conf, score = r["cls"], r["conf"], r["score"]
+        r = VARIANT_DB[hgvs]; cls, conf, score = r["cls"], r["conf"], r["score"]
     else:
         score = 0.0
         if sift < 0.05:     score += 0.4
@@ -191,19 +188,16 @@ def predict_variant(hgvs, sift, polyphen, gnomad):
         conf = "High" if (sift < 0.01 or sift > 0.9) else "Moderate"
     colour = RED if "Pathogenic" in cls else GRN
     icon   = "⚠️ WARNING" if "Pathogenic" in cls else "✅ OK"
-    explanation = PLAIN.get(cls, "")
     log_entry("S1-A | S1-R1 | OpenVariant", hgvs or f"SIFT={sift}", f"{cls} score={score}")
     return (
         f"<div style=\'background:{CARD};padding:16px;border-radius:8px;font-family:sans-serif;color:{TXT}\'>"
-        f"<p style=\'font-size:11px;color:{DIM};margin:0 0 8px\'>"
-        f"S1 · Biomedical &nbsp;›&nbsp; S1-A · PHYLO-GENOMICS &nbsp;›&nbsp; S1-R1 · OpenVariant</p>"
+        f"<p style=\'font-size:11px;color:{DIM};margin:0 0 8px\'>S1-A · PHYLO-GENOMICS · S1-R1</p>"
         f"<h3 style=\'color:{colour};margin:0 0 8px\'>{icon} {cls}</h3>"
         f"<p>Score: <b>{score:.3f}</b> &nbsp;|&nbsp; Confidence: <b>{conf}</b></p>"
-        f"<div style=\'background:#334155;border-radius:4px;height:14px\'>"
+        f"<div style=\'background:{BORDER};border-radius:4px;height:14px\'>"
         f"<div style=\'background:{colour};height:14px;border-radius:4px;width:{int(score*100)}%\'></div></div>"
-        f"<p style=\'margin-top:12px\'>{explanation}</p>"
-        f"<p style=\'font-size:11px;color:{DIM}\'>Research only. Not clinical advice.</p>"
-        f"</div>"
+        f"<p style=\'margin-top:12px\'>{PLAIN.get(cls,'')}</p>"
+        f"<p style=\'font-size:11px;color:{DIM}\'>Research only. Not clinical advice.</p></div>"
     )
 
 def predict_corona(size, zeta, peg, lipid):
@@ -216,43 +210,39 @@ def predict_corona(size, zeta, peg, lipid):
     dominant = ["ApoE","Albumin","Fibrinogen","Vitronectin","ApoA-I"][min(score, 4)]
     efficacy = "High" if score >= 4 else "Medium" if score >= 2 else "Low"
     log_entry("S1-D | S1-R6 | Corona", f"size={size},peg={peg}", f"dominant={dominant}")
-    return (f"**Dominant corona protein:** {dominant}\n\n"
-            f"**Predicted efficacy:** {efficacy}\n\n"
-            f"**Score:** {score}/6")
+    return f"**Dominant corona protein:** {dominant}\n\n**Predicted efficacy:** {efficacy}\n\n**Score:** {score}/6"
 
 def predict_cancer(c1,c2,c3,c4,c5,c6,c7,c8,c9,c10):
-    vals    = [c1,c2,c3,c4,c5,c6,c7,c8,c9,c10]
-    names   = list(BM_W.keys())
-    weights = list(BM_W.values())
-    raw     = sum(v*w for v,w in zip(vals, weights))
-    prob    = 1 / (1 + np.exp(-raw * 2))
-    label   = "CANCER" if prob > 0.5 else "HEALTHY"
-    colour  = RED if prob > 0.5 else GRN
+    vals = [c1,c2,c3,c4,c5,c6,c7,c8,c9,c10]
+    names, weights = list(BM_W.keys()), list(BM_W.values())
+    raw  = sum(v*w for v,w in zip(vals, weights))
+    prob = 1 / (1 + np.exp(-raw * 2))
+    label = "CANCER" if prob > 0.5 else "HEALTHY"
+    colour = RED if prob > 0.5 else GRN
     contribs = [v*w for v,w in zip(vals, weights)]
-    fig, ax  = plt.subplots(figsize=(6, 3.5), facecolor=CARD)
+    fig, ax = plt.subplots(figsize=(6, 3.5), facecolor=CARD)
     ax.set_facecolor(CARD)
     ax.barh(names, contribs, color=[ACC if c > 0 else ACC2 for c in contribs])
     ax.axvline(0, color=TXT, linewidth=0.8)
     ax.set_xlabel("Contribution to cancer score", color=TXT)
     ax.tick_params(colors=TXT, labelsize=8)
-    for sp in ax.spines.values(): sp.set_edgecolor("#334155")
+    for sp in ax.spines.values(): sp.set_edgecolor(BORDER)
     ax.set_title("Protein contributions", color=TXT, fontsize=10)
     plt.tight_layout()
     buf = BytesIO(); plt.savefig(buf, format="png", dpi=120, facecolor=CARD); plt.close(); buf.seek(0)
     log_entry("S1-E | S1-R9 | LiquidBiopsy", f"CTHRC1={c1},FHL2={c2}", f"{label} {prob:.2f}")
     return (
-        f"<div style=\'background:{CARD};padding:12px;border-radius:8px;font-family:sans-serif;\'>"
+        f"<div style=\'background:{CARD};padding:14px;border-radius:8px;font-family:sans-serif;\'>"
         f"<p style=\'font-size:11px;color:{DIM};margin:0 0 6px\'>S1-E · PHYLO-BIOMARKERS · S1-R9</p>"
-        f"<span style=\'color:{colour};font-size:22px;font-weight:bold\'>{label}</span><br>"
+        f"<span style=\'color:{colour};font-size:24px;font-weight:bold\'>{label}</span><br>"
         f"<span style=\'color:{TXT};font-size:14px\'>Probability: {prob:.2f}</span></div>"
     ), Image.open(buf)
 
 def predict_flow(size, zeta, peg, charge, flow_rate):
     csi = round(min((flow_rate/40)*0.6 + (peg/5)*0.2 + (1 if charge=="Cationic" else 0)*0.2, 1.0), 3)
     stability = "High remodeling" if csi > 0.6 else "Medium" if csi > 0.3 else "Stable"
-    t  = np.linspace(0, 60, 200)
-    kf = 0.03 * (1 + flow_rate/40)
-    ks = 0.038 * (1 + flow_rate/40)
+    t = np.linspace(0, 60, 200)
+    kf, ks = 0.03*(1+flow_rate/40), 0.038*(1+flow_rate/40)
     fig, ax = plt.subplots(figsize=(6, 3.5), facecolor=CARD)
     ax.set_facecolor(CARD)
     ax.plot(t, 60*np.exp(-0.03*t)+20, color="#60a5fa", ls="--", label="Albumin (static)")
@@ -260,9 +250,8 @@ def predict_flow(size, zeta, peg, charge, flow_rate):
     ax.plot(t, 14*(1-np.exp(-0.038*t))+5, color=ACC, ls="--",   label="ApoE (static)")
     ax.plot(t, 20*(1-np.exp(-ks*t))+5,    color=ACC,             label="ApoE (flow)")
     ax.set_xlabel("Time (min)", color=TXT); ax.set_ylabel("% Corona", color=TXT)
-    ax.tick_params(colors=TXT)
-    ax.legend(fontsize=7, labelcolor=TXT, facecolor=CARD)
-    for sp in ax.spines.values(): sp.set_edgecolor("#334155")
+    ax.tick_params(colors=TXT); ax.legend(fontsize=7, labelcolor=TXT, facecolor=CARD)
+    for sp in ax.spines.values(): sp.set_edgecolor(BORDER)
     ax.set_title("Vroman Effect — flow vs static", color=TXT, fontsize=9)
     plt.tight_layout()
     buf = BytesIO(); plt.savefig(buf, format="png", dpi=120, facecolor=CARD); plt.close(); buf.seek(0)
@@ -270,15 +259,15 @@ def predict_flow(size, zeta, peg, charge, flow_rate):
     return f"**Corona Shift Index: {csi}** — {stability}", Image.open(buf)
 
 def predict_bbb(smiles, pka, zeta):
-    logp     = smiles.count("C")*0.3 - smiles.count("O")*0.5 + 1.5
+    logp = smiles.count("C")*0.3 - smiles.count("O")*0.5 + 1.5
     apoe_pct = max(0, min(40, (7.0-pka)*8 + abs(zeta)*0.5 + logp*0.8))
     bbb_prob = min(0.95, apoe_pct/30)
-    tier     = "HIGH (>20%)" if apoe_pct > 20 else "MEDIUM (10-20%)" if apoe_pct > 10 else "LOW (<10%)"
-    cats     = ["ApoE%","BBB","logP","pKa fit","Zeta"]
-    vals     = [apoe_pct/40, bbb_prob, min(logp/5,1), (7-abs(pka-6.5))/7, (10-abs(zeta))/10]
-    angles   = np.linspace(0, 2*np.pi, len(cats), endpoint=False).tolist()
-    v2, a2   = vals+[vals[0]], angles+[angles[0]]
-    fig, ax  = plt.subplots(figsize=(5, 4), subplot_kw={"polar":True}, facecolor=CARD)
+    tier = "HIGH (>20%)" if apoe_pct > 20 else "MEDIUM (10-20%)" if apoe_pct > 10 else "LOW (<10%)"
+    cats = ["ApoE%","BBB","logP","pKa fit","Zeta"]
+    vals = [apoe_pct/40, bbb_prob, min(logp/5,1), (7-abs(pka-6.5))/7, (10-abs(zeta))/10]
+    angles = np.linspace(0, 2*np.pi, len(cats), endpoint=False).tolist()
+    v2, a2 = vals+[vals[0]], angles+[angles[0]]
+    fig, ax = plt.subplots(figsize=(5, 4), subplot_kw={"polar":True}, facecolor=CARD)
     ax.set_facecolor(CARD)
     ax.plot(a2, v2, color=ACC, linewidth=2); ax.fill(a2, v2, color=ACC, alpha=0.2)
     ax.set_xticks(angles); ax.set_xticklabels(cats, color=TXT, fontsize=8)
@@ -291,12 +280,11 @@ def predict_bbb(smiles, pka, zeta):
 def extract_corona(text):
     out = {"nanoparticle_composition":"","size_nm":None,"zeta_mv":None,"PDI":None,
            "protein_source":"","corona_proteins":[],"confidence":{}}
-    m = re.search(r"(\d+\.?\d*)\s*(?:nm|nanometer)", text, re.I)
-    if m: out["size_nm"] = float(m.group(1)); out["confidence"]["size_nm"] = "HIGH"
-    m = re.search(r"([+-]?\d+\.?\d*)\s*mV", text, re.I)
-    if m: out["zeta_mv"] = float(m.group(1)); out["confidence"]["zeta_mv"] = "HIGH"
-    m = re.search(r"PDI\s*[=:of]*\s*(\d+\.?\d*)", text, re.I)
-    if m: out["PDI"] = float(m.group(1)); out["confidence"]["PDI"] = "HIGH"
+    for pat, key in [(r"(\d+\.?\d*)\s*(?:nm|nanometer)","size_nm"),
+                     (r"([+-]?\d+\.?\d*)\s*mV","zeta_mv"),
+                     (r"PDI\s*[=:of]*\s*(\d+\.?\d*)","PDI")]:
+        m = re.search(pat, text, re.I)
+        if m: out[key] = float(m.group(1)); out["confidence"][key] = "HIGH"
     for src in ["human plasma","human serum","fetal bovine serum","FBS","PBS"]:
         if src.lower() in text.lower():
             out["protein_source"] = src; out["confidence"]["protein_source"] = "HIGH"; break
@@ -312,364 +300,380 @@ def extract_corona(text):
     log_entry("S1-D | S1-R10 | NLP", text[:80], f"proteins={len(out['corona_proteins'])}")
     return json.dumps(out, indent=2), summary
 
-# ── SECTION BADGE HELPER ──────────────────────────────────────────────────────
-def badge(sphere_code, sphere_name, project_code, project_name, description, metric=""):
-    metric_html = (f"<span style=\'background:#1e3a5f;color:{ACC2};padding:2px 8px;"
-                   f"border-radius:4px;font-size:11px;margin-left:8px\'>{metric}</span>"
-                   if metric else "")
+# ── HELPERS ───────────────────────────────────────────────────────────────────
+def section_header(code, name, tagline, projects_html):
+    return (
+        f"<div style=\'background:{BG};border:1px solid {BORDER};padding:14px 18px;"
+        f"border-radius:8px;margin-bottom:12px;\'>"
+        f"<div style=\'display:flex;align-items:baseline;gap:10px;\'>"
+        f"<span style=\'color:{ACC2};font-size:16px;font-weight:700\'>{code}</span>"
+        f"<span style=\'color:{TXT};font-size:14px;font-weight:600\'>{name}</span>"
+        f"<span style=\'color:{DIM};font-size:12px\'>{tagline}</span></div>"
+        f"<div style=\'margin-top:8px;font-size:12px;color:{DIM}\'>{projects_html}</div>"
+        f"</div>"
+    )
+
+def proj_badge(code, title, metric=""):
+    m = (f"<span style=\'background:#0f2a3f;color:{ACC2};padding:1px 7px;border-radius:3px;"
+         f"font-size:10px;margin-left:6px\'>{metric}</span>") if metric else ""
     return (
         f"<div style=\'background:{CARD};border-left:3px solid {ACC};"
-        f"padding:10px 14px;border-radius:0 6px 6px 0;margin-bottom:6px;\'>"
-        f"<span style=\'color:{DIM};font-size:11px\'>S1 · Biomedical &nbsp;›&nbsp; "
-        f"{sphere_code} · {sphere_name} &nbsp;›&nbsp; {project_code}</span>{metric_html}<br>"
-        f"<span style=\'color:{TXT};font-weight:bold;font-size:15px\'>{project_name}</span><br>"
-        f"<span style=\'color:{DIM};font-size:12px\'>{description}</span>"
+        f"padding:8px 12px;border-radius:0 6px 6px 0;margin-bottom:8px;\'>"
+        f"<span style=\'color:{DIM};font-size:11px\'>{code}</span>{m}<br>"
+        f"<span style=\'color:{TXT};font-size:14px;font-weight:600\'>{title}</span>"
         f"</div>"
     )
 
 # ── CSS ───────────────────────────────────────────────────────────────────────
-css = (
-    f"body,.gradio-container{{background:{BG}!important;color:{TXT}!important}}"
-    f".tab-nav button{{color:{TXT}!important;background:{CARD}!important;font-size:13px!important}}"
-    f".tab-nav button.selected{{border-bottom:2px solid {ACC}!important;color:{ACC}!important}}"
-    f"h1,h2,h3{{color:{ACC}!important}}"
-    f".gr-button-primary{{background:{ACC}!important;border:none!important}}"
-    f"footer{{display:none!important}}"
-    # section divider tabs look different
-    f".tab-nav button[data-testid*='divider']{{color:{DIM}!important;cursor:default!important;"
-    f"border-bottom:1px solid #334155!important;font-size:11px!important}}"
-)
-
-# ── CONTENT STRINGS ───────────────────────────────────────────────────────────
+css = f"""
+body, .gradio-container {{ background: {BG} !important; color: {TXT} !important; }}
+
+/* OUTER tab bar — PHYLO categories */
+.outer-tabs .tab-nav button {{
+    color: {TXT} !important;
+    background: {CARD} !important;
+    font-size: 13px !important;
+    font-weight: 600 !important;
+    padding: 8px 16px !important;
+    border-radius: 6px 6px 0 0 !important;
+}}
+.outer-tabs .tab-nav button.selected {{
+    border-bottom: 3px solid {ACC} !important;
+    color: {ACC} !important;
+    background: {BG} !important;
+}}
+
+/* INNER sub-tab bar — individual tools */
+.inner-tabs .tab-nav button {{
+    color: {DIM} !important;
+    background: {BG} !important;
+    font-size: 12px !important;
+    font-weight: 500 !important;
+    padding: 5px 12px !important;
+    border-radius: 4px 4px 0 0 !important;
+    border: 1px solid {BORDER} !important;
+    border-bottom: none !important;
+    margin-right: 3px !important;
+}}
+.inner-tabs .tab-nav button.selected {{
+    color: {ACC2} !important;
+    background: {CARD} !important;
+    border-color: {ACC2} !important;
+    border-bottom: none !important;
+}}
+.inner-tabs > .tabitem {{
+    background: {CARD} !important;
+    border: 1px solid {BORDER} !important;
+    border-radius: 0 6px 6px 6px !important;
+    padding: 14px !important;
+}}
+
+h1, h2, h3 {{ color: {ACC} !important; }}
+.gr-button-primary {{ background: {ACC} !important; border: none !important; }}
+footer {{ display: none !important; }}
+"""
+
+# ── LAB MAP HTML ──────────────────────────────────────────────────────────────
 MAP_HTML = f"""
-<div style="background:{CARD};padding:20px;border-radius:8px;font-family:monospace;
-            font-size:13px;line-height:1.9;color:{TXT}">
+<div style="background:{CARD};padding:22px;border-radius:8px;font-family:monospace;
+            font-size:13px;line-height:2.0;color:{TXT}">
 
-<span style="color:{ACC};font-size:15px;font-weight:bold">K R&D Lab · S1 Biomedical</span>
-<span style="color:{DIM};font-size:11px"> — Science Sphere, sub-direction 1</span>
+<span style="color:{ACC};font-size:16px;font-weight:bold">K R&D Lab · S1 Biomedical</span>
+<span style="color:{DIM};font-size:11px;margin-left:12px">Science Sphere — sub-direction 1</span>
 <br><br>
 
-<span style="color:{ACC2}">S1-A · PHYLO-GENOMICS</span> &nbsp;<span style="color:{DIM}">← What breaks in DNA</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R1</b> &nbsp;OpenVariant &nbsp;<span style="color:{GRN}">AUC=0.939 ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R1b</b> Somatic classifier &nbsp;<span style="color:#f59e0b">🔶 In progress</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-R12a</b> Rare variants (DIPG · UVM) &nbsp;<span style="color:{DIM}">🔴 Planned</span><br>
-<br>
-
-<span style="color:{ACC2}">S1-B · PHYLO-RNA</span> &nbsp;<span style="color:{DIM}">← How to silence it via RNA</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R2</b> &nbsp;miRNA silencing (BRCA1/2, TP53) &nbsp;<span style="color:{GRN}">✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R3</b> &nbsp;siRNA synthetic lethal (20+ cancers) &nbsp;<span style="color:{GRN}">✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R4</b> &nbsp;lncRNA-TREM2 ceRNA network &nbsp;<span style="color:{GRN}">✅</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-R4b</b> ASO designer &nbsp;<span style="color:{GRN}">✅</span><br>
-<br>
-
-<span style="color:{ACC2}">S1-C · PHYLO-DRUG</span> &nbsp;<span style="color:{DIM}">← Which molecule treats it</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R5</b> &nbsp;FGFR3 RNA-directed compounds &nbsp;<span style="color:{GRN}">✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R5b</b> Synthetic lethal drug mapping &nbsp;<span style="color:#f59e0b">🔶</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-R13</b> m6A × Ferroptosis × Circadian ⭐ &nbsp;<span style="color:{DIM}">🔴 Frontier</span><br>
-<br>
-
-<span style="color:{ACC2}">S1-D · PHYLO-LNP</span> &nbsp;<span style="color:{DIM}">← How to deliver the drug</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R6</b> &nbsp;LNP corona (serum) &nbsp;<span style="color:{GRN}">AUC=0.791 ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R7</b> &nbsp;Flow corona — Vroman effect &nbsp;<span style="color:{GRN}">✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R8</b> &nbsp;LNP brain / BBB / ApoE &nbsp;<span style="color:{GRN}">✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R10</b> AutoCorona NLP &nbsp;<span style="color:{GRN}">F1=0.71 ✅</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-R11</b> CSF · Vitreous · Bone Marrow ⭐ &nbsp;<span style="color:{DIM}">🔴 0 prior studies</span><br>
-<br>
-
-<span style="color:{ACC2}">S1-E · PHYLO-BIOMARKERS</span> &nbsp;<span style="color:{DIM}">← Detect without biopsy</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R9</b> &nbsp;Liquid Biopsy classifier &nbsp;<span style="color:{GRN}">AUC=0.992* ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R9b</b> Protein panel validator &nbsp;<span style="color:#f59e0b">🔶</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-R9c</b> ctDNA gap analysis &nbsp;<span style="color:{DIM}">🔴</span><br>
-<br>
-
-<span style="color:{ACC2}">S1-F · PHYLO-RARE</span> &nbsp;<span style="color:{DIM}">← Where nobody looked yet</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R12b</b> DIPG toolkit (H3K27M) &nbsp;<span style="color:{DIM}">🔴</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-R12c</b> UVM toolkit (GNAQ/GNA11) &nbsp;<span style="color:{DIM}">🔴</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-R12d</b> pAML toolkit (FLT3-ITD) &nbsp;<span style="color:{DIM}">🔴</span><br>
-<br>
-<span style="color:{DIM};font-size:12px">
+<span style="color:{ACC2};font-weight:600">S1-A · PHYLO-GENOMICS</span>
+<span style="color:{DIM}"> — What breaks in DNA</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R1</b> &nbsp;OpenVariant
+<span style="color:{GRN}"> AUC=0.939 ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R1b</b> Somatic classifier
+<span style="color:#f59e0b"> 🔶 In progress</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R12a</b> Rare variants (DIPG · UVM)
+<span style="color:{DIM}"> 🔴 Planned</span><br><br>
+
+<span style="color:{ACC2};font-weight:600">S1-B · PHYLO-RNA</span>
+<span style="color:{DIM}"> — How to silence it via RNA</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R2</b> &nbsp;miRNA silencing (BRCA1/2, TP53)
+<span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R3</b> &nbsp;siRNA synthetic lethal (LUAD · BRCA · COAD)
+<span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R4</b> &nbsp;lncRNA-TREM2 ceRNA network
+<span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R4b</b> ASO designer
+<span style="color:{GRN}"> ✅</span><br><br>
+
+<span style="color:{ACC2};font-weight:600">S1-C · PHYLO-DRUG</span>
+<span style="color:{DIM}"> — Which molecule treats it</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R5</b> &nbsp;FGFR3 RNA-directed compounds
+<span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R5b</b> Synthetic lethal drug mapping
+<span style="color:#f59e0b"> 🔶</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R13</b> m6A × Ferroptosis × Circadian ⭐
+<span style="color:{DIM}"> 🔴 Frontier</span><br><br>
+
+<span style="color:{ACC2};font-weight:600">S1-D · PHYLO-LNP</span>
+<span style="color:{DIM}"> — How to deliver the drug</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R6</b> &nbsp;LNP corona (serum)
+<span style="color:{GRN}"> AUC=0.791 ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R7</b> &nbsp;Flow corona — Vroman effect
+<span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R8</b> &nbsp;LNP brain / BBB / ApoE
+<span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R10</b> AutoCorona NLP
+<span style="color:{GRN}"> F1=0.71 ✅</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R11</b> CSF · Vitreous · Bone Marrow ⭐
+<span style="color:{DIM}"> 🔴 0 prior studies</span><br><br>
+
+<span style="color:{ACC2};font-weight:600">S1-E · PHYLO-BIOMARKERS</span>
+<span style="color:{DIM}"> — Detect without biopsy</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R9</b> &nbsp;Liquid Biopsy classifier
+<span style="color:{GRN}"> AUC=0.992* ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R9b</b> Protein panel validator
+<span style="color:#f59e0b"> 🔶</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R9c</b> ctDNA gap analysis
+<span style="color:{DIM}"> 🔴</span><br><br>
+
+<span style="color:{ACC2};font-weight:600">S1-F · PHYLO-RARE</span>
+<span style="color:{DIM}"> — Where nobody looked yet</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R12b</b> DIPG toolkit (H3K27M)
+<span style="color:{DIM}"> 🔴</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-R12c</b> UVM toolkit (GNAQ/GNA11)
+<span style="color:{DIM}"> 🔴</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-R12d</b> pAML toolkit (FLT3-ITD)
+<span style="color:{DIM}"> 🔴</span><br><br>
+
+<span style="color:{DIM};font-size:11px">
 ✅ Active in this demo &nbsp;·&nbsp; 🔶 In progress &nbsp;·&nbsp; 🔴 Planned / Frontier<br>
-⭐ = gap research (0–1 prior studies globally) &nbsp;·&nbsp; * = tissue proxy, plasma validation pending
+⭐ gap research (0–1 prior studies globally) &nbsp;·&nbsp; * tissue proxy, plasma validation pending
 </span>
 </div>
 """
 
-LEARNING_CASES = f"""
-## 🧪 Guided Investigations — S1 Biomedical
-
-> Progress through levels: 🟢 Beginner → 🟡 Intermediate → 🔴 Advanced
-
----
-
-### 🟢 Case 1 · S1-A · S1-R1 — Variant Pathogenicity
-**Why does the same position give two different outcomes?**
-
-1. **OpenVariant** → `BRCA1:p.R1699Q` → Benign
-2. **OpenVariant** → `BRCA1:p.R1699W` → Pathogenic
-3. Same position (R1699), different amino acid (Q vs W). What changed?
-
-*Key concept: Amino acid polarity determines BRCT domain folding. Q is polar-uncharged; W is bulky-aromatic.*
-
----
-
-### 🟢 Case 2 · S1-D · S1-R6 + S1-R8 — PEG and Brain Delivery
-**How does PEG% change which protein coats your nanoparticle?**
-
-1. **LNP Corona** → Ionizable, Zeta=−5, Size=100, **PEG=0.5%** → note protein
-2. **PEG=2.5%** → compare dominant protein
-3. **LNP Brain** → pKa=6.5 → check ApoE%
-
-*Key concept: More PEG → steric shielding → less Fibrinogen → more ApoE → better BBB crossing.*
-
----
-
-### 🟡 Case 3 · S1-D · S1-R7 — Vroman Effect Under Flow
-**Does blood flow speed reshape the corona over time?**
-
-1. **Flow Corona** → Flow=0, Ionizable → observe ApoE plateau minute
-2. **Flow=40** (arterial) → compare same curve
-3. At what minute does ApoE dominate under arterial flow?
-
-*Key concept: Albumin adsorbs first (abundance), then displaced by ApoE (affinity). Flow accelerates exchange 3–4×.*
-
----
-
-### 🟡 Case 4 · S1-B · S1-R3 — Novel siRNA Targets
-**Which cancer type has the most undrugged therapeutic targets?**
-
-1. **TP53 siRNA** → LUAD → count Drug_status = "Novel"
-2. Repeat for BRCA, COAD
-3. Pick one Novel gene → search: `[gene name] cancer PubMed`
-
-*Key concept: "Novel" = no approved or clinical drug yet — highest opportunity for new therapeutic development.*
-
----
-
-### 🔴 Case 5 · S1-E · S1-R9 — Cancer Detection Threshold
-**What is the minimum signal that flips diagnosis to CANCER?**
-
-1. **Liquid Biopsy** → all sliders = 0 → HEALTHY
-2. Set CTHRC1=2.5, FHL2=2.0, LDHA=1.8 → observe
-3. Reset all. Increase only CTHRC1 step by step. At what value does it tip?
-
-*Key concept: CTHRC1 weight=0.18 is the dominant feature. One protein can outweigh a full panel.*
-
----
-
-### 🔴 Case 6 · S1-B + S1-C · Cross-tool — Convergent Evidence
-**Is there target overlap between RNA silencing and drug discovery?**
-
-1. **miRNA** → gene=TP53 → find top silenced targets (BCL2, CDK6)
-2. **FGFR3 Drug** → P1 → find if CDK6 pathway appears in compound targets
-3. **siRNA** → BRCA → does CDK6 appear in synthetic lethal list?
-
-*Key concept: Convergence across S1-B and S1-C = higher-confidence therapeutic hypothesis. This is how real pipelines are built.*
-"""
-
-# ── BLOCKS UI ─────────────────────────────────────────────────────────────────
+# ── UI ────────────────────────────────────────────────────────────────────────
 with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
 
     gr.Markdown(
-        "# 🔬 K R&D Lab · Science Sphere\n"
-        "**S1 Biomedical** — 10 active projects · "
-        "[Oksana Kolisnyk](https://kosatiks-group.pp.ua) · KOSATIKS GROUP\n"
-        "> Research only. Not clinical advice. &nbsp;|&nbsp; "
+        "# 🔬 K R&D Lab · Science Sphere — S1 Biomedical\n"
+        "**Oksana Kolisnyk** · [KOSATIKS GROUP](https://kosatiks-group.pp.ua) &nbsp;|&nbsp; "
         "[GitHub](https://github.com/K-RnD-Lab) &nbsp; "
-        "[HuggingFace](https://huggingface.co/K-RnD-Lab)"
+        "[HuggingFace](https://huggingface.co/K-RnD-Lab) &nbsp;|&nbsp; "
+        "*Research only. Not clinical advice.*"
     )
 
-    with gr.Tabs():
+    # ═══════════════════════════════════════════════════════
+    # OUTER TABS — one per PHYLO-* category
+    # ═══════════════════════════════════════════════════════
+    with gr.Tabs(elem_classes="outer-tabs"):
 
-        # ── MAP ───────────────────────────────────────────────────────────────
+        # ── 🗺️ MAP ─────────────────────────────────────────
         with gr.TabItem("🗺️ Lab Map"):
             gr.HTML(MAP_HTML)
 
-        # ── S1-A · GENOMICS ───────────────────────────────────────────────────
-        with gr.TabItem("── S1-A · GENOMICS ──"):
-            gr.HTML(
-                f"<div style=\'background:{CARD};padding:14px;border-radius:8px;\'>"
-                f"<b style=\'color:{ACC2}\'>S1-A · PHYLO-GENOMICS</b>"
-                f"<span style=\'color:{DIM};font-size:12px\'> — What breaks in DNA</span><br>"
-                f"<span style=\'color:{TXT};font-size:13px\'>Projects: S1-R1 (active) · S1-R1b (in progress) · S1-R12a (planned)</span>"
-                f"</div>"
-            )
-
-        with gr.TabItem("S1-R1 · OpenVariant"):
-            gr.HTML(badge("S1-A","PHYLO-GENOMICS","S1-R1","OpenVariant",
-                          "SNV pathogenicity classifier · ClinVar 2026","AUC = 0.939"))
-            hgvs = gr.Textbox(label="HGVS notation", placeholder="BRCA1:p.R1699Q")
-            gr.Markdown("**Or enter functional scores manually:**")
-            with gr.Row():
-                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()
-            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)
-
-        # ── S1-B · RNA THERAPEUTICS ───────────────────────────────────────────
-        with gr.TabItem("── S1-B · RNA THER ──"):
-            gr.HTML(
-                f"<div style=\'background:{CARD};padding:14px;border-radius:8px;\'>"
-                f"<b style=\'color:{ACC2}\'>S1-B · PHYLO-RNA</b>"
-                f"<span style=\'color:{DIM};font-size:12px\'> — How to silence it via RNA</span><br>"
-                f"<span style=\'color:{TXT};font-size:13px\'>Projects: S1-R2 · S1-R3 · S1-R4 · S1-R4b (all active)</span>"
-                f"</div>"
-            )
-
-        with gr.TabItem("S1-R2 · miRNA"):
-            gr.HTML(badge("S1-B","PHYLO-RNA","S1-R2","miRNA Silencing",
-                          "Downregulated miRNAs in tumor suppressor knockouts · 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"],["TP53"]], inputs=[g1])
-            b1.click(predict_mirna, g1, o1)
-
-        with gr.TabItem("S1-R3 · siRNA"):
-            gr.HTML(badge("S1-B","PHYLO-RNA","S1-R3","siRNA Synthetic Lethal",
-                          "TP53-null cancer — novel undrugged SL targets · LUAD · BRCA · COAD"))
-            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)
-
-        with gr.TabItem("S1-R4 · lncRNA"):
-            gr.HTML(badge("S1-B","PHYLO-RNA","S1-R4","lncRNA-TREM2 Network",
-                          "ceRNA axis in Alzheimer neuroinflammation · CYTOR→miR-138-5p→AKT1"))
-            b3 = gr.Button("Load Results", variant="primary")
-            o3a = gr.Dataframe(label="ceRNA Network")
-            o3b = gr.Dataframe(label="ASO Candidates (S1-R4b)")
-            b3.click(get_lncrna, [], [o3a, o3b])
-
-        # ── S1-C · DRUG DISCOVERY ─────────────────────────────────────────────
-        with gr.TabItem("── S1-C · DRUG ──"):
-            gr.HTML(
-                f"<div style=\'background:{CARD};padding:14px;border-radius:8px;\'>"
-                f"<b style=\'color:{ACC2}\'>S1-C · PHYLO-DRUG</b>"
-                f"<span style=\'color:{DIM};font-size:12px\'> — Which molecule treats it</span><br>"
-                f"<span style=\'color:{TXT};font-size:13px\'>Projects: S1-R5 (active) · S1-R5b (in progress) · S1-R13 (frontier)</span>"
-                f"</div>"
-            )
-
-        with gr.TabItem("S1-R5 · Drug"):
-            gr.HTML(badge("S1-C","PHYLO-DRUG","S1-R5","FGFR3 RNA-Directed Drug Discovery",
-                          "Small molecules binding FGFR3 RNA structural pockets · ChEMBL screen",
-                          "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])
-
-        # ── S1-D · LNP DELIVERY ───────────────────────────────────────────────
-        with gr.TabItem("── S1-D · LNP ──"):
-            gr.HTML(
-                f"<div style=\'background:{CARD};padding:14px;border-radius:8px;\'>"
-                f"<b style=\'color:{ACC2}\'>S1-D · PHYLO-LNP</b>"
-                f"<span style=\'color:{DIM};font-size:12px\'> — How to deliver the drug to the cell</span><br>"
-                f"<span style=\'color:{TXT};font-size:13px\'>Active: S1-R6 · S1-R7 · S1-R8 · S1-R10 &nbsp;|&nbsp; "
-                f"Frontier: S1-R11 (CSF/Vitreous/Bone Marrow — 0 prior studies)</span>"
-                f"</div>"
-            )
-
-        with gr.TabItem("S1-R6 · Corona"):
-            gr.HTML(badge("S1-D","PHYLO-LNP","S1-R6","LNP Protein Corona (Serum)",
-                          "Dominant corona protein from formulation parameters","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-R7 · Flow"):
-            gr.HTML(badge("S1-D","PHYLO-LNP","S1-R7","Flow Corona — Vroman Effect",
-                          "Blood flow reshapes LNP corona over time · albumin→ApoE exchange kinetics"))
-            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 rate cm/s (aorta=40)")
-            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-R8 · Brain"):
-            gr.HTML(badge("S1-D","PHYLO-LNP","S1-R8","LNP Brain Delivery",
-                          "ApoE corona % and blood-brain barrier crossing 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])
-
-        with gr.TabItem("S1-R10 · NLP"):
-            gr.HTML(badge("S1-D","PHYLO-LNP","S1-R10","AutoCorona NLP",
-                          "Extract structured LNP data from PubMed/PMC abstracts","F1 = 0.71"))
-            txt  = gr.Textbox(lines=6,label="Paper abstract",placeholder="Paste abstract text 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])
-
-        # ── S1-E · BIOMARKERS ─────────────────────────────────────────────────
-        with gr.TabItem("── S1-E · BIO ──"):
-            gr.HTML(
-                f"<div style=\'background:{CARD};padding:14px;border-radius:8px;\'>"
-                f"<b style=\'color:{ACC2}\'>S1-E · PHYLO-BIOMARKERS</b>"
-                f"<span style=\'color:{DIM};font-size:12px\'> — Detect cancer without tissue biopsy</span><br>"
-                f"<span style=\'color:{TXT};font-size:13px\'>Projects: S1-R9 (active) · S1-R9b (in progress) · S1-R9c (planned)</span>"
-                f"</div>"
-            )
-
-        with gr.TabItem("S1-R9 · Biopsy"):
-            gr.HTML(badge("S1-E","PHYLO-BIOMARKERS","S1-R9","Liquid Biopsy Classifier",
-                          "CTHRC1 · FHL2 · LDHA panel · tissue proteomics proxy","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],[0,0,0,0,0,0,0,0,0,0]],
-                        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])
-
-        # ── LAB ───────────────────────────────────────────────────────────────
+        # ── 🧬 S1-A · PHYLO-GENOMICS ───────────────────────
+        with gr.TabItem("🧬 PHYLO-GENOMICS"):
+            gr.HTML(section_header(
+                "S1-A", "PHYLO-GENOMICS", "— What breaks in DNA",
+                "S1-R1 OpenVariant ✅ &nbsp;·&nbsp; S1-R1b Somatic classifier 🔶 &nbsp;·&nbsp; S1-R12a Rare variants 🔴"
+            ))
+            with gr.Tabs(elem_classes="inner-tabs"):
+
+                with gr.TabItem("S1-R1 · OpenVariant"):
+                    gr.HTML(proj_badge("S1-A · PHYLO-GENOMICS · S1-R1",
+                                       "OpenVariant — SNV Pathogenicity Classifier", "AUC = 0.939"))
+                    hgvs = gr.Textbox(label="HGVS notation", placeholder="BRCA1:p.R1699Q")
+                    gr.Markdown("**Or enter functional scores manually:**")
+                    with gr.Row():
+                        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")
+                    b_v = gr.Button("Predict Pathogenicity", variant="primary")
+                    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])
+                    b_v.click(predict_variant, [hgvs,sift,pp,gn], o_v)
+
+                with gr.TabItem("S1-R1b · Somatic 🔶"):
+                    gr.HTML(proj_badge("S1-A · PHYLO-GENOMICS · S1-R1b",
+                                       "Somatic Mutation Classifier — BRCA · LUAD panels", "🔶 In progress"))
+                    gr.Markdown("> This module is in active development. Coming in the next release.")
+
+        # ── 🔬 S1-B · PHYLO-RNA ────────────────────────────
+        with gr.TabItem("🔬 PHYLO-RNA"):
+            gr.HTML(section_header(
+                "S1-B", "PHYLO-RNA", "— How to silence it via RNA",
+                "S1-R2 miRNA ✅ &nbsp;·&nbsp; S1-R3 siRNA ✅ &nbsp;·&nbsp; S1-R4 lncRNA ✅ &nbsp;·&nbsp; S1-R4b ASO ✅"
+            ))
+            with gr.Tabs(elem_classes="inner-tabs"):
+
+                with gr.TabItem("S1-R2 · miRNA"):
+                    gr.HTML(proj_badge("S1-B · PHYLO-RNA · S1-R2",
+                                       "miRNA Silencing — BRCA1/2 · TP53 tumor suppressors"))
+                    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)
+
+                with gr.TabItem("S1-R3 · siRNA"):
+                    gr.HTML(proj_badge("S1-B · PHYLO-RNA · S1-R3",
+                                       "siRNA Synthetic Lethal — TP53-null · LUAD · BRCA · COAD"))
+                    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)
+
+                with gr.TabItem("S1-R4 · lncRNA + ASO"):
+                    gr.HTML(proj_badge("S1-B · PHYLO-RNA · S1-R4 + S1-R4b",
+                                       "lncRNA-TREM2 ceRNA Network + ASO Candidates · Alzheimer neuroinflammation"))
+                    b3 = gr.Button("Load Results", variant="primary")
+                    o3a = gr.Dataframe(label="ceRNA Network (S1-R4)")
+                    o3b = gr.Dataframe(label="ASO Candidates (S1-R4b)")
+                    b3.click(get_lncrna, [], [o3a, o3b])
+
+        # ── 💊 S1-C · PHYLO-DRUG ───────────────────────────
+        with gr.TabItem("💊 PHYLO-DRUG"):
+            gr.HTML(section_header(
+                "S1-C", "PHYLO-DRUG", "— Which molecule treats it",
+                "S1-R5 FGFR3 ✅ &nbsp;·&nbsp; S1-R5b SL drug mapping 🔶 &nbsp;·&nbsp; S1-R13 m6A×Ferroptosis×Circadian 🔴⭐"
+            ))
+            with gr.Tabs(elem_classes="inner-tabs"):
+
+                with gr.TabItem("S1-R5 · FGFR3"):
+                    gr.HTML(proj_badge("S1-C · PHYLO-DRUG · S1-R5",
+                                       "FGFR3 RNA-Directed Drug Discovery · ChEMBL screen",
+                                       "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])
+
+                with gr.TabItem("S1-R13 · Frontier 🔴⭐"):
+                    gr.HTML(proj_badge("S1-C · PHYLO-DRUG · S1-R13",
+                                       "m6A × Ferroptosis × Circadian — Pan-cancer triad", "🔴 Frontier"))
+                    gr.Markdown(
+                        "> **Research gap:** This triple intersection has never been studied as an integrated system.\n\n"
+                        "> **Planned datasets:** TCGA-PAAD · GEO m6A atlases · Circadian gene panels\n\n"
+                        "> **Expected timeline:** Q3 2026"
+                    )
+
+        # ── 🧪 S1-D · PHYLO-LNP ────────────────────────────
+        with gr.TabItem("🧪 PHYLO-LNP"):
+            gr.HTML(section_header(
+                "S1-D", "PHYLO-LNP", "— How to deliver the drug to the cell",
+                "S1-R6 Corona ✅ · S1-R7 Flow ✅ · S1-R8 Brain ✅ · S1-R10 NLP ✅ · S1-R11 CSF/BM 🔴⭐"
+            ))
+            with gr.Tabs(elem_classes="inner-tabs"):
+
+                with gr.TabItem("S1-R6 · Corona"):
+                    gr.HTML(proj_badge("S1-D · PHYLO-LNP · S1-R6",
+                                       "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-R7 · Flow"):
+                    gr.HTML(proj_badge("S1-D · PHYLO-LNP · S1-R7",
+                                       "Flow Corona — Vroman Effect · albumin→ApoE kinetics"))
+                    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 (aorta=40)")
+                    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-R8 · Brain"):
+                    gr.HTML(proj_badge("S1-D · PHYLO-LNP · S1-R8",
+                                       "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])
+
+                with gr.TabItem("S1-R10 · NLP"):
+                    gr.HTML(proj_badge("S1-D · PHYLO-LNP · S1-R10",
+                                       "AutoCorona NLP — structured data from PMC 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])
+
+                with gr.TabItem("S1-R11 · CSF/BM 🔴⭐"):
+                    gr.HTML(proj_badge("S1-D · PHYLO-LNP · S1-R11",
+                                       "LNP Corona in CSF · Vitreous · Bone Marrow", "🔴 0 prior studies"))
+                    gr.Markdown(
+                        "> **Research gap:** Protein corona has only been characterized in serum/plasma. "
+                        "CSF, vitreous humor, and bone marrow interstitial fluid remain completely unstudied.\n\n"
+                        "> **Target cancers:** DIPG (CSF) · UVM (vitreous) · pAML (bone marrow)\n\n"
+                        "> **Expected timeline:** Q2–Q3 2026"
+                    )
+
+        # ── 🩸 S1-E · PHYLO-BIOMARKERS ─────────────────────
+        with gr.TabItem("🩸 PHYLO-BIOMARKERS"):
+            gr.HTML(section_header(
+                "S1-E", "PHYLO-BIOMARKERS", "— Detect cancer without tissue biopsy",
+                "S1-R9 Liquid Biopsy ✅ &nbsp;·&nbsp; S1-R9b Panel validator 🔶 &nbsp;·&nbsp; S1-R9c ctDNA gap 🔴"
+            ))
+            with gr.Tabs(elem_classes="inner-tabs"):
+
+                with gr.TabItem("S1-R9 · Liquid Biopsy"):
+                    gr.HTML(proj_badge("S1-E · PHYLO-BIOMARKERS · S1-R9",
+                                       "Liquid Biopsy Classifier — CTHRC1 · FHL2 · LDHA panel",
+                                       "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-R9b · Validator 🔶"):
+                    gr.HTML(proj_badge("S1-E · PHYLO-BIOMARKERS · S1-R9b",
+                                       "Protein Panel Validator — multi-cancer plasma validation", "🔶 In progress"))
+                    gr.Markdown("> Coming next — validates S1-R9 results against GEO plasma proteomics datasets.")
+
+        # ── 📓 JOURNAL ──────────────────────────────────────
         with gr.TabItem("📓 Journal"):
-            gr.Markdown("### Lab Journal · Auto-logged by project code\nEvery tool call is saved with its S1-X · S1-RX label.")
+            gr.Markdown("### Lab Journal\nEvery tool call auto-logged with project code.")
             with gr.Row():
-                note_text = gr.Textbox(label="📝 Observation / conclusion",
-                                       placeholder="What did you discover?", lines=3)
+                note_text = gr.Textbox(label="📝 Observation", placeholder="What did you discover?", lines=3)
                 note_tab  = gr.Textbox(label="Project code (e.g. S1-R1)", value="General")
             note_last  = gr.Textbox(visible=False)
             save_btn   = gr.Button("💾 Save", variant="primary")
@@ -679,36 +683,73 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
             refresh_btn.click(load_journal, [], journal_df)
             save_btn.click(save_note, [note_text,note_tab,note_last], [save_msg,journal_df])
 
+        # ── 📚 LEARNING ─────────────────────────────────────
         with gr.TabItem("📚 Learning"):
-            gr.Markdown(LEARNING_CASES)
-            gr.Markdown("""---
-### 📖 Quick Reference — S1 Active Tools
-
-| Code | Sub-dir | Tool | Key metric |
-|------|---------|------|------------|
-| S1-R1 | S1-A | OpenVariant | AUC=0.939 |
-| S1-R2 | S1-B | miRNA silencing | hsa-miR-148a-3p top hit |
-| S1-R3 | S1-B | siRNA SL targets | SPC24 top in LUAD |
-| S1-R4 | S1-B | lncRNA-TREM2 | CYTOR→AKT1 |
-| S1-R5 | S1-C | FGFR3 drug | score=0.793 |
-| S1-R6 | S1-D | LNP corona | AUC=0.791 |
-| S1-R7 | S1-D | Flow corona | 3–4× ApoE acceleration |
-| S1-R8 | S1-D | LNP brain | pKa 6.2–6.8 optimal |
-| S1-R9 | S1-E | Liquid biopsy | AUC=0.992* |
-| S1-R10 | S1-D | AutoCorona NLP | F1=0.71 |
-
-### 🔗 Resources
-[PubMed](https://pubmed.ncbi.nlm.nih.gov) · [ClinVar](https://ncbi.nlm.nih.gov/clinvar/) · 
-[miRBase](https://mirbase.org) · [ChEMBL](https://ebi.ac.uk/chembl/) · 
-[DepMap](https://depmap.org) · [UniProt](https://uniprot.org)
+            gr.Markdown("""
+## 🧪 Guided Investigations — S1 Biomedical
+> 🟢 Beginner → 🟡 Intermediate → 🔴 Advanced
+
+---
+### 🟢 Case 1 · S1-A · S1-R1
+**Why does the same position give two different outcomes?**
+1. PHYLO-GENOMICS → OpenVariant → `BRCA1:p.R1699Q` → Benign
+2. Enter `BRCA1:p.R1699W` → Pathogenic
+3. Same position, different amino acid — Q (polar) vs W (bulky-aromatic)
+
+---
+### 🟢 Case 2 · S1-D · S1-R6 + S1-R8
+**How does PEG% control which protein coats the nanoparticle?**
+1. PHYLO-LNP → Corona → Ionizable, Zeta=−5, PEG=**0.5%** → note protein
+2. Change PEG=**2.5%** → compare
+3. Brain tab → pKa=6.5 → check ApoE%
+
+---
+### 🟡 Case 3 · S1-D · S1-R7
+**Does blood flow reshape the corona over time?**
+1. PHYLO-LNP → Flow → Flow=0 → observe ApoE curve
+2. Flow=40 (arterial) → compare
+3. At what minute does ApoE dominate?
+
+---
+### 🟡 Case 4 · S1-B · S1-R3
+**Which cancer has the most novel (undrugged) siRNA targets?**
+1. PHYLO-RNA → siRNA → LUAD → count "Novel"
+2. Repeat BRCA, COAD
+
+---
+### 🔴 Case 5 · S1-E · S1-R9
+**Minimum protein signal that flips to CANCER?**
+1. PHYLO-BIOMARKERS → Liquid Biopsy → all=0 → HEALTHY
+2. Set CTHRC1=2.5, FHL2=2.0, LDHA=1.8 → observe
+3. Reset. Increase only CTHRC1 step by step.
+
+---
+### 🔴 Case 6 · S1-B + S1-C — Cross-tool convergence
+1. PHYLO-RNA → miRNA → TP53 → find top targets (BCL2, CDK6)
+2. PHYLO-DRUG → FGFR3 → check CDK6 pathway overlap
+3. PHYLO-RNA → siRNA → BRCA → does CDK6 appear?
+
+---
+### 📖 Tool Index
+| Code | Module | Tool | Metric |
+|------|--------|------|--------|
+| S1-R1 | PHYLO-GENOMICS | OpenVariant | AUC=0.939 |
+| S1-R2 | PHYLO-RNA | miRNA silencing | top: hsa-miR-148a |
+| S1-R3 | PHYLO-RNA | siRNA SL | SPC24 top LUAD |
+| S1-R4 | PHYLO-RNA | lncRNA-TREM2 | CYTOR→AKT1 |
+| S1-R5 | PHYLO-DRUG | FGFR3 drug | score=0.793 |
+| S1-R6 | PHYLO-LNP | Corona | AUC=0.791 |
+| S1-R7 | PHYLO-LNP | Flow Vroman | 3–4× faster |
+| S1-R8 | PHYLO-LNP | LNP Brain | pKa 6.2–6.8 |
+| S1-R9 | PHYLO-BIOMARKERS | Liquid Biopsy | AUC=0.992* |
+| S1-R10 | PHYLO-LNP | AutoCorona NLP | F1=0.71 |
 """)
 
     gr.Markdown(
-        f"---\n"
-        f"**K R&D Lab** · Science Sphere · S1 Biomedical · "
-        f"[GitHub](https://github.com/K-RnD-Lab) · "
-        f"[HuggingFace](https://huggingface.co/K-RnD-Lab) · "
-        f"[KOSATIKS GROUP 🦈](https://kosatiks-group.pp.ua) · MIT License"
+        "---\n**K R&D Lab** · Science Sphere · S1 Biomedical · "
+        "[GitHub](https://github.com/K-RnD-Lab) · "
+        "[HuggingFace](https://huggingface.co/K-RnD-Lab) · "
+        "[KOSATIKS GROUP 🦈](https://kosatiks-group.pp.ua) · MIT License"
     )
 
 demo.launch(server_name="0.0.0.0", server_port=7860)