Update app.py

app.py

@@ -11,7 +11,7 @@ from datetime import datetime
 from pathlib import Path
 import plotly.graph_objects as go
 import plotly.express as px
-import tabulate  # для коректної роботи to_markdown()
+import tabulate
 
 # ========== Діагностичний друк ==========
 print("Gradio version:", gr.__version__)
@@ -43,22 +43,27 @@ def log_entry(tab, inputs, result, note=""):
     except Exception as e:
         print(f"Log error: {e}")
 
-def load_journal():
+def load_journal(category=None):
+    """Load journal entries, optionally filtered by category. Returns markdown string."""
     try:
         if not LOG_PATH.exists() or LOG_PATH.stat().st_size == 0:
             return "No entries yet."
         df = pd.read_csv(LOG_PATH)
         if df.empty:
             return "No entries yet."
-        return df.tail(20).to_markdown(index=False)
+        if category and category != "All":
+            df = df[df["tab"] == category]
+        if df.empty:
+            return "No entries for this category."
+        return df.tail(50).to_markdown(index=False)
     except Exception as e:
         print(f"Journal load error: {e}")
         return "Error loading journal."
 
-def save_note(note, tab):
+def save_note(note, category):
     if note.strip():
-        log_entry(tab, "manual note", note, note)
-    return load_journal()
+        log_entry(category, "manual note", note, note)
+    return "Note saved."
 
 def clear_journal():
     try:
@@ -169,6 +174,17 @@ PROTEINS = ["albumin","apolipoprotein","fibrinogen","vitronectin",
             "clusterin","igm","iga","igg","complement","transferrin",
             "alpha-2-macroglobulin"]
 
+# ========== Список кодів проєктів для випадаючих списків ==========
+PROJECT_CODES = [
+    "S1-A·R1a", "S1-A·R1b",
+    "S1-B·R1a", "S1-B·R2a", "S1-B·R3a", "S1-B·R3b",
+    "S1-C·R1a", "S1-C·R1b", "S1-C·R2a",
+    "S1-D·R1a", "S1-D·R2a", "S1-D·R3a", "S1-D·R4a", "S1-D·R5a",
+    "S1-E·R1a", "S1-E·R1b",
+    "S1-F·R1a", "S1-F·R2a", "S1-F·R3a",
+    "S1-G·General"
+]
+
 # ---------- 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"},
@@ -229,20 +245,20 @@ def safe_img_from_fig(fig):
 
 def predict_mirna(gene):
     df = pd.DataFrame(MIRNA_DB.get(gene, []))
-    log_entry("S1-B · R1a · miRNA", gene, f"{len(df)} miRNAs")
+    log_entry("S1-B·R1a", gene, f"{len(df)} miRNAs")
     return df
 
 def predict_sirna(cancer):
     df = pd.DataFrame(SIRNA_DB.get(cancer, []))
-    log_entry("S1-B · R2a · siRNA", cancer, f"{len(df)} targets")
+    log_entry("S1-B·R2a", cancer, f"{len(df)} targets")
     return df
 
 def get_lncrna():
-    log_entry("S1-B · R3a · lncRNA", "load", "ceRNA")
+    log_entry("S1-B·R3a", "load", "ceRNA")
     return pd.DataFrame(CERNA)
 
 def get_aso():
-    log_entry("S1-B · R3b · ASO", "load", "ASO")
+    log_entry("S1-B·R3b", "load", "ASO")
     return pd.DataFrame(ASO)
 
 def predict_drug(pocket):
@@ -259,10 +275,10 @@ def predict_drug(pocket):
         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'}")
+        log_entry("S1-C·R1a", 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)}")
+        log_entry("S1-C·R1a", pocket, f"Error: {str(e)}")
         return pd.DataFrame(), None
 
 def predict_variant(hgvs, sift, polyphen, gnomad):
@@ -279,10 +295,10 @@ def predict_variant(hgvs, sift, polyphen, gnomad):
         conf = "High" if (sift < 0.01 or sift > 0.9) else "Moderate"
     colour = RED if "Pathogenic" in cls else GRN
     icon   = "⚠️ WARNING" if "Pathogenic" in cls else "✅ OK"
-    log_entry("S1-A · R1a · OpenVariant", hgvs or f"SIFT={sift}", f"{cls} score={score}")
+    log_entry("S1-A·R1a", hgvs or f"SIFT={sift}", f"{cls} score={score}")
     return (
         f"<div style=\'background:{CARD};padding:16px;border-radius:8px;font-family:sans-serif;color:{TXT}\'>"
-        f"<p style=\'font-size:11px;color:{DIM};margin:0 0 8px\'>S1-A · R1a · OpenVariant</p>"
+        f"<p style=\'font-size:11px;color:{DIM};margin:0 0 8px\'>S1-A·R1a · OpenVariant</p>"
         f"<h3 style=\'color:{colour};margin:0 0 8px\'>{icon} {cls}</h3>"
         f"<p>Score: <b>{score:.3f}</b> &nbsp;|&nbsp; Confidence: <b>{conf}</b></p>"
         f"<div style=\'background:{BORDER};border-radius:4px;height:14px\'>"
@@ -301,7 +317,7 @@ def predict_corona(size, zeta, peg, lipid):
         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}")
+        log_entry("S1-D·R1a", 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)}"
@@ -325,10 +341,10 @@ def predict_cancer(c1,c2,c3,c4,c5,c6,c7,c8,c9,c10):
         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}")
+        log_entry("S1-E·R1a", 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"<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>"
         )
@@ -354,7 +370,7 @@ def predict_flow(size, zeta, peg, charge, flow_rate):
         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}")
+        log_entry("S1-D·R2a", 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
@@ -376,7 +392,7 @@ def predict_bbb(smiles, pka, zeta):
         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}%")
+        log_entry("S1-D·R3a", 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
@@ -402,7 +418,7 @@ def extract_corona(text):
         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'])}")
+        log_entry("S1-D·R4a", 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"
@@ -410,7 +426,7 @@ def extract_corona(text):
 def dipg_variants(sort_by):
     df = pd.DataFrame(DIPG_VARIANTS).sort_values(
         "Freq_pct" if sort_by == "Frequency" else "Drug_status", ascending=False)
-    log_entry("S1-F · R1a · DIPG", sort_by, f"{len(df)} variants")
+    log_entry("S1-F·R1a", sort_by, f"{len(df)} variants")
     return df
 
 def dipg_csf(peg, size):
@@ -428,14 +444,14 @@ def dipg_csf(peg, size):
         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")
+        log_entry("S1-F·R1a", 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)
-    log_entry("S1-F · R2a · UVM", "load", f"{len(df)} variants")
+    log_entry("S1-F·R2a", "load", f"{len(df)} variants")
     return df
 
 def uvm_vitreous():
@@ -451,7 +467,7 @@ def uvm_vitreous():
         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")
+        log_entry("S1-F·R2a", "load", "vitreous LNP ranking")
         return df, img
     except Exception as e:
         return pd.DataFrame(), None
@@ -475,7 +491,7 @@ def paml_ferroptosis(variant):
         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}")
+        log_entry("S1-F·R3a", variant, f"ferr={ferr_score:.2f}")
         _v  = row["Variant"]
         _p  = row["Pathway"]
         _d  = row["Drug_status"]
@@ -483,7 +499,7 @@ def paml_ferroptosis(variant):
         _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"<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>"
@@ -597,7 +613,12 @@ def plot_corona():
 css = f"""
 body, .gradio-container {{ background: {BG} !important; color: {TXT} !important; }}
 
-/* Вкладки верхнього рівня (категорії) */
+/* Вкладки верхнього рівня (категорії) з переносом на новий ряд */
+.tabs-outer .tab-nav {{
+    display: flex;
+    flex-wrap: wrap;
+    gap: 2px;
+}}
 .tabs-outer .tab-nav button {{
     color: {TXT} !important;
     background: {CARD} !important;
@@ -605,6 +626,11 @@ body, .gradio-container {{ background: {BG} !important; color: {TXT} !important;
     font-weight: 600 !important;
     padding: 8px 16px !important;
     border-radius: 6px 6px 0 0 !important;
+    border: 1px solid {BORDER};
+    border-bottom: none;
+    margin-right: 2px;
+    margin-bottom: 2px;
+    white-space: nowrap;
 }}
 .tabs-outer .tab-nav button.selected {{
     border-bottom: 3px solid {ACC} !important;
@@ -676,14 +702,14 @@ body, .gradio-container {{ background: {BG} !important; color: {TXT} !important;
     margin-left: 6px;
 }}
 
-/* Журнал */
-.journal {{
+/* Бічна панель з журналом (тільки введення) */
+.sidebar-journal {{
     background: {CARD};
     border: 1px solid {BORDER};
     border-radius: 8px;
     padding: 14px;
 }}
-.journal h3 {{
+.sidebar-journal h3 {{
     color: {ACC};
     margin-top: 0;
 }}
@@ -701,30 +727,30 @@ MAP_HTML = f"""
 <span style="color:{DIM};font-size:11px;margin-left:12px">Science Sphere — sub-direction 1</span>
 <br><br>
 <span style="color:{ACC2};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:{GRN}"> AUC=0.939 ✅</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-A · R1b</b> Somatic classifier <span style="color:#f59e0b"> 🔶 In progress</span><br><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-A·R1a</b> OpenVariant <span style="color:{GRN}"> 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:{ACC2};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:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-B · R2a</b> siRNA synthetic lethal <span style="color:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-B · R3a</b> lncRNA-TREM2 ceRNA <span style="color:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-B · R3b</b> ASO designer <span style="color:{GRN}"> ✅</span><br><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-B·R1a</b> miRNA silencing <span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-B·R2a</b> siRNA synthetic lethal <span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-B·R3a</b> lncRNA-TREM2 ceRNA <span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-B·R3b</b> ASO designer <span style="color:{GRN}"> ✅</span><br><br>
 <span style="color:{ACC2};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:{GRN}"> ✅</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:{DIM}"> 🔴 Frontier</span><br><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-C·R1a</b> FGFR3 RNA-directed compounds <span style="color:{GRN}"> ✅</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:{DIM}"> 🔴 Frontier</span><br><br>
 <span style="color:{ACC2};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:{GRN}"> AUC=0.791 ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-D · R2a</b> Flow corona — Vroman effect <span style="color:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-D · R3a</b> LNP brain / BBB / ApoE <span style="color:{GRN}"> ✅</span><br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-D · R4a</b> AutoCorona NLP <span style="color:{GRN}"> F1=0.71 ✅</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-D · R5a</b> CSF · Vitreous · Bone Marrow <span style="color:{DIM}"> 🔴 0 prior studies</span><br><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-D·R1a</b> LNP corona (serum) <span style="color:{GRN}"> AUC=0.791 ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-D·R2a</b> Flow corona — Vroman effect <span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-D·R3a</b> LNP brain / BBB / ApoE <span style="color:{GRN}"> ✅</span><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-D·R4a</b> AutoCorona NLP <span style="color:{GRN}"> F1=0.71 ✅</span><br>
+&nbsp;&nbsp;&nbsp;└─ <b>S1-D·R5a</b> CSF · Vitreous · Bone Marrow <span style="color:{DIM}"> 🔴 0 prior studies</span><br><br>
 <span style="color:{ACC2};font-weight:600">S1-E · PHYLO-BIOMARKERS</span> — Detect without biopsy<br>
-&nbsp;&nbsp;&nbsp;├─ <b>S1-E · R1a</b> Liquid Biopsy classifier <span style="color:{GRN}"> AUC=0.992* ✅</span><br>
-&nbsp;&nbsp;&nbsp;└─ <b>S1-E · R1b</b> Protein panel validator <span style="color:#f59e0b"> 🔶</span><br><br>
+&nbsp;&nbsp;&nbsp;├─ <b>S1-E·R1a</b> Liquid Biopsy classifier <span style="color:{GRN}"> 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:{ACC2};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>
+&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:{ACC2};font-weight:600">S1-G · PHYLO-SIM</span> — 3D Models & Simulations<br>
 &nbsp;&nbsp;&nbsp;├─ <b>Nanoparticle</b> Interactive 3D model <span style="color:{GRN}"> ✅</span><br>
 &nbsp;&nbsp;&nbsp;├─ <b>DNA Helix</b> Double helix visualization <span style="color:{GRN}"> ✅</span><br>
@@ -750,7 +776,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                     gr.HTML(MAP_HTML)
 
                 # === S1-A · PHYLO-GENOMICS ===
-                with gr.TabItem("🧬 S1-A · PHYLO-GENOMICS"):
+                with gr.TabItem("🧬 S1-A"):
                     gr.HTML(section_header(
                         "S1-A", "PHYLO-GENOMICS", "— What breaks in DNA",
                         "R1a OpenVariant ✅ · R1b Somatic classifier 🔶"
@@ -761,7 +787,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                             with gr.Tabs(elem_classes="sub-tabs"):
                                 # R1a · OpenVariant
                                 with gr.TabItem("R1a · OpenVariant"):
-                                    gr.HTML(proj_badge("S1-A · R1a", "OpenVariant — SNV Pathogenicity Classifier", "AUC=0.939"))
+                                    gr.HTML(proj_badge("S1-A·R1a", "OpenVariant — SNV Pathogenicity Classifier", "AUC=0.939"))
                                     hgvs = gr.Textbox(label="HGVS notation", placeholder="BRCA1:p.R1699Q")
                                     gr.Markdown("**Or enter functional scores manually:**")
                                     with gr.Row():
@@ -776,11 +802,11 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                                     b_v.click(predict_variant, [hgvs,sift,pp,gn], o_v)
                                 # R1b · Somatic Classifier (в розробці)
                                 with gr.TabItem("R1b · Somatic Classifier 🔶"):
-                                    gr.HTML(proj_badge("S1-A · R1b", "Somatic Mutation Classifier", "🔶 In progress"))
+                                    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.")
 
                 # === S1-B · PHYLO-RNA ===
-                with gr.TabItem("🔬 S1-B · PHYLO-RNA"):
+                with gr.TabItem("🔬 S1-B"):
                     gr.HTML(section_header(
                         "S1-B", "PHYLO-RNA", "— How to silence it via RNA",
                         "R1a miRNA ✅ · R2a siRNA ✅ · R3a lncRNA ✅ · R3b ASO ✅"
@@ -790,7 +816,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                         with gr.TabItem("R1 · miRNA silencing"):
                             with gr.Tabs(elem_classes="sub-tabs"):
                                 with gr.TabItem("R1a · BRCA2 miRNA"):
-                                    gr.HTML(proj_badge("S1-B · R1a", "miRNA Silencing — BRCA1/2 · TP53"))
+                                    gr.HTML(proj_badge("S1-B·R1a", "miRNA Silencing — BRCA1/2 · TP53"))
                                     g1 = gr.Dropdown(["BRCA2","BRCA1","TP53"], value="BRCA2", label="Gene")
                                     b1 = gr.Button("Find miRNAs", variant="primary")
                                     o1 = gr.Dataframe(label="Top 5 downregulated miRNAs")
@@ -800,7 +826,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                         with gr.TabItem("R2 · siRNA SL"):
                             with gr.Tabs(elem_classes="sub-tabs"):
                                 with gr.TabItem("R2a · TP53 siRNA"):
-                                    gr.HTML(proj_badge("S1-B · R2a", "siRNA Synthetic Lethal — TP53-null"))
+                                    gr.HTML(proj_badge("S1-B·R2a", "siRNA Synthetic Lethal — TP53-null"))
                                     g2 = gr.Dropdown(["LUAD","BRCA","COAD"], value="LUAD", label="Cancer type")
                                     b2 = gr.Button("Find Targets", variant="primary")
                                     o2 = gr.Dataframe(label="Top 5 synthetic lethal targets")
@@ -810,18 +836,18 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                         with gr.TabItem("R3 · lncRNA + ASO"):
                             with gr.Tabs(elem_classes="sub-tabs"):
                                 with gr.TabItem("R3a · lncRNA-TREM2"):
-                                    gr.HTML(proj_badge("S1-B · R3a", "lncRNA-TREM2 ceRNA Network"))
+                                    gr.HTML(proj_badge("S1-B·R3a", "lncRNA-TREM2 ceRNA Network"))
                                     b3a = gr.Button("Load ceRNA", variant="primary")
                                     o3a = gr.Dataframe(label="ceRNA Network (R3a)")
                                     b3a.click(lambda: pd.DataFrame(CERNA), [], o3a)
                                 with gr.TabItem("R3b · ASO Designer"):
-                                    gr.HTML(proj_badge("S1-B · R3b", "ASO Designer"))
+                                    gr.HTML(proj_badge("S1-B·R3b", "ASO Designer"))
                                     b3b = gr.Button("Load ASO Candidates", variant="primary")
                                     o3b = gr.Dataframe(label="ASO Candidates (R3b)")
                                     b3b.click(lambda: pd.DataFrame(ASO), [], o3b)
 
                 # === S1-C · PHYLO-DRUG ===
-                with gr.TabItem("💊 S1-C · PHYLO-DRUG"):
+                with gr.TabItem("💊 S1-C"):
                     gr.HTML(section_header(
                         "S1-C", "PHYLO-DRUG", "— Which molecule treats it",
                         "R1a FGFR3 ✅ · R1b SL drug mapping 🔶 · R2a Frontier 🔴⭐"
@@ -831,7 +857,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                         with gr.TabItem("R1 · RNA-directed drug"):
                             with gr.Tabs(elem_classes="sub-tabs"):
                                 with gr.TabItem("R1a · FGFR3 RNA Drug"):
-                                    gr.HTML(proj_badge("S1-C · R1a", "FGFR3 RNA-Directed Drug Discovery", "top score 0.793"))
+                                    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")
@@ -840,13 +866,13 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                                     gr.Examples([["P1 (hairpin loop)"],["P10 (G-quadruplex)"]], inputs=[g4])
                                     b4_drug.click(predict_drug, [g4], [o4t, o4p])
                                 with gr.TabItem("R1b · SL Drug Mapping 🔶"):
-                                    gr.HTML(proj_badge("S1-C · R1b", "Synthetic Lethal Drug Mapping", "🔶 In progress"))
+                                    gr.HTML(proj_badge("S1-C·R1b", "Synthetic Lethal Drug Mapping", "🔶 In progress"))
                                     gr.Markdown("> In development. Coming soon.")
                         # R2 · Frontier
                         with gr.TabItem("R2 · Frontier"):
                             with gr.Tabs(elem_classes="sub-tabs"):
                                 with gr.TabItem("R2a · m6A×Ferroptosis×Circadian 🔴⭐"):
-                                    gr.HTML(proj_badge("S1-C · R2a", "m6A × Ferroptosis × Circadian", "🔴 Frontier"))
+                                    gr.HTML(proj_badge("S1-C·R2a", "m6A × Ferroptosis × Circadian", "🔴 Frontier"))
                                     gr.Markdown(
                                         "> **Research gap:** This triple intersection has never been studied as an integrated system.\n\n"
                                         "> **Planned datasets:** TCGA-PAAD · GEO m6A atlases · Circadian gene panels\n\n"
@@ -854,7 +880,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                                     )
 
                 # === S1-D · PHYLO-LNP ===
-                with gr.TabItem("🧪 S1-D · PHYLO-LNP"):
+                with gr.TabItem("🧪 S1-D"):
                     gr.HTML(section_header(
                         "S1-D", "PHYLO-LNP", "— How to deliver the drug",
                         "R1a Corona ✅ · R2a Flow ✅ · R3a Brain ✅ · R4a NLP ✅ · R5a CSF/BM 🔴⭐"
@@ -864,7 +890,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                         with gr.TabItem("R1 · Serum corona"):
                             with gr.Tabs(elem_classes="sub-tabs"):
                                 with gr.TabItem("R1a · LNP Corona ML"):
-                                    gr.HTML(proj_badge("S1-D · R1a", "LNP Protein Corona (Serum)", "AUC=0.791"))
+                                    gr.HTML(proj_badge("S1-D·R1a", "LNP Protein Corona (Serum)", "AUC=0.791"))
                                     with gr.Row():
                                         sz = gr.Slider(50,300,value=100,step=1,label="Size (nm)")
                                         zt = gr.Slider(-40,10,value=-5,step=1,label="Zeta (mV)")
@@ -879,7 +905,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                         with gr.TabItem("R2 · Flow corona"):
                             with gr.Tabs(elem_classes="sub-tabs"):
                                 with gr.TabItem("R2a · Flow Corona"):
-                                    gr.HTML(proj_badge("S1-D · R2a", "Flow Corona — Vroman Effect"))
+                                    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)")
@@ -896,7 +922,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                         with gr.TabItem("R3 · Brain BBB"):
                             with gr.Tabs(elem_classes="sub-tabs"):
                                 with gr.TabItem("R3a · LNP Brain"):
-                                    gr.HTML(proj_badge("S1-D · R3a", "LNP Brain Delivery"))
+                                    gr.HTML(proj_badge("S1-D·R3a", "LNP Brain Delivery"))
                                     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():
@@ -911,7 +937,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                         with gr.TabItem("R4 · NLP"):
                             with gr.Tabs(elem_classes="sub-tabs"):
                                 with gr.TabItem("R4a · AutoCorona NLP"):
-                                    gr.HTML(proj_badge("S1-D · R4a", "AutoCorona NLP", "F1=0.71"))
+                                    gr.HTML(proj_badge("S1-D·R4a", "AutoCorona NLP", "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")
@@ -926,7 +952,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                         with gr.TabItem("R5 · Exotic fluids 🔴⭐"):
                             with gr.Tabs(elem_classes="sub-tabs"):
                                 with gr.TabItem("R5a · CSF/Vitreous/BM"):
-                                    gr.HTML(proj_badge("S1-D · R5a", "LNP Corona in CSF · Vitreous · Bone Marrow", "🔴 0 prior studies"))
+                                    gr.HTML(proj_badge("S1-D·R5a", "LNP Corona in CSF · Vitreous · Bone Marrow", "🔴 0 prior studies"))
                                     gr.Markdown(
                                         "> **Research gap:** Protein corona has only been characterized in serum/plasma. "
                                         "CSF, vitreous humor, and bone marrow interstitial fluid remain completely unstudied.\n\n"
@@ -935,7 +961,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                                     )
 
                 # === S1-E · PHYLO-BIOMARKERS ===
-                with gr.TabItem("🩸 S1-E · PHYLO-BIOMARKERS"):
+                with gr.TabItem("🩸 S1-E"):
                     gr.HTML(section_header(
                         "S1-E", "PHYLO-BIOMARKERS", "— Detect without biopsy",
                         "R1a Liquid Biopsy ✅ · R1b Protein validator 🔶"
@@ -944,7 +970,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                         with gr.TabItem("R1 · Liquid biopsy"):
                             with gr.Tabs(elem_classes="sub-tabs"):
                                 with gr.TabItem("R1a · Liquid Biopsy"):
-                                    gr.HTML(proj_badge("S1-E · R1a", "Liquid Biopsy Classifier", "AUC=0.992*"))
+                                    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")
@@ -964,11 +990,11 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                                                 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("R1b · Protein Validator 🔶"):
-                                    gr.HTML(proj_badge("S1-E · R1b", "Protein Panel Validator", "🔶 In progress"))
+                                    gr.HTML(proj_badge("S1-E·R1b", "Protein Panel Validator", "🔶 In progress"))
                                     gr.Markdown("> Coming next — validates R1a results against GEO plasma proteomics datasets.")
 
                 # === S1-F · PHYLO-RARE ===
-                with gr.TabItem("🧠 S1-F · PHYLO-RARE"):
+                with gr.TabItem("🧠 S1-F"):
                     gr.HTML(section_header(
                         "S1-F", "PHYLO-RARE", "— Where almost nobody has looked yet",
                         "<b style='color:#ef4444'>⚠️ <300 cases/yr · <5% survival · 0–1 prior studies per gap</b><br>"
@@ -979,7 +1005,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                         with gr.TabItem("R1 · DIPG"):
                             with gr.Tabs(elem_classes="sub-tabs"):
                                 with gr.TabItem("R1a · DIPG Toolkit"):
-                                    gr.HTML(proj_badge("S1-F · R1a", "DIPG Toolkit", "PBTA · GSE126319"))
+                                    gr.HTML(proj_badge("S1-F·R1a", "DIPG Toolkit", "PBTA · GSE126319"))
                                     gr.Markdown(
                                         "> **Why DIPG?** Diffuse Intrinsic Pontine Glioma — median survival 9–11 months. "
                                         "H3K27M oncohistone in **78%** cases. "
@@ -1013,7 +1039,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                         with gr.TabItem("R2 · UVM"):
                             with gr.Tabs(elem_classes="sub-tabs"):
                                 with gr.TabItem("R2a · UVM Toolkit"):
-                                    gr.HTML(proj_badge("S1-F · R2a", "UVM Toolkit", "TCGA-UVM n=80"))
+                                    gr.HTML(proj_badge("S1-F·R2a", "UVM Toolkit", "TCGA-UVM n=80"))
                                     gr.Markdown(
                                         "> **Why UVM?** Uveal Melanoma — metastatic 5-yr survival **15%**. "
                                         "GNAQ/GNA11 mutations in 78% cases. "
@@ -1043,7 +1069,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                         with gr.TabItem("R3 · pAML"):
                             with gr.Tabs(elem_classes="sub-tabs"):
                                 with gr.TabItem("R3a · pAML Toolkit"):
-                                    gr.HTML(proj_badge("S1-F · R3a", "pAML Toolkit", "TARGET-AML n≈197"))
+                                    gr.HTML(proj_badge("S1-F·R3a", "pAML Toolkit", "TARGET-AML n≈197"))
                                     gr.Markdown(
                                         "> **Why pAML?** Pediatric AML — relapse OS **<30%**. "
                                         "FLT3-ITD in 25% cases. "
@@ -1077,7 +1103,7 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
                                             )
 
                 # === S1-G · 3D Lab ===
-                with gr.TabItem("🧊 S1-G · 3D Lab"):
+                with gr.TabItem("🧊 S1-G"):
                     gr.HTML(section_header(
                         "S1-G", "PHYLO-SIM", "— 3D Models & Simulations",
                         "Interactive visualizations for learning"
@@ -1110,29 +1136,53 @@ with gr.Blocks(css=css, title="K R&D Lab · S1 Biomedical") as demo:
 ## 🧪 Guided Investigations
 > 🟢 Beginner → 🟡 Intermediate → 🔴 Advanced
 
-**S1-A · R1a** OpenVariant – try BRCA1:p.R1699Q vs R1699W  
-**S1-D · R1a** Corona – vary PEG% and observe dominant protein  
-**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  
-**S1-G · 3D Lab** – explore nanoparticle, DNA, and protein corona models
+**S1-A·R1a** OpenVariant – try BRCA1:p.R1699Q vs R1699W  
+**S1-D·R1a** Corona – vary PEG% and observe dominant protein  
+**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  
+**S1-G·3D Lab** – explore nanoparticle, DNA, and protein corona models
                     """)
 
-        # Права колонка з журналом
-        with gr.Column(scale=1, min_width=300):
-            with gr.Group(elem_classes="journal"):
-                gr.Markdown("## 📓 Lab Journal")
-                note_input = gr.Textbox(label="📝 Observation", placeholder="Your observation...", lines=2)
-                note_tab  = gr.Textbox(label="Project code (e.g. S1-A·R1a)", value="General", visible=False)
-                with gr.Row():
-                    save_btn = gr.Button("💾 Save", size="sm", variant="primary")
-                    refresh_btn = gr.Button("🔄 Refresh", size="sm")
-                    clear_btn = gr.Button("🗑️ Clear", size="sm")
-                journal_display = gr.Markdown(value=load_journal())
+                # === Journal (окрема вкладка) ===
+                with gr.TabItem("📓 Journal"):
+                    gr.Markdown("## Lab Journal — Full History")
+                    with gr.Row():
+                        filter_dropdown = gr.Dropdown(
+                            choices=["All"] + PROJECT_CODES,
+                            value="All",
+                            label="Filter by project code"
+                        )
+                        refresh_btn = gr.Button("🔄 Refresh", size="sm")
+                        clear_btn = gr.Button("🗑️ Clear Journal", size="sm")
+                    journal_display = gr.Markdown(value=load_journal())
+                    
+                    refresh_btn.click(load_journal, inputs=[filter_dropdown], outputs=journal_display)
+                    clear_btn.click(clear_journal, [], journal_display).then(
+                        load_journal, inputs=[filter_dropdown], outputs=journal_display
+                    )
+                    filter_dropdown.change(load_journal, inputs=[filter_dropdown], outputs=journal_display)
+
+        # Права колонка – швидке введення нотаток (без відображення)
+        with gr.Column(scale=1, min_width=260):
+            with gr.Group(elem_classes="sidebar-journal"):
+                gr.Markdown("## 📝 Quick Note")
+                note_dropdown = gr.Dropdown(
+                    choices=PROJECT_CODES,
+                    value=PROJECT_CODES[0],
+                    label="Project code"
+                )
+                note_input = gr.Textbox(label="Your observation", placeholder="Type here...", lines=3)
+                save_btn = gr.Button("💾 Save Note", variant="primary", size="sm")
+                save_status = gr.Markdown("")
+                
+                def quick_save(note, code):
+                    if note.strip():
+                        log_entry(code, "quick note", note, note)
+                        return "✅ Note saved."
+                    return "⚠️ Note is empty."
                 
-                save_btn.click(save_note, [note_input, note_tab], journal_display)
-                refresh_btn.click(load_journal, [], journal_display)
-                clear_btn.click(clear_journal, [], journal_display)
+                save_btn.click(quick_save, [note_input, note_dropdown], save_status)
 
     gr.Markdown(
         "---\n**K R&D Lab** · MIT License · "