Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import pandas as pd
 import numpy as np
-import json, re, csv, os
+import json, re, csv
 import matplotlib
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
@@ -16,179 +16,125 @@ ACC  = "#f97316"
 ACC2 = "#38bdf8"
 TXT  = "#f1f5f9"
 
-# ── Logging ──────────────────────────────────────────────────────────────────
 LOG_PATH = Path("./lab_journal.csv")
 
 def log_entry(tab, inputs, result, note=""):
     write_header = not LOG_PATH.exists()
     with open(LOG_PATH, "a", newline="", encoding="utf-8") as f:
-        w = csv.DictWriter(f,
-            fieldnames=["timestamp","tab","inputs","result","note"])
+        w = csv.DictWriter(f, fieldnames=["timestamp","tab","inputs","result","note"])
         if write_header:
             w.writeheader()
         w.writerow({
             "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M"),
-            "tab":       tab,
-            "inputs":    str(inputs),
-            "result":    str(result)[:200],
-            "note":      note
+            "tab": tab,
+            "inputs": str(inputs),
+            "result": str(result)[:200],
+            "note": note
         })
 
 def load_journal():
     if not LOG_PATH.exists():
-        return pd.DataFrame(columns=
-            ["timestamp","tab","inputs","result","note"])
+        return pd.DataFrame(columns=["timestamp","tab","inputs","result","note"])
     return pd.read_csv(LOG_PATH)
 
 def save_note(note, tab, last_result):
     log_entry(tab, "", last_result, note)
     return "✅ Saved!", load_journal()
 
-# ── All original DB dicts (unchanged) ────────────────────────────────────────
 MIRNA_DB = {
     "BRCA2": [
-        {"miRNA":"hsa-miR-148a-3p","log2FC":-0.70,"padj":0.013,
-         "targets":"DNMT1, AKT2","pathway":"Epigenetic reprogramming"},
-        {"miRNA":"hsa-miR-30e-5p","log2FC":-0.49,"padj":0.032,
-         "targets":"MYC, KRAS","pathway":"Oncogene suppression"},
-        {"miRNA":"hsa-miR-551b-3p","log2FC":-0.59,"padj":0.048,
-         "targets":"SMAD4, CDK6","pathway":"TGF-beta / CDK4/6"},
-        {"miRNA":"hsa-miR-22-3p","log2FC":-0.43,"padj":0.041,
-         "targets":"HIF1A, PTEN","pathway":"Hypoxia / PI3K"},
-        {"miRNA":"hsa-miR-200c-3p","log2FC":-0.38,"padj":0.044,
-         "targets":"ZEB1, ZEB2","pathway":"EMT suppression"},
+        {"miRNA":"hsa-miR-148a-3p","log2FC":-0.70,"padj":0.013,"targets":"DNMT1, AKT2","pathway":"Epigenetic reprogramming"},
+        {"miRNA":"hsa-miR-30e-5p","log2FC":-0.49,"padj":0.032,"targets":"MYC, KRAS","pathway":"Oncogene suppression"},
+        {"miRNA":"hsa-miR-551b-3p","log2FC":-0.59,"padj":0.048,"targets":"SMAD4, CDK6","pathway":"TGF-beta / CDK4/6"},
+        {"miRNA":"hsa-miR-22-3p","log2FC":-0.43,"padj":0.041,"targets":"HIF1A, PTEN","pathway":"Hypoxia / PI3K"},
+        {"miRNA":"hsa-miR-200c-3p","log2FC":-0.38,"padj":0.044,"targets":"ZEB1, ZEB2","pathway":"EMT suppression"},
     ],
     "BRCA1": [
-        {"miRNA":"hsa-miR-155-5p","log2FC":-0.81,"padj":0.008,
-         "targets":"SHIP1, SOCS1","pathway":"Immune evasion"},
-        {"miRNA":"hsa-miR-146a-5p","log2FC":-0.65,"padj":0.019,
-         "targets":"TRAF6, IRAK1","pathway":"NF-kB signalling"},
-        {"miRNA":"hsa-miR-21-5p","log2FC":-0.55,"padj":0.027,
-         "targets":"PTEN, PDCD4","pathway":"Apoptosis"},
-        {"miRNA":"hsa-miR-17-5p","log2FC":-0.47,"padj":0.036,
-         "targets":"RB1, E2F1","pathway":"Cell cycle"},
-        {"miRNA":"hsa-miR-34a-5p","log2FC":-0.41,"padj":0.049,
-         "targets":"BCL2, CDK6","pathway":"p53 axis"},
+        {"miRNA":"hsa-miR-155-5p","log2FC":-0.81,"padj":0.008,"targets":"SHIP1, SOCS1","pathway":"Immune evasion"},
+        {"miRNA":"hsa-miR-146a-5p","log2FC":-0.65,"padj":0.019,"targets":"TRAF6, IRAK1","pathway":"NF-kB signalling"},
+        {"miRNA":"hsa-miR-21-5p","log2FC":-0.55,"padj":0.027,"targets":"PTEN, PDCD4","pathway":"Apoptosis"},
+        {"miRNA":"hsa-miR-17-5p","log2FC":-0.47,"padj":0.036,"targets":"RB1, E2F1","pathway":"Cell cycle"},
+        {"miRNA":"hsa-miR-34a-5p","log2FC":-0.41,"padj":0.049,"targets":"BCL2, CDK6","pathway":"p53 axis"},
     ],
     "TP53": [
-        {"miRNA":"hsa-miR-34a-5p","log2FC":-1.10,"padj":0.001,
-         "targets":"BCL2, CDK6","pathway":"p53-miR-34 axis"},
-        {"miRNA":"hsa-miR-192-5p","log2FC":-0.90,"padj":0.005,
-         "targets":"MDM2, DHFR","pathway":"p53 feedback"},
-        {"miRNA":"hsa-miR-145-5p","log2FC":-0.75,"padj":0.012,
-         "targets":"MYC, EGFR","pathway":"Growth suppression"},
-        {"miRNA":"hsa-miR-107","log2FC":-0.62,"padj":0.023,
-         "targets":"CDK6, HIF1B","pathway":"Hypoxia / cell cycle"},
-        {"miRNA":"hsa-miR-215-5p","log2FC":-0.51,"padj":0.038,
-         "targets":"DTL, DHFR","pathway":"DNA damage response"},
+        {"miRNA":"hsa-miR-34a-5p","log2FC":-1.10,"padj":0.001,"targets":"BCL2, CDK6","pathway":"p53-miR-34 axis"},
+        {"miRNA":"hsa-miR-192-5p","log2FC":-0.90,"padj":0.005,"targets":"MDM2, DHFR","pathway":"p53 feedback"},
+        {"miRNA":"hsa-miR-145-5p","log2FC":-0.75,"padj":0.012,"targets":"MYC, EGFR","pathway":"Growth suppression"},
+        {"miRNA":"hsa-miR-107","log2FC":-0.62,"padj":0.023,"targets":"CDK6, HIF1B","pathway":"Hypoxia / cell cycle"},
+        {"miRNA":"hsa-miR-215-5p","log2FC":-0.51,"padj":0.038,"targets":"DTL, DHFR","pathway":"DNA damage response"},
     ],
 }
 
 SIRNA_DB = {
-    "LUAD":[
-        {"Gene":"SPC24","dCERES":-0.175,"log2FC":1.13,
-         "Drug_status":"Novel","siRNA":"GCAGCUGAAGAAACUGAAU"},
-        {"Gene":"BUB1B","dCERES":-0.119,"log2FC":1.12,
-         "Drug_status":"Novel","siRNA":"CCAAAGAGCUGAAGAACAU"},
-        {"Gene":"CDC45","dCERES":-0.144,"log2FC":1.26,
-         "Drug_status":"Novel","siRNA":"GCAUCAAGAUGAAGGAGAU"},
-        {"Gene":"PLK1","dCERES":-0.239,"log2FC":1.03,
-         "Drug_status":"Clinical","siRNA":"GACGCUCAAGAUGCAGAUU"},
-        {"Gene":"CDK1","dCERES":-0.201,"log2FC":1.00,
-         "Drug_status":"Clinical","siRNA":"GCAGAAGCACUGAAGAUUU"},
+    "LUAD": [
+        {"Gene":"SPC24","dCERES":-0.175,"log2FC":1.13,"Drug_status":"Novel","siRNA":"GCAGCUGAAGAAACUGAAU"},
+        {"Gene":"BUB1B","dCERES":-0.119,"log2FC":1.12,"Drug_status":"Novel","siRNA":"CCAAAGAGCUGAAGAACAU"},
+        {"Gene":"CDC45","dCERES":-0.144,"log2FC":1.26,"Drug_status":"Novel","siRNA":"GCAUCAAGAUGAAGGAGAU"},
+        {"Gene":"PLK1","dCERES":-0.239,"log2FC":1.03,"Drug_status":"Clinical","siRNA":"GACGCUCAAGAUGCAGAUU"},
+        {"Gene":"CDK1","dCERES":-0.201,"log2FC":1.00,"Drug_status":"Clinical","siRNA":"GCAGAAGCACUGAAGAUUU"},
     ],
-    "BRCA":[
-        {"Gene":"AURKA","dCERES":-0.165,"log2FC":1.20,
-         "Drug_status":"Clinical","siRNA":"GCACUGAAGAUGCAGAAUU"},
-        {"Gene":"AURKB","dCERES":-0.140,"log2FC":1.15,
-         "Drug_status":"Clinical","siRNA":"CCUGAAGACGCUCAAGGUU"},
-        {"Gene":"CENPW","dCERES":-0.125,"log2FC":0.95,
-         "Drug_status":"Novel","siRNA":"GCAGAAGCACUGAAGAUUU"},
-        {"Gene":"RFC2","dCERES":-0.136,"log2FC":0.50,
-         "Drug_status":"Novel","siRNA":"GCAAGAUGCAGAAGCACUU"},
-        {"Gene":"TYMS","dCERES":-0.131,"log2FC":0.72,
-         "Drug_status":"Approved","siRNA":"GGACGCUCAAGAUGCAGAU"},
+    "BRCA": [
+        {"Gene":"AURKA","dCERES":-0.165,"log2FC":1.20,"Drug_status":"Clinical","siRNA":"GCACUGAAGAUGCAGAAUU"},
+        {"Gene":"AURKB","dCERES":-0.140,"log2FC":1.15,"Drug_status":"Clinical","siRNA":"CCUGAAGACGCUCAAGGUU"},
+        {"Gene":"CENPW","dCERES":-0.125,"log2FC":0.95,"Drug_status":"Novel","siRNA":"GCAGAAGCACUGAAGAUUU"},
+        {"Gene":"RFC2","dCERES":-0.136,"log2FC":0.50,"Drug_status":"Novel","siRNA":"GCAAGAUGCAGAAGCACUU"},
+        {"Gene":"TYMS","dCERES":-0.131,"log2FC":0.72,"Drug_status":"Approved","siRNA":"GGACGCUCAAGAUGCAGAU"},
     ],
-    "COAD":[
-        {"Gene":"KRAS","dCERES":-0.210,"log2FC":0.80,
-         "Drug_status":"Clinical","siRNA":"GCUGGAGCUGGUGGUAGUU"},
-        {"Gene":"WEE1","dCERES":-0.180,"log2FC":1.05,
-         "Drug_status":"Clinical","siRNA":"GCAGCUGAAGAAACUGAAU"},
-        {"Gene":"CHEK1","dCERES":-0.155,"log2FC":0.90,
-         "Drug_status":"Clinical","siRNA":"CCAAAGAGCUGAAGAACAU"},
-        {"Gene":"RFC2","dCERES":-0.130,"log2FC":0.55,
-         "Drug_status":"Novel","siRNA":"GCAUCAAGAUGAAGGAGAU"},
-        {"Gene":"PKMYT1","dCERES":-0.122,"log2FC":1.07,
-         "Drug_status":"Clinical","siRNA":"GACGCUCAAGAUGCAGAUU"},
+    "COAD": [
+        {"Gene":"KRAS","dCERES":-0.210,"log2FC":0.80,"Drug_status":"Clinical","siRNA":"GCUGGAGCUGGUGGUAGUU"},
+        {"Gene":"WEE1","dCERES":-0.180,"log2FC":1.05,"Drug_status":"Clinical","siRNA":"GCAGCUGAAGAAACUGAAU"},
+        {"Gene":"CHEK1","dCERES":-0.155,"log2FC":0.90,"Drug_status":"Clinical","siRNA":"CCAAAGAGCUGAAGAACAU"},
+        {"Gene":"RFC2","dCERES":-0.130,"log2FC":0.55,"Drug_status":"Novel","siRNA":"GCAUCAAGAUGAAGGAGAU"},
+        {"Gene":"PKMYT1","dCERES":-0.122,"log2FC":1.07,"Drug_status":"Clinical","siRNA":"GACGCUCAAGAUGCAGAUU"},
     ],
 }
 
 CERNA = [
-    {"lncRNA":"CYTOR","miRNA":"hsa-miR-138-5p",
-     "target":"AKT1","pathway":"TREM2 core signaling"},
-    {"lncRNA":"CYTOR","miRNA":"hsa-miR-138-5p",
-     "target":"NFKB1","pathway":"Neuroinflammation"},
-    {"lncRNA":"GAS5","miRNA":"hsa-miR-21-5p",
-     "target":"PTEN","pathway":"Neuroinflammation"},
-    {"lncRNA":"GAS5","miRNA":"hsa-miR-222-3p",
-     "target":"IL1B","pathway":"Neuroinflammation"},
-    {"lncRNA":"HOTAIRM1","miRNA":"hsa-miR-9-5p",
-     "target":"TREM2","pathway":"Direct TREM2 regulation"},
+    {"lncRNA":"CYTOR","miRNA":"hsa-miR-138-5p","target":"AKT1","pathway":"TREM2 core signaling"},
+    {"lncRNA":"CYTOR","miRNA":"hsa-miR-138-5p","target":"NFKB1","pathway":"Neuroinflammation"},
+    {"lncRNA":"GAS5","miRNA":"hsa-miR-21-5p","target":"PTEN","pathway":"Neuroinflammation"},
+    {"lncRNA":"GAS5","miRNA":"hsa-miR-222-3p","target":"IL1B","pathway":"Neuroinflammation"},
+    {"lncRNA":"HOTAIRM1","miRNA":"hsa-miR-9-5p","target":"TREM2","pathway":"Direct TREM2 regulation"},
 ]
 ASO = [
-    {"lncRNA":"GAS5","position":119,"accessibility":0.653,
-     "GC_pct":50,"Tm":47.2,"priority":"HIGH"},
-    {"lncRNA":"CYTOR","position":507,"accessibility":0.653,
-     "GC_pct":50,"Tm":46.8,"priority":"HIGH"},
-    {"lncRNA":"HOTAIRM1","position":234,"accessibility":0.621,
-     "GC_pct":44,"Tm":44.1,"priority":"MEDIUM"},
-    {"lncRNA":"LINC00847","position":89,"accessibility":0.598,
-     "GC_pct":56,"Tm":48.3,"priority":"MEDIUM"},
-    {"lncRNA":"ZFAS1","position":312,"accessibility":0.571,
-     "GC_pct":48,"Tm":45.5,"priority":"MEDIUM"},
+    {"lncRNA":"GAS5","position":119,"accessibility":0.653,"GC_pct":50,"Tm":47.2,"priority":"HIGH"},
+    {"lncRNA":"CYTOR","position":507,"accessibility":0.653,"GC_pct":50,"Tm":46.8,"priority":"HIGH"},
+    {"lncRNA":"HOTAIRM1","position":234,"accessibility":0.621,"GC_pct":44,"Tm":44.1,"priority":"MEDIUM"},
+    {"lncRNA":"LINC00847","position":89,"accessibility":0.598,"GC_pct":56,"Tm":48.3,"priority":"MEDIUM"},
+    {"lncRNA":"ZFAS1","position":312,"accessibility":0.571,"GC_pct":48,"Tm":45.5,"priority":"MEDIUM"},
 ]
 
 FGFR3 = {
-    "P1 (hairpin loop)":[
-        {"Compound":"CHEMBL1575701","RNA_score":0.809,
-         "Toxicity":0.01,"Final_score":0.793},
-        {"Compound":"CHEMBL15727","RNA_score":0.805,
-         "Toxicity":0.00,"Final_score":0.789},
-        {"Compound":"Thioguanine","RNA_score":0.888,
-         "Toxicity":32.5,"Final_score":0.742},
-        {"Compound":"Deazaguanine","RNA_score":0.888,
-         "Toxicity":35.0,"Final_score":0.735},
-        {"Compound":"CHEMBL441","RNA_score":0.775,
-         "Toxicity":5.2,"Final_score":0.721},
+    "P1 (hairpin loop)": [
+        {"Compound":"CHEMBL1575701","RNA_score":0.809,"Toxicity":0.01,"Final_score":0.793},
+        {"Compound":"CHEMBL15727","RNA_score":0.805,"Toxicity":0.00,"Final_score":0.789},
+        {"Compound":"Thioguanine","RNA_score":0.888,"Toxicity":32.5,"Final_score":0.742},
+        {"Compound":"Deazaguanine","RNA_score":0.888,"Toxicity":35.0,"Final_score":0.735},
+        {"Compound":"CHEMBL441","RNA_score":0.775,"Toxicity":5.2,"Final_score":0.721},
     ],
-    "P10 (G-quadruplex)":[
-        {"Compound":"CHEMBL15727","RNA_score":0.805,
-         "Toxicity":0.00,"Final_score":0.789},
-        {"Compound":"CHEMBL5411515","RNA_score":0.945,
-         "Toxicity":37.1,"Final_score":0.761},
-        {"Compound":"CHEMBL90","RNA_score":0.760,
-         "Toxicity":2.1,"Final_score":0.745},
-        {"Compound":"CHEMBL102","RNA_score":0.748,
-         "Toxicity":8.4,"Final_score":0.712},
-        {"Compound":"Berberine","RNA_score":0.735,
-         "Toxicity":3.2,"Final_score":0.708},
+    "P10 (G-quadruplex)": [
+        {"Compound":"CHEMBL15727","RNA_score":0.805,"Toxicity":0.00,"Final_score":0.789},
+        {"Compound":"CHEMBL5411515","RNA_score":0.945,"Toxicity":37.1,"Final_score":0.761},
+        {"Compound":"CHEMBL90","RNA_score":0.760,"Toxicity":2.1,"Final_score":0.745},
+        {"Compound":"CHEMBL102","RNA_score":0.748,"Toxicity":8.4,"Final_score":0.712},
+        {"Compound":"Berberine","RNA_score":0.735,"Toxicity":3.2,"Final_score":0.708},
     ],
 }
 
 VARIANT_DB = {
-    "BRCA1:p.R1699Q":{"score":0.03,"cls":"Benign","conf":"High"},
-    "BRCA1:p.R1699W":{"score":0.97,"cls":"Pathogenic","conf":"High"},
-    "BRCA2:p.D2723A":{"score":0.999,"cls":"Pathogenic","conf":"High"},
-    "TP53:p.R248W":  {"score":0.998,"cls":"Pathogenic","conf":"High"},
-    "TP53:p.R248Q":  {"score":0.995,"cls":"Pathogenic","conf":"High"},
-    "EGFR:p.L858R":  {"score":0.96,"cls":"Pathogenic","conf":"High"},
-    "ALK:p.F1174L":  {"score":0.94,"cls":"Pathogenic","conf":"High"},
+    "BRCA1:p.R1699Q": {"score":0.03,"cls":"Benign","conf":"High"},
+    "BRCA1:p.R1699W": {"score":0.97,"cls":"Pathogenic","conf":"High"},
+    "BRCA2:p.D2723A": {"score":0.999,"cls":"Pathogenic","conf":"High"},
+    "TP53:p.R248W":   {"score":0.998,"cls":"Pathogenic","conf":"High"},
+    "TP53:p.R248Q":   {"score":0.995,"cls":"Pathogenic","conf":"High"},
+    "EGFR:p.L858R":   {"score":0.96,"cls":"Pathogenic","conf":"High"},
+    "ALK:p.F1174L":   {"score":0.94,"cls":"Pathogenic","conf":"High"},
 }
 PLAIN = {
-    "Pathogenic":"This variant is likely to cause disease. Clinical follow-up is strongly recommended.",
+    "Pathogenic":      "This variant is likely to cause disease. Clinical follow-up is strongly recommended.",
     "Likely Pathogenic":"This variant is probably harmful. Discuss with your doctor.",
-    "Benign":"This variant is likely harmless. Common in the general population.",
-    "Likely Benign":"This variant is probably harmless. No strong reason for concern.",
+    "Benign":          "This variant is likely harmless. Common in the general population.",
+    "Likely Benign":   "This variant is probably harmless. No strong reason for concern.",
 }
 BM_W = {
     "CTHRC1":0.18,"FHL2":0.15,"LDHA":0.14,"P4HA1":0.13,
@@ -199,17 +145,14 @@ PROTEINS = ["albumin","apolipoprotein","fibrinogen","vitronectin",
             "clusterin","igm","iga","igg","complement","transferrin",
             "alpha-2-macroglobulin"]
 
-# ── Core functions (unchanged logic, + logging) ───────────────────────────────
 def predict_mirna(gene):
     df = pd.DataFrame(MIRNA_DB.get(gene, []))
-    log_entry("BRCA2 miRNA", gene,
-              f"Found {len(df)} miRNAs for {gene}")
+    log_entry("BRCA2 miRNA", gene, f"Found {len(df)} miRNAs for {gene}")
     return df
 
 def predict_sirna(cancer):
     df = pd.DataFrame(SIRNA_DB.get(cancer, []))
-    log_entry("TP53 siRNA", cancer,
-              f"Found {len(df)} targets for {cancer}")
+    log_entry("TP53 siRNA", cancer, f"Found {len(df)} targets for {cancer}")
     return df
 
 def get_lncrna():
@@ -231,8 +174,7 @@ def predict_drug(pocket):
     plt.savefig(buf, format="png", dpi=120, facecolor=CARD)
     plt.close()
     buf.seek(0)
-    top = df.iloc[0]["Compound"] if len(df) else "none"
-    log_entry("FGFR3 Drug", pocket, f"Top: {top}")
+    log_entry("FGFR3 Drug", pocket, f"Top: {df.iloc[0]['Compound'] if len(df) else 'none'}")
     return df, Image.open(buf)
 
 def predict_variant(hgvs, sift, polyphen, gnomad):
@@ -242,9 +184,9 @@ def predict_variant(hgvs, sift, polyphen, gnomad):
         cls, conf, score = r["cls"], r["conf"], r["score"]
     else:
         score = 0.0
-        if sift < 0.05:       score += 0.4
-        if polyphen > 0.85:   score += 0.35
-        if gnomad < 0.0001:   score += 0.25
+        if sift < 0.05:      score += 0.4
+        if polyphen > 0.85:  score += 0.35
+        if gnomad < 0.0001:  score += 0.25
         score = round(score, 3)
         cls  = ("Pathogenic" if score > 0.6 else
                 "Likely Pathogenic" if score > 0.4 else "Benign")
@@ -253,16 +195,14 @@ def predict_variant(hgvs, sift, polyphen, gnomad):
     icon   = "⚠️ WARNING" if "Pathogenic" in cls else "✅ OK"
     bar_w  = int(score * 100)
     explanation = PLAIN.get(cls, "")
-    log_entry("OpenVariant", hgvs or f"SIFT={sift}",
-              f"{cls} score={score}")
+    log_entry("OpenVariant", hgvs or f"SIFT={sift}", f"{cls} score={score}")
     return (
         f"<div style='background:{CARD};padding:16px;border-radius:8px;"
         f"font-family:sans-serif;color:{TXT}'>"
         f"<h3 style='color:{colour}'>{icon} {cls}</h3>"
         f"<p>Score: <b>{score:.3f}</b> &nbsp;|&nbsp; Confidence: <b>{conf}</b></p>"
         f"<div style='background:#334155;border-radius:4px;height:16px'>"
-        f"<div style='background:{colour};height:16px;border-radius:4px;"
-        f"width:{bar_w}%'></div></div>"
+        f"<div style='background:{colour};height:16px;border-radius:4px;width:{bar_w}%'></div></div>"
         f"<p style='margin-top:12px'>{explanation}</p>"
         f"<p style='font-size:11px;color:#64748b'>Research only. Not clinical.</p>"
         f"</div>"
@@ -277,15 +217,12 @@ def predict_corona(size, zeta, peg, lipid):
     if size < 100:     score += 1
     proteins = ["ApoE","Albumin","Fibrinogen","Vitronectin","ApoA-I"]
     dominant = proteins[min(score, 4)]
-    efficacy = ("High" if score >= 4 else
-                "Medium" if score >= 2 else "Low")
-    result = (f"**Dominant corona protein:** {dominant}\n\n"
-              f"**Predicted efficacy class:** {efficacy}\n\n"
-              f"**Composite score:** {score}/6")
-    log_entry("LNP Corona",
-              f"size={size},zeta={zeta},peg={peg},lipid={lipid}",
+    efficacy = ("High" if score >= 4 else "Medium" if score >= 2 else "Low")
+    log_entry("LNP Corona", f"size={size},zeta={zeta},peg={peg},lipid={lipid}",
               f"dominant={dominant},efficacy={efficacy}")
-    return result
+    return (f"**Dominant corona protein:** {dominant}\n\n"
+            f"**Predicted efficacy class:** {efficacy}\n\n"
+            f"**Composite score:** {score}/6")
 
 def predict_cancer(c1,c2,c3,c4,c5,c6,c7,c8,c9,c10):
     vals    = [c1,c2,c3,c4,c5,c6,c7,c8,c9,c10]
@@ -311,15 +248,12 @@ def predict_cancer(c1,c2,c3,c4,c5,c6,c7,c8,c9,c10):
     plt.savefig(buf, format="png", dpi=120, facecolor=CARD)
     plt.close()
     buf.seek(0)
-    log_entry("Liquid Biopsy",
-              f"CTHRC1={c1},FHL2={c2},LDHA={c3}...",
-              f"{label} prob={prob:.2f}")
+    log_entry("Liquid Biopsy", f"CTHRC1={c1},FHL2={c2}...", f"{label} prob={prob:.2f}")
     return (
         f"<div style='background:{CARD};padding:12px;border-radius:8px;"
         f"color:{colour};font-size:20px;font-family:sans-serif'>"
         f"<b>{label}</b><br>"
-        f"<span style='color:{TXT};font-size:14px'>"
-        f"Probability: {prob:.2f}</span></div>"
+        f"<span style='color:{TXT};font-size:14px'>Probability: {prob:.2f}</span></div>"
     ), Image.open(buf)
 
 def predict_flow(size, zeta, peg, charge, flow_rate):
@@ -333,14 +267,10 @@ def predict_flow(size, zeta, peg, charge, flow_rate):
     ks = 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.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)
@@ -353,28 +283,21 @@ def predict_flow(size, zeta, peg, charge, flow_rate):
     plt.savefig(buf, format="png", dpi=120, facecolor=CARD)
     plt.close()
     buf.seek(0)
-    log_entry("Flow Corona",
-              f"flow={flow_rate},charge={charge}",
-              f"CSI={csi},{stability}")
-    return (f"**Corona Shift Index: {csi}** — {stability}",
-            Image.open(buf))
+    log_entry("Flow Corona", f"flow={flow_rate},charge={charge}", f"CSI={csi},{stability}")
+    return f"**Corona Shift Index: {csi}** — {stability}", Image.open(buf)
 
 def predict_bbb(smiles, pka, zeta):
     logp     = smiles.count("C")*0.3 - smiles.count("O")*0.5 + 1.5
-    apoe_pct = max(0, min(40,
-               (7.0-pka)*8 + abs(zeta)*0.5 + logp*0.8))
+    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
+    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]
+    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)
+    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)
@@ -386,18 +309,15 @@ def predict_bbb(smiles, pka, zeta):
     plt.savefig(buf, format="png", dpi=120, facecolor=CARD)
     plt.close()
     buf.seek(0)
-    log_entry("LNP Brain",
-              f"pka={pka},zeta={zeta}",
-              f"ApoE={apoe_pct:.1f}%,BBB={bbb_prob:.2f}")
-    result_text = (f"**Predicted ApoE:** {apoe_pct:.1f}% — {tier}\n\n"
-                   f"**BBB Probability:** {bbb_prob:.2f}")
-    return result_text, Image.open(buf)
+    log_entry("LNP Brain", f"pka={pka},zeta={zeta}", f"ApoE={apoe_pct:.1f}%,BBB={bbb_prob:.2f}")
+    return (f"**Predicted ApoE:** {apoe_pct:.1f}% — {tier}\n\n"
+            f"**BBB Probability:** {bbb_prob:.2f}"), Image.open(buf)
 
 def extract_corona(text):
     out = {
-        "nanoparticle_composition":"",
-        "size_nm":None,"zeta_mv":None,"PDI":None,
-        "protein_source":"","corona_proteins":[],"confidence":{}
+        "nanoparticle_composition": "",
+        "size_nm": None, "zeta_mv": None, "PDI": None,
+        "protein_source": "", "corona_proteins": [], "confidence": {}
     }
     m = re.search(r"(\d+\.?\d*)\s*(?:nm|nanometer)", text, re.I)
     if m:
@@ -411,41 +331,31 @@ def extract_corona(text):
     if m:
         out["PDI"] = float(m.group(1))
         out["confidence"]["PDI"] = "HIGH"
-    for src in ["human plasma","human serum",
-                "fetal bovine serum","FBS","PBS"]:
+    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()
+        {"name": p, "confidence": "MEDIUM"} for p in PROTEINS if p in text.lower()
     ]
-    for lip in ["DSPC","DOPE","MC3","DLin",
-                "cholesterol","PEG","DOTAP"]:
+    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()
+    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("AutoCorona NLP",
-              text[:80]+"...",
+    summary = "All key fields extracted" if not flags else " | ".join(flags)
+    log_entry("AutoCorona NLP", text[:80]+"...",
               f"proteins={len(out['corona_proteins'])},{summary}")
     return json.dumps(out, indent=2), summary
 
-# ── CSS ───────────────────────────────────────────────────────────────────────
 css = (
-    f"body,.gradio-container{{background:{BG}!important;"
-    f"color:{TXT}!important}}"
-    f".tab-nav button{{color:{TXT}!important;"
-    f"background:{CARD}!important}}"
-    f".tab-nav button.selected{{border-bottom:2px solid {ACC}!important;"
-    f"color:{ACC}!important}}"
+    f"body,.gradio-container{{background:{BG}!important;color:{TXT}!important}}"
+    f".tab-nav button{{color:{TXT}!important;background:{CARD}!important}}"
+    f".tab-nav button.selected{{border-bottom:2px solid {ACC}!important;color:{ACC}!important}}"
     f"h1,h2,h3{{color:{ACC}!important}}"
     f".gr-button-primary{{background:{ACC}!important;border:none!important}}"
     f"footer{{display:none!important}}"
@@ -454,105 +364,76 @@ css = (
 LEARNING_CASES = """
 ## 🧪 Top 5 Guided Investigations
 
----
 ### Case 1 — Beginner 🟢
 **Question:** Why is the same gene position benign vs pathogenic?
-
-**Steps:**
-1. OpenVariant tab → enter `BRCA1:p.R1699Q` → note: Benign
-2. Enter `BRCA1:p.R1699W` → note: Pathogenic
-3. Same position (R1699), different amino acid change
-4. Write in your notes: *what changed and why?*
+1. OpenVariant → enter `BRCA1:p.R1699Q` → Benign
+2. Enter `BRCA1:p.R1699W` → Pathogenic
+3. Same position, different amino acid — what changed?
 
 **Key concept:** Amino acid polarity determines protein folding impact.
 
 ---
 ### Case 2 — Beginner 🟢
 **Question:** How does PEG% change what protein sticks to LNPs?
+1. LNP Corona → Ionizable, Zeta=-5, Size=100, PEG=0.5% → note protein
+2. PEG=2.5% → compare
+3. LNP Brain → pKa=6.5 → compare ApoE%
 
-**Steps:**
-1. LNP Corona tab → Lipid=Ionizable, Zeta=-5, Size=100
-2. Set PEG=0.5% → note dominant protein
-3. Set PEG=2.5% → compare
-4. LNP Brain tab → same pKa=6.5, compare ApoE%
-
-**Key concept:** PEG shields the surface → less Fibrinogen, more ApoE.
+**Key concept:** More PEG → less Fibrinogen, more ApoE.
 
 ---
 ### Case 3 — Intermediate 🟡
 **Question:** Does blood flow change corona composition?
+1. Flow Corona → Flow=0, Ionizable
+2. Flow=40 (arterial) → compare ApoE curve
+3. At what minute does ApoE plateau?
 
-**Steps:**
-1. Flow Corona tab → Flow=0, Ionizable → screenshot mentally
-2. Flow=40 (arterial) → same lipid → compare ApoE curve
-3. Note: at what minute does ApoE plateau in each case?
-4. Question: why does brain delivery need ApoE in the corona?
-
-**Key concept:** Vroman effect — fast proteins (albumin) displaced by
-slow but higher-affinity proteins (ApoE) under flow.
+**Key concept:** Vroman effect — albumin displaced by ApoE under flow.
 
 ---
 ### Case 4 — Intermediate 🟡
-**Question:** Which cancer type has the most novel siRNA targets?
-
-**Steps:**
-1. TP53 siRNA tab → LUAD → count "Novel" in Drug_status
-2. Repeat for BRCA and COAD
-3. Which has most untapped targets?
-4. Pick one "Novel" gene → Google: "[gene] cancer therapeutic target"
-
-**Key concept:** Novel = no approved drug yet = research opportunity.
+**Question:** Which cancer has the most novel siRNA targets?
+1. TP53 siRNA → LUAD → count "Novel"
+2. Repeat BRCA, COAD
+3. Pick one Novel gene → Google: "[gene] cancer therapeutic target"
 
 ---
 ### Case 5 — Advanced 🔴
-**Question:** Can you identify a cancer sample from protein levels?
-
-**Steps:**
-1. Liquid Biopsy tab → all sliders at 0 → should say HEALTHY
+**Question:** Can you identify cancer from protein levels?
+1. Liquid Biopsy → all sliders=0 → HEALTHY
 2. Set CTHRC1=2.5, FHL2=2.0, LDHA=1.8 → observe
-3. Try to find the minimum CTHRC1 value that tips to CANCER
-4. AutoCorona NLP → paste a PubMed abstract about cancer proteomics
-5. Check: does the abstract mention any of the 10 biomarkers?
+3. Find minimum CTHRC1 that tips to CANCER
 
-**Key concept:** CTHRC1 is the single strongest cancer indicator
-in the panel — its weight (0.18) dominates the score.
+**Key concept:** CTHRC1 weight (0.18) dominates the score.
 """
 
-# ── BUILD ─────────────────────────────────────────────────────────────────────
 with gr.Blocks(css=css, title="K R&D Lab") as demo:
-    last_result_state = gr.State("")
 
     gr.Markdown(
         "# 🧬 K R&D Lab — Computational Biology Suite\n"
         "**Oksana Kolisnyk** · ML Engineer · "
         "[KOSATIKS GROUP](https://kosatiks-group.pp.ua)\n"
-        "> 10 open-source tools + lab journal. "
-        "Hypothesis testing across disciplines."
+        "> 10 open-source tools + lab journal."
     )
 
     with gr.Tabs():
 
-        # ── Tab 1 ──
         with gr.TabItem("🧬 BRCA2 miRNA"):
             gr.Markdown("### Tumor Suppressor miRNAs")
-            g1 = gr.Dropdown(["BRCA2","BRCA1","TP53"],
-                             value="BRCA2", label="Gene")
+            g1 = gr.Dropdown(["BRCA2","BRCA1","TP53"], value="BRCA2", label="Gene")
             b1 = gr.Button("Find miRNAs", variant="primary")
             o1 = gr.Dataframe(label="Top 5 downregulated miRNAs")
             gr.Examples([["BRCA2"],["TP53"]], inputs=[g1])
             b1.click(predict_mirna, g1, o1)
 
-        # ── Tab 2 ──
         with gr.TabItem("💉 TP53 siRNA"):
             gr.Markdown("### Synthetic Lethal siRNA Targets")
-            g2 = gr.Dropdown(["LUAD","BRCA","COAD"],
-                             value="LUAD", label="Cancer type")
+            g2 = gr.Dropdown(["LUAD","BRCA","COAD"], value="LUAD", label="Cancer type")
             b2 = gr.Button("Find Targets", variant="primary")
             o2 = gr.Dataframe(label="Top 5 siRNA targets")
             gr.Examples([["LUAD"],["BRCA"]], inputs=[g2])
             b2.click(predict_sirna, g2, o2)
 
-        # ── Tab 3 ──
         with gr.TabItem("🧠 lncRNA-TREM2"):
             gr.Markdown("### lncRNA Networks in Alzheimer's")
             b3 = gr.Button("Load Results", variant="primary")
@@ -560,35 +441,25 @@ with gr.Blocks(css=css, title="K R&D Lab") as demo:
             o3b = gr.Dataframe(label="ASO Candidates")
             b3.click(get_lncrna, [], [o3a, o3b])
 
-        # ── Tab 4 ──
         with gr.TabItem("💊 FGFR3 Drug"):
             gr.Markdown("### RNA-Directed Drug Discovery: FGFR3")
-            g4 = gr.Radio(
-                ["P1 (hairpin loop)","P10 (G-quadruplex)"],
-                value="P1 (hairpin loop)", label="Target pocket")
+            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])
+            gr.Examples([["P1 (hairpin loop)"],["P10 (G-quadruplex)"]], inputs=[g4])
             b4.click(predict_drug, g4, [o4t, o4p])
 
-        # ── Tab 5 ──
         with gr.TabItem("🔬 OpenVariant"):
-            gr.Markdown(
-                "### OpenVariant — Pathogenicity Classifier\n"
-                "AUC=0.939 on ClinVar 2026.")
-            hgvs = gr.Textbox(label="HGVS notation",
-                              placeholder="BRCA1:p.R1699Q")
+            gr.Markdown("### OpenVariant — Pathogenicity Classifier\nAUC=0.939 on ClinVar 2026.")
+            hgvs = gr.Textbox(label="HGVS notation", placeholder="BRCA1:p.R1699Q")
             gr.Markdown("**Or enter scores manually:**")
             with gr.Row():
-                sift = gr.Slider(0,1,0.5,label="SIFT (0=damaging)")
-                pp   = gr.Slider(0,1,0.5,label="PolyPhen-2")
-                gn   = gr.Slider(0,0.01,0.001,
-                                 label="gnomAD AF",step=0.0001)
-            b5 = gr.Button("Predict Pathogenicity",
-                           variant="primary")
+                sift = gr.Slider(0,1,0.5, label="SIFT (0=damaging)")
+                pp   = gr.Slider(0,1,0.5, label="PolyPhen-2")
+                gn   = gr.Slider(0,0.01,0.001, label="gnomAD AF", step=0.0001)
+            b5 = gr.Button("Predict Pathogenicity", variant="primary")
             o5 = gr.HTML(label="Result")
             gr.Examples(
                 [["BRCA1:p.R1699Q",0.82,0.05,0.0012],
@@ -597,30 +468,22 @@ with gr.Blocks(css=css, title="K R&D Lab") as demo:
                 inputs=[hgvs,sift,pp,gn])
             b5.click(predict_variant, [hgvs,sift,pp,gn], o5)
 
-        # ── Tab 6 ──
         with gr.TabItem("🧪 LNP Corona"):
             gr.Markdown("### LNP Protein Corona Prediction")
             with gr.Row():
-                sz = gr.Slider(50,300,100,label="Size (nm)")
-                zt = gr.Slider(-40,10,-5, label="Zeta (mV)")
+                sz = gr.Slider(50,300,100, label="Size (nm)")
+                zt = gr.Slider(-40,10,-5,  label="Zeta (mV)")
             with gr.Row():
-                pg = gr.Slider(0,5,1.5,label="PEG mol%")
-                lp = gr.Dropdown(
-                    ["Ionizable","Cationic","Anionic","Neutral"],
-                    value="Ionizable", label="Lipid type")
+                pg = gr.Slider(0,5,1.5, label="PEG mol%")
+                lp = gr.Dropdown(["Ionizable","Cationic","Anionic","Neutral"],
+                                 value="Ionizable", label="Lipid type")
             b6 = gr.Button("Predict", variant="primary")
             o6 = gr.Markdown()
-            gr.Examples(
-                [[100,-5,1.5,"Ionizable"],
-                 [80,5,0.5,"Cationic"]],
-                inputs=[sz,zt,pg,lp])
+            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)
 
-        # ── Tab 7 ──
         with gr.TabItem("🩸 Liquid Biopsy"):
-            gr.Markdown(
-                "### Protein Corona Cancer Diagnostics\n"
-                "Classify cancer vs healthy from protein z-scores.")
+            gr.Markdown("### Protein Corona Cancer Diagnostics\nClassify cancer vs healthy.")
             with gr.Row():
                 p1=gr.Slider(-3,3,0,label="CTHRC1")
                 p2=gr.Slider(-3,3,0,label="FHL2")
@@ -640,135 +503,99 @@ with gr.Blocks(css=css, title="K R&D Lab") as demo:
                 [[2,2,1.5,1.8,1.6,-1,-1.2,-0.8,1.4,-1.1],
                  [0,0,0,0,0,0,0,0,0,0]],
                 inputs=[p1,p2,p3,p4,p5,p6,p7,p8,p9,p10])
-            b7.click(predict_cancer,
-                     [p1,p2,p3,p4,p5,p6,p7,p8,p9,p10],
-                     [o7t, o7p])
+            b7.click(predict_cancer, [p1,p2,p3,p4,p5,p6,p7,p8,p9,p10], [o7t,o7p])
 
-        # ── Tab 8 ──
         with gr.TabItem("🌊 Flow Corona"):
             gr.Markdown("### Corona Remodeling Under Blood Flow")
             with gr.Row():
-                s8  = gr.Slider(50,300,100,label="Size (nm)")
-                z8  = gr.Slider(-40,10,-5, label="Zeta (mV)")
-                pg8 = gr.Slider(0,5,1.5,  label="PEG mol%")
+                s8  = gr.Slider(50,300,100, label="Size (nm)")
+                z8  = gr.Slider(-40,10,-5,  label="Zeta (mV)")
+                pg8 = gr.Slider(0,5,1.5,    label="PEG mol%")
             with gr.Row():
-                ch8 = gr.Dropdown(
-                    ["Ionizable","Cationic","Anionic","Neutral"],
-                    value="Ionizable", label="Charge type")
-                fl8 = gr.Slider(0,40,20,
-                                label="Flow rate cm/s (aorta=40)")
+                ch8 = gr.Dropdown(["Ionizable","Cationic","Anionic","Neutral"],
+                                  value="Ionizable", label="Charge type")
+                fl8 = gr.Slider(0,40,20, label="Flow rate cm/s (aorta=40)")
             b8  = gr.Button("Model Vroman Effect", variant="primary")
             o8t = gr.Markdown()
             o8p = gr.Image(label="Kinetics plot")
-            gr.Examples(
-                [[100,-5,1.5,"Ionizable",40],
-                 [150,5,0.5,"Cationic",10]],
-                inputs=[s8,z8,pg8,ch8,fl8])
+            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])
 
-        # ── Tab 9 ──
         with gr.TabItem("🧠 LNP Brain"):
             gr.Markdown("### LNP Brain Delivery Predictor")
-            smi = gr.Textbox(
-                label="Ionizable lipid SMILES",
-                value="CC(C)CC(=O)OCC(COC(=O)CC(C)C)OC(=O)CC(C)C")
+            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(4,8,6.5,step=0.1,label="pKa")
-                zt9 = gr.Slider(-20,10,-3,       label="Zeta (mV)")
+                pk  = gr.Slider(4,8,6.5, step=0.1, label="pKa")
+                zt9 = gr.Slider(-20,10,-3,          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])
+            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])
 
-        # ── Tab 10 ──
         with gr.TabItem("📄 AutoCorona NLP"):
-            gr.Markdown(
-                "### AutoCorona NLP Extraction\n"
-                "Paste any paper abstract to extract corona data.")
-            txt = gr.Textbox(lines=6, label="Paper abstract",
-                             placeholder="Paste text here...")
+            gr.Markdown("### AutoCorona NLP Extraction\nPaste any paper abstract.")
+            txt  = gr.Textbox(lines=6, label="Paper abstract", placeholder="Paste text here...")
             b10  = gr.Button("Extract Data", variant="primary")
             o10j = gr.Code(label="Extracted JSON", language="json")
             o10f = gr.Textbox(label="Validation flags")
             gr.Examples([[
-                "LNPs composed of MC3, DSPC, Cholesterol "
-                "(50:10:40 mol%) with 1.5% PEG-DMG. "
-                "Hydrodynamic diameter was 98 nm, "
-                "zeta potential -3.2 mV, PDI 0.12. "
-                "Incubated in human plasma. "
-                "Corona: albumin, apolipoprotein E, fibrinogen."
+                "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])
 
-        # ── Tab 11 — Lab Journal ──────────────────────────────────
         with gr.TabItem("📓 Lab Journal"):
-            gr.Markdown(
-                "### Your Research Log\n"
-                "Every query is auto-saved. "
-                "Add notes to any result below.")
+            gr.Markdown("### Your Research Log\nEvery query is auto-saved.")
             with gr.Row():
                 note_text = gr.Textbox(
                     label="📝 Add observation / conclusion",
-                    placeholder=
-                    "What did you discover? What's your next question?",
+                    placeholder="What did you discover? What's your next question?",
                     lines=3)
-                note_tab = gr.Textbox(
-                    label="Which tool? (auto-fill or type)",
-                    value="General")
-            note_last = gr.Textbox(
-                label="Result to annotate", visible=False)
-            save_btn  = gr.Button("💾 Save Observation",
-                                  variant="primary")
+                note_tab = gr.Textbox(label="Which tool?", value="General")
+            note_last = gr.Textbox(label="Result to annotate", visible=False)
+            save_btn  = gr.Button("💾 Save Observation", variant="primary")
             save_msg  = gr.Markdown()
             journal_df = gr.Dataframe(
                 label="📋 Full History",
                 value=load_journal,
-                every=30,
                 interactive=False)
             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])
-            gr.Markdown(
-                "📥 **Download your log:** "
-                "file saved as `lab_journal.csv` in the app folder.")
-
-        # ── Tab 12 — Learning Mode ────────────────────────────────
+            save_btn.click(save_note, [note_text, note_tab, note_last], [save_msg, journal_df])
+            gr.Markdown("📥 Log saved as `lab_journal.csv` in the app folder.")
+
         with gr.TabItem("📚 Learning Mode"):
             gr.Markdown(LEARNING_CASES)
-            gr.Markdown("---")
-            gr.Markdown("### 📖 Quick Reference — What each tool does")
+            gr.Markdown("---\n### 📖 Quick Reference")
             gr.Markdown("""
-| Tool | What it predicts | Key input | Why it matters |
-|------|-----------------|-----------|----------------|
-| OpenVariant | Pathogenic / Benign | Gene mutation | Clinical genetics |
-| LNP Corona | Dominant corona protein | Formulation params | Drug delivery |
-| Flow Corona | Vroman exchange kinetics | Flow rate | In vivo realism |
-| LNP Brain | ApoE% + BBB probability | pKa + zeta | GBM therapy |
-| Liquid Biopsy | Cancer vs Healthy | Protein z-scores | Diagnostics |
-| BRCA2 miRNA | Downregulated miRNAs | Gene name | RNA therapy |
-| TP53 siRNA | Synthetic lethal targets | Cancer type | Target discovery |
-| lncRNA-TREM2 | ceRNA network + ASOs | — | Alzheimer's |
-| FGFR3 Drug | Small molecule candidates | Pocket type | RNA drug design |
-| AutoCorona NLP | Structured data from text | Abstract | Literature mining |
+| Tool | Predicts | Key input |
+|------|----------|-----------|
+| OpenVariant | Pathogenic/Benign | Gene mutation |
+| LNP Corona | Dominant protein | Formulation |
+| Flow Corona | Vroman kinetics | Flow rate |
+| LNP Brain | ApoE% + BBB prob | pKa + zeta |
+| Liquid Biopsy | Cancer/Healthy | Protein z-scores |
+| BRCA2 miRNA | Downregulated miRNAs | Gene name |
+| TP53 siRNA | Synthetic lethal targets | Cancer type |
+| lncRNA-TREM2 | ceRNA + ASOs | — |
+| FGFR3 Drug | Small molecules | Pocket type |
+| AutoCorona NLP | Structured data | Abstract text |
 """)
             gr.Markdown("""
-### 🔗 Essential Resources for Beginners
-- **PubMed:** https://pubmed.ncbi.nlm.nih.gov
-- **ClinVar (real variants):** https://www.ncbi.nlm.nih.gov/clinvar/
-- **UniProt (proteins):** https://www.uniprot.org
-- **ChEMBL (compounds):** https://www.ebi.ac.uk/chembl/
-- **KEGG (pathways):** https://www.genome.jp/kegg/
+### 🔗 Resources
+- [PubMed](https://pubmed.ncbi.nlm.nih.gov)
+- [ClinVar](https://www.ncbi.nlm.nih.gov/clinvar/)
+- [UniProt](https://www.uniprot.org)
+- [ChEMBL](https://www.ebi.ac.uk/chembl/)
 """)
 
     gr.Markdown(
-        "---\n**K R&D Lab** | Research tool only — not clinical | "
+        "---\n**K R&D Lab** | Research only — not clinical | "
         "[GitHub](https://github.com/TEZv/K-RnD-Lab-PHYLO-03_2026) | "
         "[KOSATIKS GROUP 🦈](https://kosatiks-group.pp.ua)"
     )