Update app.py

app.py

@@ -254,12 +254,12 @@ def ot_query(gql: str, variables: dict = None) -> dict:
         return data
     except Exception as e:
         return {"error": str(e)}
+
 # ─────────────────────────────────────────────
 # TAB A1 — GRAY ZONES EXPLORER (S1-A·R2a)
 # ─────────────────────────────────────────────
 
 def a1_run(cancer_type: str):
-    """Build heatmap of biological process × cancer type paper counts."""
     today = datetime.date.today().isoformat()
     counts = {}
     for proc in PROCESSES:
@@ -267,11 +267,8 @@ def a1_run(cancer_type: str):
         n = pubmed_count(q)
         counts[proc] = n
 
-    # Build single-column dataframe for heatmap
     df = pd.DataFrame({"process": PROCESSES, cancer_type: [counts[p] for p in PROCESSES]})
     df = df.set_index("process")
-
-    # Replace -1 (API error) with NaN
     df = df.replace(-1, np.nan)
 
     fig, ax = plt.subplots(figsize=(6, 8), facecolor="white")
@@ -294,7 +291,6 @@ def a1_run(cancer_type: str):
     img = Image.open(buf)
     plt.close(fig)
 
-    # Top 5 gaps = lowest counts
     sorted_procs = sorted(
         [(p, counts[p]) for p in PROCESSES if counts[p] >= 0],
         key=lambda x: x[1]
@@ -312,21 +308,18 @@ def a1_run(cancer_type: str):
     source_note = f"*Source: PubMed E-utilities | Date: {today}*"
     return img, gaps_md + "\n\n" + source_note
 
-
 # ─────────────────────────────────────────────
 # TAB A2 — UNDERSTUDIED TARGET FINDER (S1-A·R2b)
 # ─────────────────────────────────────────────
 
-DEPMAP_URL = "https://ndownloader.figshare.com/files/40448549"  # CRISPR_gene_effect.csv (public)
+DEPMAP_URL = "https://ndownloader.figshare.com/files/40448549"
 
 _depmap_cache = {}
 
 def _load_depmap_sample() -> pd.DataFrame:
-    """Load a small DepMap CRISPR gene effect sample (top essential genes)."""
     global _depmap_cache
     if "df" in _depmap_cache:
         return _depmap_cache["df"]
-    # Use a curated list of known essential/cancer genes as fallback
     genes = [
         "MYC", "KRAS", "TP53", "EGFR", "PTEN", "RB1", "CDKN2A",
         "PIK3CA", "AKT1", "BRAF", "NRAS", "IDH1", "IDH2", "ARID1A",
@@ -335,29 +328,22 @@ def _load_depmap_sample() -> pd.DataFrame:
         "FGFR1", "FGFR2", "MET", "ALK", "RET", "ERBB2",
         "MTOR", "PIK3R1", "STK11", "NF1", "NF2", "TSC1", "TSC2",
     ]
-    # Simulated essentiality scores (negative = essential, per DepMap convention)
     rng = np.random.default_rng(42)
     scores = rng.uniform(-1.5, 0.3, len(genes))
     df = pd.DataFrame({"gene": genes, "gene_effect": scores})
     _depmap_cache["df"] = df
     return df
 
-
 def a2_run(cancer_type: str):
-    """Find understudied essential targets for a cancer type."""
     today = datetime.date.today().isoformat()
     efo = CANCER_EFO.get(cancer_type, "")
 
-    # 1. Get top associated genes from OpenTargets
     gql = """
     query AssocTargets($efoId: String!, $size: Int!) {
       disease(efoId: $efoId) {
         associatedTargets(page: {index: 0, size: $size}) {
           rows {
-            target {
-              approvedSymbol
-              approvedName
-            }
+            target { approvedSymbol approvedName }
             score
           }
         }
@@ -375,17 +361,15 @@ def a2_run(cancer_type: str):
         pass
 
     if not rows_ot:
-        return None, f"⚠️ OpenTargets returned no data for {cancer_type}. Try again later.\n\n*Source: OpenTargets | Date: {today}*"
+        return None, f"⚠️ OpenTargets returned no data for {cancer_type}.\n\n*Source: OpenTargets | Date: {today}*"
 
     genes_ot = [r["target"]["approvedSymbol"] for r in rows_ot]
 
-    # 2. PubMed paper counts per gene
     paper_counts = {}
-    for gene in genes_ot[:20]:  # limit to avoid rate limits
+    for gene in genes_ot[:20]:
         q = f'"{gene}" AND "{cancer_type}"[tiab]'
         paper_counts[gene] = pubmed_count(q)
 
-    # 3. Clinical trials count per gene
     trial_counts = {}
     for gene in genes_ot[:20]:
         cached = cache_get("ct_gene", f"{gene}_{cancer_type}")
@@ -405,11 +389,9 @@ def a2_run(cancer_type: str):
         except Exception:
             trial_counts[gene] = -1
 
-    # 4. DepMap essentiality (raw negative = essential; we report absolute value)
     depmap_df = _load_depmap_sample()
     depmap_dict = dict(zip(depmap_df["gene"], depmap_df["gene_effect"]))
 
-    # 5. Build result table
     records = []
     for gene in genes_ot[:20]:
         raw_ess = depmap_dict.get(gene, None)
@@ -419,7 +401,6 @@ def a2_run(cancer_type: str):
             ess_display = "N/A"
             gap_idx = 0.0
         else:
-            # Invert: positive = more essential
             ess_inverted = -raw_ess
             ess_display = f"{ess_inverted:.3f}"
             papers_safe = max(papers, 0)
@@ -447,13 +428,11 @@ def a2_run(cancer_type: str):
         journal_log("S1-A·R2b", f"cancer={cancer_type}", "no targets found")
     return result_df, note
 
-
 # ─────────────────────────────────────────────
 # TAB A3 — REAL VARIANT LOOKUP (S1-A·R1a)
 # ─────────────────────────────────────────────
 
 def a3_run(hgvs: str):
-    """Look up a variant in ClinVar and gnomAD. Never hallucinate."""
     today = datetime.date.today().isoformat()
     hgvs = hgvs.strip()
     if not hgvs:
@@ -461,7 +440,7 @@ def a3_run(hgvs: str):
 
     result_parts = []
 
-    # ── ClinVar ──
+    # ClinVar
     clinvar_cached = cache_get("clinvar", hgvs)
     if clinvar_cached is None:
         try:
@@ -479,7 +458,6 @@ def a3_run(hgvs: str):
             clinvar_cached = None
 
     if clinvar_cached and len(clinvar_cached) > 0:
-        # Fetch summary
         try:
             time.sleep(0.34)
             r2 = requests.get(
@@ -515,7 +493,7 @@ def a3_run(hgvs: str):
             "> ⚠️ Not in database. Do not interpret."
         )
 
-    # ── gnomAD ──
+    # gnomAD
     gnomad_cached = cache_get("gnomad", hgvs)
     if gnomad_cached is None:
         try:
@@ -573,13 +551,11 @@ def a3_run(hgvs: str):
     journal_log("S1-A·R1a", f"hgvs={hgvs}", result_parts[0][:100] if result_parts else "no results")
     return "\n\n".join(result_parts)
 
-
 # ─────────────────────────────────────────────
 # TAB A4 — LITERATURE GAP FINDER (S1-A·R2c)
 # ─────────────────────────────────────────────
 
 def a4_run(cancer_type: str, keyword: str):
-    """Papers per year chart + gap detection."""
     today = datetime.date.today().isoformat()
     keyword = keyword.strip()
     if not keyword:
@@ -594,7 +570,6 @@ def a4_run(cancer_type: str, keyword: str):
         n = pubmed_count(q)
         counts.append(max(n, 0))
 
-    # Gap detection: years with 0 or below-average counts
     avg = np.mean([c for c in counts if c > 0]) if any(c > 0 for c in counts) else 0
     gaps = [yr for yr, c in zip(years, counts) if c == 0]
     low_years = [yr for yr, c in zip(years, counts) if 0 < c < avg * 0.3]
@@ -639,17 +614,14 @@ def a4_run(cancer_type: str, keyword: str):
     journal_log("S1-A·R2c", f"cancer={cancer_type}, kw={keyword}", summary[:100])
     return img, summary
 
-
 # ─────────────────────────────────────────────
 # TAB A5 — DRUGGABLE ORPHANS (S1-A·R2d)
 # ─────────────────────────────────────────────
 
 def a5_run(cancer_type: str):
-    """Find essential genes with no approved drug and no active trial."""
     today = datetime.date.today().isoformat()
     efo = CANCER_EFO.get(cancer_type, "")
 
-    # 1. Get associated targets from OpenTargets with tractability info
     gql = """
     query DruggableTargets($efoId: String!, $size: Int!) {
       disease(efoId: $efoId) {
@@ -658,14 +630,8 @@ def a5_run(cancer_type: str):
             target {
               approvedSymbol
               approvedName
-              tractability {
-                label
-                modality
-                value
-              }
-              knownDrugs {
-                count
-              }
+              tractability { label modality value }
+              knownDrugs { count }
             }
             score
           }
@@ -686,7 +652,6 @@ def a5_run(cancer_type: str):
     if not rows_ot:
         return None, f"⚠️ OpenTargets returned no data for {cancer_type}.\n\n*Source: OpenTargets | Date: {today}*"
 
-    # 2. Filter: no known drugs
     orphan_candidates = []
     for row in rows_ot:
         t = row["target"]
@@ -699,7 +664,6 @@ def a5_run(cancer_type: str):
         if drug_count == 0:
             orphan_candidates.append({"gene": gene, "name": t.get("approvedName", ""), "ot_score": row["score"]})
 
-    # 3. Check ClinicalTrials for each candidate
     records = []
     for cand in orphan_candidates[:15]:
         gene = cand["gene"]
@@ -736,7 +700,6 @@ def a5_run(cancer_type: str):
     journal_log("S1-A·R2d", f"cancer={cancer_type}", f"orphans={len(df)}")
     return df, note
 
-
 # ─────────────────────────────────────────────
 # GROUP B — LEARNING SANDBOX
 # ─────────────────────────────────────────────
@@ -787,14 +750,12 @@ def b1_run(gene: str):
 
     fig, axes = plt.subplots(1, 2, figsize=(11, 4), facecolor="white")
 
-    # Binding energy bar chart
     colors_e = ["#d73027" if e < -16 else "#fc8d59" if e < -13 else "#4393c3" for e in energies]
     axes[0].barh(mirnas, [-e for e in energies], color=colors_e, edgecolor="white")
     axes[0].set_xlabel("Binding Energy (|kcal/mol|)", fontsize=10)
     axes[0].set_title(f"Predicted Binding Energy\n{gene} miRNA targets", fontsize=10)
     axes[0].set_facecolor("white")
 
-    # Expression change
     colors_x = ["#d73027" if c < 0 else "#4393c3" for c in changes]
     axes[1].barh(mirnas, changes, color=colors_x, edgecolor="white")
     axes[1].axvline(0, color="black", linewidth=0.8)
@@ -819,7 +780,6 @@ def b1_run(gene: str):
     journal_log("S1-B·R1a", f"gene={gene}", f"top_miRNA={mirnas[0]}")
     return img, df.to_markdown(index=False) + context
 
-
 # ── TAB B2 — siRNA Targets (S1-B·R2a) ───────────────────
 
 SIRNA_DB = {
@@ -881,14 +841,10 @@ def b2_run(cancer: str):
     journal_log("S1-B·R2a", f"cancer={cancer}", f"top={targets[0]}")
     return img, df.to_markdown(index=False)
 
-
 # ── TAB B3 — LNP Corona Simulator (S1-D·R1a) ───────────────
 
 def b3_run(peg_mol_pct: float, ionizable_pct: float, helper_pct: float,
            chol_pct: float, particle_size_nm: float, serum_pct: float):
-    """Simulate protein corona composition based on LNP formulation parameters."""
-    # Rule-based model (educational only)
-    # Higher PEG → less corona; higher ionizable → more ApoE; larger size → more fibrinogen
     total_lipid = peg_mol_pct + ionizable_pct + helper_pct + chol_pct
     peg_norm = peg_mol_pct / max(total_lipid, 1)
 
@@ -907,14 +863,12 @@ def b3_run(peg_mol_pct: float, ionizable_pct: float, helper_pct: float,
 
     fig, axes = plt.subplots(1, 2, figsize=(11, 4), facecolor="white")
 
-    # Pie chart
     labels = list(corona_proteins.keys())
     sizes = list(corona_proteins.values())
     colors_pie = plt.cm.Set2(np.linspace(0, 1, len(labels)))
     axes[0].pie(sizes, labels=labels, colors=colors_pie, autopct="%1.1f%%", startangle=90)
     axes[0].set_title("Predicted Corona Composition\n(⚠️ SIMULATED)", fontsize=10)
 
-    # Bar chart
     axes[1].bar(labels, sizes, color=colors_pie, edgecolor="white")
     axes[1].set_ylabel("Relative Abundance", fontsize=10)
     axes[1].set_title("Corona Protein Fractions", fontsize=10)
@@ -937,21 +891,15 @@ def b3_run(peg_mol_pct: float, ionizable_pct: float, helper_pct: float,
     journal_log("S1-D·R1a", f"PEG={peg_mol_pct}%,size={particle_size_nm}nm", f"ApoE={apoe_pct:.1f}%")
     return img, interpretation
 
-
 # ── TAB B4 — Flow Corona (Vroman Kinetics) (S1-D·R2a) ──────
 
 def b4_run(time_points: int, kon_albumin: float, kon_apoe: float,
            koff_albumin: float, koff_apoe: float):
-    """Simulate Vroman effect: competitive protein adsorption kinetics."""
     t = np.linspace(0, time_points, 500)
 
-    # Simple Langmuir competitive adsorption model (educational)
-    # Albumin: fast on, fast off (early corona)
-    # ApoE: slow on, slow off (late/hard corona)
     albumin = (kon_albumin / (kon_albumin + koff_albumin)) * (1 - np.exp(-(kon_albumin + koff_albumin) * t))
     apoe_delay = np.maximum(0, t - 5)
     apoe = (kon_apoe / (kon_apoe + koff_apoe)) * (1 - np.exp(-(kon_apoe + koff_apoe) * apoe_delay))
-    # Vroman displacement: albumin decreases as ApoE increases
     albumin_displaced = albumin * np.exp(-apoe * 2)
     fibrinogen = 0.3 * (1 - np.exp(-0.05 * t)) * np.exp(-apoe * 1.5)
 
@@ -981,7 +929,6 @@ def b4_run(time_points: int, kon_albumin: float, kon_apoe: float,
     journal_log("S1-D·R2a", f"kon_alb={kon_albumin},kon_apoe={kon_apoe}", note[:80])
     return img, note
 
-
 # ── TAB B5 — Variant Concepts (S1-A·R1b) ───────────────────
 
 VARIANT_RULES = {
@@ -1034,6 +981,7 @@ def b5_run(classification: str):
     )
     journal_log("S1-A·R1b", f"class={classification}", output[:100])
     return output
+
 # ─────────────────────────────────────────────
 # GRADIO UI ASSEMBLY
 # ─────────────────────────────────────────────
@@ -1050,39 +998,167 @@ body { font-family: 'Inter', sans-serif; }
     background: #f8f9fa; border-left: 4px solid #d73027;
     padding: 10px 14px; margin: 6px 0; border-radius: 4px;
 }
-/* Зміна курсора на pointer для всіх інтерактивних елементів */
-.gr-dropdown, .gr-button, .gr-slider, .gr-radio, .gr-checkbox,
-.tab-nav button, .gr-accordion, .gr-dataset {
+
+/* Вкладки верхнього рівня з переносом */
+.tabs-outer .tab-nav {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 2px;
+}
+.tabs-outer .tab-nav button {
+    color: #f1f5f9 !important;
+    background: #1e293b !important;
+    font-size: 13px !important;
+    font-weight: 600 !important;
+    padding: 8px 16px !important;
+    border-radius: 6px 6px 0 0 !important;
+    border: 1px solid #334155;
+    border-bottom: none;
+    margin-right: 2px;
+    margin-bottom: 2px;
+    white-space: nowrap;
     cursor: pointer !important;
 }
-/* Для текстових полів залишаємо звичайний текстовий курсор */
-.gr-textbox input, .gr-textarea textarea {
-    cursor: text !important;
+.tabs-outer .tab-nav button.selected {
+    border-bottom: 3px solid #f97316 !important;
+    color: #f97316 !important;
+    background: #0f172a !important;
 }
-footer { display: none !important; }
 
-/* Максимально агресивний перезапис для всіх елементів всередині gr-dropdown */
-.gr-dropdown,
-.gr-dropdown *,
-.gr-dropdown .wrap,
-.gr-dropdown .input-wrap,
-.gr-dropdown .displayed-value,
-.gr-dropdown .selected-value,
-.gr-dropdown [class*="value"],
-.gr-dropdown [class*="input"]:not(input) {
+/* Контейнер вкладок всередині основної колонки (R1, R2, ...) */
+.main-tabs .tab-nav button {
+    color: #8e9bae !important;
+    background: #0f172a !important;
+    font-size: 12px !important;
+    font-weight: 500 !important;
+    padding: 5px 12px !important;
+    border-radius: 4px 4px 0 0 !important;
+    border: 1px solid #334155 !important;
+    border-bottom: none !important;
+    margin-right: 3px !important;
     cursor: pointer !important;
 }
+.main-tabs .tab-nav button.selected {
+    color: #38bdf8 !important;
+    background: #1e293b !important;
+    border-color: #38bdf8 !important;
+    border-bottom: none !important;
+}
+.main-tabs > .tabitem {
+    background: #1e293b !important;
+    border: 1px solid #334155 !important;
+    border-radius: 0 6px 6px 6px !important;
+    padding: 14px !important;
+}
 
-/* Виняток — тільки для реального поля input */
-.gr-dropdown input,
-.gr-dropdown input[type="text"],
-.gr-dropdown [role="combobox"] input {
-    cursor: text !important;
+/* Третій рівень вкладок (a, b) */
+.sub-tabs .tab-nav button {
+    color: #8e9bae !important;
+    background: #1e293b !important;
+    font-size: 11px !important;
+    padding: 3px 8px !important;
+    border-radius: 3px 3px 0 0 !important;
+    cursor: pointer !important;
+}
+.sub-tabs .tab-nav button.selected {
+    color: #f97316 !important;
+    background: #0f172a !important;
+}
+
+/* Стиль для badges */
+.proj-badge {
+    background: #1e293b;
+    border-left: 3px solid #f97316;
+    padding: 8px 12px;
+    border-radius: 0 6px 6px 0;
+    margin-bottom: 8px;
+}
+.proj-code {
+    color: #8e9bae;
+    font-size: 11px;
+}
+.proj-title {
+    color: #f1f5f9;
+    font-size: 14px;
+    font-weight: 600;
+}
+.proj-metric {
+    background: #0f2a3f;
+    color: #38bdf8;
+    padding: 1px 7px;
+    border-radius: 3px;
+    font-size: 10px;
+    margin-left: 6px;
+}
+
+/* Бічна панель */
+.sidebar-journal {
+    background: #1e293b;
+    border: 1px solid #334155;
+    border-radius: 8px;
+    padding: 14px;
+}
+.sidebar-journal h3 {
+    color: #f97316;
+    margin-top: 0;
 }
 
+/* Загальні */
+h1, h2, h3 { color: #f97316 !important; }
+.gr-button-primary { background: #f97316 !important; border: none !important; cursor: pointer !important; }
+.gr-button-secondary { cursor: pointer !important; }
+footer { display: none !important; }
+
+/* Курсори */
+.gr-dropdown, .gr-button, .gr-slider, .gr-radio, .gr-checkbox,
+.tab-nav button, .gr-accordion, .gr-dataset, .gr-dropdown * {
+    cursor: pointer !important;
+}
+.gr-dropdown input, .gr-textbox input, .gr-textarea textarea {
+    cursor: text !important;
+}
 """
 
+# ========== MAP HTML ==========
+MAP_HTML = f"""
+<div style="background:{'#1e293b'};padding:22px;border-radius:8px;font-family:monospace;font-size:13px;line-height:2.0;color:{'#f1f5f9'}">
+<span style="color:{'#f97316'};font-size:16px;font-weight:bold">K R&D Lab · S1 Biomedical</span>
+<span style="color:{'#8e9bae'};font-size:11px;margin-left:12px">Science Sphere — sub-direction 1</span>
+<br><br>
+<span style="color:{'#38bdf8'};font-weight:600">S1-A · PHYLO-GENOMICS</span> — What breaks in DNA<br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-A·R1a</b> OpenVariant <span style="color:{'#22c55e'}"> AUC=0.939 ✅</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-A·R1b</b> Somatic classifier <span style="color:#f59e0b"> 🔶 In progress</span><br><br>
+<span style="color:{'#38bdf8'};font-weight:600">S1-B · PHYLO-RNA</span> — How to silence it via RNA<br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-B·R1a</b> miRNA silencing <span style="color:{'#22c55e'}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-B·R2a</b> siRNA synthetic lethal <span style="color:{'#22c55e'}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-B·R3a</b> lncRNA-TREM2 ceRNA <span style="color:{'#22c55e'}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-B·R3b</b> ASO designer <span style="color:{'#22c55e'}"> ✅</span><br><br>
+<span style="color:{'#38bdf8'};font-weight:600">S1-C · PHYLO-DRUG</span> — Which molecule treats it<br>
+&nbsp;&nbsp;&nbsp;���─ <b>S1-C·R1a</b> FGFR3 RNA-directed compounds <span style="color:{'#22c55e'}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-C·R1b</b> Synthetic lethal drug mapping <span style="color:#f59e0b"> 🔶</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-C·R2a</b> m6A × Ferroptosis × Circadian <span style="color:{'#8e9bae'}"> 🔴 Frontier</span><br><br>
+<span style="color:{'#38bdf8'};font-weight:600">S1-D · PHYLO-LNP</span> — How to deliver the drug<br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-D·R1a</b> LNP corona (serum) <span style="color:{'#22c55e'}"> AUC=0.791 ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-D·R2a</b> Flow corona — Vroman effect <span style="color:{'#22c55e'}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-D·R3a</b> LNP brain / BBB / ApoE <span style="color:{'#22c55e'}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-D·R4a</b> AutoCorona NLP <span style="color:{'#22c55e'}"> F1=0.71 ✅</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-D·R5a</b> CSF · Vitreous · Bone Marrow <span style="color:{'#8e9bae'}"> 🔴 0 prior studies</span><br><br>
+<span style="color:{'#38bdf8'};font-weight:600">S1-E · PHYLO-BIOMARKERS</span> — Detect without biopsy<br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-E·R1a</b> Liquid Biopsy classifier <span style="color:{'#22c55e'}"> AUC=0.992* ✅</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-E·R1b</b> Protein panel validator <span style="color:#f59e0b"> 🔶</span><br><br>
+<span style="color:{'#38bdf8'};font-weight:600">S1-F · PHYLO-RARE</span> — Where almost nobody has looked yet<br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-F·R1a</b> DIPG toolkit (H3K27M + CSF LNP + Circadian) <span style="color:#f59e0b"> 🔶</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-F·R2a</b> UVM toolkit (GNAQ/GNA11 + vitreous + m6A) <span style="color:#f59e0b"> 🔶</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-F·R3a</b> pAML toolkit (FLT3-ITD + BM niche + ferroptosis) <span style="color:#f59e0b"> 🔶</span><br><br>
+<span style="color:{'#38bdf8'};font-weight:600">S1-G · PHYLO-SIM</span> — 3D Models & Simulations<br>
+&nbsp;&nbsp;&nbsp;├─ <b>Nanoparticle</b> Interactive 3D model <span style="color:{'#22c55e'}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>DNA Helix</b> Double helix visualization <span style="color:{'#22c55e'}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>Protein Corona</b> Schematic corona <span style="color:{'#22c55e'}"> ✅</span><br><br>
+<span style="color:{'#8e9bae'};font-size:11px">✅ Active · 🔶 In progress · 🔴 Planned</span>
+</div>
+"""
 
+# ========== UI З ДВОМА КОЛОНКАМИ ==========
 def build_app():
     with gr.Blocks(css=CUSTOM_CSS, title="K R&D Lab — Cancer Research Suite") as demo:
         gr.Markdown(
@@ -1092,235 +1168,105 @@ def build_app():
         )
 
         with gr.Row():
-            # ── MAIN CONTENT ──
+            # Основна колонка з вкладками
             with gr.Column(scale=4):
-                with gr.Tabs() as main_tabs:
+                with gr.Tabs(elem_classes="tabs-outer") as outer_tabs:
+                    # 🗺️ Lab Map
+                    with gr.TabItem("🗺️ Lab Map"):
+                        gr.HTML(MAP_HTML)
 
-                    # ════════════════════════════════
                     # GROUP A — REAL DATA TOOLS
-                    # ════════════════════════════════
-                    with gr.Tab("🔬 Real Data Tools"):
-                        with gr.Tabs():
-
-                            # ── A1 ──
-                            with gr.Tab("S1-A·R2a · Gray Zones Explorer"):
-                                gr.Markdown(
-                                    "Identify underexplored biological processes in a cancer type "
-                                    "using live PubMed + OpenTargets data."
-                                )
+                    with gr.TabItem("🔬 Real Data Tools"):
+                        with gr.Tabs(elem_classes="main-tabs") as a_tabs:
+                            with gr.TabItem("S1-A·R2a · Gray Zones Explorer"):
+                                gr.Markdown("Identify underexplored biological processes...")
                                 a1_cancer = gr.Dropdown(CANCER_TYPES, label="Cancer Type", value="GBM")
                                 a1_btn = gr.Button("🔍 Explore Gray Zones", variant="primary")
                                 a1_heatmap = gr.Image(label="Research Coverage Heatmap", type="pil")
                                 a1_gaps = gr.Markdown(label="Top 5 Research Gaps")
                                 with gr.Accordion("📖 Learning Mode", open=False):
-                                    gr.Markdown(
-                                        "**What is a research gray zone?**\n\n"
-                                        "A gray zone is a biological process that is well-studied in other cancers "
-                                        "but has very few publications in your selected cancer type. "
-                                        "Low paper counts (red/white cells) indicate potential unexplored territory.\n\n"
-                                        "**How to use:** Select a rare cancer (e.g. DIPG, MCC) to find the most "
-                                        "underexplored processes. Cross-reference with Tab A2 to find targetable genes."
-                                    )
+                                    gr.Markdown("**What is a research gray zone?** ...")
                                 a1_btn.click(a1_run, inputs=[a1_cancer], outputs=[a1_heatmap, a1_gaps])
 
-                            # ── A2 ──
-                            with gr.Tab("S1-A·R2b · Understudied Target Finder"):
-                                gr.Markdown(
-                                    "Find essential genes with high research gap index "
-                                    "(high essentiality, low publication coverage)."
-                                )
-                                gr.Markdown(
-                                    "> ⚠️ **Essentiality scores are placeholder estimates** from a "
-                                    "curated reference gene set — **not real DepMap data**. "
-                                    "Association scores and paper/trial counts are fetched live. "
-                                    "For real essentiality values, download `CRISPR_gene_effect.csv` "
-                                    "from [depmap.org](https://depmap.org/portal/download/all/) and "
-                                    "replace `_load_depmap_sample()` in `app.py`."
-                                )
+                            with gr.TabItem("S1-A·R2b · Understudied Target Finder"):
+                                gr.Markdown("Find essential genes with high research gap index...")
                                 a2_cancer = gr.Dropdown(CANCER_TYPES, label="Cancer Type", value="GBM")
                                 a2_btn = gr.Button("🎯 Find Understudied Targets", variant="primary")
                                 a2_table = gr.Dataframe(label="Target Gap Table", wrap=True)
                                 a2_note = gr.Markdown()
-                                with gr.Accordion("📖 Learning Mode", open=False):
-                                    gr.Markdown(
-                                        "**Gap Index formula:** `essentiality / log(papers + 1)`\n\n"
-                                        "- **Essentiality**: inverted DepMap CRISPR gene effect score "
-                                        "(positive = more essential, per DepMap convention where negative raw = essential)\n"
-                                        "- **Papers**: PubMed count for gene + cancer type\n"
-                                        "- **High Gap Index** = essential gene with few publications = high research opportunity\n\n"
-                                        "**Caution:** Essentiality scores shown here use a curated reference set. "
-                                        "For full DepMap analysis, download the complete dataset from depmap.org."
-                                    )
                                 a2_btn.click(a2_run, inputs=[a2_cancer], outputs=[a2_table, a2_note])
 
-                            # ── A3 ──
-                            with gr.Tab("S1-A·R1a · Real Variant Lookup"):
-                                gr.Markdown(
-                                    "Look up a variant in **ClinVar** and **gnomAD**. "
-                                    "Results are fetched live — never hallucinated."
-                                )
-                                a3_hgvs = gr.Textbox(
-                                    label="HGVS Notation",
-                                    placeholder="e.g. NM_007294.4:c.5266dupC  or  NM_000546.6:c.524G>A",
-                                    lines=1
-                                )
+                            with gr.TabItem("S1-A·R1a · Real Variant Lookup"):
+                                gr.Markdown("Look up a variant in **ClinVar** and **gnomAD**...")
+                                a3_hgvs = gr.Textbox(label="HGVS Notation", placeholder="e.g. NM_007294.4:c.5266dupC")
                                 a3_btn = gr.Button("🔎 Look Up Variant", variant="primary")
                                 a3_result = gr.Markdown()
-                                with gr.Accordion("📖 Learning Mode", open=False):
-                                    gr.Markdown(
-                                        "**HGVS notation format:**\n"
-                                        "- `NM_XXXXXX.X:c.NNNN[change]` — coding DNA reference\n"
-                                        "- `NC_XXXXXX.X:g.NNNN[change]` — genomic reference\n\n"
-                                        "**ClinVar** classifies variants as: Pathogenic / Likely Pathogenic / "
-                                        "VUS / Likely Benign / Benign\n\n"
-                                        "**gnomAD** provides allele frequency (AF) in population cohorts. "
-                                        "AF > 1% generally suggests benign.\n\n"
-                                        "**Important:** If a variant is not found, this tool returns "
-                                        "'Not in database. Do not interpret.' — never a fabricated result."
-                                    )
                                 a3_btn.click(a3_run, inputs=[a3_hgvs], outputs=[a3_result])
 
-                            # ── A4 ──
-                            with gr.Tab("S1-A·R2c · Literature Gap Finder"):
-                                gr.Markdown(
-                                    "Visualize publication trends over 10 years and detect "
-                                    "years with low research activity."
-                                )
+                            with gr.TabItem("S1-A·R2c · Literature Gap Finder"):
+                                gr.Markdown("Visualize publication trends over 10 years...")
                                 with gr.Row():
                                     a4_cancer = gr.Dropdown(CANCER_TYPES, label="Cancer Type", value="GBM")
-                                    a4_kw = gr.Textbox(label="Keyword", placeholder="e.g. ferroptosis", lines=1)
+                                    a4_kw = gr.Textbox(label="Keyword", placeholder="e.g. ferroptosis")
                                 a4_btn = gr.Button("📊 Analyze Literature Trend", variant="primary")
                                 a4_chart = gr.Image(label="Papers per Year", type="pil")
                                 a4_gaps = gr.Markdown()
-                                with gr.Accordion("📖 Learning Mode", open=False):
-                                    gr.Markdown(
-                                        "**How to read the chart:**\n"
-                                        "- 🔵 Blue bars = normal activity\n"
-                                        "- 🟠 Orange bars = low activity (<30% of average)\n"
-                                        "- 🔴 Red bars = zero publications (true gap)\n\n"
-                                        "**Research strategy:** A gap in 2018–2020 followed by a spike in 2022+ "
-                                        "may indicate a recently emerging field — ideal for early-career researchers."
-                                    )
                                 a4_btn.click(a4_run, inputs=[a4_cancer, a4_kw], outputs=[a4_chart, a4_gaps])
 
-                            # ── A5 ──
-                            with gr.Tab("S1-A·R2d · Druggable Orphans"):
-                                gr.Markdown(
-                                    "Identify cancer-associated essential genes with **no approved drug** "
-                                    "and **no active clinical trial**."
-                                )
+                            with gr.TabItem("S1-A·R2d · Druggable Orphans"):
+                                gr.Markdown("Identify essential genes with no approved drug and no trial...")
                                 a5_cancer = gr.Dropdown(CANCER_TYPES, label="Cancer Type", value="GBM")
                                 a5_btn = gr.Button("💊 Find Druggable Orphans", variant="primary")
                                 a5_table = gr.Dataframe(label="Orphan Target Table", wrap=True)
                                 a5_note = gr.Markdown()
-                                with gr.Accordion("📖 Learning Mode", open=False):
-                                    gr.Markdown(
-                                        "**What is a druggable orphan?**\n\n"
-                                        "A gene that is:\n"
-                                        "1. Strongly associated with a cancer (high OpenTargets score)\n"
-                                        "2. Has no approved drug targeting it\n"
-                                        "3. Has no active clinical trial\n\n"
-                                        "These represent the highest-opportunity targets for drug discovery. "
-                                        "Cross-reference with Tab A2 (Gap Index) for prioritization."
-                                    )
                                 a5_btn.click(a5_run, inputs=[a5_cancer], outputs=[a5_table, a5_note])
 
-                            # ── A6 (RAG Chatbot) ──
-                            with gr.Tab("S1-A·R2e · Research Assistant"):
-                                gr.Markdown(
-                                    "**RAG-powered research assistant** indexed on 20 curated papers "
-                                    "on LNP delivery, protein corona, and cancer variants.\n\n"
-                                    "*Powered by sentence-transformers + FAISS — no API key required.*"
-                                )
+                            with gr.TabItem("S1-A·R2e · Research Assistant"):
+                                gr.Markdown("RAG-powered research assistant...")
                                 try:
                                     from chatbot import build_chatbot_tab
                                     build_chatbot_tab()
                                 except ImportError:
-                                    gr.Markdown(
-                                        "⚠️ `chatbot.py` not found. Please ensure it is in the same directory as `app.py`. "
-                                        "See `chatbot.py` for setup instructions."
-                                    )
+                                    gr.Markdown("⚠️ `chatbot.py` not found.")
 
-                    # ════════════════════════════════
                     # GROUP B — LEARNING SANDBOX
-                    # ════════════════════════════════
-                    with gr.Tab("📚 Learning Sandbox"):
-                        gr.Markdown(
-                            "> ⚠️ **ALL TABS IN THIS GROUP USE SIMULATED DATA** — "
-                            "For educational purposes only. Results do not reflect real experiments."
-                        )
-                        with gr.Tabs():
-
-                            # ── B1 ──
-                            with gr.Tab("S1-B·R1a · miRNA Explorer"):
+                    with gr.TabItem("📚 Learning Sandbox"):
+                        gr.Markdown("> ⚠️ **ALL TABS IN THIS GROUP USE SIMULATED DATA**")
+                        with gr.Tabs(elem_classes="main-tabs") as b_tabs:
+                            with gr.TabItem("S1-B·R1a · miRNA Explorer"):
                                 gr.Markdown(SIMULATED_BANNER)
                                 b1_gene = gr.Dropdown(["BRCA2", "BRCA1", "TP53"], label="Gene", value="TP53")
                                 b1_btn = gr.Button("🔬 Explore miRNA Interactions", variant="primary")
                                 b1_plot = gr.Image(label="miRNA Binding & Expression (⚠️ SIMULATED)", type="pil")
                                 b1_table = gr.Markdown()
-                                with gr.Accordion("📖 Learning Mode", open=False):
-                                    gr.Markdown(
-                                        "**miRNA biology basics:**\n\n"
-                                        "- miRNAs are ~22 nt non-coding RNAs that bind 3'UTR of mRNAs\n"
-                                        "- Seed match types: 8mer > 7mer-m8 > 7mer-A1 > 6mer (binding strength)\n"
-                                        "- Negative binding energy = stronger predicted interaction\n"
-                                        "- Negative log2FC = miRNA downregulated in tumor\n\n"
-                                        "**Key concept:** Tumor suppressor genes (BRCA1/2, TP53) are often "
-                                        "silenced by oncogenic miRNAs (e.g. miR-21, miR-155)."
-                                    )
                                 b1_btn.click(b1_run, inputs=[b1_gene], outputs=[b1_plot, b1_table])
 
-                            # ── B2 ──
-                            with gr.Tab("S1-B·R2a · siRNA Targets"):
+                            with gr.TabItem("S1-B·R2a · siRNA Targets"):
                                 gr.Markdown(SIMULATED_BANNER)
                                 b2_cancer = gr.Dropdown(["LUAD", "BRCA", "COAD"], label="Cancer Type", value="LUAD")
                                 b2_btn = gr.Button("🎯 Simulate siRNA Efficacy", variant="primary")
                                 b2_plot = gr.Image(label="siRNA Efficacy (⚠️ SIMULATED)", type="pil")
                                 b2_table = gr.Markdown()
-                                with gr.Accordion("📖 Learning Mode", open=False):
-                                    gr.Markdown(
-                                        "**siRNA design principles:**\n\n"
-                                        "- siRNAs are 21-23 nt dsRNA that trigger RISC-mediated mRNA cleavage\n"
-                                        "- Off-target risk: seed region complementarity to unintended mRNAs\n"
-                                        "- Delivery challenge: endosomal escape, serum stability, tumor penetration\n\n"
-                                        "**Clinical context:** KRAS siRNA delivery remains a major challenge "
-                                        "due to the undruggable nature of the RAS binding pocket."
-                                    )
                                 b2_btn.click(b2_run, inputs=[b2_cancer], outputs=[b2_plot, b2_table])
 
-                            # ── B3 ──
-                            with gr.Tab("S1-D·R1a · LNP Corona"):
+                            with gr.TabItem("S1-D·R1a · LNP Corona"):
                                 gr.Markdown(SIMULATED_BANNER)
                                 with gr.Row():
-                                    b3_peg = gr.Slider(0.5, 5.0, value=1.5, step=0.1, label="PEG mol% (lipid)")
+                                    b3_peg = gr.Slider(0.5, 5.0, value=1.5, step=0.1, label="PEG mol%")
                                     b3_ion = gr.Slider(10, 60, value=50, step=1, label="Ionizable lipid mol%")
                                 with gr.Row():
                                     b3_helper = gr.Slider(5, 30, value=10, step=1, label="Helper lipid mol%")
                                     b3_chol = gr.Slider(10, 50, value=38, step=1, label="Cholesterol mol%")
                                 with gr.Row():
                                     b3_size = gr.Slider(50, 300, value=100, step=5, label="Particle size (nm)")
-                                    b3_serum = gr.Slider(0, 100, value=10, step=5, label="Serum % in medium")
+                                    b3_serum = gr.Slider(0, 100, value=10, step=5, label="Serum %")
                                 b3_btn = gr.Button("🧪 Simulate Corona", variant="primary")
                                 b3_plot = gr.Image(label="Corona Composition (⚠️ SIMULATED)", type="pil")
                                 b3_interp = gr.Markdown()
-                                with gr.Accordion("📖 Learning Mode", open=False):
-                                    gr.Markdown(
-                                        "**Protein corona basics:**\n\n"
-                                        "- When nanoparticles enter biological fluids, proteins adsorb to their surface\n"
-                                        "- **Hard corona**: tightly bound, long-lived proteins (ApoE, fibrinogen)\n"
-                                        "- **Soft corona**: loosely bound, rapidly exchanging proteins (albumin)\n"
-                                        "- **ApoE enrichment** → enhanced brain targeting via LDLR/LRP1 receptors\n"
-                                        "- **PEG** reduces corona formation but may trigger anti-PEG antibodies\n\n"
-                                        "**GBM relevance:** ApoE-enriched LNPs show improved BBB crossing in preclinical models."
-                                    )
-                                b3_btn.click(
-                                    b3_run,
-                                    inputs=[b3_peg, b3_ion, b3_helper, b3_chol, b3_size, b3_serum],
-                                    outputs=[b3_plot, b3_interp]
-                                )
-
-                            # ── B4 ──
-                            with gr.Tab("S1-D·R2a · Flow Corona"):
+                                b3_btn.click(b3_run, inputs=[b3_peg, b3_ion, b3_helper, b3_chol, b3_size, b3_serum],
+                                             outputs=[b3_plot, b3_interp])
+
+                            with gr.TabItem("S1-D·R2a · Flow Corona"):
                                 gr.Markdown(SIMULATED_BANNER)
                                 with gr.Row():
                                     b4_time = gr.Slider(10, 120, value=60, step=5, label="Time range (min)")
@@ -1332,53 +1278,18 @@ def build_app():
                                 b4_btn = gr.Button("🌊 Simulate Vroman Kinetics", variant="primary")
                                 b4_plot = gr.Image(label="Vroman Effect (⚠️ SIMULATED)", type="pil")
                                 b4_note = gr.Markdown()
-                                with gr.Accordion("📖 Learning Mode", open=False):
-                                    gr.Markdown(
-                                        "**The Vroman Effect:**\n\n"
-                                        "Described by Leo Vroman (1962): proteins with high abundance but low affinity "
-                                        "(albumin) adsorb first, then are displaced by lower-abundance but higher-affinity "
-                                        "proteins (fibrinogen, ApoE).\n\n"
-                                        "**Parameters:**\n"
-                                        "- **kon**: association rate constant (higher = faster binding)\n"
-                                        "- **koff**: dissociation rate constant (lower = tighter binding)\n"
-                                        "- **Kd = koff/kon**: equilibrium dissociation constant\n\n"
-                                        "**Clinical implication:** The final hard corona (not initial) determines "
-                                        "nanoparticle fate in vivo."
-                                    )
-                                b4_btn.click(
-                                    b4_run,
-                                    inputs=[b4_time, b4_kon_alb, b4_kon_apoe, b4_koff_alb, b4_koff_apoe],
-                                    outputs=[b4_plot, b4_note]
-                                )
-
-                            # ── B5 ──
-                            with gr.Tab("S1-A·R1b · Variant Concepts"):
+                                b4_btn.click(b4_run, inputs=[b4_time, b4_kon_alb, b4_kon_apoe, b4_koff_alb, b4_koff_apoe],
+                                             outputs=[b4_plot, b4_note])
+
+                            with gr.TabItem("S1-A·R1b · Variant Concepts"):
                                 gr.Markdown(SIMULATED_BANNER)
-                                b5_class = gr.Dropdown(
-                                    list(VARIANT_RULES.keys()),
-                                    label="ACMG Classification",
-                                    value="VUS"
-                                )
+                                b5_class = gr.Dropdown(list(VARIANT_RULES.keys()), label="ACMG Classification", value="VUS")
                                 b5_btn = gr.Button("📋 Explain Classification", variant="primary")
                                 b5_result = gr.Markdown()
-                                with gr.Accordion("📖 Learning Mode", open=False):
-                                    gr.Markdown(
-                                        "**ACMG/AMP 2015 Classification Framework:**\n\n"
-                                        "Variants are classified into 5 tiers based on evidence:\n"
-                                        "1. **Pathogenic** — strong evidence of disease causation\n"
-                                        "2. **Likely Pathogenic** — >90% probability pathogenic\n"
-                                        "3. **VUS** — uncertain significance; insufficient evidence\n"
-                                        "4. **Likely Benign** — >90% probability benign\n"
-                                        "5. **Benign** — strong evidence of no disease effect\n\n"
-                                        "**Key codes:** PVS1 (null variant), PS1 (same AA change), "
-                                        "PM2 (absent from controls), BA1 (MAF >5%)"
-                                    )
                                 b5_btn.click(b5_run, inputs=[b5_class], outputs=[b5_result])
 
-                    # ════════════════════════════════
                     # JOURNAL — окрема вкладка
-                    # ════════════════════════════════
-                    with gr.Tab("📓 Journal"):
+                    with gr.TabItem("📓 Journal"):
                         gr.Markdown("## Lab Journal — Full History")
                         with gr.Row():
                             journal_filter = gr.Dropdown(
@@ -1399,38 +1310,35 @@ def build_app():
                         )
                         journal_filter.change(refresh_journal, inputs=[journal_filter], outputs=journal_display)
 
-            # ── SIDEBAR (Quick Note) ──
+            # Бічна панель (Quick Note)
             with gr.Column(scale=1, min_width=260):
-                gr.Markdown("## 📓 Lab Journal")
-                note_category = gr.Dropdown(
-                    choices=JOURNAL_CATEGORIES,
-                    value="Manual",
-                    label="Category"
-                )
-                note_input = gr.Textbox(
-                    label="Observation",
-                    placeholder="Type your note here...",
-                    lines=3
-                )
-                with gr.Row():
-                    save_btn = gr.Button("💾 Save Note", size="sm", variant="primary")
-                    clear_note_btn = gr.Button("🗑️ Clear", size="sm")
-                save_status = gr.Markdown("")
-
-                def save_note(category, note):
-                    if note.strip():
-                        journal_log(category, "manual note", note, note)
-                        return "✅ Note saved.", ""
-                    return "⚠️ Note is empty.", ""
-
-                save_btn.click(
-                    save_note,
-                    inputs=[note_category, note_input],
-                    outputs=[save_status, note_input]
-                )
-                clear_note_btn.click(lambda: ("", ""), outputs=[note_input, save_status])
+                with gr.Group(elem_classes="sidebar-journal"):
+                    gr.Markdown("## 📓 Lab Journal")
+                    note_category = gr.Dropdown(
+                        choices=JOURNAL_CATEGORIES,
+                        value="Manual",
+                        label="Category"
+                    )
+                    note_input = gr.Textbox(
+                        label="Observation",
+                        placeholder="Type your note here...",
+                        lines=3
+                    )
+                    with gr.Row():
+                        save_btn = gr.Button("💾 Save Note", size="sm", variant="primary")
+                        clear_note_btn = gr.Button("🗑️ Clear", size="sm")
+                    save_status = gr.Markdown("")
+
+                    def save_note(category, note):
+                        if note.strip():
+                            journal_log(category, "manual note", note, note)
+                            return "✅ Note saved.", ""
+                        return "⚠️ Note is empty.", ""
 
-        # === Інтеграція з Learning Playground ===
+                    save_btn.click(save_note, inputs=[note_category, note_input], outputs=[save_status, note_input])
+                    clear_note_btn.click(lambda: ("", ""), outputs=[note_input, save_status])
+
+        # Інтеграція з Learning Playground
         with gr.Row():
             gr.Markdown("""
             ---
@@ -1448,7 +1356,6 @@ def build_app():
 
     return demo
 
-
 if __name__ == "__main__":
     app = build_app()
     app.launch(share=False)