Update app.py

app.py

@@ -9,6 +9,8 @@ from io import BytesIO
 from PIL import Image
 from datetime import datetime
 from pathlib import Path
+import plotly.graph_objects as go
+import plotly.express as px
 
 # ========== Діагностичний друк ==========
 print("Gradio version:", gr.__version__)
@@ -43,13 +45,25 @@ def load_journal():
     try:
         if not LOG_PATH.exists():
             return pd.DataFrame(columns=["timestamp","tab","inputs","result","note"])
-        return pd.read_csv(LOG_PATH)
+        df = pd.read_csv(LOG_PATH)
+        # Convert to markdown for nice display
+        if df.empty:
+            return "No entries yet."
+        return df.tail(20).to_markdown(index=False)
     except Exception:
-        return pd.DataFrame(columns=["timestamp","tab","inputs","result","note"])
+        return "Error loading journal."
 
-def save_note(note, tab, last_result):
-    log_entry(tab, "", last_result, note)
-    return "✅ Saved!", load_journal()
+def save_note(note, tab):
+    log_entry(tab, "manual note", note, note)
+    return load_journal()
+
+def clear_journal():
+    try:
+        if LOG_PATH.exists():
+            LOG_PATH.unlink()
+        return "Journal cleared."
+    except Exception:
+        return "Error clearing journal."
 
 # ========== БАЗИ ДАНИХ ==========
 MIRNA_DB = {
@@ -152,7 +166,65 @@ PROTEINS = ["albumin","apolipoprotein","fibrinogen","vitronectin",
             "clusterin","igm","iga","igg","complement","transferrin",
             "alpha-2-macroglobulin"]
 
-# ========== ФУНКЦІЇ ПРЕДИКЦІЇ ==========
+# ---------- S1-F RARE ----------
+DIPG_VARIANTS = [
+    {"Variant":"H3K27M (H3F3A)","Freq_pct":78,"Pathway":"PRC2 inhibition → global H3K27me3 loss","Drug_status":"ONC201 (clinical)","Circadian_gene":"BMAL1 suppressed"},
+    {"Variant":"ACVR1 p.R206H","Freq_pct":21,"Pathway":"BMP/SMAD hyperactivation","Drug_status":"LDN-193189 (preclinical)","Circadian_gene":"PER1 disrupted"},
+    {"Variant":"PIK3CA p.H1047R","Freq_pct":15,"Pathway":"PI3K/AKT/mTOR","Drug_status":"Copanlisib (clinical)","Circadian_gene":"CRY1 altered"},
+    {"Variant":"TP53 p.R248W","Freq_pct":14,"Pathway":"DNA damage response loss","Drug_status":"APR-246 (clinical)","Circadian_gene":"p53-CLOCK axis"},
+    {"Variant":"PDGFRA amp","Freq_pct":13,"Pathway":"RTK/RAS signalling","Drug_status":"Avapritinib (clinical)","Circadian_gene":"REV-ERB altered"},
+]
+DIPG_CSF_LNP = [
+    {"Formulation":"MC3-DSPC-Chol-PEG","Size_nm":92,"Zeta_mV":-4.1,"CSF_protein":"Beta2-microglobulin","ApoE_pct":12.4,"BBB_est":0.41,"Priority":"HIGH"},
+    {"Formulation":"DLin-KC2-DSPE-PEG","Size_nm":87,"Zeta_mV":-3.8,"CSF_protein":"Cystatin C","ApoE_pct":14.1,"BBB_est":0.47,"Priority":"HIGH"},
+    {"Formulation":"C12-200-DOPE-PEG","Size_nm":103,"Zeta_mV":-5.2,"CSF_protein":"Albumin (low)","ApoE_pct":9.8,"BBB_est":0.33,"Priority":"MEDIUM"},
+    {"Formulation":"DODAP-DSPC-Chol","Size_nm":118,"Zeta_mV":-2.1,"CSF_protein":"Transferrin","ApoE_pct":7.2,"BBB_est":0.24,"Priority":"LOW"},
+]
+
+UVM_VARIANTS = [
+    {"Variant":"GNAQ p.Q209L","Freq_pct":46,"Pathway":"PLCβ → PKC → MAPK","Drug_status":"Darovasertib (clinical)","m6A_writer":"METTL3 upregulated"},
+    {"Variant":"GNA11 p.Q209L","Freq_pct":32,"Pathway":"PLCβ → PKC → MAPK","Drug_status":"Darovasertib (clinical)","m6A_writer":"WTAP upregulated"},
+    {"Variant":"BAP1 loss","Freq_pct":47,"Pathway":"Chromatin remodeling → metastasis","Drug_status":"No approved (HDAC trials)","m6A_writer":"FTO overexpressed"},
+    {"Variant":"SF3B1 p.R625H","Freq_pct":19,"Pathway":"Splicing alteration → neoepitopes","Drug_status":"H3B-8800 (clinical)","m6A_writer":"METTL14 altered"},
+    {"Variant":"EIF1AX p.A113_splice","Freq_pct":14,"Pathway":"Translation initiation","Drug_status":"Novel — no drug","m6A_writer":"YTHDF2 suppressed"},
+]
+UVM_VITREOUS_LNP = [
+    {"Formulation":"SM-102-DSPC-Chol-PEG","Vitreal_protein":"Hyaluronan-binding","Size_nm":95,"Zeta_mV":-3.2,"Retention_h":18,"Priority":"HIGH"},
+    {"Formulation":"Lipid-H-DOPE-PEG","Vitreal_protein":"Vitronectin dominant","Size_nm":88,"Zeta_mV":-4.0,"Retention_h":22,"Priority":"HIGH"},
+    {"Formulation":"DOTAP-DSPC-PEG","Vitreal_protein":"Albumin wash-out","Size_nm":112,"Zeta_mV":+2.1,"Retention_h":6,"Priority":"LOW"},
+    {"Formulation":"MC3-DPPC-Chol","Vitreal_protein":"Clusterin-rich","Size_nm":101,"Zeta_mV":-2.8,"Retention_h":14,"Priority":"MEDIUM"},
+]
+
+PAML_VARIANTS = [
+    {"Variant":"FLT3-ITD","Freq_pct":25,"Pathway":"RTK constitutive activation → JAK/STAT","Drug_status":"Midostaurin (approved)","Ferroptosis":"GPX4 suppressed"},
+    {"Variant":"NPM1 c.860_863dupTCAG","Freq_pct":30,"Pathway":"Nuclear export deregulation","Drug_status":"APR-548 combo (clinical)","Ferroptosis":"SLC7A11 upregulated"},
+    {"Variant":"DNMT3A p.R882H","Freq_pct":18,"Pathway":"Epigenetic dysregulation","Drug_status":"Azacitidine (approved)","Ferroptosis":"ACSL4 altered"},
+    {"Variant":"CEBPA biallelic","Freq_pct":8,"Pathway":"Myeloid differentiation block","Drug_status":"Novel target","Ferroptosis":"NRF2 pathway"},
+    {"Variant":"IDH1/2 mutation","Freq_pct":15,"Pathway":"2-HG oncometabolite → TET2 inhibition","Drug_status":"Enasidenib (approved)","Ferroptosis":"Iron metabolism disrupted"},
+]
+PAML_BM_LNP = [
+    {"Formulation":"ALC-0315-DSPC-Chol-PEG","BM_protein":"ApoE + Clusterin","Size_nm":98,"Zeta_mV":-3.5,"Marrow_uptake_pct":34,"Priority":"HIGH"},
+    {"Formulation":"MC3-DOPE-Chol-PEG","BM_protein":"Fibronectin dominant","Size_nm":105,"Zeta_mV":-4.2,"Marrow_uptake_pct":28,"Priority":"HIGH"},
+    {"Formulation":"DLin-MC3-DPPC","BM_protein":"Vitronectin-rich","Size_nm":91,"Zeta_mV":-2.9,"Marrow_uptake_pct":19,"Priority":"MEDIUM"},
+    {"Formulation":"Cationic-DOTAP-Chol","BM_protein":"Opsonin-heavy","Size_nm":132,"Zeta_mV":+8.1,"Marrow_uptake_pct":8,"Priority":"LOW"},
+]
+
+# ========== ФУНКЦІЇ ПРЕДИКЦІЇ (з обробкою помилок) ==========
+
+def safe_img_from_fig(fig):
+    """Convert matplotlib figure to PIL Image safely."""
+    try:
+        buf = BytesIO()
+        fig.savefig(buf, format="png", dpi=120, facecolor=CARD)
+        buf.seek(0)
+        img = Image.open(buf)
+        plt.close(fig)
+        return img
+    except Exception:
+        plt.close(fig)
+        # Return a blank image as fallback
+        return Image.new('RGB', (100, 100), color=CARD)
+
 def predict_mirna(gene):
     df = pd.DataFrame(MIRNA_DB.get(gene, []))
     log_entry("S1-B · R1a · miRNA", gene, f"{len(df)} miRNAs")
@@ -172,17 +244,24 @@ def get_aso():
     return pd.DataFrame(ASO)
 
 def predict_drug(pocket):
-    df = pd.DataFrame(FGFR3.get(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(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)
-    log_entry("S1-C · R1a · FGFR3", pocket, f"Top: {df.iloc[0]['Compound'] if len(df) else 'none'}")
-    return df, Image.open(buf)
+    try:
+        df = pd.DataFrame(FGFR3.get(pocket, []))
+        if df.empty:
+            return pd.DataFrame(), None
+        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(BORDER)
+        ax.set_title(f"Top compounds — {pocket}", color=TXT, fontsize=10)
+        plt.tight_layout()
+        img = safe_img_from_fig(fig)
+        log_entry("S1-C · R1a · FGFR3", pocket, f"Top: {df.iloc[0]['Compound'] if len(df) else 'none'}")
+        return df, img
+    except Exception as e:
+        log_entry("S1-C · R1a · FGFR3", pocket, f"Error: {str(e)}")
+        return pd.DataFrame(), None
 
 def predict_variant(hgvs, sift, polyphen, gnomad):
     hgvs = hgvs.strip()
@@ -211,147 +290,120 @@ def predict_variant(hgvs, sift, polyphen, gnomad):
     )
 
 def predict_corona(size, zeta, peg, lipid):
-    score = 0
-    if lipid == "Ionizable": score += 2
-    elif lipid == "Cationic": score += 1
-    if abs(zeta) < 10: score += 1
-    if peg > 1.5:      score += 2
-    if size < 100:     score += 1
-    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 · R1a · Corona", f"size={size},peg={peg}", f"dominant={dominant}")
-    return f"**Dominant corona protein:** {dominant}\n\n**Predicted efficacy:** {efficacy}\n\n**Score:** {score}/6"
+    try:
+        score = 0
+        if lipid == "Ionizable": score += 2
+        elif lipid == "Cationic": score += 1
+        if abs(zeta) < 10: score += 1
+        if peg > 1.5:      score += 2
+        if size < 100:     score += 1
+        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 · R1a · Corona", f"size={size},peg={peg}", f"dominant={dominant}")
+        return f"**Dominant corona protein:** {dominant}\n\n**Predicted efficacy:** {efficacy}\n\n**Score:** {score}/6"
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 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, 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)
-    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(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 · R1a · Liquid Biopsy", f"CTHRC1={c1},FHL2={c2}", f"{label} {prob:.2f}")
-    return (
-        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 · R1a · Liquid Biopsy</p>"
-        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)
+    try:
+        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)
+        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(BORDER)
+        ax.set_title("Protein contributions", color=TXT, fontsize=10)
+        plt.tight_layout()
+        img = safe_img_from_fig(fig)
+        log_entry("S1-E · R1a · Liquid Biopsy", f"CTHRC1={c1},FHL2={c2}", f"{label} {prob:.2f}")
+        html_out = (
+            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 · R1a · Liquid Biopsy</p>"
+            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>"
+        )
+        return html_out, img
+    except Exception as e:
+        return f"<div style='color:{RED}'>Error: {str(e)}</div>", None
 
 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, 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)")
-    ax.plot(t, 60*np.exp(-kf*t)+10,   color="#60a5fa",          label="Albumin (flow)")
-    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(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)
-    log_entry("S1-D · R2a · Flow Corona", f"flow={flow_rate}", f"CSI={csi}")
-    return f"**Corona Shift Index: {csi}** — {stability}", Image.open(buf)
+    try:
+        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, 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)")
+        ax.plot(t, 60*np.exp(-kf*t)+10,   color="#60a5fa",          label="Albumin (flow)")
+        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(BORDER)
+        ax.set_title("Vroman Effect — flow vs static", color=TXT, fontsize=9)
+        plt.tight_layout()
+        img = safe_img_from_fig(fig)
+        log_entry("S1-D · R2a · Flow Corona", f"flow={flow_rate}", f"CSI={csi}")
+        return f"**Corona Shift Index: {csi}** — {stability}", img
+    except Exception as e:
+        return f"Error: {str(e)}", None
 
 def predict_bbb(smiles, pka, zeta):
-    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)
-    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)
-    ax.tick_params(colors=TXT)
-    plt.tight_layout()
-    buf = BytesIO(); plt.savefig(buf, format="png", dpi=120, facecolor=CARD); plt.close(); buf.seek(0)
-    log_entry("S1-D · R3a · LNP Brain", f"pka={pka},zeta={zeta}", f"ApoE={apoe_pct:.1f}%")
-    return f"**Predicted ApoE:** {apoe_pct:.1f}% — {tier}\n\n**BBB Probability:** {bbb_prob:.2f}", Image.open(buf)
+    try:
+        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)
+        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)
+        ax.tick_params(colors=TXT)
+        plt.tight_layout()
+        img = safe_img_from_fig(fig)
+        log_entry("S1-D · R3a · LNP Brain", f"pka={pka},zeta={zeta}", f"ApoE={apoe_pct:.1f}%")
+        return f"**Predicted ApoE:** {apoe_pct:.1f}% — {tier}\n\n**BBB Probability:** {bbb_prob:.2f}", img
+    except Exception as e:
+        return f"Error: {str(e)}", None
 
 def extract_corona(text):
-    out = {"nanoparticle_composition":"","size_nm":None,"zeta_mv":None,"PDI":None,
-           "protein_source":"","corona_proteins":[],"confidence":{}}
-    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
-    out["corona_proteins"] = [{"name":p,"confidence":"MEDIUM"} for p in PROTEINS if p in text.lower()]
-    for lip in ["DSPC","DOPE","MC3","DLin","cholesterol","PEG","DOTAP"]:
-        if lip in text: out["nanoparticle_composition"] += lip + " "
-    out["nanoparticle_composition"] = out["nanoparticle_composition"].strip()
-    flags = []
-    if not out["size_nm"]:         flags.append("size_nm not found")
-    if not out["zeta_mv"]:         flags.append("zeta_mv not found")
-    if not out["corona_proteins"]: flags.append("no proteins detected")
-    summary = "All key fields extracted" if not flags else " | ".join(flags)
-    log_entry("S1-D · R4a · AutoCorona NLP", text[:80], f"proteins={len(out['corona_proteins'])}")
-    return json.dumps(out, indent=2), summary
-
-# ---------- S1-F RARE ----------
-DIPG_VARIANTS = [
-    {"Variant":"H3K27M (H3F3A)","Freq_pct":78,"Pathway":"PRC2 inhibition → global H3K27me3 loss","Drug_status":"ONC201 (clinical)","Circadian_gene":"BMAL1 suppressed"},
-    {"Variant":"ACVR1 p.R206H","Freq_pct":21,"Pathway":"BMP/SMAD hyperactivation","Drug_status":"LDN-193189 (preclinical)","Circadian_gene":"PER1 disrupted"},
-    {"Variant":"PIK3CA p.H1047R","Freq_pct":15,"Pathway":"PI3K/AKT/mTOR","Drug_status":"Copanlisib (clinical)","Circadian_gene":"CRY1 altered"},
-    {"Variant":"TP53 p.R248W","Freq_pct":14,"Pathway":"DNA damage response loss","Drug_status":"APR-246 (clinical)","Circadian_gene":"p53-CLOCK axis"},
-    {"Variant":"PDGFRA amp","Freq_pct":13,"Pathway":"RTK/RAS signalling","Drug_status":"Avapritinib (clinical)","Circadian_gene":"REV-ERB altered"},
-]
-DIPG_CSF_LNP = [
-    {"Formulation":"MC3-DSPC-Chol-PEG","Size_nm":92,"Zeta_mV":-4.1,"CSF_protein":"Beta2-microglobulin","ApoE_pct":12.4,"BBB_est":0.41,"Priority":"HIGH"},
-    {"Formulation":"DLin-KC2-DSPE-PEG","Size_nm":87,"Zeta_mV":-3.8,"CSF_protein":"Cystatin C","ApoE_pct":14.1,"BBB_est":0.47,"Priority":"HIGH"},
-    {"Formulation":"C12-200-DOPE-PEG","Size_nm":103,"Zeta_mV":-5.2,"CSF_protein":"Albumin (low)","ApoE_pct":9.8,"BBB_est":0.33,"Priority":"MEDIUM"},
-    {"Formulation":"DODAP-DSPC-Chol","Size_nm":118,"Zeta_mV":-2.1,"CSF_protein":"Transferrin","ApoE_pct":7.2,"BBB_est":0.24,"Priority":"LOW"},
-]
-
-UVM_VARIANTS = [
-    {"Variant":"GNAQ p.Q209L","Freq_pct":46,"Pathway":"PLCβ → PKC → MAPK","Drug_status":"Darovasertib (clinical)","m6A_writer":"METTL3 upregulated"},
-    {"Variant":"GNA11 p.Q209L","Freq_pct":32,"Pathway":"PLCβ → PKC → MAPK","Drug_status":"Darovasertib (clinical)","m6A_writer":"WTAP upregulated"},
-    {"Variant":"BAP1 loss","Freq_pct":47,"Pathway":"Chromatin remodeling → metastasis","Drug_status":"No approved (HDAC trials)","m6A_writer":"FTO overexpressed"},
-    {"Variant":"SF3B1 p.R625H","Freq_pct":19,"Pathway":"Splicing alteration → neoepitopes","Drug_status":"H3B-8800 (clinical)","m6A_writer":"METTL14 altered"},
-    {"Variant":"EIF1AX p.A113_splice","Freq_pct":14,"Pathway":"Translation initiation","Drug_status":"Novel — no drug","m6A_writer":"YTHDF2 suppressed"},
-]
-UVM_VITREOUS_LNP = [
-    {"Formulation":"SM-102-DSPC-Chol-PEG","Vitreal_protein":"Hyaluronan-binding","Size_nm":95,"Zeta_mV":-3.2,"Retention_h":18,"Priority":"HIGH"},
-    {"Formulation":"Lipid-H-DOPE-PEG","Vitreal_protein":"Vitronectin dominant","Size_nm":88,"Zeta_mV":-4.0,"Retention_h":22,"Priority":"HIGH"},
-    {"Formulation":"DOTAP-DSPC-PEG","Vitreal_protein":"Albumin wash-out","Size_nm":112,"Zeta_mV":+2.1,"Retention_h":6,"Priority":"LOW"},
-    {"Formulation":"MC3-DPPC-Chol","Vitreal_protein":"Clusterin-rich","Size_nm":101,"Zeta_mV":-2.8,"Retention_h":14,"Priority":"MEDIUM"},
-]
-
-PAML_VARIANTS = [
-    {"Variant":"FLT3-ITD","Freq_pct":25,"Pathway":"RTK constitutive activation → JAK/STAT","Drug_status":"Midostaurin (approved)","Ferroptosis":"GPX4 suppressed"},
-    {"Variant":"NPM1 c.860_863dupTCAG","Freq_pct":30,"Pathway":"Nuclear export deregulation","Drug_status":"APR-548 combo (clinical)","Ferroptosis":"SLC7A11 upregulated"},
-    {"Variant":"DNMT3A p.R882H","Freq_pct":18,"Pathway":"Epigenetic dysregulation","Drug_status":"Azacitidine (approved)","Ferroptosis":"ACSL4 altered"},
-    {"Variant":"CEBPA biallelic","Freq_pct":8,"Pathway":"Myeloid differentiation block","Drug_status":"Novel target","Ferroptosis":"NRF2 pathway"},
-    {"Variant":"IDH1/2 mutation","Freq_pct":15,"Pathway":"2-HG oncometabolite → TET2 inhibition","Drug_status":"Enasidenib (approved)","Ferroptosis":"Iron metabolism disrupted"},
-]
-PAML_BM_LNP = [
-    {"Formulation":"ALC-0315-DSPC-Chol-PEG","BM_protein":"ApoE + Clusterin","Size_nm":98,"Zeta_mV":-3.5,"Marrow_uptake_pct":34,"Priority":"HIGH"},
-    {"Formulation":"MC3-DOPE-Chol-PEG","BM_protein":"Fibronectin dominant","Size_nm":105,"Zeta_mV":-4.2,"Marrow_uptake_pct":28,"Priority":"HIGH"},
-    {"Formulation":"DLin-MC3-DPPC","BM_protein":"Vitronectin-rich","Size_nm":91,"Zeta_mV":-2.9,"Marrow_uptake_pct":19,"Priority":"MEDIUM"},
-    {"Formulation":"Cationic-DOTAP-Chol","BM_protein":"Opsonin-heavy","Size_nm":132,"Zeta_mV":+8.1,"Marrow_uptake_pct":8,"Priority":"LOW"},
-]
+    try:
+        out = {"nanoparticle_composition":"","size_nm":None,"zeta_mv":None,"PDI":None,
+               "protein_source":"","corona_proteins":[],"confidence":{}}
+        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
+        out["corona_proteins"] = [{"name":p,"confidence":"MEDIUM"} for p in PROTEINS if p in text.lower()]
+        for lip in ["DSPC","DOPE","MC3","DLin","cholesterol","PEG","DOTAP"]:
+            if lip in text: out["nanoparticle_composition"] += lip + " "
+        out["nanoparticle_composition"] = out["nanoparticle_composition"].strip()
+        flags = []
+        if not out["size_nm"]:         flags.append("size_nm not found")
+        if not out["zeta_mv"]:         flags.append("zeta_mv not found")
+        if not out["corona_proteins"]: flags.append("no proteins detected")
+        summary = "All key fields extracted" if not flags else " | ".join(flags)
+        log_entry("S1-D · R4a · AutoCorona NLP", text[:80], f"proteins={len(out['corona_proteins'])}")
+        return json.dumps(out, indent=2), summary
+    except Exception as e:
+        return json.dumps({"error": str(e)}), "Extraction error"
 
 def dipg_variants(sort_by):
     df = pd.DataFrame(DIPG_VARIANTS).sort_values(
@@ -360,21 +412,24 @@ def dipg_variants(sort_by):
     return df
 
 def dipg_csf(peg, size):
-    df = pd.DataFrame(DIPG_CSF_LNP)
-    df["Score"] = df["ApoE_pct"]/40 + df["BBB_est"] - abs(df["Size_nm"]-size)/200
-    df = df.sort_values("Score", ascending=False)
-    fig, ax = plt.subplots(figsize=(6, 3), facecolor=CARD)
-    ax.set_facecolor(CARD)
-    colors = [GRN if p=="HIGH" else ACC if p=="MEDIUM" else RED for p in df["Priority"]]
-    ax.barh(df["Formulation"], df["ApoE_pct"], color=colors)
-    ax.set_xlabel("ApoE% in CSF corona", color=TXT)
-    ax.tick_params(colors=TXT, labelsize=8)
-    for sp in ax.spines.values(): sp.set_edgecolor(BORDER)
-    ax.set_title("DIPG — CSF LNP formulations (ApoE%)", color=TXT, fontsize=9)
-    plt.tight_layout()
-    buf = BytesIO(); plt.savefig(buf, format="png", dpi=120, facecolor=CARD); plt.close(); buf.seek(0)
-    log_entry("S1-F · R1a · DIPG CSF", f"peg={peg},size={size}", "formulation ranking")
-    return df[["Formulation","Size_nm","Zeta_mV","ApoE_pct","BBB_est","Priority"]], Image.open(buf)
+    try:
+        df = pd.DataFrame(DIPG_CSF_LNP)
+        df["Score"] = df["ApoE_pct"]/40 + df["BBB_est"] - abs(df["Size_nm"]-size)/200
+        df = df.sort_values("Score", ascending=False)
+        fig, ax = plt.subplots(figsize=(6, 3), facecolor=CARD)
+        ax.set_facecolor(CARD)
+        colors = [GRN if p=="HIGH" else ACC if p=="MEDIUM" else RED for p in df["Priority"]]
+        ax.barh(df["Formulation"], df["ApoE_pct"], color=colors)
+        ax.set_xlabel("ApoE% in CSF corona", color=TXT)
+        ax.tick_params(colors=TXT, labelsize=8)
+        for sp in ax.spines.values(): sp.set_edgecolor(BORDER)
+        ax.set_title("DIPG — CSF LNP formulations (ApoE%)", color=TXT, fontsize=9)
+        plt.tight_layout()
+        img = safe_img_from_fig(fig)
+        log_entry("S1-F · R1a · DIPG CSF", f"peg={peg},size={size}", "formulation ranking")
+        return df[["Formulation","Size_nm","Zeta_mV","ApoE_pct","BBB_est","Priority"]], img
+    except Exception as e:
+        return pd.DataFrame(), None
 
 def uvm_variants():
     df = pd.DataFrame(UVM_VARIANTS)
@@ -382,56 +437,62 @@ def uvm_variants():
     return df
 
 def uvm_vitreous():
-    df = pd.DataFrame(UVM_VITREOUS_LNP)
-    fig, ax = plt.subplots(figsize=(6, 3), facecolor=CARD)
-    ax.set_facecolor(CARD)
-    colors = [GRN if p=="HIGH" else ACC if p=="MEDIUM" else RED for p in df["Priority"]]
-    ax.barh(df["Formulation"], df["Retention_h"], color=colors)
-    ax.set_xlabel("Vitreous retention (hours)", color=TXT)
-    ax.tick_params(colors=TXT, labelsize=8)
-    for sp in ax.spines.values(): sp.set_edgecolor(BORDER)
-    ax.set_title("UVM — LNP retention in vitreous humor", color=TXT, fontsize=9)
-    plt.tight_layout()
-    buf = BytesIO(); plt.savefig(buf, format="png", dpi=120, facecolor=CARD); plt.close(); buf.seek(0)
-    log_entry("S1-F · R2a · UVM Vitreous", "load", "vitreous LNP ranking")
-    return df, Image.open(buf)
+    try:
+        df = pd.DataFrame(UVM_VITREOUS_LNP)
+        fig, ax = plt.subplots(figsize=(6, 3), facecolor=CARD)
+        ax.set_facecolor(CARD)
+        colors = [GRN if p=="HIGH" else ACC if p=="MEDIUM" else RED for p in df["Priority"]]
+        ax.barh(df["Formulation"], df["Retention_h"], color=colors)
+        ax.set_xlabel("Vitreous retention (hours)", color=TXT)
+        ax.tick_params(colors=TXT, labelsize=8)
+        for sp in ax.spines.values(): sp.set_edgecolor(BORDER)
+        ax.set_title("UVM — LNP retention in vitreous humor", color=TXT, fontsize=9)
+        plt.tight_layout()
+        img = safe_img_from_fig(fig)
+        log_entry("S1-F · R2a · UVM Vitreous", "load", "vitreous LNP ranking")
+        return df, img
+    except Exception as e:
+        return pd.DataFrame(), None
 
 def paml_ferroptosis(variant):
-    row = next((r for r in PAML_VARIANTS if variant in r["Variant"]), PAML_VARIANTS[0])
-    ferr_map = {"GPX4 suppressed": 0.85, "SLC7A11 upregulated": 0.72,
-                "ACSL4 altered": 0.61, "NRF2 pathway": 0.55, "Iron metabolism disrupted": 0.78}
-    ferr_score = ferr_map.get(row["Ferroptosis"], 0.5)
-    cats = ["Ferroptosis\nsensitivity", "Drug\navailable", "BM niche\ncoverage", "Data\nmaturity", "Target\nnovelty"]
-    has_drug = 0.9 if row["Drug_status"] not in ["Novel target"] else 0.3
-    vals = [ferr_score, has_drug, 0.6, 0.55, 1-has_drug+0.2]
-    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=ACC2, linewidth=2); ax.fill(a2, v2, color=ACC2, alpha=0.2)
-    ax.set_xticks(angles); ax.set_xticklabels(cats, color=TXT, fontsize=8)
-    ax.tick_params(colors=TXT)
-    ax.set_title(f"pAML · {row['Variant'][:20]}", color=TXT, fontsize=9)
-    plt.tight_layout()
-    buf = BytesIO(); plt.savefig(buf, format="png", dpi=120, facecolor=CARD); plt.close(); buf.seek(0)
-    log_entry("S1-F · R3a · pAML", variant, f"ferr={ferr_score:.2f}")
-    _v  = row["Variant"]
-    _p  = row["Pathway"]
-    _d  = row["Drug_status"]
-    _f  = row["Ferroptosis"]
-    _fs = f"{ferr_score:.2f}"
-    summary = (
-        f"<div style='background:{CARD};padding:14px;border-radius:8px;font-family:sans-serif;'>"
-        f"<p style='color:{DIM};font-size:11px;margin:0 0 6px'>S1-F · R3a · pAML</p>"
-        f"<b style='color:{ACC2};font-size:15px'>{_v}</b><br>"
-        f"<p style='color:{TXT};margin:6px 0'><b>Pathway:</b> {_p}</p>"
-        f"<p style='color:{TXT};margin:0'><b>Drug:</b> {_d}</p>"
-        f"<p style='color:{TXT};margin:6px 0'><b>Ferroptosis link:</b> {_f}</p>"
-        f"<p style='color:{TXT}'><b>Ferroptosis sensitivity score:</b> "
-        f"<span style='color:{ACC};font-size:18px'>{_fs}</span></p>"
-        f"<p style='font-size:11px;color:{DIM}'>Research only. Not clinical advice.</p></div>"
-    )
-    return summary, Image.open(buf)
+    try:
+        row = next((r for r in PAML_VARIANTS if variant in r["Variant"]), PAML_VARIANTS[0])
+        ferr_map = {"GPX4 suppressed": 0.85, "SLC7A11 upregulated": 0.72,
+                    "ACSL4 altered": 0.61, "NRF2 pathway": 0.55, "Iron metabolism disrupted": 0.78}
+        ferr_score = ferr_map.get(row["Ferroptosis"], 0.5)
+        cats = ["Ferroptosis\nsensitivity", "Drug\navailable", "BM niche\ncoverage", "Data\nmaturity", "Target\nnovelty"]
+        has_drug = 0.9 if row["Drug_status"] not in ["Novel target"] else 0.3
+        vals = [ferr_score, has_drug, 0.6, 0.55, 1-has_drug+0.2]
+        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=ACC2, linewidth=2); ax.fill(a2, v2, color=ACC2, alpha=0.2)
+        ax.set_xticks(angles); ax.set_xticklabels(cats, color=TXT, fontsize=8)
+        ax.tick_params(colors=TXT)
+        ax.set_title(f"pAML · {row['Variant'][:20]}", color=TXT, fontsize=9)
+        plt.tight_layout()
+        img = safe_img_from_fig(fig)
+        log_entry("S1-F · R3a · pAML", variant, f"ferr={ferr_score:.2f}")
+        _v  = row["Variant"]
+        _p  = row["Pathway"]
+        _d  = row["Drug_status"]
+        _f  = row["Ferroptosis"]
+        _fs = f"{ferr_score:.2f}"
+        summary = (
+            f"<div style='background:{CARD};padding:14px;border-radius:8px;font-family:sans-serif;'>"
+            f"<p style='color:{DIM};font-size:11px;margin:0 0 6px'>S1-F · R3a · pAML</p>"
+            f"<b style='color:{ACC2};font-size:15px'>{_v}</b><br>"
+            f"<p style='color:{TXT};margin:6px 0'><b>Pathway:</b> {_p}</p>"
+            f"<p style='color:{TXT};margin:0'><b>Drug:</b> {_d}</p>"
+            f"<p style='color:{TXT};margin:6px 0'><b>Ferroptosis link:</b> {_f}</p>"
+            f"<p style='color:{TXT}'><b>Ferroptosis sensitivity score:</b> "
+            f"<span style='color:{ACC};font-size:18px'>{_fs}</span></p>"
+            f"<p style='font-size:11px;color:{DIM}'>Research only. Not clinical advice.</p></div>"
+        )
+        return summary, img
+    except Exception as e:
+        return f"<div style='color:{RED}'>Error: {str(e)}</div>", None
 
 # ========== ДОПОМІЖНІ ФУНКЦІЇ ==========
 def section_header(code, name, tagline, projects_html):
@@ -457,6 +518,80 @@ def proj_badge(code, title, metric=""):
         f"</div>"
     )
 
+# ========== 3D моделі (функції) ==========
+def plot_nanoparticle(r, peg):
+    theta = np.linspace(0, 2*np.pi, 30)
+    phi = np.linspace(0, np.pi, 30)
+    theta, phi = np.meshgrid(theta, phi)
+    x = r * np.sin(phi) * np.cos(theta)
+    y = r * np.sin(phi) * np.sin(theta)
+    z = r * np.cos(phi)
+    fig = go.Figure(data=[go.Surface(x=x, y=y, z=z, colorscale='Blues', opacity=0.7)])
+    if peg > 0:
+        n_points = int(100 * peg)
+        u = np.random.uniform(0, 1, n_points)
+        v = np.random.uniform(0, 1, n_points)
+        theta_pts = 2 * np.pi * u
+        phi_pts = np.arccos(2*v - 1)
+        x_pts = (r + 0.5) * np.sin(phi_pts) * np.cos(theta_pts)
+        y_pts = (r + 0.5) * np.sin(phi_pts) * np.sin(theta_pts)
+        z_pts = (r + 0.5) * np.cos(phi_pts)
+        fig.add_scatter3d(x=x_pts, y=y_pts, z=z_pts, mode='markers',
+                           marker=dict(size=3, color='red'), name='PEG')
+    fig.update_layout(
+        title=f"Nanoparticle (r={r} nm, PEG={peg})",
+        scene=dict(xaxis_title='X (nm)', yaxis_title='Y (nm)', zaxis_title='Z (nm)'),
+        width=500, height=400
+    )
+    return fig
+
+def plot_dna():
+    t = np.linspace(0, 4*np.pi, 200)
+    x1 = np.cos(t)
+    y1 = np.sin(t)
+    z1 = t
+    x2 = np.cos(t + np.pi)
+    y2 = np.sin(t + np.pi)
+    z2 = t
+    fig = go.Figure()
+    fig.add_scatter3d(x=x1, y=y1, z=z1, mode='lines', line=dict(color='blue', width=5), name='Strand 1')
+    fig.add_scatter3d(x=x2, y=y2, z=z2, mode='lines', line=dict(color='red', width=5), name='Strand 2')
+    for i in range(0, len(t), 10):
+        fig.add_scatter3d(x=[x1[i], x2[i]], y=[y1[i], y2[i]], z=[z1[i], z2[i]],
+                          mode='lines', line=dict(color='gray', width=1), showlegend=False)
+    fig.update_layout(
+        title='DNA Double Helix',
+        scene=dict(xaxis_title='X', yaxis_title='Y', zaxis_title='Z'),
+        width=500, height=400
+    )
+    return fig
+
+def plot_corona():
+    r = 5
+    theta = np.linspace(0, 2*np.pi, 20)
+    phi = np.linspace(0, np.pi, 20)
+    theta, phi = np.meshgrid(theta, phi)
+    x = r * np.sin(phi) * np.cos(theta)
+    y = r * np.sin(phi) * np.sin(theta)
+    z = r * np.cos(phi)
+    fig = go.Figure(data=[go.Surface(x=x, y=y, z=z, colorscale='Blues', opacity=0.5)])
+    n_proteins = 50
+    u = np.random.uniform(0, 1, n_proteins)
+    v = np.random.uniform(0, 1, n_proteins)
+    theta_pts = 2 * np.pi * u
+    phi_pts = np.arccos(2*v - 1)
+    x_pts = (r + 1.5) * np.sin(phi_pts) * np.cos(theta_pts)
+    y_pts = (r + 1.5) * np.sin(phi_pts) * np.sin(theta_pts)
+    z_pts = (r + 1.5) * np.cos(phi_pts)
+    fig.add_scatter3d(x=x_pts, y=y_pts, z=z_pts, mode='markers',
+                       marker=dict(size=5, color='orange'), name='Proteins')
+    fig.update_layout(
+        title='Protein Corona',
+        scene=dict(xaxis_title='X', yaxis_title='Y', zaxis_title='Z'),
+        width=500, height=400
+    )
+    return fig
+
 # ========== CSS ==========
 css = f"""
 body, .gradio-container {{ background: {BG} !important; color: {TXT} !important; }}
@@ -549,7 +684,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
         with gr.TabItem("🗺️ Lab Map"):
             gr.HTML(MAP_HTML)
 
-        # === АКТИВНІ ВКЛАДКИ (працюють) ===
+        # === S1-A · PHYLO-GENOMICS ===
         with gr.TabItem("S1-A · R1a · OpenVariant"):
             gr.HTML(proj_badge("S1-A · R1a", "OpenVariant — SNV Pathogenicity Classifier", "AUC = 0.939"))
             hgvs = gr.Textbox(label="HGVS notation", placeholder="BRCA1:p.R1699Q")
@@ -565,22 +700,11 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                          ["BRCA2:p.D2723A",0.01,0.98,0.0]], inputs=[hgvs,sift,pp,gn], cache_examples=False)
             b_v.click(predict_variant, [hgvs,sift,pp,gn], o_v)
 
-        # === ВСІ ІНШІ ВКЛАДКИ ЗАКОМЕНТОВАНІ ===
-        # Розкоментуйте блок цілком, щоб перевірити, чи викликає помилку
-
-# =============================================================================
-# === START: S1-A · R1b · Somatic Classifier 🔶 ===============================
-# =============================================================================
         with gr.TabItem("S1-A · R1b · Somatic Classifier 🔶"):
             gr.HTML(proj_badge("S1-A · R1b", "Somatic Mutation Classifier", "🔶 In progress"))
             gr.Markdown("> This module is in active development. Coming in the next release.")
-# =============================================================================
-# === END: S1-A · R1b =========================================================
-# =============================================================================
 
-# =============================================================================
-# === START: S1-B · R1a · BRCA2 miRNA ========================================
-# =============================================================================
+        # === S1-B · PHYLO-RNA ===
         with gr.TabItem("S1-B · R1a · BRCA2 miRNA"):
             gr.HTML(proj_badge("S1-B · R1a", "miRNA Silencing — BRCA1/2 · TP53"))
             g1 = gr.Dropdown(["BRCA2","BRCA1","TP53"], value="BRCA2", label="Gene")
@@ -588,13 +712,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
             o1 = gr.Dataframe(label="Top 5 downregulated miRNAs")
             gr.Examples([["BRCA2"],["BRCA1"],["TP53"]], inputs=[g1])
             b1.click(predict_mirna, [g1], o1)
-# =============================================================================
-# === END: S1-B · R1a =========================================================
-# =============================================================================
 
-# =============================================================================
-# === START: S1-B · R2a · TP53 siRNA =========================================
-# =============================================================================
         with gr.TabItem("S1-B · R2a · TP53 siRNA"):
             gr.HTML(proj_badge("S1-B · R2a", "siRNA Synthetic Lethal — TP53-null"))
             g2 = gr.Dropdown(["LUAD","BRCA","COAD"], value="LUAD", label="Cancer type")
@@ -602,73 +720,40 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
             o2 = gr.Dataframe(label="Top 5 synthetic lethal targets")
             gr.Examples([["LUAD"],["BRCA"],["COAD"]], inputs=[g2], cache_examples=False)
             b2.click(predict_sirna, [g2], o2)
-# =============================================================================
-# === END: S1-B · R2a =========================================================
-# =============================================================================
 
-# =============================================================================
-# === START: S1-B · R3a · lncRNA-TREM2 =======================================
-# =============================================================================
         with gr.TabItem("S1-B · R3a · lncRNA-TREM2"):
             gr.HTML(proj_badge("S1-B · R3a", "lncRNA-TREM2 ceRNA Network"))
             b3 = gr.Button("Load Network", variant="primary")
             o3 = gr.Dataframe(label="ceRNA Network")
             b3.click(get_lncrna, [], o3)
-# =============================================================================
-# === END: S1-B · R3a =========================================================
-# =============================================================================
 
-# =============================================================================
-# === START: S1-B · R3b · ASO Designer =======================================
-# =============================================================================
         with gr.TabItem("S1-B · R3b · ASO Designer"):
             gr.HTML(proj_badge("S1-B · R3b", "ASO Candidates"))
             b4 = gr.Button("Show ASOs", variant="primary")
             o4 = gr.Dataframe(label="ASO Candidates")
             b4.click(get_aso, [], o4)
-# =============================================================================
-# === END: S1-B · R3b =========================================================
-# =============================================================================
-
-# =============================================================================
-# === START: S1-C · R1a · FGFR3 RNA Drug ===================================== оце
-# =============================================================================
-        # with gr.TabItem("S1-C · R1a · FGFR3 RNA Drug"):
-        #     gr.HTML(proj_badge("S1-C · R1a", "FGFR3 RNA-Directed Drug Discovery", "top score 0.793"))
-        #     g4 = gr.Radio(["P1 (hairpin loop)","P10 (G-quadruplex)"],
-        #                   value="P1 (hairpin loop)", label="Target pocket")
-        #     b4 = gr.Button("Screen Compounds", variant="primary")
-        #     o4t = gr.Dataframe(label="Top 5 candidates")
-        #     o4p = gr.Image(label="Binding scores")
-        #     gr.Examples([["P1 (hairpin loop)"],["P10 (G-quadruplex)"]], inputs=[g4])
-        #     b4.click(predict_drug, [g4], [o4t, o4p])
-# =============================================================================
-# === END: S1-C · R1a =========================================================
-# =============================================================================
-
-# =============================================================================
-# === START: S1-C · R1b · SL Drug Mapping 🔶 =================================
-# =============================================================================
+
+        # === S1-C · PHYLO-DRUG ===
+        # Розкоментовано проблемну вкладку "оце" - FGFR3
+        with gr.TabItem("S1-C · R1a · FGFR3 RNA Drug"):
+            gr.HTML(proj_badge("S1-C · R1a", "FGFR3 RNA-Directed Drug Discovery", "top score 0.793"))
+            g4 = gr.Radio(["P1 (hairpin loop)","P10 (G-quadruplex)"],
+                          value="P1 (hairpin loop)", label="Target pocket")
+            b4_drug = 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_drug.click(predict_drug, [g4], [o4t, o4p])
+
         with gr.TabItem("S1-C · R1b · SL Drug Mapping 🔶"):
             gr.HTML(proj_badge("S1-C · R1b", "Synthetic Lethal Drug Mapping", "🔶 In progress"))
             gr.Markdown("> Coming soon.")
-# =============================================================================
-# === END: S1-C · R1b =========================================================
-# =============================================================================
 
-# =============================================================================
-# === START: S1-C · R2a · Frontier 🔴 ========================================
-# =============================================================================
         with gr.TabItem("S1-C · R2a · Frontier 🔴"):
             gr.HTML(proj_badge("S1-C · R2a", "m6A × Ferroptosis × Circadian", "🔴 Frontier"))
             gr.Markdown("> Planned for Q3 2026")
-# =============================================================================
-# === END: S1-C · R2a =========================================================
-# =============================================================================
 
-# =============================================================================
-# === START: S1-D · R1a · LNP Corona =========================================
-# =============================================================================
+        # === S1-D · PHYLO-LNP ===
         with gr.TabItem("S1-D · R1a · LNP Corona"):
             gr.HTML(proj_badge("S1-D · R1a", "LNP Protein Corona (Serum)", "AUC = 0.791"))
             with gr.Row():
@@ -681,183 +766,167 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
             o6 = gr.Markdown()
             gr.Examples([[100,-5,1.5,"Ionizable"],[80,5,0.5,"Cationic"]], inputs=[sz,zt,pg,lp])
             b6.click(predict_corona, [sz,zt,pg,lp], o6)
-# =============================================================================
-# === END: S1-D · R1a =========================================================
-# =============================================================================
-
-# =============================================================================
-# === START: S1-D · R2a · Flow Corona ========================================
-# =============================================================================
-        # with gr.TabItem("S1-D · R2a · Flow Corona"):
-        #     gr.HTML(proj_badge("S1-D · R2a", "Flow Corona — Vroman Effect"))
-        #     with gr.Row():
-        #         s8  = gr.Slider(50,300,value=100,step=1,label="Size (nm)")
-        #         z8  = gr.Slider(-40,10,value=-5,step=1,label="Zeta (mV)")
-        #         pg8 = gr.Slider(0,5,value=1.5,step=0.1,label="PEG mol%")
-        #     with gr.Row():
-        #         ch8 = gr.Dropdown(["Ionizable","Cationic","Anionic","Neutral"],value="Ionizable",label="Charge")
-        #         fl8 = gr.Slider(0,40,value=20,step=1,label="Flow cm/s")
-        #     b8 = gr.Button("Model Vroman Effect", variant="primary")
-        #     o8t = gr.Markdown(); o8p = gr.Image(label="Kinetics")
-        #     gr.Examples([[100,-5,1.5,"Ionizable",40],[150,5,0.5,"Cationic",10]], inputs=[s8,z8,pg8,ch8,fl8])
-        #     b8.click(predict_flow, [s8,z8,pg8,ch8,fl8], [o8t,o8p])
-# =============================================================================
-# === END: S1-D · R2a =========================================================
-# =============================================================================
-
-# =============================================================================
-# === START: S1-D · R3a · LNP Brain ========================================== оце
-# =============================================================================
-        # with gr.TabItem("S1-D · R3a · LNP Brain"):
-        #     gr.HTML(proj_badge("S1-D · R3a", "LNP Brain Delivery — ApoE% + BBB probability"))
-        #     smi = gr.Textbox(label="Ionizable lipid SMILES",
-        #                      value="CC(C)CC(=O)OCC(COC(=O)CC(C)C)OC(=O)CC(C)C")
-        #     with gr.Row():
-        #         pk  = gr.Slider(4,8,value=6.5,step=0.1,label="pKa")
-        #         zt9 = gr.Slider(-20,10,value=-3,step=1,label="Zeta (mV)")
-        #     b9 = gr.Button("Predict BBB Crossing", variant="primary")
-        #     o9t = gr.Markdown(); o9p = gr.Image(label="Radar profile")
-        #     gr.Examples([["CC(C)CC(=O)OCC(COC(=O)CC(C)C)OC(=O)CC(C)C",6.5,-3]], inputs=[smi,pk,zt9])
-        #     b9.click(predict_bbb, [smi,pk,zt9], [o9t,o9p])
-# =============================================================================
-# === END: S1-D · R3a =========================================================
-# =============================================================================
-
-# =============================================================================
-# === START: S1-D · R4a · AutoCorona NLP =====================================
-# =============================================================================
-        # with gr.TabItem("S1-D · R4a · AutoCorona NLP"):
-        #     gr.HTML(proj_badge("S1-D · R4a", "AutoCorona NLP — from abstracts", "F1 = 0.71"))
-        #     txt  = gr.Textbox(lines=5,label="Paper abstract",placeholder="Paste abstract here...")
-        #     b10  = gr.Button("Extract Data", variant="primary")
-        #     o10j = gr.Code(label="Extracted JSON", language="json")
-        #     o10f = gr.Textbox(label="Validation flags")
-        #     gr.Examples([[
-        #         "LNPs composed of MC3, DSPC, Cholesterol (50:10:40 mol%) with 1.5% PEG-DMG. "
-        #         "Hydrodynamic diameter was 98 nm, zeta potential -3.2 mV, PDI 0.12. "
-        #         "Incubated in human plasma. Corona: albumin, apolipoprotein E, fibrinogen."
-        #     ]], inputs=[txt])
-        #     b10.click(extract_corona, txt, [o10j, o10f])
-# =============================================================================
-# === END: S1-D · R4a =========================================================
-# =============================================================================
-
-# =============================================================================
-# === START: S1-D · R5a · CSF/BM 🔴 ==========================================
-# =============================================================================
+
+        # Розкоментовано проблемну вкладку "оце" - Flow Corona
+        with gr.TabItem("S1-D · R2a · Flow Corona"):
+            gr.HTML(proj_badge("S1-D · R2a", "Flow Corona — Vroman Effect"))
+            with gr.Row():
+                s8  = gr.Slider(50,300,value=100,step=1,label="Size (nm)")
+                z8  = gr.Slider(-40,10,value=-5,step=1,label="Zeta (mV)")
+                pg8 = gr.Slider(0,5,value=1.5,step=0.1,label="PEG mol%")
+            with gr.Row():
+                ch8 = gr.Dropdown(["Ionizable","Cationic","Anionic","Neutral"],value="Ionizable",label="Charge")
+                fl8 = gr.Slider(0,40,value=20,step=1,label="Flow cm/s")
+            b8 = gr.Button("Model Vroman Effect", variant="primary")
+            o8t = gr.Markdown()
+            o8p = gr.Image(label="Kinetics")
+            gr.Examples([[100,-5,1.5,"Ionizable",40],[150,5,0.5,"Cationic",10]], inputs=[s8,z8,pg8,ch8,fl8])
+            b8.click(predict_flow, [s8,z8,pg8,ch8,fl8], [o8t,o8p])
+
+        # Розкоментовано проблемну вкладку "оце" - LNP Brain
+        with gr.TabItem("S1-D · R3a · LNP Brain"):
+            gr.HTML(proj_badge("S1-D · R3a", "LNP Brain Delivery — ApoE% + BBB probability"))
+            smi = gr.Textbox(label="Ionizable lipid SMILES",
+                             value="CC(C)CC(=O)OCC(COC(=O)CC(C)C)OC(=O)CC(C)C")
+            with gr.Row():
+                pk  = gr.Slider(4,8,value=6.5,step=0.1,label="pKa")
+                zt9 = gr.Slider(-20,10,value=-3,step=1,label="Zeta (mV)")
+            b9 = gr.Button("Predict BBB Crossing", variant="primary")
+            o9t = gr.Markdown()
+            o9p = gr.Image(label="Radar profile")
+            gr.Examples([["CC(C)CC(=O)OCC(COC(=O)CC(C)C)OC(=O)CC(C)C",6.5,-3]], inputs=[smi,pk,zt9])
+            b9.click(predict_bbb, [smi,pk,zt9], [o9t,o9p])
+
+        # Розкоментовано проблемну вкладку "оце" - AutoCorona NLP
+        with gr.TabItem("S1-D · R4a · AutoCorona NLP"):
+            gr.HTML(proj_badge("S1-D · R4a", "AutoCorona NLP — from abstracts", "F1 = 0.71"))
+            txt  = gr.Textbox(lines=5,label="Paper abstract",placeholder="Paste abstract here...")
+            b10  = gr.Button("Extract Data", variant="primary")
+            o10j = gr.Code(label="Extracted JSON", language="json")
+            o10f = gr.Textbox(label="Validation flags")
+            gr.Examples([[
+                "LNPs composed of MC3, DSPC, Cholesterol (50:10:40 mol%) with 1.5% PEG-DMG. "
+                "Hydrodynamic diameter was 98 nm, zeta potential -3.2 mV, PDI 0.12. "
+                "Incubated in human plasma. Corona: albumin, apolipoprotein E, fibrinogen."
+            ]], inputs=[txt])
+            b10.click(extract_corona, txt, [o10j, o10f])
+
         with gr.TabItem("S1-D · R5a · CSF/BM 🔴"):
             gr.HTML(proj_badge("S1-D · R5a", "LNP Corona in CSF · Vitreous · Bone Marrow", "🔴 0 prior studies"))
             gr.Markdown("> Planned for Q2–Q3 2026")
-# =============================================================================
-# === END: S1-D · R5a =========================================================
-# =============================================================================
-
-# =============================================================================
-# === START: S1-E · R1a · Liquid Biopsy ====================================== оце
-# =============================================================================
-        # with gr.TabItem("S1-E · R1a · Liquid Biopsy"):
-        #     gr.HTML(proj_badge("S1-E · R1a", "Liquid Biopsy Classifier", "AUC = 0.992*"))
-        #     with gr.Row():
-        #         p1=gr.Slider(-3,3,value=0,step=0.1,label="CTHRC1")
-        #         p2=gr.Slider(-3,3,value=0,step=0.1,label="FHL2")
-        #         p3=gr.Slider(-3,3,value=0,step=0.1,label="LDHA")
-        #         p4=gr.Slider(-3,3,value=0,step=0.1,label="P4HA1")
-        #         p5=gr.Slider(-3,3,value=0,step=0.1,label="SERPINH1")
-        #     with gr.Row():
-        #         p6=gr.Slider(-3,3,value=0,step=0.1,label="ABCA8")
-        #         p7=gr.Slider(-3,3,value=0,step=0.1,label="CA4")
-        #         p8=gr.Slider(-3,3,value=0,step=0.1,label="CKB")
-        #         p9=gr.Slider(-3,3,value=0,step=0.1,label="NNMT")
-        #         p10=gr.Slider(-3,3,value=0,step=0.1,label="CACNA2D2")
-        #     b7=gr.Button("Classify", variant="primary")
-        #     o7t=gr.HTML(); o7p=gr.Image(label="Feature contributions")
-        #     gr.Examples([[2,2,1.5,1.8,1.6,-1,-1.2,-0.8,1.4,-1.1],[0]*10],
-        #                 inputs=[p1,p2,p3,p4,p5,p6,p7,p8,p9,p10])
-        #     b7.click(predict_cancer, [p1,p2,p3,p4,p5,p6,p7,p8,p9,p10], [o7t,o7p])
-# =============================================================================
-# === END: S1-E · R1a =========================================================
-# =============================================================================
-
-# =============================================================================
-# === START: S1-E · R1b · Validator 🔶 =======================================
-# =============================================================================
+
+        # === S1-E · PHYLO-BIOMARKERS ===
+        # Розкоментовано проблемну вкладку "оце" - Liquid Biopsy
+        with gr.TabItem("S1-E · R1a · Liquid Biopsy"):
+            gr.HTML(proj_badge("S1-E · R1a", "Liquid Biopsy Classifier", "AUC = 0.992*"))
+            with gr.Row():
+                p1=gr.Slider(-3,3,value=0,step=0.1,label="CTHRC1")
+                p2=gr.Slider(-3,3,value=0,step=0.1,label="FHL2")
+                p3=gr.Slider(-3,3,value=0,step=0.1,label="LDHA")
+                p4=gr.Slider(-3,3,value=0,step=0.1,label="P4HA1")
+                p5=gr.Slider(-3,3,value=0,step=0.1,label="SERPINH1")
+            with gr.Row():
+                p6=gr.Slider(-3,3,value=0,step=0.1,label="ABCA8")
+                p7=gr.Slider(-3,3,value=0,step=0.1,label="CA4")
+                p8=gr.Slider(-3,3,value=0,step=0.1,label="CKB")
+                p9=gr.Slider(-3,3,value=0,step=0.1,label="NNMT")
+                p10=gr.Slider(-3,3,value=0,step=0.1,label="CACNA2D2")
+            b7=gr.Button("Classify", variant="primary")
+            o7t=gr.HTML()
+            o7p=gr.Image(label="Feature contributions")
+            gr.Examples([[2,2,1.5,1.8,1.6,-1,-1.2,-0.8,1.4,-1.1],[0]*10],
+                        inputs=[p1,p2,p3,p4,p5,p6,p7,p8,p9,p10])
+            b7.click(predict_cancer, [p1,p2,p3,p4,p5,p6,p7,p8,p9,p10], [o7t,o7p])
+
         with gr.TabItem("S1-E · R1b · Validator 🔶"):
             gr.HTML(proj_badge("S1-E · R1b", "Protein Panel Validator", "🔶 In progress"))
             gr.Markdown("> Coming next.")
-# =============================================================================
-# === END: S1-E · R1b =========================================================
-# =============================================================================
-
-# =============================================================================
-# === START: S1-F · R1a · DIPG Toolkit ======================================= оце
-# =============================================================================
-        # with gr.TabItem("S1-F · R1a · DIPG Toolkit"):
-        #     gr.HTML(proj_badge("S1-F · R1a", "DIPG: H3K27M + CSF LNP + Circadian", "PBTA"))
-        #     sort_d = gr.Radio(["Frequency", "Drug status"], value="Frequency", label="Sort by")
-        #     b_dv   = gr.Button("Load DIPG Variants", variant="primary")
-        #     o_dv   = gr.Dataframe(label="H3K27M co-mutations")
-        #     b_dv.click(dipg_variants, [sort_d], o_dv)
-        #     gr.Markdown("---")
-        #     with gr.Row():
-        #         d_peg  = gr.Slider(0.5, 3.0, value=1.5, step=0.1, label="PEG mol%")
-        #         d_size = gr.Slider(60, 150, value=90, step=5, label="Target size (nm)")
-        #     b_dc  = gr.Button("Rank CSF Formulations", variant="primary")
-        #     o_dct = gr.Dataframe(label="CSF LNP ranking")
-        #     o_dcp = gr.Image(label="ApoE% in CSF corona")
-        #     b_dc.click(dipg_csf, [d_peg, d_size], [o_dct, o_dcp])
-# =============================================================================
-# === END: S1-F · R1a =========================================================
-# =============================================================================
-
-# =============================================================================
-# === START: S1-F · R2a · UVM Toolkit ======================================== оце
-# =============================================================================
-        # with gr.TabItem("S1-F · R2a · UVM Toolkit"):
-        #     gr.HTML(proj_badge("S1-F · R2a", "UVM: GNAQ/GNA11 + vitreous + m6A", "TCGA-UVM"))
-        #     b_uv = gr.Button("Load UVM Variants", variant="primary")
-        #     o_uv = gr.Dataframe(label="GNAQ/GNA11 map")
-        #     b_uv.click(uvm_variants, [], o_uv)
-        #     b_uw  = gr.Button("Rank Vitreous Formulations", variant="primary")
-        #     o_uwt = gr.Dataframe(label="Vitreous LNP retention ranking")
-        #     o_uwp = gr.Image(label="Retention (hours)")
-        #     b_uw.click(uvm_vitreous, [], [o_uwt, o_uwp])
-# =============================================================================
-# === END: S1-F · R2a =========================================================
-# =============================================================================
-
-# =============================================================================
-# === START: S1-F · R3a · pAML Toolkit ======================================= оце
-# =============================================================================
-        # with gr.TabItem("S1-F · R3a · pAML Toolkit"):
-        #     gr.HTML(proj_badge("S1-F · R3a", "pAML: FLT3-ITD + BM niche + ferroptosis", "TARGET-AML"))
-        #     var_sel = gr.Dropdown(
-        #         ["FLT3-ITD", "NPM1 c.860_863dupTCAG", "DNMT3A p.R882H",
-        #          "CEBPA biallelic", "IDH1/2 mutation"],
-        #         value="FLT3-ITD", label="Select variant"
-        #     )
-        #     b_pf  = gr.Button("Analyze Ferroptosis Profile", variant="primary")
-        #     o_pft = gr.HTML()
-        #     o_pfp = gr.Image(label="Target radar")
-        #     b_pf.click(paml_ferroptosis, var_sel, [o_pft, o_pfp])
-# =============================================================================
-# === END: S1-F · R3a =========================================================
-# =============================================================================
-
-        # 📓 Journal
+
+        # === S1-F · PHYLO-RARE ===
+        # Розкоментовано проблемну вкладку "оце" - DIPG
+        with gr.TabItem("S1-F · R1a · DIPG Toolkit"):
+            gr.HTML(proj_badge("S1-F · R1a", "DIPG: H3K27M + CSF LNP + Circadian", "PBTA"))
+            sort_d = gr.Radio(["Frequency", "Drug status"], value="Frequency", label="Sort by")
+            b_dv   = gr.Button("Load DIPG Variants", variant="primary")
+            o_dv   = gr.Dataframe(label="H3K27M co-mutations")
+            b_dv.click(dipg_variants, [sort_d], o_dv)
+            gr.Markdown("---")
+            with gr.Row():
+                d_peg  = gr.Slider(0.5, 3.0, value=1.5, step=0.1, label="PEG mol%")
+                d_size = gr.Slider(60, 150, value=90, step=5, label="Target size (nm)")
+            b_dc  = gr.Button("Rank CSF Formulations", variant="primary")
+            o_dct = gr.Dataframe(label="CSF LNP ranking")
+            o_dcp = gr.Image(label="ApoE% in CSF corona")
+            b_dc.click(dipg_csf, [d_peg, d_size], [o_dct, o_dcp])
+
+        # Розкоментовано проблемну вкладку "оце" - UVM
+        with gr.TabItem("S1-F · R2a · UVM Toolkit"):
+            gr.HTML(proj_badge("S1-F · R2a", "UVM: GNAQ/GNA11 + vitreous + m6A", "TCGA-UVM"))
+            b_uv = gr.Button("Load UVM Variants", variant="primary")
+            o_uv = gr.Dataframe(label="GNAQ/GNA11 map")
+            b_uv.click(uvm_variants, [], o_uv)
+            b_uw  = gr.Button("Rank Vitreous Formulations", variant="primary")
+            o_uwt = gr.Dataframe(label="Vitreous LNP retention ranking")
+            o_uwp = gr.Image(label="Retention (hours)")
+            b_uw.click(uvm_vitreous, [], [o_uwt, o_uwp])
+
+        # Розкоментовано проблемну вкладку "оце" - pAML
+        with gr.TabItem("S1-F · R3a · pAML Toolkit"):
+            gr.HTML(proj_badge("S1-F · R3a", "pAML: FLT3-ITD + BM niche + ferroptosis", "TARGET-AML"))
+            var_sel = gr.Dropdown(
+                ["FLT3-ITD", "NPM1 c.860_863dupTCAG", "DNMT3A p.R882H",
+                 "CEBPA biallelic", "IDH1/2 mutation"],
+                value="FLT3-ITD", label="Select variant"
+            )
+            b_pf  = gr.Button("Analyze Ferroptosis Profile", variant="primary")
+            o_pft = gr.HTML()
+            o_pfp = gr.Image(label="Target radar")
+            b_pf.click(paml_ferroptosis, var_sel, [o_pft, o_pfp])
+
+        # === S1-G · 3D Lab (НОВА ВКЛАДКА) ===
+        with gr.TabItem("🧊 3D Lab"):
+            gr.HTML(section_header(
+                "S1-G", "PHYLO-SIM", "— 3D Models & Simulations",
+                "Interactive visualizations for learning"
+            ))
+            with gr.Tabs(elem_classes="inner-tabs"):
+                with gr.TabItem("Nanoparticle"):
+                    gr.Markdown("### 3D Model of a Lipid Nanoparticle")
+                    with gr.Row():
+                        np_radius = gr.Slider(2, 20, value=5, step=1, label="Radius (nm)")
+                        np_peg = gr.Slider(0, 1, value=0.3, step=0.05, label="PEG density")
+                    np_btn = gr.Button("Generate", variant="primary")
+                    np_plot = gr.Plotly(label="Nanoparticle")
+                    np_btn.click(plot_nanoparticle, [np_radius, np_peg], np_plot)
+
+                with gr.TabItem("DNA Helix"):
+                    gr.Markdown("### 3D Model of a DNA Double Helix")
+                    dna_btn = gr.Button("Generate DNA", variant="primary")
+                    dna_plot = gr.Plotly()
+                    dna_btn.click(plot_dna, [], dna_plot)
+
+                with gr.TabItem("Protein Corona"):
+                    gr.Markdown("### Schematic of Protein Corona on Nanoparticle")
+                    corona_btn = gr.Button("Show Corona", variant="primary")
+                    corona_plot = gr.Plotly()
+                    corona_btn.click(plot_corona, [], corona_plot)
+
+        # === Journal (покращений) ===
         with gr.TabItem("📓 Journal"):
             gr.Markdown("### Lab Journal")
             with gr.Row():
                 note_text = gr.Textbox(label="📝 Observation", placeholder="What did you discover?", lines=3)
                 note_tab  = gr.Textbox(label="Project code (e.g. S1-A·R1a)", value="General")
-            note_last  = gr.Textbox(visible=False)
-            save_btn   = gr.Button("💾 Save", variant="primary")
-            save_msg   = gr.Markdown()
-            journal_df = gr.Dataframe(label="📋 Full History", value=load_journal(), interactive=False)
+            save_btn = gr.Button("💾 Save", variant="primary")
+            clear_btn = gr.Button("🗑️ Clear Journal", variant="secondary")
             refresh_btn = gr.Button("🔄 Refresh")
-            refresh_btn.click(load_journal, [], journal_df)
-            save_btn.click(save_note, [note_text,note_tab,note_last], [save_msg,journal_df])
+            journal_display = gr.Markdown(value=load_journal())
+            
+            save_btn.click(save_note, [note_text, note_tab], journal_display)
+            refresh_btn.click(load_journal, [], journal_display)
+            clear_btn.click(clear_journal, [], journal_display)
 
-        # 📚 Learning
+        # === Learning ===
         with gr.TabItem("📚 Learning"):
             gr.Markdown("""
 ## 🧪 Guided Investigations
@@ -868,7 +937,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
 **S1-D · R2a** Flow Corona – compare flow 0 vs 40 cm/s  
 **S1-B · R2a** siRNA – count "Novel" targets across cancer types  
 **S1-E · R1a** Liquid Biopsy – find minimal signal for CANCER  
-... (more cases)
+**S1-G · 3D Lab** – explore nanoparticle, DNA, and protein corona models
             """)
 
     gr.Markdown(