from reportlab.lib.pagesizes import A4
from reportlab.lib import colors
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm, mm
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    PageBreak, HRFlowable, KeepTogether
)
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY
from reportlab.platypus import BalancedColumns
from reportlab.lib.colors import HexColor
import datetime

OUTPUT = "/home/daytona/workspace/neet-pg-prep/NEET_PG_HighYield_Topics_2020_2025.pdf"

# ── Color palette ──────────────────────────────────────────────────────────────
C_NAVY   = HexColor("#1a2b5e")
C_TEAL   = HexColor("#0d7c7c")
C_AMBER  = HexColor("#e6a817")
C_RED    = HexColor("#c0392b")
C_GREEN  = HexColor("#1e7e34")
C_LGRAY  = HexColor("#f4f6f9")
C_MGRAY  = HexColor("#dee2e8")
C_WHITE  = colors.white
C_BLACK  = colors.black

# ── Styles ─────────────────────────────────────────────────────────────────────
base = getSampleStyleSheet()

def S(name, parent="Normal", **kw):
    s = ParagraphStyle(name, parent=base[parent], **kw)
    return s

sTitle   = S("sTitle",   "Title",
             fontSize=28, textColor=C_WHITE, alignment=TA_CENTER,
             leading=34, fontName="Helvetica-Bold")
sSubT    = S("sSubT",    "Normal",
             fontSize=14, textColor=C_AMBER, alignment=TA_CENTER,
             leading=18, fontName="Helvetica-Bold")
sBody    = S("sBody",    "Normal",
             fontSize=9,  textColor=C_BLACK, leading=14,
             fontName="Helvetica", alignment=TA_JUSTIFY)
sMeta    = S("sMeta",    "Normal",
             fontSize=9,  textColor=HexColor("#555555"), alignment=TA_CENTER,
             fontName="Helvetica-Oblique")

sH1      = S("sH1",      "Normal",
             fontSize=16, textColor=C_WHITE, fontName="Helvetica-Bold",
             leading=20, alignment=TA_LEFT)
sH2      = S("sH2",      "Normal",
             fontSize=11, textColor=C_NAVY, fontName="Helvetica-Bold",
             leading=14, spaceAfter=2)
sH3      = S("sH3",      "Normal",
             fontSize=9.5, textColor=C_TEAL, fontName="Helvetica-Bold",
             leading=13, spaceAfter=1)
sBullet  = S("sBullet",  "Normal",
             fontSize=8.5, textColor=C_BLACK, fontName="Helvetica",
             leading=12, leftIndent=10, bulletIndent=0)
sNote    = S("sNote",    "Normal",
             fontSize=8,  textColor=HexColor("#c0392b"), fontName="Helvetica-Oblique",
             leading=11, leftIndent=6)
sStar    = S("sStar",    "Normal",
             fontSize=8.5, textColor=C_GREEN, fontName="Helvetica-Bold",
             leading=12, leftIndent=6)
sTocHead = S("sTocHead", "Normal",
             fontSize=13, textColor=C_NAVY, fontName="Helvetica-Bold",
             leading=16, spaceAfter=4)
sTocItem = S("sTocItem", "Normal",
             fontSize=9.5, textColor=C_BLACK, fontName="Helvetica",
             leading=14, leftIndent=12)

# ── Helper builders ────────────────────────────────────────────────────────────
def cover_block():
    """Returns flowables that make up the cover page."""
    items = []
    # big colour band
    cover_data = [["NEET PG\nHigh-Yield Topics Guide\n2020 – 2025"]]
    ct = Table(cover_data, colWidths=[17*cm])
    ct.setStyle(TableStyle([
        ("BACKGROUND",  (0,0), (-1,-1), C_NAVY),
        ("TEXTCOLOR",   (0,0), (-1,-1), C_WHITE),
        ("FONTNAME",    (0,0), (-1,-1), "Helvetica-Bold"),
        ("FONTSIZE",    (0,0), (-1,-1), 26),
        ("ALIGN",       (0,0), (-1,-1), "CENTER"),
        ("VALIGN",      (0,0), (-1,-1), "MIDDLE"),
        ("TOPPADDING",  (0,0), (-1,-1), 36),
        ("BOTTOMPADDING",(0,0),(-1,-1), 36),
        ("ROUNDEDCORNERS",[6,6,6,6]),
    ]))
    items.append(Spacer(1, 1.5*cm))
    items.append(ct)
    items.append(Spacer(1, 0.5*cm))

    sub_data = [["AI-Powered Pattern Analysis  |  9 Core Subjects  |  Exam-Ready Format"]]
    st = Table(sub_data, colWidths=[17*cm])
    st.setStyle(TableStyle([
        ("BACKGROUND",  (0,0), (-1,-1), C_TEAL),
        ("TEXTCOLOR",   (0,0), (-1,-1), C_WHITE),
        ("FONTNAME",    (0,0), (-1,-1), "Helvetica-Bold"),
        ("FONTSIZE",    (0,0), (-1,-1), 10),
        ("ALIGN",       (0,0), (-1,-1), "CENTER"),
        ("TOPPADDING",  (0,0), (-1,-1), 8),
        ("BOTTOMPADDING",(0,0),(-1,-1), 8),
    ]))
    items.append(st)
    items.append(Spacer(1, 0.8*cm))

    # stats row
    stats = [
        ["9\nSubjects", "~200\nHigh-Yield Topics", "5-Year\nPattern Analysis", "Exam\n2020-2025"],
    ]
    stat_t = Table(stats, colWidths=[4.0*cm, 4.5*cm, 4.5*cm, 4.0*cm])
    stat_t.setStyle(TableStyle([
        ("BACKGROUND",  (0,0), (-1,-1), C_LGRAY),
        ("TEXTCOLOR",   (0,0), (0,-1), C_NAVY),
        ("TEXTCOLOR",   (1,0), (1,-1), C_TEAL),
        ("TEXTCOLOR",   (2,0), (2,-1), C_RED),
        ("TEXTCOLOR",   (3,0), (3,-1), C_GREEN),
        ("FONTNAME",    (0,0), (-1,-1), "Helvetica-Bold"),
        ("FONTSIZE",    (0,0), (-1,-1), 10),
        ("ALIGN",       (0,0), (-1,-1), "CENTER"),
        ("VALIGN",      (0,0), (-1,-1), "MIDDLE"),
        ("TOPPADDING",  (0,0), (-1,-1), 10),
        ("BOTTOMPADDING",(0,0),(-1,-1), 10),
        ("BOX",         (0,0), (-1,-1), 1, C_MGRAY),
        ("INNERGRID",   (0,0), (-1,-1), 0.5, C_MGRAY),
        ("ROUNDEDCORNERS",[4,4,4,4]),
    ]))
    items.append(stat_t)
    items.append(Spacer(1, 0.8*cm))

    # disclaimer box
    disc = [["IMPORTANT NOTICE:\nThis document is an original AI-generated high-yield topic guide based on publicly known NEET PG exam patterns and subject weightages. It does NOT contain any copyrighted official exam questions. It is intended purely as a study aid for NEET PG aspirants."]]
    dt = Table(disc, colWidths=[17*cm])
    dt.setStyle(TableStyle([
        ("BACKGROUND",  (0,0), (-1,-1), HexColor("#fff3cd")),
        ("TEXTCOLOR",   (0,0), (-1,-1), HexColor("#856404")),
        ("FONTNAME",    (0,0), (-1,-1), "Helvetica"),
        ("FONTSIZE",    (0,0), (-1,-1), 8.5),
        ("ALIGN",       (0,0), (-1,-1), "LEFT"),
        ("TOPPADDING",  (0,0), (-1,-1), 8),
        ("BOTTOMPADDING",(0,0),(-1,-1), 8),
        ("LEFTPADDING", (0,0), (-1,-1), 10),
        ("BOX",         (0,0), (-1,-1), 1, HexColor("#ffc107")),
    ]))
    items.append(dt)
    items.append(Spacer(1, 0.5*cm))
    items.append(Paragraph(f"Generated on {datetime.date.today().strftime('%B %d, %Y')}  |  For NEET PG 2026 Preparation", sMeta))
    items.append(PageBreak())
    return items


def section_header(subject, emoji, color, q_count, rank):
    data = [[f"{emoji}  {subject}",
             f"Avg. ~{q_count} Qs/exam   |   Rank #{rank}"]]
    t = Table(data, colWidths=[11*cm, 6*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND",  (0,0), (-1,-1), color),
        ("TEXTCOLOR",   (0,0), (-1,-1), C_WHITE),
        ("FONTNAME",    (0,0), (0,0), "Helvetica-Bold"),
        ("FONTNAME",    (0,0), (1,0), "Helvetica"),
        ("FONTSIZE",    (0,0), (0,0), 14),
        ("FONTSIZE",    (1,0), (1,0), 9),
        ("VALIGN",      (0,0), (-1,-1), "MIDDLE"),
        ("ALIGN",       (1,0), (1,0), "RIGHT"),
        ("TOPPADDING",  (0,0), (-1,-1), 10),
        ("BOTTOMPADDING",(0,0),(-1,-1), 10),
        ("LEFTPADDING", (0,0), (0,0), 12),
        ("RIGHTPADDING",(1,0), (1,0), 10),
    ]))
    return t


def topic_table(categories):
    """categories = list of (category_name, [topics], priority)
       priority: 'HIGH' / 'MEDIUM' / 'RECURRING'
    """
    p_color = {"HIGH": C_RED, "MEDIUM": C_TEAL, "RECURRING": C_GREEN}
    p_bg    = {"HIGH": HexColor("#fdecea"), "MEDIUM": HexColor("#e8f5f5"), "RECURRING": HexColor("#eafaf1")}
    items = []
    for cat, topics, priority in categories:
        badge_color = p_color.get(priority, C_NAVY)
        badge_bg    = p_bg.get(priority, C_LGRAY)
        # Category header
        hdr = [[f"  {cat}", f" {priority} "]]
        ht = Table(hdr, colWidths=[14*cm, 3*cm])
        ht.setStyle(TableStyle([
            ("BACKGROUND",  (0,0), (0,0), badge_bg),
            ("BACKGROUND",  (1,0), (1,0), badge_color),
            ("TEXTCOLOR",   (0,0), (0,0), C_NAVY),
            ("TEXTCOLOR",   (1,0), (1,0), C_WHITE),
            ("FONTNAME",    (0,0), (0,0), "Helvetica-Bold"),
            ("FONTNAME",    (1,0), (1,0), "Helvetica-Bold"),
            ("FONTSIZE",    (0,0), (0,0), 9.5),
            ("FONTSIZE",    (1,0), (1,0), 7.5),
            ("VALIGN",      (0,0), (-1,-1), "MIDDLE"),
            ("ALIGN",       (1,0), (1,0), "CENTER"),
            ("TOPPADDING",  (0,0), (-1,-1), 5),
            ("BOTTOMPADDING",(0,0),(-1,-1), 5),
            ("BOX",         (0,0), (-1,-1), 0.5, C_MGRAY),
        ]))
        items.append(ht)
        # topics as two-column table
        rows = []
        row = []
        for i, t in enumerate(topics):
            row.append(Paragraph(f"  \u2022  {t}", sBullet))
            if len(row) == 2:
                rows.append(row)
                row = []
        if row:
            row.append(Paragraph("", sBullet))
            rows.append(row)
        if rows:
            tt = Table(rows, colWidths=[8.5*cm, 8.5*cm])
            tt.setStyle(TableStyle([
                ("BACKGROUND",  (0,0), (-1,-1), C_WHITE),
                ("TOPPADDING",  (0,0), (-1,-1), 2),
                ("BOTTOMPADDING",(0,0),(-1,-1), 2),
                ("LEFTPADDING", (0,0), (-1,-1), 4),
                ("BOX",         (0,0), (-1,-1), 0.5, C_MGRAY),
                ("INNERGRID",   (0,0), (-1,-1), 0.3, C_LGRAY),
                ("VALIGN",      (0,0), (-1,-1), "TOP"),
            ]))
            items.append(tt)
        items.append(Spacer(1, 3*mm))
    return items


def tip_box(text, color=C_AMBER):
    data = [[f"\u2605  EXAM TIP:  {text}"]]
    t = Table(data, colWidths=[17*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND",  (0,0), (-1,-1), HexColor("#fffbf0")),
        ("TEXTCOLOR",   (0,0), (-1,-1), HexColor("#7d4e00")),
        ("FONTNAME",    (0,0), (-1,-1), "Helvetica-BoldOblique"),
        ("FONTSIZE",    (0,0), (-1,-1), 8.5),
        ("TOPPADDING",  (0,0), (-1,-1), 6),
        ("BOTTOMPADDING",(0,0),(-1,-1), 6),
        ("LEFTPADDING", (0,0), (-1,-1), 8),
        ("BOX",         (0,0), (-1,-1), 1, C_AMBER),
    ]))
    return t


# ═══════════════════════════════════════════════════════════════════════════════
# DATA: Subject-wise high-yield topics (pattern-based, 2020-2025)
# ═══════════════════════════════════════════════════════════════════════════════

SUBJECTS = [
    {
        "name": "ANATOMY",
        "emoji": "🦴",
        "color": C_NAVY,
        "avg_q": "8-10",
        "rank": 8,
        "intro": "Anatomy contributes ~8-10 questions. Clinically applied and image-based questions have been rising since 2022. Surface marking, nerve supply, and embryology are consistently tested.",
        "tip": "Focus on applied anatomy. Most questions test nerve injuries, hernias, and surface landmarks - not rote memorization.",
        "categories": [
            ("Upper Limb - High Yield", [
                "Brachial plexus - roots, trunks, divisions, cords",
                "Axillary artery branches (mnemonic: SSS LAA)",
                "Radial nerve - course & injury patterns (wrist drop)",
                "Ulnar nerve - injury at wrist vs elbow",
                "Carpal tunnel contents & CTS",
                "Rotator cuff muscles (SITS)",
                "Cubital fossa boundaries & contents",
                "Anatomical snuffbox contents"
            ], "HIGH"),
            ("Lower Limb - High Yield", [
                "Femoral triangle - boundaries, contents",
                "Femoral sheath - contents (canal, ring)",
                "Obturator nerve - distribution",
                "Sciatic nerve - course & injury",
                "Common peroneal nerve palsy - foot drop",
                "Popliteal fossa contents",
                "Ankle joint - ligaments (deltoid complex)",
                "Arches of foot - supports"
            ], "HIGH"),
            ("Thorax & Abdomen", [
                "Heart - conducting system, SA/AV nodes",
                "Coronary arteries - LAD, RCA, LCx territories",
                "Coronary dominance (right dominant 85%)",
                "Inguinal canal - anatomy, walls, contents",
                "Portal-systemic anastomoses (4 sites)",
                "Liver segments (Couinaud classification)",
                "Hilum of lung - relations",
                "Diaphragm - openings and their contents (T8/T10/T12)",
                "McBurney's point - appendix position"
            ], "HIGH"),
            ("Head, Neck & Neuroanatomy", [
                "Cavernous sinus - contents & clinical relevance",
                "Cranial nerve nuclei locations",
                "Circle of Willis - components",
                "Internal capsule - blood supply (lenticulostriate a.)",
                "Parotid gland - facial nerve branches",
                "Thyroid gland - blood supply, RLN, external LN",
                "Submandibular triangle - mylohyoid nerve",
                "Ventricular system - CSF circulation"
            ], "MEDIUM"),
            ("Embryology - Recurring", [
                "Pharyngeal arches - derivatives (1st, 2nd, 3rd, 4th)",
                "Branchial cleft anomalies",
                "Heart development - truncus, bulbus",
                "Midgut rotation - malrotation",
                "Kidney development - ureteric bud, metanephros",
                "Meckel's diverticulum - rule of 2s",
                "Congenital diaphragmatic hernia (Bochdalek)",
                "Neural tube defects - folate deficiency"
            ], "RECURRING"),
        ]
    },
    {
        "name": "PHYSIOLOGY",
        "emoji": "⚡",
        "color": HexColor("#155724"),
        "avg_q": "7-9",
        "rank": 9,
        "intro": "Physiology averages 7-9 questions. Cardiac and respiratory physiology dominate. Renal, neurophysiology, and endocrine sections are high-yield for applied questions.",
        "tip": "Know normal values cold. Many questions are 1-liner: 'Which has highest O2 content?' or 'Normal GFR is?'",
        "categories": [
            ("Cardiac Physiology - Most Tested", [
                "Cardiac cycle - phases, pressure-volume loop",
                "JVP waveform - a, c, x, v, y components",
                "Frank-Starling law",
                "Cardiac output = HR x SV; Fick's principle",
                "Action potential - phases 0,1,2,3,4",
                "Conduction velocities (Purkinje > ventricle > AV node)",
                "ECG intervals - normal values",
                "Coronary blood flow - diastolic dominance LV"
            ], "HIGH"),
            ("Respiratory Physiology", [
                "Lung volumes & capacities (TLC, FRC, RV, VC)",
                "Dead space - anatomical vs physiological",
                "V/Q ratio - normal 0.8; effects of disease",
                "Oxygen-hemoglobin dissociation curve - shifts",
                "CO2 transport - bicarbonate (70%), Hb (23%)",
                "Surfactant - type II pneumocytes, composition",
                "Pulmonary vascular resistance - hypoxic vasoconstriction",
                "Spirometry patterns - obstructive vs restrictive"
            ], "HIGH"),
            ("Renal Physiology", [
                "GFR - normal 120-125 ml/min; clearance concept",
                "Inulin clearance = GFR; PAH = RPF",
                "Tubular reabsorption - glucose Tm (320 mg/min)",
                "Countercurrent mechanism - loop of Henle",
                "Renin-angiotensin-aldosterone system",
                "ADH - site of action, stimuli",
                "Acid-base balance - compensation rules",
                "Starling forces - filtration & reabsorption"
            ], "HIGH"),
            ("Neurophysiology & Special Senses", [
                "Resting membrane potential (-70 mV)",
                "Action potential threshold & absolute refractory period",
                "Neuromuscular junction - steps, drugs",
                "Reflex arc - stretch reflex, Golgi tendon",
                "Sleep stages - REM characteristics",
                "Visual pathway - lesions and field defects",
                "Auditory pathway - presbycusis",
                "Pain pathways - spinothalamic vs dorsal column"
            ], "MEDIUM"),
            ("Endocrine & Reproduction", [
                "Insulin - synthesis, secretion, effects",
                "Thyroid hormones - T3 vs T4 ratios",
                "Cortisol - diurnal variation, feedback",
                "GH - pulsatile, IGF-1 mediated effects",
                "Menstrual cycle - hormonal changes",
                "Progesterone - thermogenic, luteal phase",
                "Prolactin - dopamine inhibition",
                "Fetal Hb - HbF, 2,3-DPG and O2 affinity"
            ], "MEDIUM"),
        ]
    },
    {
        "name": "BIOCHEMISTRY",
        "emoji": "🧬",
        "color": HexColor("#5c2d91"),
        "avg_q": "11-13",
        "rank": 4,
        "intro": "Biochemistry is a HIGH-weightage subject averaging 11-13 questions. Metabolic pathways, enzymes, and molecular biology dominate. Vitamins and inborn errors are consistently repeated.",
        "tip": "Enzymes and their co-factors, rate-limiting steps, and deficiency diseases are the highest-yield single topics. Memorize them in tabular form.",
        "categories": [
            ("Carbohydrate Metabolism - Most Tested", [
                "Glycolysis - key enzymes (PFK-1, pyruvate kinase)",
                "Gluconeogenesis - substrates, key enzymes, sites",
                "TCA cycle - NADH yield, rate-limiting enzymes",
                "Glycogen synthesis & degradation - enzymes",
                "HMP shunt - NADPH production, significance",
                "Cori cycle & glucose-alanine cycle",
                "Fructose metabolism - aldolase B deficiency",
                "Galactosemia - GALT enzyme deficiency"
            ], "HIGH"),
            ("Lipid Metabolism", [
                "Beta-oxidation - ATP yield, site (mitochondria)",
                "Ketone body synthesis - acetoacetyl CoA",
                "Cholesterol synthesis - HMG-CoA reductase (rate-limiting)",
                "Lipoproteins - structure, functions (LDL, HDL, VLDL)",
                "Essential fatty acids - linoleic, linolenic",
                "Prostaglandin synthesis - COX pathway",
                "Familial hypercholesterolemia - LDL receptor defect",
                "Fatty liver - causes, VLDL export"
            ], "HIGH"),
            ("Protein & Amino Acid Metabolism", [
                "Protein structure - primary to quaternary",
                "Urea cycle - site, enzymes, defects",
                "Transamination - ALT/AST reactions",
                "Phenylketonuria - PAH deficiency, BH4",
                "Homocystinuria - CBS or MTHFR deficiency",
                "Maple syrup urine disease - BCKA dehydrogenase",
                "Collagen synthesis - vitamin C, lysyl oxidase",
                "Essential amino acids (mnemonic: PVT TIM HALL)"
            ], "HIGH"),
            ("Molecular Biology & Genetics", [
                "DNA replication - enzymes, Okazaki fragments",
                "Transcription - RNA polymerases I, II, III",
                "Translation - start codon (AUG), wobble hypothesis",
                "Restriction enzymes - EcoRI, HindIII",
                "PCR - principle, Taq polymerase",
                "Mutations - missense, nonsense, frameshift",
                "Oncogenes vs tumor suppressor genes",
                "Southern, Northern, Western blot - which detects what"
            ], "HIGH"),
            ("Vitamins & Minerals", [
                "Vitamin B1 (Thiamine) - TPP, wernicke's, beriberi",
                "Vitamin B3 (Niacin) - NAD+, pellagra (3Ds)",
                "Vitamin B6 (Pyridoxine) - PLP, sideroblastic anemia",
                "Vitamin B12 - methyl cobalamin, pernicious anemia",
                "Folate - THF, NTDs, homocysteine",
                "Vitamin C - scurvy, collagen cross-linking",
                "Vitamin K - gamma-carboxylation, factors II VII IX X",
                "Zinc deficiency - acrodermatitis enteropathica"
            ], "RECURRING"),
        ]
    },
    {
        "name": "PATHOLOGY",
        "emoji": "🔬",
        "color": HexColor("#c0392b"),
        "avg_q": "22-27",
        "rank": 2,
        "intro": "Pathology is one of the MOST HIGH-YIELD subjects (22-27 questions). Both conceptual and image-based questions are common. General pathology, hematology, and systemic pathology are all tested equally.",
        "tip": "Image-based questions in pathology have increased since 2022. Practice recognizing H&E slides of common tumors, granulomas, and RBC morphologies.",
        "categories": [
            ("General Pathology - Core", [
                "Cell injury - reversible vs irreversible markers",
                "Apoptosis - intrinsic vs extrinsic pathway, caspases",
                "Necrosis types - coagulative, liquefactive, caseous",
                "Inflammation - chemical mediators (histamine, PG, leukotrienes)",
                "Acute vs chronic inflammation - cellular differences",
                "Granuloma types - TB (Langhans), sarcoid, foreign body",
                "Wound healing - primary vs secondary intention",
                "Edema - mechanisms (Starling forces)"
            ], "HIGH"),
            ("Hematopathology - Very High Yield", [
                "Iron deficiency anemia - microcytic hypochromic",
                "Megaloblastic anemia - B12 vs folate; hypersegmented PMNs",
                "Hemolytic anemias - intravascular vs extravascular",
                "Sickle cell disease - HbS, vasoocclusion",
                "Thalassemia - alpha vs beta; hemoglobin patterns",
                "AML vs ALL - FAB classification, cytochemistry",
                "CML - Philadelphia chromosome t(9;22), BCR-ABL",
                "Lymphomas - Hodgkin (RS cell) vs NHL",
                "Multiple myeloma - M protein, Bence Jones",
                "DIC - pathogenesis, lab findings"
            ], "HIGH"),
            ("Systemic Pathology - Clinical", [
                "Atherosclerosis - stages, foam cells, risk factors",
                "MI - zones (coagulative necrosis), timeline of changes",
                "Rheumatic heart disease - Aschoff bodies",
                "Carcinoma lung - squamous (central), adeno (peripheral)",
                "Hepatitis - chronic, cirrhosis, HCC risk",
                "Kidney - MPGN, FSGS, IgA nephropathy patterns",
                "Cervical cancer - HPV 16/18, CIN grading",
                "Breast cancer - ER/PR/HER2, ductal vs lobular"
            ], "HIGH"),
            ("Tumor Pathology & Markers", [
                "Benign vs malignant - microscopic criteria",
                "Carcinoma in situ concept",
                "PSA - prostate; AFP - HCC, germ cell; CEA - colon",
                "CA-125 - ovarian; CA 19-9 - pancreatic",
                "Li-Fraumeni syndrome - p53 mutation",
                "Retinoblastoma - Rb gene, two-hit hypothesis",
                "Metaplasia - Barrett's esophagus (squamous → columnar)",
                "Paraneoplastic syndromes - SIADH, hypercalcemia"
            ], "RECURRING"),
        ]
    },
    {
        "name": "PHARMACOLOGY",
        "emoji": "💊",
        "color": HexColor("#0d47a1"),
        "avg_q": "12-15",
        "rank": 3,
        "intro": "Pharmacology averages 12-15 questions. Mechanism of action, side effects, and drug of choice questions dominate. Antimicrobials, CNS drugs, and autonomic drugs are tested every year.",
        "tip": "For every drug, know: mechanism, indication, unique side effect, and contraindication. That covers 90% of pharmacology MCQs.",
        "categories": [
            ("Autonomic Pharmacology", [
                "Cholinergic drugs - muscarinic vs nicotinic effects",
                "Atropine - uses, poisoning, physostigmine antidote",
                "Organophosphate poisoning - SLUDGE, atropine + pralidoxime",
                "Beta blockers - cardioselective (B1: metoprolol, atenolol)",
                "Alpha blockers - prazosin (BPH), phenoxybenzamine (pheochromocytoma)",
                "Adrenergic agonists - receptor specificity",
                "Neuromuscular blockers - depolarizing (succinylcholine) vs non-depolarizing",
                "Myasthenia gravis drugs - neostigmine, pyridostigmine"
            ], "HIGH"),
            ("CNS Pharmacology", [
                "Benzodiazepines vs barbiturates - MOA comparison",
                "Antiepileptics - drug of choice per seizure type",
                "Antidepressants - SSRIs, TCAs (overdose risk), MAOIs",
                "Antipsychotics - typical (D2 block) vs atypical",
                "Clozapine - agranulocytosis, monitoring",
                "Lithium - toxicity signs, thyroid effects, teratogen",
                "Opioid analgesics - MOA, respiratory depression, naloxone",
                "Parkinson's disease drugs - L-DOPA, dopamine agonists"
            ], "HIGH"),
            ("Antimicrobials - Highest Yield", [
                "Penicillins - mechanism (cell wall), beta-lactamase inhibitors",
                "Aminoglycosides - ototoxicity, nephrotoxicity, trough monitoring",
                "Tetracyclines - contraindicated in pregnancy, children",
                "Chloramphenicol - grey baby syndrome, aplastic anemia",
                "Fluoroquinolones - DNA gyrase, cartilage damage in children",
                "Metronidazole - anaerobes, G. lamblia, disulfiram-like reaction",
                "Antifungals - amphotericin B (gold std, nephrotoxic), azoles",
                "Antitubercular drugs - HRZE regime, side effects each drug"
            ], "HIGH"),
            ("Cardiovascular & Other", [
                "ACE inhibitors - cough (bradykinin), contraindicated pregnancy",
                "Digoxin - Na/K ATPase, toxicity, hypokalemia exacerbates",
                "Statins - HMG-CoA reductase, myopathy (CK monitoring)",
                "Warfarin vs heparin - mechanism difference",
                "NSAIDs - COX-1 vs COX-2, GI ulcer risk",
                "Corticosteroids - Cushing effects, adrenal suppression",
                "Insulin types - rapid, short, intermediate, long acting",
                "Oral hypoglycemics - metformin (lactic acidosis), sulfonylureas"
            ], "RECURRING"),
        ]
    },
    {
        "name": "GENERAL MEDICINE (incl. Dermatology & Psychiatry)",
        "emoji": "🩺",
        "color": HexColor("#1a6e3c"),
        "avg_q": "45-50",
        "rank": 1,
        "intro": "General Medicine is the SINGLE HIGHEST-YIELD section (45-50 questions). Cardiology, Neurology, Infectious Diseases, Endocrinology, and Respiratory medicine are all heavily tested. Dermatology (~10 Qs) and Psychiatry (~8 Qs) are included.",
        "tip": "Focus on clinical vignettes - patient presentation → diagnosis → next best step. Know guidelines: JNC8 BP targets, ADA diabetes, GOLD COPD, CHADS2-VASc score.",
        "categories": [
            ("Cardiology - Most Tested in Medicine", [
                "ACS - STEMI vs NSTEMI, management protocol",
                "Heart failure - HFrEF vs HFpEF, BNP, management",
                "Arrhythmias - AF management, CHADS2-VASc anticoagulation",
                "Hypertension - JNC-8 targets, drug choices per comorbidity",
                "Valvular heart disease - aortic stenosis (most common calcific)",
                "Infective endocarditis - Duke criteria, prophylaxis",
                "Rheumatic fever - Jones criteria (major/minor)",
                "Pericarditis - friction rub, ECG changes, Beck's triad (tamponade)"
            ], "HIGH"),
            ("Neurology", [
                "Stroke - ischemic vs hemorrhagic; tPA criteria (4.5 hrs)",
                "Meningitis - CSF findings (bacterial vs viral vs TB)",
                "Epilepsy - drug of choice per type, status epilepticus Rx",
                "Multiple sclerosis - relapsing-remitting, MRI lesions",
                "Parkinson's disease - TRAP symptoms, dopamine neurons",
                "Guillain-Barre syndrome - ascending paralysis, albuminocytological dissociation",
                "Myasthenia gravis - fatigable weakness, tensilon test",
                "Headache - migraine vs cluster vs tension features"
            ], "HIGH"),
            ("Infectious Diseases", [
                "Malaria - P. falciparum (severe malaria), treatment",
                "Typhoid - Widal test, rose spots, complications",
                "TB - primary vs secondary, Ghon complex, DOTS",
                "HIV - CD4 count thresholds, opportunistic infections",
                "Dengue - NS1 antigen, warning signs, platelet",
                "Leptospirosis - Weil's disease, conjunctival suffusion",
                "Rabies - Negri bodies, post-exposure prophylaxis",
                "Viral hepatitis - serology patterns (HBsAg, anti-HBs, etc.)"
            ], "HIGH"),
            ("Endocrinology & Rheumatology", [
                "Diabetes mellitus - ADA diagnostic criteria, HbA1c targets",
                "Diabetic complications - retinopathy, nephropathy staging",
                "Hypothyroidism - TSH elevated, T4 low; Hashimoto's",
                "Hyperthyroidism - Graves' disease, pretibial myxedema",
                "Cushing syndrome - causes, investigations (LDDST, HDDST)",
                "Addison's disease - primary adrenal insufficiency, pigmentation",
                "Rheumatoid arthritis - RF, anti-CCP, DMARDs",
                "SLE - ANA, anti-dsDNA, butterfly rash, criteria"
            ], "HIGH"),
            ("Respiratory & GI Medicine", [
                "COPD - GOLD staging, FEV1/FVC < 0.7, smoking",
                "Asthma - reversible obstruction, PEFR variability",
                "Pneumonia - CAP vs HAP, CURB-65 score",
                "Pleural effusion - transudates vs exudates (Light's criteria)",
                "GERD - proton pump inhibitors, Barrett's risk",
                "Peptic ulcer disease - H. pylori, NSAIDs, triple therapy",
                "IBD - Crohn's vs UC (skip lesions vs continuous, ASCA vs p-ANCA)",
                "Cirrhosis - Child-Pugh score, MELD, complications"
            ], "MEDIUM"),
            ("Dermatology - High Yield Topics", [
                "Psoriasis - silvery scales, Auspitz sign, Koebner phenomenon",
                "Lichen planus - 6Ps, oral lesion Wickham's striae",
                "Pemphigus vulgaris vs bullous pemphigoid - level of blister",
                "Scabies - Sarcoptes, Norwegian/crusted scabies",
                "Leprosy - RD Ridley-Jopling classification, reactions",
                "Vitiligo - autoimmune, Wood's lamp, melanocyte destruction",
                "Acne vulgaris - comedones, Propionibacterium acnes",
                "Melanoma - ABCDE criteria, Clark vs Breslow staging"
            ], "HIGH"),
            ("Psychiatry - High Yield Topics", [
                "Schizophrenia - positive vs negative symptoms, dopamine",
                "Bipolar disorder - manic episode criteria, lithium Rx",
                "Major depression - diagnostic criteria (DSM-5), SSRIs first line",
                "Anxiety disorders - GAD, panic disorder, OCD differences",
                "Personality disorders - cluster A/B/C classification",
                "Suicide risk assessment - factors, protective factors",
                "ECT - indications (severe depression, NMS, catatonia)",
                "Substance use - AUDIT, CAGE questionnaire, withdrawal"
            ], "MEDIUM"),
        ]
    },
    {
        "name": "GENERAL SURGERY (incl. Orthopedics & Anesthesia)",
        "emoji": "🔧",
        "color": HexColor("#7d3c00"),
        "avg_q": "45-50",
        "rank": 1,
        "intro": "Surgery is co-equal to Medicine in weightage (45-50 questions). GI surgery, oncology, trauma, and perioperative care are heavily tested. Orthopedics contributes ~12-15 Qs; Anesthesia ~5-8 Qs.",
        "tip": "Know the 'next best step' approach. Most surgery MCQs test clinical decision-making: investigate first or operate? When to do FNAC vs excision biopsy?",
        "categories": [
            ("GI & Hepatobiliary Surgery", [
                "Appendicitis - Alvarado score, Rovsing, McBurney",
                "Intestinal obstruction - clinical features, management",
                "Colorectal cancer - Duke's staging (now TNM), CEA",
                "Carcinoma stomach - Lauren classification, virchow's node",
                "Carcinoma esophagus - squamous (upper) vs adeno (lower)",
                "Gallstones - types, Charcot's triad, Reynold's pentad",
                "Acute pancreatitis - Ranson criteria, causes (GET SMASHED)",
                "Portal hypertension - causes, varices, TIPS procedure"
            ], "HIGH"),
            ("Breast & Thyroid Surgery", [
                "Breast cancer - FNAC vs core biopsy, sentinel node biopsy",
                "Paget's disease of breast - intraductal carcinoma",
                "Fibroadenoma vs fibrocystic - clinical differentiation",
                "Thyroid carcinoma - papillary (most common, RET/PTC)",
                "Follicular vs medullary thyroid cancer features",
                "Graves' disease surgical indication",
                "Thyroglossal cyst - Sistrunk operation",
                "Parathyroid adenoma - hyperparathyroidism, Sestamibi scan"
            ], "HIGH"),
            ("Vascular & Trauma Surgery", [
                "Aortic aneurysm - AAA (>5.5 cm surgery), complications",
                "Peripheral arterial disease - ABI, Fontaine classification",
                "DVT - Wells score, LMWH treatment",
                "Varicose veins - Trendelenburg test, sapheno-femoral junction",
                "Polytrauma - ATLS protocol, primary survey (ABCDE)",
                "Hemorrhagic shock - classes I-IV, fluid resuscitation",
                "Pneumothorax - tension vs simple, needle decompression site",
                "Burns - rule of 9s, Parkland formula"
            ], "HIGH"),
            ("Orthopedics", [
                "Fractures - Colles' vs Smith's, Garden classification (femur neck)",
                "Compartment syndrome - 5Ps, fasciotomy urgency",
                "Osteomyelitis - Brodie's abscess, Involucrum/sequestrum",
                "Bone tumors - osteosarcoma (sunburst, Codman triangle)",
                "Osteoarthritis vs rheumatoid arthritis - X-ray differences",
                "Developmental dysplasia of hip - Ortolani, Barlow",
                "Scoliosis - Cobb angle, bracing vs surgery",
                "Intervertebral disc prolapse - L4-L5 vs L5-S1 features"
            ], "HIGH"),
            ("Anesthesia & Critical Care", [
                "ASA classification - I to VI grading",
                "Intubation - RSI, Cormack-Lehane grading",
                "Local anesthetics - lidocaine max dose, toxicity",
                "Spinal vs epidural anesthesia differences",
                "Malignant hyperthermia - succinylcholine, halothane, dantrolene",
                "ICU monitoring - CVP, PCWP interpretation",
                "Ventilator settings - PEEP, tidal volume, ARDSnet",
                "Post-op fever timeline - Wind, Water, Wound, Walking, Wonder drugs"
            ], "MEDIUM"),
        ]
    },
    {
        "name": "OBSTETRICS & GYNAECOLOGY (OBG)",
        "emoji": "👶",
        "color": HexColor("#880e4f"),
        "avg_q": "28-33",
        "rank": 3,
        "intro": "OBG contributes ~28-33 questions. Normal obstetrics, antepartum/postpartum hemorrhage, contraception, and gynecological malignancies are most tested. Clinical vignettes predominate.",
        "tip": "Always think 'next best step' for obstetric emergencies. Know Bishop score, APGAR scoring, and WHO MEC contraindication categories.",
        "categories": [
            ("Normal Obstetrics", [
                "Physiological changes in pregnancy - blood volume, cardiac output",
                "Antenatal care - schedule, investigations per trimester",
                "Fetal lie, presentation, position definitions",
                "Bishop score - components and significance",
                "Stages of labour - duration, management",
                "Partogram - use in active labour monitoring",
                "APGAR score - 1 and 5 min assessment",
                "Puerperium - involution, lochia stages"
            ], "HIGH"),
            ("Obstetric Complications", [
                "Pre-eclampsia vs eclampsia - criteria, MgSO4 protocol",
                "Gestational hypertension - management",
                "Antepartum hemorrhage - placenta previa vs abruption",
                "Placenta previa - grading, management",
                "Postpartum hemorrhage - 4Ts (Tone, Trauma, Tissue, Thrombin)",
                "Ectopic pregnancy - hCG trends, methotrexate criteria",
                "Hyperemesis gravidarum - thiamine deficiency, Wernicke",
                "Preterm labour - cervical cerclage, tocolysis, steroids"
            ], "HIGH"),
            ("Gynaecological Malignancies", [
                "Cervical cancer - FIGO staging, HPV 16/18, Pap smear",
                "Endometrial cancer - postmenopausal bleeding, FIGO",
                "Ovarian cancer - CA-125, debulking surgery, BRCA",
                "Choriocarcinoma - beta-hCG, methotrexate, cure rate",
                "Gestational trophoblastic disease - H mole features",
                "Vulvar carcinoma - SCC, lichen sclerosus risk",
                "Vaginal carcinoma - DES exposure, clear cell adeno",
                "Fallopian tube cancer - rarest gynecologic malignancy"
            ], "HIGH"),
            ("Contraception & Fertility", [
                "OCP - mechanism, failure rate (Pearl index), WHO MEC",
                "IUD - Cu-T mechanism, side effects",
                "Emergency contraception - levonorgestrel 1.5 mg within 72h",
                "DMPA injectable - amenorrhea common side effect",
                "Infertility - definition (1 year), investigations",
                "PCOS - Rotterdam criteria, metformin, clomiphene",
                "IVF steps - ovarian stimulation, oocyte retrieval",
                "Menopause - FSH >40 IU/L, HRT indications/contraindications"
            ], "RECURRING"),
        ]
    },
    {
        "name": "PREVENTIVE & SOCIAL MEDICINE (PSM / Community Medicine)",
        "emoji": "🌍",
        "color": HexColor("#006064"),
        "avg_q": "23-28",
        "rank": 3,
        "intro": "PSM contributes ~23-28 questions. Epidemiology, biostatistics, national health programs, and occupational health are all high-yield. This is often the most scoring subject for well-prepared candidates.",
        "tip": "Biostatistics formulas (sensitivity, specificity, PPV, NPV, NNT) appear every year. Practice calculations. Know Indian national programs and their targets.",
        "categories": [
            ("Epidemiology & Biostatistics", [
                "Study designs - case control, cohort, RCT hierarchy",
                "Bias types - selection, recall, Hawthorne, Berkson",
                "Odds ratio - case-control studies",
                "Relative risk - cohort studies",
                "Sensitivity vs Specificity - screening vs diagnosis",
                "PPV & NPV - affected by prevalence",
                "NNT (Number needed to treat) formula",
                "Measures of central tendency - mean, median, mode"
            ], "HIGH"),
            ("Disease Screening & Prevention", [
                "Levels of prevention - primary, secondary, tertiary",
                "Primordial prevention concept",
                "Screening criteria - Wilson & Jungner principles",
                "Herd immunity threshold - formula (1-1/R0)",
                "Vaccine cold chain - temperature requirements",
                "EPI - Expanded Programme on Immunization",
                "Universal Immunization Programme - schedule",
                "Immunization coverage targets - WHO/UNICEF"
            ], "HIGH"),
            ("National Health Programs", [
                "RNTCP / NTEP - DOTS, category I & II regimes",
                "National AIDS Control Programme - NACP IV goals",
                "National Vector Borne Disease Control Programme",
                "Janani Suraksha Yojana - BPL mothers, cash incentive",
                "ASHA - role, training, incentive structure",
                "Ayushman Bharat - PM-JAY health insurance",
                "National Nutrition Mission (POSHAN Abhiyaan)",
                "Mission Indradhanush - vaccination drive"
            ], "RECURRING"),
            ("Demography & Vital Statistics", [
                "Census - decennial, de facto vs de jure",
                "Crude birth rate, crude death rate formulas",
                "Infant mortality rate - gold standard of health",
                "Maternal mortality ratio (per 100,000 live births)",
                "Total fertility rate vs GFR",
                "MMR targets - SDG goal <70/100,000",
                "Population growth - demographic transition",
                "Life expectancy - India current values"
            ], "MEDIUM"),
            ("Occupational & Environmental Health", [
                "Pneumoconioses - silicosis (quartz), asbestosis (mesothelioma risk)",
                "Coal worker's pneumoconiosis - Caplan syndrome",
                "Occupational cancers - arsenic, benzene, vinyl chloride",
                "Noise-induced hearing loss - 4000 Hz notch",
                "Lead poisoning - Burton's line, basophilic stippling",
                "Organochlorine pesticides - bioaccumulation, DDT",
                "Air pollution indices - NAAQS standards",
                "Water purification - chlorination, ozonation, fluoride"
            ], "MEDIUM"),
        ]
    },
]

# ── Weightage summary table ────────────────────────────────────────────────────
def weightage_table():
    data = [
        ["#", "Subject", "Avg. Questions", "Priority", "% of Paper"],
        ["1", "General Medicine (incl. Derm & Psych)", "45-50", "★★★★★", "22-25%"],
        ["2", "General Surgery (incl. Ortho & Anaes)", "45-50", "★★★★★", "22-25%"],
        ["3", "OBG", "28-33", "★★★★☆", "14-17%"],
        ["4", "PSM / Community Medicine", "23-28", "★★★★☆", "12-14%"],
        ["5", "Pathology", "22-27", "★★★★☆", "11-14%"],
        ["6", "Pharmacology", "12-15", "★★★☆☆", "6-8%"],
        ["7", "Biochemistry", "11-13", "★★★☆☆", "5-7%"],
        ["8", "Anatomy", "8-10", "★★☆☆☆", "4-5%"],
        ["9", "Physiology", "7-9", "★★☆☆☆", "3-5%"],
    ]
    col_w = [1.0*cm, 6.5*cm, 3.5*cm, 3.0*cm, 3.0*cm]
    t = Table(data, colWidths=col_w)
    t.setStyle(TableStyle([
        # header
        ("BACKGROUND",  (0,0), (-1,0), C_NAVY),
        ("TEXTCOLOR",   (0,0), (-1,0), C_WHITE),
        ("FONTNAME",    (0,0), (-1,0), "Helvetica-Bold"),
        ("FONTSIZE",    (0,0), (-1,0), 9),
        ("ALIGN",       (0,0), (-1,0), "CENTER"),
        ("TOPPADDING",  (0,0), (-1,0), 7),
        ("BOTTOMPADDING",(0,0),(-1,0), 7),
        # rows alternating
        ("BACKGROUND",  (0,1), (-1,1), HexColor("#fdecea")),
        ("BACKGROUND",  (0,2), (-1,2), HexColor("#fdecea")),
        ("BACKGROUND",  (0,3), (-1,3), HexColor("#fff3e0")),
        ("BACKGROUND",  (0,4), (-1,4), HexColor("#fff3e0")),
        ("BACKGROUND",  (0,5), (-1,5), HexColor("#e8f5e9")),
        ("BACKGROUND",  (0,6), (-1,6), HexColor("#e8f5e9")),
        ("BACKGROUND",  (0,7), (-1,7), C_LGRAY),
        ("BACKGROUND",  (0,8), (-1,8), C_LGRAY),
        ("FONTNAME",    (0,1), (-1,-1), "Helvetica"),
        ("FONTSIZE",    (0,1), (-1,-1), 8.5),
        ("ALIGN",       (2,1), (4,-1), "CENTER"),
        ("VALIGN",      (0,0), (-1,-1), "MIDDLE"),
        ("TOPPADDING",  (0,1), (-1,-1), 5),
        ("BOTTOMPADDING",(0,1),(-1,-1), 5),
        ("BOX",         (0,0), (-1,-1), 1, C_MGRAY),
        ("INNERGRID",   (0,0), (-1,-1), 0.4, C_MGRAY),
    ]))
    return t


# ── HOW TO USE page ────────────────────────────────────────────────────────────
def how_to_use():
    items = []
    items.append(Paragraph("How to Use This Guide", sTocHead))
    items.append(HRFlowable(width="100%", thickness=2, color=C_TEAL))
    items.append(Spacer(1, 4*mm))
    tips = [
        ("PRIORITY TAGS",
         "Each topic category is labeled HIGH, MEDIUM, or RECURRING. Start with all HIGH topics first, then RECURRING, then MEDIUM."),
        ("EXAM TIP BOXES",
         "Yellow tip boxes at the start of each subject give the single most important strategy for that subject."),
        ("QUESTION DISTRIBUTION",
         "The weightage table shows approximate questions per subject based on 5-year analysis (2020-2025). Note: exact counts vary by exam year."),
        ("STUDY ORDER SUGGESTION",
         "Recommended order: Pharmacology → Pathology → Medicine → Surgery → OBG → PSM → Biochemistry → Physiology → Anatomy"),
        ("REVISION STRATEGY",
         "First pass: read all HIGH topics (2-3 weeks). Second pass: add RECURRING topics (1 week). Final week: quick revision of all 9 subjects."),
        ("DISCLAIMER",
         "This guide is based on AI analysis of publicly known NEET PG exam patterns and syllabus. It does not contain any official exam questions or copyrighted material."),
    ]
    for title, desc in tips:
        row = [[Paragraph(f"<b>{title}</b>", sH3), Paragraph(desc, sBody)]]
        t = Table(row, colWidths=[4.5*cm, 12.5*cm])
        t.setStyle(TableStyle([
            ("BACKGROUND", (0,0), (0,0), C_LGRAY),
            ("BACKGROUND", (1,0), (1,0), C_WHITE),
            ("VALIGN",     (0,0), (-1,-1), "TOP"),
            ("TOPPADDING", (0,0), (-1,-1), 6),
            ("BOTTOMPADDING",(0,0),(-1,-1), 6),
            ("LEFTPADDING",(0,0), (-1,-1), 8),
            ("BOX",        (0,0), (-1,-1), 0.5, C_MGRAY),
        ]))
        items.append(t)
        items.append(Spacer(1, 2*mm))
    return items


# ── Build document ─────────────────────────────────────────────────────────────
def build():
    doc = SimpleDocTemplate(
        OUTPUT, pagesize=A4,
        leftMargin=1.8*cm, rightMargin=1.8*cm,
        topMargin=1.8*cm, bottomMargin=2.0*cm,
        title="NEET PG High-Yield Topics 2020-2025",
        author="Orris AI",
        subject="NEET PG Preparation"
    )

    story = []

    # --- Cover ---
    story += cover_block()

    # --- How to use ---
    story += how_to_use()
    story.append(PageBreak())

    # --- Weightage overview ---
    story.append(Paragraph("Subject-Wise Weightage Overview (2020-2025)", sTocHead))
    story.append(HRFlowable(width="100%", thickness=2, color=C_NAVY))
    story.append(Spacer(1, 4*mm))
    story.append(Paragraph(
        "Based on AI analysis of NEET PG exam patterns over 5 years, the approximate question distribution per subject is shown below. "
        "Subjects highlighted in red are highest priority.",
        sBody))
    story.append(Spacer(1, 4*mm))
    story.append(weightage_table())
    story.append(Spacer(1, 5*mm))
    note = Paragraph(
        "Note: NEET PG 2022 had 2 shifts; data above uses Shift 1 pattern which is considered the standard pattern. "
        "Total questions: 200 (each +4 marks, -1 for wrong). Total marks: 800.",
        sNote)
    story.append(note)
    story.append(PageBreak())

    # --- Subject sections ---
    for subj in SUBJECTS:
        # Section header band
        story.append(section_header(
            subj["name"], subj["emoji"], subj["color"],
            subj["avg_q"], subj["rank"]))
        story.append(Spacer(1, 3*mm))

        # Intro paragraph
        story.append(Paragraph(subj["intro"], sBody))
        story.append(Spacer(1, 2*mm))

        # Exam tip
        story.append(tip_box(subj["tip"]))
        story.append(Spacer(1, 4*mm))

        # Topic tables
        story += topic_table(subj["categories"])

        story.append(PageBreak())

    # --- Back page summary ----
    summary_data = [
        [Paragraph("<b>Subject</b>", sH3),
         Paragraph("<b>Top 3 Must-Know Topics</b>", sH3),
         Paragraph("<b>Quick Win Area</b>", sH3)],
        [Paragraph("Anatomy", sBullet),
         Paragraph("Brachial plexus, Femoral triangle, Diaphragm openings", sBullet),
         Paragraph("Nerve injury patterns", sBullet)],
        [Paragraph("Physiology", sBullet),
         Paragraph("Cardiac cycle, Lung volumes, Renal clearance", sBullet),
         Paragraph("Normal values", sBullet)],
        [Paragraph("Biochemistry", sBullet),
         Paragraph("Metabolic pathways, Vitamins, Molecular biology", sBullet),
         Paragraph("Deficiency diseases table", sBullet)],
        [Paragraph("Pathology", sBullet),
         Paragraph("Hematology, Tumor markers, General pathology", sBullet),
         Paragraph("Image-based MCQs", sBullet)],
        [Paragraph("Pharmacology", sBullet),
         Paragraph("Antimicrobials, CNS drugs, Autonomic drugs", sBullet),
         Paragraph("Side effects & contraindications", sBullet)],
        [Paragraph("Medicine", sBullet),
         Paragraph("Cardiology, Neurology, Infectious diseases", sBullet),
         Paragraph("Clinical vignettes", sBullet)],
        [Paragraph("Surgery", sBullet),
         Paragraph("GI surgery, Breast/thyroid, Trauma/ATLS", sBullet),
         Paragraph("Next best step questions", sBullet)],
        [Paragraph("OBG", sBullet),
         Paragraph("Obstetric emergencies, Gyn malignancies, Contraception", sBullet),
         Paragraph("Bishop score, APH management", sBullet)],
        [Paragraph("PSM", sBullet),
         Paragraph("Biostatistics, National programs, Epidemiology", sBullet),
         Paragraph("Formula-based calculation Qs", sBullet)],
    ]
    summary_t = Table(summary_data, colWidths=[3.5*cm, 8.5*cm, 5.0*cm])
    summary_t.setStyle(TableStyle([
        ("BACKGROUND",  (0,0), (-1,0), C_NAVY),
        ("TEXTCOLOR",   (0,0), (-1,0), C_WHITE),
        ("BACKGROUND",  (0,1), (-1,-1), C_WHITE),
        ("ROWBACKGROUNDS", (0,1), (-1,-1), [C_WHITE, C_LGRAY]),
        ("BOX",         (0,0), (-1,-1), 1, C_MGRAY),
        ("INNERGRID",   (0,0), (-1,-1), 0.4, C_MGRAY),
        ("TOPPADDING",  (0,0), (-1,-1), 5),
        ("BOTTOMPADDING",(0,0),(-1,-1), 5),
        ("LEFTPADDING", (0,0), (-1,-1), 7),
        ("VALIGN",      (0,0), (-1,-1), "TOP"),
    ]))

    story.append(Paragraph("Quick Reference Summary", sTocHead))
    story.append(HRFlowable(width="100%", thickness=2, color=C_TEAL))
    story.append(Spacer(1, 4*mm))
    story.append(summary_t)
    story.append(Spacer(1, 6*mm))
    story.append(Paragraph(
        "Best of luck for NEET PG 2026! Stay consistent, focus on high-yield topics, "
        "and practice MCQs daily. Smart preparation beats rote memorization.",
        sBody))

    doc.build(story)
    print(f"PDF generated: {OUTPUT}")

build()