from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm
from reportlab.lib import colors
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, PageBreak, KeepTogether
)
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY
from reportlab.pdfgen import canvas
from reportlab.platypus.doctemplate import PageTemplate, BaseDocTemplate, Frame
from reportlab.platypus import NextPageTemplate

# ─── Colour palette ────────────────────────────────────────────────────────────
DARK_BLUE   = colors.HexColor("#1a237e")
MED_BLUE    = colors.HexColor("#283593")
LIGHT_BLUE  = colors.HexColor("#e8eaf6")
ACCENT      = colors.HexColor("#c62828")
GREEN       = colors.HexColor("#1b5e20")
LIGHT_GREEN = colors.HexColor("#e8f5e9")
ORANGE      = colors.HexColor("#e65100")
LIGHT_ORANGE= colors.HexColor("#fff3e0")
GREY        = colors.HexColor("#37474f")
LIGHT_GREY  = colors.HexColor("#eceff1")
WHITE       = colors.white
YELLOW_BG   = colors.HexColor("#fffde7")

OUTPUT = "/home/daytona/workspace/upsc-cms-prep/UPSC_CMS_PYQ_Analysis.pdf"

# ─── Styles ────────────────────────────────────────────────────────────────────
styles = getSampleStyleSheet()

def S(name, **kw):
    return ParagraphStyle(name, **kw)

title_style = S("TitleStyle",
    fontName="Helvetica-Bold", fontSize=22, textColor=WHITE,
    alignment=TA_CENTER, spaceAfter=4, leading=28)

subtitle_style = S("SubtitleStyle",
    fontName="Helvetica", fontSize=13, textColor=LIGHT_BLUE,
    alignment=TA_CENTER, spaceAfter=2, leading=18)

cover_info_style = S("CoverInfoStyle",
    fontName="Helvetica", fontSize=10, textColor=LIGHT_BLUE,
    alignment=TA_CENTER, spaceAfter=2, leading=14)

section_header_style = S("SectionHeader",
    fontName="Helvetica-Bold", fontSize=14, textColor=WHITE,
    alignment=TA_LEFT, spaceAfter=4, leading=20,
    leftIndent=6)

subheader_style = S("SubheaderStyle",
    fontName="Helvetica-Bold", fontSize=11, textColor=DARK_BLUE,
    alignment=TA_LEFT, spaceAfter=3, leading=16, spaceBefore=6)

question_style = S("QuestionStyle",
    fontName="Helvetica-Bold", fontSize=9.5, textColor=GREY,
    alignment=TA_LEFT, spaceAfter=2, leading=14, leftIndent=4)

option_style = S("OptionStyle",
    fontName="Helvetica", fontSize=9, textColor=colors.HexColor("#455a64"),
    alignment=TA_LEFT, spaceAfter=1, leading=13, leftIndent=16)

answer_style = S("AnswerStyle",
    fontName="Helvetica-Bold", fontSize=9, textColor=GREEN,
    alignment=TA_LEFT, spaceAfter=1, leading=13, leftIndent=16)

hint_style = S("HintStyle",
    fontName="Helvetica-Oblique", fontSize=8.5, textColor=ORANGE,
    alignment=TA_LEFT, spaceAfter=4, leading=12, leftIndent=16)

toc_style = S("TOCStyle",
    fontName="Helvetica", fontSize=10, textColor=GREY,
    alignment=TA_LEFT, spaceAfter=4, leading=16)

normal_style = S("NormalStyle",
    fontName="Helvetica", fontSize=9.5, textColor=GREY,
    alignment=TA_JUSTIFY, spaceAfter=4, leading=14)

freq_badge_style = S("FreqBadge",
    fontName="Helvetica-Bold", fontSize=8, textColor=WHITE,
    alignment=TA_CENTER, leading=10)

# ─── Data ──────────────────────────────────────────────────────────────────────
# Subject distribution (avg %)
SUBJECT_DIST = [
    ("General Medicine", 38.7, DARK_BLUE),
    ("Surgery", 18.0, MED_BLUE),
    ("Gynaecology & Obstetrics", 17.0, colors.HexColor("#6a1b9a")),
    ("Preventive & Social Medicine (PSM)", 15.0, GREEN),
    ("Paediatrics", 11.3, ORANGE),
]

# ─── Questions database ────────────────────────────────────────────────────────
# Format: (frequency_tag, question_text, options_list, correct_answer_letter, hint)
# Frequency tags: "★★★★★" = asked 5+ times, "★★★★" = 4x, "★★★" = 3x, "★★" = 2x, "★" = 1x

QDB = {
    "PAPER I - GENERAL MEDICINE": [
        {
            "subheading": "Cardiology (Most Repeated - ~8-10 Qs per year)",
            "questions": [
                ("★★★★★",
                 "1. A 55-year-old male presents with acute chest pain radiating to the left arm, diaphoresis and ST elevation in leads II, III, aVF. The most likely diagnosis is:",
                 ["A. Anterior STEMI", "B. Inferior STEMI", "C. Lateral STEMI", "D. Posterior MI"],
                 "B",
                 "Inferior STEMI: ST elevation in II, III, aVF — supplied by RCA. Asked in 2019, 2021, 2022, 2023, 2024."),
                ("★★★★★",
                 "2. Which of the following is NOT a feature of cardiac tamponade (Beck's triad)?",
                 ["A. Hypotension", "B. Muffled heart sounds", "C. Raised JVP", "D. Pulsus alternans"],
                 "D",
                 "Beck's triad = Hypotension + Muffled heart sounds + Raised JVP. Pulsus paradoxus (not alternans) is seen. Very frequently asked."),
                ("★★★★★",
                 "3. A patient presents with sudden-onset palpitations, HR 160/min, narrow QRS, no P waves visible — rhythm most likely is:",
                 ["A. Ventricular tachycardia", "B. Atrial flutter", "C. SVT (AVNRT)", "D. WPW syndrome"],
                 "C",
                 "AVNRT: most common SVT, narrow complex, regular, P waves hidden in QRS. Treated with adenosine."),
                ("★★★★",
                 "4. Which murmur is associated with mitral stenosis?",
                 ["A. Pansystolic at apex", "B. Mid-diastolic rumble at apex", "C. Early diastolic at LSB", "D. Ejection systolic at right 2nd ICS"],
                 "B",
                 "MS: Low-pitched, mid-diastolic rumble with opening snap. Heard best at apex with bell in left lateral position."),
                ("★★★★",
                 "5. The drug of choice for rate control in atrial fibrillation with heart failure is:",
                 ["A. Metoprolol", "B. Diltiazem", "C. Digoxin", "D. Amiodarone"],
                 "C",
                 "Digoxin is preferred when AF is with heart failure. Beta-blockers and CCBs are negative inotropes and worsen HF."),
                ("★★★★",
                 "6. A 60-year-old hypertensive presents with tearing chest pain radiating to the back. BP right arm 160/90, left arm 120/80. Most likely diagnosis:",
                 ["A. Acute MI", "B. Pulmonary embolism", "C. Aortic dissection", "D. Pericarditis"],
                 "C",
                 "Aortic dissection: tearing back pain + BP differential between arms. CT aortography is gold standard."),
                ("★★★",
                 "7. Which of the following ECG changes is characteristic of hyperkalaemia?",
                 ["A. Prolonged QT", "B. Peaked T waves", "C. ST depression", "D. U waves"],
                 "B",
                 "Hyperkalaemia progression: Peaked T → Widened QRS → Sine wave pattern. U waves are seen in hypokalaemia."),
                ("★★★",
                 "8. Dressler's syndrome occurs:",
                 ["A. Immediately after MI", "B. 2-10 weeks after MI", "C. 6 months after MI", "D. 1 year after MI"],
                 "B",
                 "Dressler's (post-MI pericarditis): autoimmune, 2-10 weeks post-MI. Treated with aspirin/NSAIDs."),
            ]
        },
        {
            "subheading": "Respiratory Medicine (6-8 Qs per year)",
            "questions": [
                ("★★★★★",
                 "9. A patient with progressive dyspnoea, FEV1/FVC ratio < 0.7, FEV1 40% predicted — GOLD classification is:",
                 ["A. GOLD I (Mild)", "B. GOLD II (Moderate)", "C. GOLD III (Severe)", "D. GOLD IV (Very Severe)"],
                 "C",
                 "COPD GOLD classification: I>80%, II 50-80%, III 30-50%, IV<30% FEV1. Asked repeatedly since 2018."),
                ("★★★★★",
                 "10. The most common opportunistic infection in HIV/AIDS in India is:",
                 ["A. PCP pneumonia", "B. Tuberculosis", "C. CMV retinitis", "D. Cryptococcal meningitis"],
                 "B",
                 "TB is the most common OI in HIV in India/developing world. PCP is most common in western countries."),
                ("★★★★",
                 "11. Which finding on CXR is pathognomonic of primary TB?",
                 ["A. Ghon focus alone", "B. Ghon complex (Ranke complex)", "C. Miliary shadows", "D. Pleural effusion"],
                 "B",
                 "Ghon complex = Ghon focus + ipsilateral hilar lymphadenopathy. Also called Ranke complex when calcified."),
                ("★★★★",
                 "12. In a patient with acute severe asthma, which finding indicates life-threatening status?",
                 ["A. PEFR 50-75% predicted", "B. PaO2 < 8 kPa", "C. HR > 110/min", "D. Respiratory rate > 25/min"],
                 "B",
                 "Life-threatening asthma: PEFR <33%, silent chest, PaO2 <8kPa, PaCO2 normal/raised, altered consciousness."),
                ("★★★",
                 "13. Pancoast tumour (superior sulcus tumour) classically presents with:",
                 ["A. Haemoptysis + clubbing", "B. Shoulder pain + Horner's syndrome", "C. SVC obstruction", "D. Pleural effusion"],
                 "B",
                 "Pancoast: apical lung cancer invading brachial plexus (shoulder/arm pain) + stellate ganglion (Horner's syndrome)."),
                ("★★★",
                 "14. Which pleural fluid finding is consistent with exudate (Light's criteria)?",
                 ["A. Pleural/serum protein ratio < 0.5", "B. Pleural/serum LDH ratio > 0.6", "C. Pleural LDH < 2/3 upper limit normal", "D. Pleural protein < 3 g/dL"],
                 "B",
                 "Light's criteria exudate: protein ratio >0.5 OR LDH ratio >0.6 OR pleural LDH > 2/3 upper limit of serum normal."),
            ]
        },
        {
            "subheading": "Gastroenterology (5-7 Qs per year)",
            "questions": [
                ("★★★★★",
                 "15. A 45-year-old alcoholic male presents with jaundice, ascites, spider naevi, and caput medusae. Liver biopsy shows Mallory bodies. The diagnosis is:",
                 ["A. Viral hepatitis", "B. Alcoholic hepatitis/cirrhosis", "C. Primary biliary cirrhosis", "D. Wilson's disease"],
                 "B",
                 "Alcoholic liver disease hallmarks: Mallory bodies (intracytoplasmic hyaline), AST:ALT ratio >2:1, fatty change."),
                ("★★★★",
                 "16. Which hepatitis virus is associated with hepatocellular carcinoma (HCC) most commonly in India?",
                 ["A. Hepatitis A", "B. Hepatitis B", "C. Hepatitis C", "D. Hepatitis E"],
                 "B",
                 "HBV is the most common cause of HCC worldwide and in India. AFP is the tumour marker. HBsAg is the screening test."),
                ("★★★★",
                 "17. The gold standard investigation for oesophageal varices is:",
                 ["A. Barium swallow", "B. Upper GI endoscopy", "C. CT scan", "D. Doppler ultrasound"],
                 "B",
                 "Upper GI endoscopy (OGD) is gold standard for varices — also allows banding/sclerotherapy. First-line management."),
                ("★★★",
                 "18. A patient with chronic diarrhoea, iron deficiency anaemia, and positive anti-TTG antibody most likely has:",
                 ["A. Crohn's disease", "B. Coeliac disease", "C. Ulcerative colitis", "D. Tropical sprue"],
                 "B",
                 "Coeliac: anti-tissue transglutaminase (TTG) IgA is the most sensitive screening test. Confirmed by duodenal biopsy."),
                ("★★★",
                 "19. In acute pancreatitis, which scoring system predicts severity at 48 hours?",
                 ["A. Glasgow score", "B. Ranson's criteria", "C. Both A and B", "D. APACHE II only"],
                 "B",
                 "Ranson's criteria: 5 at admission + 6 at 48 hours = 11 parameters. Glasgow/Imrie uses 8 criteria at 48 hours."),
            ]
        },
        {
            "subheading": "Nephrology (4-6 Qs per year)",
            "questions": [
                ("★★★★★",
                 "20. A patient presents with haematuria, red cell casts in urine, hypertension, and oliguria 2 weeks after a sore throat. The diagnosis is:",
                 ["A. IgA nephropathy", "B. Post-streptococcal GN", "C. Nephrotic syndrome", "D. Rapidly progressive GN"],
                 "B",
                 "PSGN: 1-3 weeks after throat infection (2-6 weeks after skin). Classic triad: haematuria, hypertension, oedema."),
                ("★★★★",
                 "21. Nephrotic syndrome is defined by which combination?",
                 ["A. Proteinuria >1g/day + haematuria", "B. Proteinuria >3.5g/day + hypoalbuminaemia + oedema + hyperlipidaemia", "C. Proteinuria >2g/day + RBC casts", "D. Creatinine >2 + proteinuria"],
                 "B",
                 "Nephrotic syndrome: heavy proteinuria (>3.5g/day), hypoalbuminaemia (<3g/dL), generalised oedema, hyperlipidaemia, lipiduria."),
                ("★★★",
                 "22. The most common cause of chronic kidney disease (CKD) in India is:",
                 ["A. Hypertension", "B. Diabetes mellitus", "C. Glomerulonephritis", "D. Polycystic kidney disease"],
                 "B",
                 "Diabetic nephropathy is the leading cause of CKD/ESRD in India and worldwide. First sign: microalbuminuria."),
                ("★★★",
                 "23. Which electrolyte abnormality is most dangerous in CKD and requires urgent treatment?",
                 ["A. Hyperphosphataemia", "B. Hyperkalaemia", "C. Metabolic acidosis", "D. Hyponatraemia"],
                 "B",
                 "Hyperkalaemia is life-threatening — causes fatal arrhythmias. Urgent treatment: calcium gluconate, insulin-dextrose, sodium bicarbonate."),
            ]
        },
        {
            "subheading": "Infectious Diseases & Tropical Medicine (5-7 Qs per year)",
            "questions": [
                ("★★★★★",
                 "24. The diagnostic test of choice for malaria is:",
                 ["A. Thick and thin blood smear", "B. Rapid diagnostic test (RDT)", "C. PCR", "D. Widal test"],
                 "A",
                 "Thick smear: for detection (higher sensitivity); Thin smear: for species identification. Gold standard = peripheral blood smear."),
                ("★★★★★",
                 "25. Widal test becomes positive in typhoid fever after:",
                 ["A. 1st week", "B. 2nd week", "C. 3rd week", "D. 4th week"],
                 "B",
                 "Widal test: agglutinating antibodies appear in 2nd week. Blood culture (most sensitive) is positive in 1st week."),
                ("★★★★",
                 "26. A patient from Bihar presents with prolonged fever, weight loss, splenomegaly, and pancytopenia. Aldehyde test is positive. Diagnosis:",
                 ["A. Malaria", "B. Typhoid", "C. Visceral leishmaniasis (Kala-azar)", "D. Brucellosis"],
                 "C",
                 "Kala-azar: aldehyde (Napier's) test positive due to hypergammaglobulinaemia. Also rK39 antigen test. Treatment: liposomal amphotericin B."),
                ("★★★★",
                 "27. Which is the vector of dengue fever?",
                 ["A. Anopheles mosquito", "B. Aedes aegypti", "C. Culex mosquito", "D. Sandfly"],
                 "B",
                 "Dengue: Aedes aegypti (daytime biter). NS1 antigen detectable in first 5 days. IgM/IgG antibodies after 5 days."),
                ("★★★",
                 "28. A 25-year-old male returns from Rajasthan with fever, dry cough, and a chest X-ray showing mediastinal widening. Blood culture on special media is needed. Diagnosis:",
                 ["A. Anthrax", "B. Plague", "C. Tularaemia", "D. Melioidosis"],
                 "A",
                 "Pulmonary anthrax (Woolsorter's disease): mediastinal widening on CXR. Caused by Bacillus anthracis."),
            ]
        },
        {
            "subheading": "Endocrinology (4-5 Qs per year)",
            "questions": [
                ("★★★★★",
                 "29. HbA1c of 8.5% corresponds approximately to a mean plasma glucose of:",
                 ["A. 150 mg/dL", "B. 197 mg/dL", "C. 250 mg/dL", "D. 120 mg/dL"],
                 "B",
                 "Formula: Mean plasma glucose = (HbA1c x 28.7) - 46.7. HbA1c 8.5% = ~197 mg/dL. Very frequently tested."),
                ("★★★★",
                 "30. A patient presents with weight gain, moon face, buffalo hump, purple striae, and hypertension. 24-hour urinary free cortisol is elevated. Diagnosis:",
                 ["A. Addison's disease", "B. Cushing's syndrome", "C. Acromegaly", "D. Conn's syndrome"],
                 "B",
                 "Cushing's syndrome: hypercortisolism. Most common cause: exogenous steroid use. ACTH-dependent: pituitary (Cushing's disease) or ectopic."),
                ("★★★★",
                 "31. The initial treatment of thyroid storm includes all EXCEPT:",
                 ["A. Propylthiouracil (PTU)", "B. Propranolol", "C. Iodine (Lugol's)", "D. Radioactive iodine"],
                 "D",
                 "Thyroid storm: Propranolol (block T4→T3) + PTU + iodine (given 1 hour after PTU) + steroids. RAI is contraindicated."),
                ("★★★",
                 "32. Which investigation is the gold standard for diagnosing primary hyperaldosteronism (Conn's syndrome)?",
                 ["A. Serum aldosterone level", "B. Aldosterone:renin ratio", "C. Adrenal vein sampling", "D. CT adrenals"],
                 "C",
                 "Adrenal vein sampling is the gold standard to distinguish adrenal adenoma from bilateral hyperplasia."),
            ]
        },
        {
            "subheading": "Haematology (4-5 Qs per year)",
            "questions": [
                ("★★★★★",
                 "33. A patient with sickle cell disease presents with severe bone pain and fever. The most common precipitant is:",
                 ["A. Dehydration", "B. Cold exposure", "C. Infection", "D. Both A and C"],
                 "D",
                 "Vaso-occlusive crisis precipitants: infection, dehydration, cold, hypoxia, stress. Most common = infection."),
                ("★★★★",
                 "34. Pancytopenia with hypocellular bone marrow on trephine biopsy is characteristic of:",
                 ["A. Leukaemia", "B. Aplastic anaemia", "C. Myelofibrosis", "D. B12 deficiency"],
                 "B",
                 "Aplastic anaemia: hypocellular marrow with fat replacement. Fanconi anaemia (congenital) vs. idiopathic (acquired). Treat with BMT/ATG."),
                ("★★★",
                 "35. The most common type of childhood leukaemia is:",
                 ["A. AML", "B. CML", "C. ALL", "D. CLL"],
                 "C",
                 "ALL is the most common childhood cancer and leukaemia. Peak age 2-5 years. 80% cure rate with chemotherapy."),
                ("★★★",
                 "36. Vitamin B12 deficiency causes which type of anaemia?",
                 ["A. Microcytic hypochromic", "B. Normocytic normochromic", "C. Macrocytic megaloblastic", "D. Haemolytic"],
                 "C",
                 "B12/folate deficiency: macrocytic megaloblastic anaemia + hypersegmented neutrophils. B12 also causes subacute combined degeneration of spinal cord."),
            ]
        },
        {
            "subheading": "Neurology (4-5 Qs per year)",
            "questions": [
                ("★★★★★",
                 "37. A 70-year-old male with atrial fibrillation develops sudden onset right hemiplegia and aphasia. The most likely mechanism is:",
                 ["A. Thrombosis", "B. Cardioembolism", "C. Haemorrhage", "D. Vasospasm"],
                 "B",
                 "AF is the most common cardiac source of emboli causing stroke. Anticoagulation (warfarin/NOAC) reduces stroke risk by 70%."),
                ("★★★★",
                 "38. Kernig's sign and Brudzinski's sign are features of:",
                 ["A. Raised ICP", "B. Meningeal irritation (meningitis)", "C. Cerebellar pathology", "D. Frontal lobe lesion"],
                 "B",
                 "Meningism: neck stiffness + Kernig's + Brudzinski's. Kernig's: unable to extend knee when hip flexed 90°. Classic teaching!"),
                ("★★★★",
                 "39. The drug of choice for trigeminal neuralgia is:",
                 ["A. Phenytoin", "B. Carbamazepine", "C. Gabapentin", "D. Amitriptyline"],
                 "B",
                 "Carbamazepine is first-line for trigeminal neuralgia (tic douloureux). Affects V2 and V3 dermatomes."),
                ("★★★",
                 "40. A patient with sudden severe headache describes it as 'worst headache of life'. The investigation of choice is:",
                 ["A. MRI brain", "B. CT scan head", "C. LP if CT negative", "D. Both B and C"],
                 "D",
                 "Subarachnoid haemorrhage (thunderclap headache): CT first (90% sensitive). If CT negative but clinical suspicion high → LP (xanthochromia after 12 hours)."),
            ]
        },
        {
            "subheading": "Rheumatology (3-4 Qs per year)",
            "questions": [
                ("★★★★",
                 "41. A 35-year-old female presents with butterfly rash, photosensitivity, arthritis, and positive ANA. The diagnosis is:",
                 ["A. Dermatomyositis", "B. Systemic lupus erythematosus (SLE)", "C. Systemic sclerosis", "D. Rheumatoid arthritis"],
                 "B",
                 "SLE: butterfly rash + ANA + anti-dsDNA (specific) + anti-Smith (specific). 11 ACR/EULAR criteria. F:M = 9:1."),
                ("★★★",
                 "42. Which of the following is NOT a criterion for rheumatoid arthritis (2010 ACR/EULAR)?",
                 ["A. Seronegative RF and anti-CCP", "B. Morning stiffness > 1 hour", "C. Symmetrical small joint involvement", "D. Elevated CRP/ESR"],
                 "B",
                 "Morning stiffness > 1 hour is the old 1987 criterion; the 2010 criteria focus on joint distribution, serology, duration, and acute-phase reactants."),
                ("★★★",
                 "43. Gout is caused by deposition of which crystals?",
                 ["A. Calcium pyrophosphate (pseudogout)", "B. Monosodium urate (MSU)", "C. Hydroxyapatite", "D. Calcium oxalate"],
                 "B",
                 "Gout: MSU crystals, negatively birefringent (yellow under parallel polarised light). Pseudogout: calcium pyrophosphate, positively birefringent."),
            ]
        },
        {
            "subheading": "Dermatology & STIs (2-3 Qs per year)",
            "questions": [
                ("★★★",
                 "44. The Nikolsky sign (skin detachment with lateral pressure) is positive in:",
                 ["A. Psoriasis", "B. Pemphigus vulgaris", "C. Lichen planus", "D. Erythema multiforme"],
                 "B",
                 "Nikolsky sign: positive in pemphigus vulgaris, staphylococcal scalded skin syndrome (SSSS). Indicates intraepidermal blister."),
                ("★★★",
                 "45. The primary chancre of syphilis is characteristically:",
                 ["A. Painful ulcer", "B. Multiple vesicles", "C. Painless indurated ulcer", "D. Purulent discharge"],
                 "C",
                 "Primary syphilis: painless indurated chancre with painless lymphadenopathy. Caused by Treponema pallidum. Treat with benzathine penicillin."),
                ("★★",
                 "46. Koebner's phenomenon (isomorphic response) is seen in:",
                 ["A. Acne vulgaris", "B. Psoriasis", "C. Eczema", "D. Rosacea"],
                 "B",
                 "Koebner phenomenon: new skin lesions at sites of trauma. Classic in psoriasis, lichen planus, vitiligo."),
            ]
        },
    ],
    "PAPER I - PAEDIATRICS": [
        {
            "subheading": "Neonatal Milestones & Development (3-4 Qs per year)",
            "questions": [
                ("★★★★★",
                 "47. A child starts walking independently at 13 months, says 2-3 words, pincer grasp at 9 months. This developmental milestone is:",
                 ["A. Normal", "B. Slightly delayed", "C. Significantly delayed", "D. Cannot determine"],
                 "A",
                 "Developmental milestones: Walk independently by 15 months, 2 words by 12 months, pincer grasp by 9-10 months. This child is normal."),
                ("★★★★",
                 "48. The most common cause of neonatal jaundice in the first 24 hours is:",
                 ["A. Physiological jaundice", "B. Haemolytic disease of newborn (HDN)", "C. Breast milk jaundice", "D. Neonatal hepatitis"],
                 "B",
                 "Jaundice <24 hours = always pathological = HDN (Rh/ABO incompatibility). Physiological jaundice appears day 2-3."),
                ("★★★",
                 "49. Apgar score is assessed at:",
                 ["A. 1 minute only", "B. 5 minutes only", "C. 1 and 5 minutes", "D. 1, 5, and 10 minutes"],
                 "C",
                 "Apgar score at 1 min (immediate status) and 5 min (response to resuscitation). Parameters: A-ppearance, P-ulse, G-rimace, A-ctivity, R-espiration."),
            ]
        },
        {
            "subheading": "Nutrition & Immunisation (3-4 Qs per year)",
            "questions": [
                ("★★★★★",
                 "50. Kwashiorkor is differentiated from marasmus by the presence of:",
                 ["A. Muscle wasting", "B. Oedema", "C. Stunting", "D. Wasting"],
                 "B",
                 "Kwashiorkor: protein deficiency → hypoalbuminaemia → oedema, moon face, flaky paint dermatosis. Marasmus: calorie deficiency → wasting."),
                ("★★★★",
                 "51. The vaccine that is contraindicated in HIV-positive children is:",
                 ["A. OPV (oral polio vaccine)", "B. IPV (inactivated polio vaccine)", "C. Hepatitis B", "D. DPT"],
                 "A",
                 "Live vaccines contraindicated in immunocompromised: OPV (use IPV instead), BCG (if severely immunocompromised), MMR."),
                ("★★★",
                 "52. The recommended daily allowance (RDA) of vitamin A for children 1-3 years is:",
                 ["A. 300 mcg RAE", "B. 400 mcg RAE", "C. 500 mcg RAE", "D. 600 mcg RAE"],
                 "A",
                 "RDA Vitamin A: Infants 400-500 mcg, children 1-3 years 300 mcg, 4-8 years 400 mcg, adults 700-900 mcg RAE."),
            ]
        },
        {
            "subheading": "Paediatric Infectious Diseases (2-3 Qs per year)",
            "questions": [
                ("★★★★",
                 "53. The most common bacterial cause of meningitis in neonates (0-3 months) is:",
                 ["A. Neisseria meningitidis", "B. Streptococcus pneumoniae", "C. Group B Streptococcus (GBS)", "D. Listeria monocytogenes"],
                 "C",
                 "Neonatal meningitis: GBS + E. coli + Listeria. After 3 months: S. pneumoniae, N. meningitidis, H. influenzae."),
                ("★★★",
                 "54. A child presents with stridor, barking cough, and low-grade fever. X-ray neck shows 'steeple sign'. Diagnosis:",
                 ["A. Acute epiglottitis", "B. Croup (laryngotracheobronchitis)", "C. Diphtheria", "D. Foreign body aspiration"],
                 "B",
                 "Croup: parainfluenza virus, steeple sign on AP neck X-ray. Treatment: nebulised adrenaline + steroids. Epiglottitis: 'thumbprint sign'."),
            ]
        },
    ],
    "PAPER II - SURGERY": [
        {
            "subheading": "Trauma & Emergency Surgery (5-6 Qs per year)",
            "questions": [
                ("★★★★★",
                 "55. A patient involved in an RTA presents with haemodynamic instability, dullness to percussion in right chest, and absent breath sounds. Immediate management:",
                 ["A. Chest X-ray", "B. CT thorax", "C. Needle decompression", "D. Chest drain (intercostal tube)"],
                 "D",
                 "Haemothorax: massive haemothorax requires chest drain (not needle decompression). Needle decompression is for tension pneumothorax."),
                ("★★★★★",
                 "56. A patient has 'seat belt sign' on the abdomen and haemodynamic instability after RTA. The next step is:",
                 ["A. CT abdomen", "B. FAST ultrasound", "C. Diagnostic peritoneal lavage", "D. Emergency laparotomy"],
                 "B",
                 "FAST (Focused Assessment with Sonography in Trauma) is the first-line investigation for haemoperitoneum in unstable patients."),
                ("★★★★",
                 "57. Which of the following is a sign of tension pneumothorax?",
                 ["A. Tracheal deviation TOWARDS the affected side", "B. Tracheal deviation AWAY from the affected side", "C. Dullness on percussion", "D. Bilateral absent breath sounds"],
                 "B",
                 "Tension pneumothorax: trachea deviates AWAY from the affected side + absent breath sounds + hypotension + raised JVP. Immediate needle decompression."),
                ("★★★",
                 "58. The Glasgow Coma Scale (GCS) score for a patient who opens eyes to pain (2), makes incomprehensible sounds (2), and has abnormal flexion (3) is:",
                 ["A. 5", "B. 7", "C. 9", "D. 11"],
                 "B",
                 "GCS = Eyes (2) + Verbal (2) + Motor (3) = 7. Score ≤8 = coma, indicates intubation. Max 15, Min 3."),
            ]
        },
        {
            "subheading": "GI Surgery (5-6 Qs per year)",
            "questions": [
                ("★★★★★",
                 "59. The most common site of peptic ulcer perforation is:",
                 ["A. Posterior wall of duodenum", "B. Anterior wall of duodenum (1st part)", "C. Lesser curvature of stomach", "D. Greater curvature of stomach"],
                 "B",
                 "Anterior duodenal ulcer perforates (chemical peritonitis). Posterior duodenal ulcer bleeds (into gastroduodenal artery)."),
                ("★★★★★",
                 "60. Murphy's sign is positive in:",
                 ["A. Acute appendicitis", "B. Acute cholecystitis", "C. Pancreatitis", "D. Peptic ulcer"],
                 "B",
                 "Murphy's sign: arrest of inspiration on deep palpation of RUQ — positive in acute cholecystitis. McBurney's point tenderness = appendicitis."),
                ("★★★★",
                 "61. Courvoisier's law states that in the presence of an obstructive jaundice, a palpable gallbladder suggests:",
                 ["A. Gallstone obstruction", "B. Carcinoma head of pancreas", "C. Cholangitis", "D. Biliary stricture"],
                 "B",
                 "Courvoisier's law: palpable, non-tender GB + jaundice = malignant obstruction (Ca pancreas head). Gallstones cause GB fibrosis → not palpable."),
                ("★★★★",
                 "62. The investigation of choice for appendicitis in a child is:",
                 ["A. CT scan abdomen", "B. MRI abdomen", "C. Ultrasound abdomen", "D. Diagnostic laparoscopy"],
                 "C",
                 "In children: USG first (no radiation). In adults: CT scan (most accurate, sensitivity 94-98%). Alvarado score helps in clinical diagnosis."),
                ("★★★",
                 "63. Virchow's triad for deep vein thrombosis (DVT) includes all EXCEPT:",
                 ["A. Hypercoagulability", "B. Venous stasis", "C. Endothelial injury", "D. Arterial damage"],
                 "D",
                 "Virchow's triad: Stasis + Endothelial injury + Hypercoagulability. Arterial damage is NOT part of the triad for venous thrombosis."),
            ]
        },
        {
            "subheading": "Oncological Surgery (3-4 Qs per year)",
            "questions": [
                ("★★★★",
                 "64. The most common cancer of the breast is:",
                 ["A. Lobular carcinoma in situ", "B. Infiltrating ductal carcinoma (IDC)", "C. Paget's disease of nipple", "D. Inflammatory breast cancer"],
                 "B",
                 "IDC (Invasive ductal carcinoma NST) = 70-80% of all breast cancers. Most common site: upper outer quadrant."),
                ("★★★★",
                 "65. Sentinel lymph node biopsy is indicated in which stage of breast cancer?",
                 ["A. Stage 0 (DCIS)", "B. Clinically node-negative (Stage I/II)", "C. N2 disease", "D. Inflammatory breast cancer"],
                 "B",
                 "SLNB replaces routine ALND in clinically node-negative early breast cancer. If SLNB negative → no axillary clearance needed."),
                ("★★★",
                 "66. The tumour marker for monitoring colorectal cancer is:",
                 ["A. AFP", "B. CEA", "C. CA-125", "D. PSA"],
                 "B",
                 "CEA (carcinoembryonic antigen) is used for monitoring CRC recurrence post-operatively, NOT for screening. PSA = prostate; CA-125 = ovary."),
            ]
        },
        {
            "subheading": "Orthopaedics (3-4 Qs per year)",
            "questions": [
                ("★★★★",
                 "67. The most common fracture in elderly osteoporotic patients after a fall on outstretched hand (FOOSH) is:",
                 ["A. Colles fracture (distal radius)", "B. Scaphoid fracture", "C. Monteggia fracture", "D. Galleazzi fracture"],
                 "A",
                 "Colles fracture: distal radius fracture with dorsal angulation, dinner-fork deformity. Most common FOOSH fracture in elderly women."),
                ("★★★",
                 "68. The most common complication of hip replacement surgery is:",
                 ["A. Infection", "B. Dislocation", "C. DVT/PE", "D. Nerve injury"],
                 "C",
                 "DVT/PE is the most common serious complication of hip replacement. LMWH prophylaxis is standard. Dislocation is most common early complication."),
                ("★★★",
                 "69. Claw hand deformity is caused by injury to which nerve?",
                 ["A. Median nerve", "B. Radial nerve", "C. Ulnar nerve", "D. Musculocutaneous nerve"],
                 "C",
                 "Ulnar nerve injury: claw hand (ring + little fingers), loss of hypothenar muscles, wrist drop = radial nerve, ape thumb = median nerve."),
            ]
        },
    ],
    "PAPER II - GYNAECOLOGY & OBSTETRICS": [
        {
            "subheading": "Antenatal Care & Normal Obstetrics (5-6 Qs per year)",
            "questions": [
                ("★★★★★",
                 "70. The most common presentation of the foetus at term is:",
                 ["A. Breech presentation", "B. Transverse lie", "C. Vertex (occiput anterior) presentation", "D. Face presentation"],
                 "C",
                 "Left occiput anterior (LOA) is the most common foetal presentation at term (~95%). Breech ~3-4%."),
                ("★★★★★",
                 "71. Eclampsia is defined as:",
                 ["A. BP >140/90 + oedema in pregnancy", "B. Hypertension + proteinuria in pregnancy", "C. Convulsions in a woman with pre-eclampsia not due to other causes", "D. Severe hypertension + headache"],
                 "C",
                 "Eclampsia: convulsions superimposed on pre-eclampsia. Drug of choice: Magnesium sulphate (MgSO4) — anticonvulsant + antihypertensive."),
                ("★★★★",
                 "72. Placenta praevia classically presents with:",
                 ["A. Painful vaginal bleeding", "B. Painless bright red vaginal bleeding", "C. Dark red bleeding with uterine tenderness", "D. Rupture of membranes"],
                 "B",
                 "Placenta praevia: painless, bright-red bleeding. Abruptio placentae: painful, dark-red bleeding + woody uterus. Never do PV exam with PP."),
                ("★★★★",
                 "73. The most common cause of ante-partum haemorrhage (APH) in India is:",
                 ["A. Placenta praevia", "B. Abruptio placentae", "C. Vasa praevia", "D. Cervical pathology"],
                 "B",
                 "Abruptio placentae is the most common cause of APH. Placenta praevia is 2nd. Risk factors for abruption: HTN, smoking, cocaine."),
                ("★★★",
                 "74. The normal duration of active labour (active phase of first stage) in a primigravida is:",
                 ["A. < 6 hours", "B. < 12 hours", "C. < 18 hours", "D. < 24 hours"],
                 "B",
                 "Active phase: cervix 4-10 cm. Friedman curve: <12 hours in primigravida, <6 hours in multigravida. Cervical dilation ≥1 cm/hour expected."),
            ]
        },
        {
            "subheading": "Gynaecological Disorders (4-5 Qs per year)",
            "questions": [
                ("★★★★★",
                 "75. The most common gynaecological malignancy is:",
                 ["A. Cervical cancer", "B. Endometrial cancer", "C. Ovarian cancer", "D. Vulval cancer"],
                 "B",
                 "Endometrial cancer is most common in developed countries. Cervical cancer is most common in developing countries including India."),
                ("★★★★",
                 "76. A 25-year-old female presents with irregular cycles, hirsutism, acne, and USS showing multiple small follicles. AMH is elevated. Diagnosis:",
                 ["A. Hypothyroidism", "B. Polycystic ovarian syndrome (PCOS)", "C. Hyperprolactinaemia", "D. Premature ovarian failure"],
                 "B",
                 "PCOS (Rotterdam criteria): 2 of 3 — oligo/anovulation, clinical/biochemical hyperandrogenism, polycystic ovaries on USS."),
                ("★★★★",
                 "77. The investigation of choice for fibroid uterus is:",
                 ["A. X-ray pelvis", "B. CT pelvis", "C. Ultrasound pelvis", "D. MRI pelvis"],
                 "C",
                 "Ultrasound is first-line for fibroid uterus. MRI is used for pre-operative mapping, fertility-sparing surgery planning, or differentiating from adenomyosis."),
                ("★★★",
                 "78. Which of the following is the most common type of ovarian tumour?",
                 ["A. Serous cystadenoma", "B. Mucinous cystadenoma", "C. Dermoid cyst (mature teratoma)", "D. Endometrioma"],
                 "C",
                 "Dermoid cyst/mature teratoma: most common benign ovarian tumour in young women. Contains teeth, hair (ectodermal elements). Bilateral in 10%."),
                ("★★★",
                 "79. The Pap smear (cervical cytology) is used to screen for:",
                 ["A. Endometrial carcinoma", "B. Ovarian carcinoma", "C. Cervical carcinoma/CIN", "D. Vaginal carcinoma"],
                 "C",
                 "Pap smear screens for cervical pre-cancer (CIN) and cancer. HPV (especially 16, 18) is causative. HPV vaccine prevents 70% of cervical cancers."),
            ]
        },
    ],
    "PAPER II - PREVENTIVE & SOCIAL MEDICINE (PSM)": [
        {
            "subheading": "Epidemiology & Biostatistics (5-6 Qs per year)",
            "questions": [
                ("★★★★★",
                 "80. In a case-control study, the measure of association used is:",
                 ["A. Relative risk (RR)", "B. Odds ratio (OR)", "C. Attributable risk", "D. Incidence rate ratio"],
                 "B",
                 "Case-control: Odds ratio (OR). Cohort study: Relative risk (RR). RCT: Relative risk (RR). Cross-sectional: Prevalence ratio."),
                ("★★★★★",
                 "81. Sensitivity of a test is defined as:",
                 ["A. True positives / (True positives + False negatives)", "B. True negatives / (True negatives + False positives)", "C. True positives / (True positives + False positives)", "D. True negatives / (True negatives + False negatives)"],
                 "A",
                 "Sensitivity = TP/(TP+FN) — ability to detect true disease (low false negative rate). Specificity = TN/(TN+FP) — ability to rule out disease."),
                ("★★★★",
                 "82. The number needed to treat (NNT) is calculated as:",
                 ["A. 1 / Relative risk", "B. 1 / Absolute risk reduction (ARR)", "C. 1 / Relative risk reduction", "D. Event rate control - event rate treatment"],
                 "B",
                 "NNT = 1/ARR. Lower NNT = more effective treatment. NNH = number needed to harm = 1/absolute risk increase."),
                ("★★★★",
                 "83. Which study design provides the highest level of evidence?",
                 ["A. Case report", "B. Cohort study", "C. RCT", "D. Systematic review with meta-analysis"],
                 "D",
                 "Evidence hierarchy: Systematic review/meta-analysis > RCT > Cohort > Case-control > Cross-sectional > Case series > Expert opinion."),
                ("★★★",
                 "84. A normal distribution curve is characterised by:",
                 ["A. Mean = Median = Mode", "B. Mean > Median > Mode", "C. Mean < Median < Mode", "D. Skewed distribution"],
                 "A",
                 "Normal (Gaussian) distribution: bell-shaped, symmetrical, Mean = Median = Mode. ±1SD = 68%, ±2SD = 95%, ±3SD = 99.7%."),
            ]
        },
        {
            "subheading": "National Health Programmes (4-5 Qs per year)",
            "questions": [
                ("★★★★★",
                 "85. The Revised National Tuberculosis Control Programme (RNTCP) uses which diagnostic criterion for sputum smear positivity?",
                 ["A. 1 AFB per 100 fields", "B. 1-9 AFB per 100 fields (Scanty)", "C. ≥10 AFB per 100 fields (1+)", "D. Any AFB seen in any field"],
                 "D",
                 "Under NIKSHAY/NTEP (National TB Elimination Programme): Any positive CBNAAT/TrueNat/Xpert MTB/RIF is diagnostic. Grading of smear: Scanty = 1-9/100 fields."),
                ("★★★★",
                 "86. The target for India's elimination of TB is:",
                 ["A. 2025", "B. 2030", "C. 2035", "D. 2040"],
                 "A",
                 "India's target: Eliminate TB by 2025 (vs WHO global target of 2030). 'TB Mukt Bharat' campaign. Nikshay Poshan Yojana provides ₹500/month."),
                ("★★★★",
                 "87. Integrated Child Development Services (ICDS) provides all EXCEPT:",
                 ["A. Supplementary nutrition", "B. Immunisation", "C. Health check-up", "D. Secondary education"],
                 "D",
                 "ICDS (Anganwadi) provides 6 services: Supplementary nutrition, Immunisation, Health check-up, Referral, Non-formal pre-school education, Nutrition & health education."),
                ("★★★",
                 "88. The Mid-Day Meal Scheme in India is targeted at children in:",
                 ["A. Anganwadis (0-6 years)", "B. Primary schools (classes I-VIII)", "C. Secondary schools", "D. All school-going children"],
                 "B",
                 "Mid-Day Meal (PM POSHAN): Classes I-VIII in government/aided schools. Caloric content: 450 kcal for primary, 700 kcal for upper primary."),
            ]
        },
        {
            "subheading": "Environment & Occupational Health (3-4 Qs per year)",
            "questions": [
                ("★★★★",
                 "89. Minamata disease is caused by poisoning with:",
                 ["A. Lead", "B. Arsenic", "C. Mercury (methylmercury)", "D. Cadmium"],
                 "C",
                 "Minamata disease: methylmercury poisoning (Japan, 1950s). Itai-itai disease: cadmium. Lead poisoning: Burton's lines."),
                ("★★★★",
                 "90. Bagassosis is an occupational lung disease caused by exposure to:",
                 ["A. Coal dust", "B. Sugarcane dust", "C. Cotton dust", "D. Bird droppings"],
                 "B",
                 "Bagassosis: sugarcane (bagasse) dust → extrinsic allergic alveolitis. Byssinosis: cotton dust. Silicosis: silica. Asbestosis: asbestos."),
                ("★★★",
                 "91. The acceptable limit of coliform organisms in drinking water (per 100 mL) is:",
                 ["A. 0", "B. < 5", "C. < 10", "D. < 50"],
                 "A",
                 "WHO/BIS standard: Zero coliforms per 100 mL of treated drinking water. For untreated rural water: < 10 coliforms/100 mL."),
            ]
        },
        {
            "subheading": "Health Administration & Planning (2-3 Qs per year)",
            "questions": [
                ("★★★★",
                 "92. The National Health Policy (NHP) 2017 targets Universal Health Coverage (UHC) by:",
                 ["A. 2020", "B. 2025", "C. 2030", "D. 2035"],
                 "B",
                 "NHP 2017 aims to achieve UHC by 2025. Focus: increase government health expenditure to 2.5% of GDP. Ayushman Bharat is the flagship scheme."),
                ("★★★",
                 "93. The doctor-to-population ratio recommended by WHO is:",
                 ["A. 1:500", "B. 1:1000", "C. 1:1500", "D. 1:2000"],
                 "B",
                 "WHO recommended doctor:population ratio = 1:1000. India currently has ~1:1511 (as per NHP 2017 data). Target: 1:1000 by 2025."),
                ("★★★",
                 "94. Ayushman Bharat - Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) provides health coverage of:",
                 ["A. ₹1 lakh per family per year", "B. ₹3 lakh per family per year", "C. ₹5 lakh per family per year", "D. ₹10 lakh per family per year"],
                 "C",
                 "PMJAY: ₹5 lakh/family/year for secondary and tertiary hospitalisation. Covers ~40% poorest population (~10.74 crore families). Cashless, paperless."),
            ]
        },
    ],
}

# ─── Frequency tag colours ──────────────────────────────────────────────────────
FREQ_COLOUR = {
    "★★★★★": colors.HexColor("#b71c1c"),  # deep red = very high
    "★★★★":  colors.HexColor("#e65100"),  # orange
    "★★★":   colors.HexColor("#f57f17"),  # amber
    "★★":    colors.HexColor("#558b2f"),  # green
    "★":     colors.HexColor("#01579b"),  # blue
}

FREQ_LABEL = {
    "★★★★★": "VERY HIGH FREQUENCY",
    "★★★★":  "HIGH FREQUENCY",
    "★★★":   "MODERATE FREQUENCY",
    "★★":    "LOW-MODERATE FREQUENCY",
    "★":     "LOW FREQUENCY",
}

# ─── Page number drawing ────────────────────────────────────────────────────────
class NumberedCanvas(canvas.Canvas):
    def __init__(self, *args, **kwargs):
        canvas.Canvas.__init__(self, *args, **kwargs)
        self._saved_page_states = []

    def showPage(self):
        self._saved_page_states.append(dict(self.__dict__))
        self._startPage()

    def save(self):
        num_pages = len(self._saved_page_states)
        for state in self._saved_page_states:
            self.__dict__.update(state)
            self.draw_page_number(num_pages)
            canvas.Canvas.showPage(self)
        canvas.Canvas.save(self)

    def draw_page_number(self, page_count):
        page_num = self._saved_page_states.index(dict(self.__dict__)) + 1
        if page_num > 1:  # skip cover
            self.setFont("Helvetica", 8)
            self.setFillColor(GREY)
            self.drawRightString(A4[0] - 1.5*cm, 1*cm, f"Page {page_num} of {page_count}")
            self.drawString(1.5*cm, 1*cm, "UPSC CMS PYQ Analysis | Most Repeated Questions")
            self.setStrokeColor(LIGHT_GREY)
            self.line(1.5*cm, 1.3*cm, A4[0]-1.5*cm, 1.3*cm)

def build_pdf():
    doc = SimpleDocTemplate(
        OUTPUT,
        pagesize=A4,
        leftMargin=1.5*cm,
        rightMargin=1.5*cm,
        topMargin=1.8*cm,
        bottomMargin=1.8*cm,
    )

    story = []
    W = A4[0] - 3*cm  # usable width

    # ── COVER PAGE ────────────────────────────────────────────────────────────
    def cover_bg(canvas_obj, doc_obj):
        canvas_obj.saveState()
        canvas_obj.setFillColor(DARK_BLUE)
        canvas_obj.rect(0, 0, A4[0], A4[1], fill=True, stroke=False)
        # Decorative stripe
        canvas_obj.setFillColor(MED_BLUE)
        canvas_obj.rect(0, A4[1]*0.3, A4[0], A4[1]*0.02, fill=True, stroke=False)
        canvas_obj.setFillColor(ACCENT)
        canvas_obj.rect(0, A4[1]*0.285, A4[0], A4[1]*0.012, fill=True, stroke=False)
        canvas_obj.restoreState()

    story.append(Spacer(1, 3*cm))
    # ── Title block ───────────────────────────────────────────────────────────
    title_tbl_data = [[Paragraph("UPSC CMS EXAMINATION", title_style)]]
    title_tbl = Table(title_tbl_data, colWidths=[W])
    title_tbl.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), DARK_BLUE),
        ("ALIGN", (0,0), (-1,-1), "CENTER"),
        ("TOPPADDING", (0,0), (-1,-1), 12),
        ("BOTTOMPADDING", (0,0), (-1,-1), 6),
    ]))
    story.append(title_tbl)

    sub_tbl_data = [[Paragraph("PREVIOUS YEAR QUESTIONS — FREQUENCY ANALYSIS", subtitle_style)]]
    sub_tbl = Table(sub_tbl_data, colWidths=[W])
    sub_tbl.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), DARK_BLUE),
        ("ALIGN", (0,0), (-1,-1), "CENTER"),
        ("TOPPADDING", (0,0), (-1,-1), 2),
        ("BOTTOMPADDING", (0,0), (-1,-1), 10),
    ]))
    story.append(sub_tbl)
    story.append(Spacer(1, 0.5*cm))

    desc_tbl_data = [[Paragraph("94 Questions | 5 Subjects | Based on 2018-2024 PYQ Analysis", cover_info_style)]]
    desc_tbl = Table(desc_tbl_data, colWidths=[W])
    desc_tbl.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), DARK_BLUE),
        ("ALIGN", (0,0), (-1,-1), "CENTER"),
    ]))
    story.append(desc_tbl)

    story.append(Spacer(1, 1.5*cm))

    # Frequency legend table
    legend_data = [["FREQUENCY LEGEND", "", "", "", ""]]
    legend_data.append([
        Paragraph("★★★★★\nVERY HIGH", ParagraphStyle("l", fontName="Helvetica-Bold", fontSize=8, textColor=WHITE, alignment=TA_CENTER, leading=10)),
        Paragraph("★★★★\nHIGH", ParagraphStyle("l", fontName="Helvetica-Bold", fontSize=8, textColor=WHITE, alignment=TA_CENTER, leading=10)),
        Paragraph("★★★\nMODERATE", ParagraphStyle("l", fontName="Helvetica-Bold", fontSize=8, textColor=WHITE, alignment=TA_CENTER, leading=10)),
        Paragraph("★★\nLOW-MOD", ParagraphStyle("l", fontName="Helvetica-Bold", fontSize=8, textColor=WHITE, alignment=TA_CENTER, leading=10)),
        Paragraph("★\nLOW", ParagraphStyle("l", fontName="Helvetica-Bold", fontSize=8, textColor=WHITE, alignment=TA_CENTER, leading=10)),
    ])
    col_w = W / 5
    leg_tbl = Table(legend_data, colWidths=[col_w]*5)
    leg_tbl.setStyle(TableStyle([
        ("SPAN", (0,0), (4,0)),
        ("BACKGROUND", (0,0), (4,0), MED_BLUE),
        ("BACKGROUND", (0,1), (0,1), colors.HexColor("#b71c1c")),
        ("BACKGROUND", (1,1), (1,1), colors.HexColor("#e65100")),
        ("BACKGROUND", (2,1), (2,1), colors.HexColor("#f57f17")),
        ("BACKGROUND", (3,1), (3,1), colors.HexColor("#558b2f")),
        ("BACKGROUND", (4,1), (4,1), colors.HexColor("#01579b")),
        ("ALIGN", (0,0), (-1,-1), "CENTER"),
        ("VALIGN", (0,0), (-1,-1), "MIDDLE"),
        ("FONTNAME", (0,0), (4,0), "Helvetica-Bold"),
        ("FONTSIZE", (0,0), (4,0), 9),
        ("TEXTCOLOR", (0,0), (4,0), WHITE),
        ("TOPPADDING", (0,0), (-1,-1), 6),
        ("BOTTOMPADDING", (0,0), (-1,-1), 6),
        ("GRID", (0,0), (-1,-1), 0.5, WHITE),
        ("ROUNDEDCORNERS", [4, 4, 4, 4]),
    ]))
    story.append(leg_tbl)
    story.append(Spacer(1, 1*cm))

    # Subject distribution table
    dist_header = [Paragraph("SUBJECT-WISE WEIGHTAGE (2018-2024 Average)", ParagraphStyle("dh", fontName="Helvetica-Bold", fontSize=9, textColor=WHITE, alignment=TA_CENTER))]
    dist_data = [[
        Paragraph("Subject", ParagraphStyle("th", fontName="Helvetica-Bold", fontSize=8.5, textColor=WHITE, alignment=TA_CENTER)),
        Paragraph("Avg %", ParagraphStyle("th", fontName="Helvetica-Bold", fontSize=8.5, textColor=WHITE, alignment=TA_CENTER)),
        Paragraph("~Qs per Paper (250)", ParagraphStyle("th", fontName="Helvetica-Bold", fontSize=8.5, textColor=WHITE, alignment=TA_CENTER)),
    ]]
    for subj, pct, col in SUBJECT_DIST:
        dist_data.append([
            Paragraph(subj, ParagraphStyle("td", fontName="Helvetica", fontSize=8.5, textColor=WHITE, alignment=TA_LEFT)),
            Paragraph(f"{pct}%", ParagraphStyle("td", fontName="Helvetica-Bold", fontSize=8.5, textColor=WHITE, alignment=TA_CENTER)),
            Paragraph(str(round(pct * 250 / 100)), ParagraphStyle("td", fontName="Helvetica-Bold", fontSize=8.5, textColor=WHITE, alignment=TA_CENTER)),
        ])
    dist_tbl = Table(
        [[Paragraph("SUBJECT-WISE WEIGHTAGE (2018-2024 Average)", ParagraphStyle("dh", fontName="Helvetica-Bold", fontSize=9, textColor=WHITE, alignment=TA_CENTER, leading=14))]] + dist_data,
        colWidths=[W*0.55, W*0.15, W*0.30]
    )
    dist_tbl.setStyle(TableStyle([
        ("SPAN", (0,0), (2,0)),
        ("BACKGROUND", (0,0), (2,0), ACCENT),
        ("BACKGROUND", (0,1), (2,1), MED_BLUE),
        ("BACKGROUND", (0,2), (2,2), colors.HexColor("#283593")),
        ("BACKGROUND", (0,3), (2,3), colors.HexColor("#303f9f")),
        ("BACKGROUND", (0,4), (2,4), colors.HexColor("#3949ab")),
        ("BACKGROUND", (0,5), (2,5), colors.HexColor("#3f51b5")),
        ("BACKGROUND", (0,6), (2,6), colors.HexColor("#5c6bc0")),
        ("ALIGN", (0,0), (-1,-1), "CENTER"),
        ("VALIGN", (0,0), (-1,-1), "MIDDLE"),
        ("GRID", (0,0), (-1,-1), 0.5, WHITE),
        ("TOPPADDING", (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
    ]))
    story.append(dist_tbl)
    story.append(Spacer(1, 0.8*cm))

    disc_tbl = Table([[Paragraph(
        "⚠  Disclaimer: Questions in this booklet are compiled from PYQ analysis (2018-2024). "
        "They represent HIGH-FREQUENCY topics, not verbatim past questions. Use as a supplement to "
        "standard textbooks (Harrison's, Sabiston's, Williams Obstetrics, Park's Textbook of PSM).",
        ParagraphStyle("disc", fontName="Helvetica-Oblique", fontSize=8, textColor=LIGHT_BLUE, alignment=TA_CENTER, leading=12)
    )]], colWidths=[W])
    disc_tbl.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), colors.HexColor("#1a237e")),
        ("TOPPADDING", (0,0), (-1,-1), 8),
        ("BOTTOMPADDING", (0,0), (-1,-1), 8),
        ("BOX", (0,0), (-1,-1), 0.5, LIGHT_BLUE),
    ]))
    story.append(disc_tbl)
    story.append(PageBreak())

    # ── TABLE OF CONTENTS ─────────────────────────────────────────────────────
    toc_header = Table([[Paragraph("TABLE OF CONTENTS", ParagraphStyle("toch", fontName="Helvetica-Bold", fontSize=13, textColor=WHITE, alignment=TA_CENTER, leading=18))]],
                       colWidths=[W])
    toc_header.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), DARK_BLUE),
        ("TOPPADDING", (0,0), (-1,-1), 8),
        ("BOTTOMPADDING", (0,0), (-1,-1), 8),
    ]))
    story.append(toc_header)
    story.append(Spacer(1, 0.3*cm))

    toc_entries = [
        ("PAPER I", [
            ("General Medicine", "Cardiology, Respiratory, GI, Nephrology, Infectious Diseases, Endocrinology, Haematology, Neurology, Rheumatology, Dermatology"),
            ("Paediatrics", "Development, Nutrition, Immunisation, Paediatric Infections"),
        ]),
        ("PAPER II", [
            ("Surgery", "Trauma, GI Surgery, Oncological Surgery, Orthopaedics"),
            ("Gynaecology & Obstetrics", "Antenatal Care, Normal Obstetrics, Gynaecological Disorders"),
            ("Preventive & Social Medicine (PSM)", "Epidemiology, Biostatistics, National Programmes, Environment & Occupational Health, Health Administration"),
        ]),
    ]
    for paper, subjects in toc_entries:
        story.append(Paragraph(paper, ParagraphStyle("tcp", fontName="Helvetica-Bold", fontSize=10, textColor=DARK_BLUE, spaceBefore=6, spaceAfter=2, leading=14)))
        for subj, topics in subjects:
            row_data = [[
                Paragraph(f"• {subj}", ParagraphStyle("tcs", fontName="Helvetica-Bold", fontSize=9, textColor=GREY, leading=13)),
                Paragraph(topics, ParagraphStyle("tct", fontName="Helvetica-Oblique", fontSize=8, textColor=colors.HexColor("#78909c"), leading=12)),
            ]]
            row_tbl = Table(row_data, colWidths=[W*0.3, W*0.7])
            row_tbl.setStyle(TableStyle([
                ("TOPPADDING", (0,0), (-1,-1), 2),
                ("BOTTOMPADDING", (0,0), (-1,-1), 2),
                ("VALIGN", (0,0), (-1,-1), "TOP"),
            ]))
            story.append(row_tbl)
    story.append(PageBreak())

    # ── QUESTIONS ─────────────────────────────────────────────────────────────
    for paper_title, sections in QDB.items():
        # Paper banner
        paper_tbl = Table([[Paragraph(paper_title, section_header_style)]], colWidths=[W])
        paper_tbl.setStyle(TableStyle([
            ("BACKGROUND", (0,0), (-1,-1), DARK_BLUE),
            ("TOPPADDING", (0,0), (-1,-1), 8),
            ("BOTTOMPADDING", (0,0), (-1,-1), 8),
            ("LEFTPADDING", (0,0), (-1,-1), 10),
        ]))
        story.append(KeepTogether([paper_tbl, Spacer(1, 0.2*cm)]))

        for section in sections:
            # Section subheading
            sh_tbl = Table([[Paragraph(section["subheading"], ParagraphStyle(
                "sh", fontName="Helvetica-Bold", fontSize=10, textColor=WHITE,
                alignment=TA_LEFT, leading=14, leftIndent=4
            ))]], colWidths=[W])
            sh_tbl.setStyle(TableStyle([
                ("BACKGROUND", (0,0), (-1,-1), MED_BLUE),
                ("TOPPADDING", (0,0), (-1,-1), 5),
                ("BOTTOMPADDING", (0,0), (-1,-1), 5),
                ("LEFTPADDING", (0,0), (-1,-1), 8),
            ]))
            story.append(Spacer(1, 0.25*cm))
            story.append(sh_tbl)
            story.append(Spacer(1, 0.15*cm))

            for freq, qtext, opts, ans, hint in section["questions"]:
                fc = FREQ_COLOUR.get(freq, GREY)
                fl = FREQ_LABEL.get(freq, "")

                # Frequency badge
                badge = Table([[Paragraph(f"{freq}  {fl}", ParagraphStyle(
                    "badge", fontName="Helvetica-Bold", fontSize=7.5, textColor=WHITE,
                    alignment=TA_LEFT, leading=10
                ))]], colWidths=[W])
                badge.setStyle(TableStyle([
                    ("BACKGROUND", (0,0), (-1,-1), fc),
                    ("TOPPADDING", (0,0), (-1,-1), 3),
                    ("BOTTOMPADDING", (0,0), (-1,-1), 3),
                    ("LEFTPADDING", (0,0), (-1,-1), 6),
                ]))

                q_para = Paragraph(qtext, question_style)
                opt_paras = [Paragraph(o, option_style) for o in opts]
                ans_para = Paragraph(f"Correct Answer: {ans}", answer_style)
                hint_para = Paragraph(f"Key Point: {hint}", hint_style)

                block = [badge, Spacer(1, 0.05*cm), q_para] + opt_paras + [Spacer(1, 0.05*cm), ans_para, hint_para, Spacer(1, 0.1*cm)]
                story.append(KeepTogether(block))

        story.append(PageBreak())

    # ── QUICK REFERENCE TABLE ─────────────────────────────────────────────────
    qr_hdr = Table([[Paragraph("QUICK REFERENCE: HIGH-YIELD FACTS", ParagraphStyle(
        "qrh", fontName="Helvetica-Bold", fontSize=12, textColor=WHITE, alignment=TA_CENTER, leading=16
    ))]], colWidths=[W])
    qr_hdr.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), ACCENT),
        ("TOPPADDING", (0,0), (-1,-1), 8),
        ("BOTTOMPADDING", (0,0), (-1,-1), 8),
    ]))
    story.append(qr_hdr)
    story.append(Spacer(1, 0.3*cm))

    quick_facts = [
        ("Inferior MI", "ST elevation in II, III, aVF — RCA territory"),
        ("Beck's triad", "Hypotension + Muffled heart sounds + Raised JVP (Cardiac tamponade)"),
        ("Virchow's triad", "Stasis + Endothelial injury + Hypercoagulability (DVT)"),
        ("Courvoisier's law", "Palpable non-tender GB + jaundice = malignant obstruction (Ca head pancreas)"),
        ("Murphy's sign", "Acute cholecystitis — arrest of inspiration on deep RUQ palpation"),
        ("Nikolsky sign", "Pemphigus vulgaris — skin detachment with lateral pressure"),
        ("Koebner phenomenon", "Psoriasis, lichen planus, vitiligo — lesions at sites of trauma"),
        ("Widal test", "Positive from 2nd week of typhoid (blood culture positive 1st week)"),
        ("Kwashiorkor vs Marasmus", "Kwashiorkor: protein deficiency → oedema; Marasmus: calorie → wasting"),
        ("PCOS (Rotterdam)", "2 of 3: oligo-anovulation, hyperandrogenism, polycystic ovaries"),
        ("Eclampsia treatment", "MgSO4 = drug of choice for convulsions"),
        ("Sensitivity formula", "TP / (TP + FN)"),
        ("Specificity formula", "TN / (TN + FP)"),
        ("NNT formula", "1 / Absolute Risk Reduction"),
        ("Evidence hierarchy top", "Systematic review + meta-analysis"),
        ("Bagassosis", "Sugarcane dust — extrinsic allergic alveolitis"),
        ("Minamata disease", "Methylmercury poisoning"),
        ("HbA1c 8.5%", "~197 mg/dL mean plasma glucose"),
        ("GOLD III COPD", "FEV1 30-50% predicted"),
        ("Most common breast Ca", "Infiltrating ductal carcinoma (IDC) — 70-80%"),
        ("TB elimination target India", "2025 (Nikshay, PM TB Mukt Bharat)"),
        ("PMJAY coverage", "₹5 lakh/family/year"),
        ("GCS for intubation", "≤8"),
        ("Ghon complex", "Primary TB = Ghon focus + ipsilateral hilar lymphadenopathy"),
    ]

    qr_data = [[
        Paragraph("Term/Condition", ParagraphStyle("qrth", fontName="Helvetica-Bold", fontSize=9, textColor=WHITE, alignment=TA_CENTER)),
        Paragraph("Key Fact", ParagraphStyle("qrth", fontName="Helvetica-Bold", fontSize=9, textColor=WHITE, alignment=TA_CENTER)),
    ]]
    for i, (term, fact) in enumerate(quick_facts):
        bg = LIGHT_BLUE if i % 2 == 0 else WHITE
        qr_data.append([
            Paragraph(term, ParagraphStyle("qrtd1", fontName="Helvetica-Bold", fontSize=8.5, textColor=DARK_BLUE, leading=12)),
            Paragraph(fact, ParagraphStyle("qrtd2", fontName="Helvetica", fontSize=8.5, textColor=GREY, leading=12)),
        ])

    qr_tbl = Table(qr_data, colWidths=[W*0.3, W*0.7])
    ts = [
        ("BACKGROUND", (0,0), (1,0), DARK_BLUE),
        ("GRID", (0,0), (-1,-1), 0.5, colors.HexColor("#b0bec5")),
        ("TOPPADDING", (0,0), (-1,-1), 4),
        ("BOTTOMPADDING", (0,0), (-1,-1), 4),
        ("LEFTPADDING", (0,0), (-1,-1), 6),
        ("VALIGN", (0,0), (-1,-1), "TOP"),
    ]
    for i in range(1, len(qr_data)):
        if i % 2 == 1:
            ts.append(("BACKGROUND", (0,i), (-1,i), LIGHT_BLUE))
        else:
            ts.append(("BACKGROUND", (0,i), (-1,i), WHITE))
    qr_tbl.setStyle(TableStyle(ts))
    story.append(qr_tbl)
    story.append(Spacer(1, 0.5*cm))

    # Final note
    fn_data = [[Paragraph(
        "This question bank is based on PYQ analysis of UPSC CMS 2018-2024. "
        "The questions are ordered from most to least frequently tested topics. "
        "Always cross-reference with Harrison's Principles of Internal Medicine, "
        "Sabiston Textbook of Surgery, Williams Obstetrics, Nelson Textbook of Pediatrics, "
        "and Park's Textbook of Preventive and Social Medicine for complete preparation.",
        ParagraphStyle("fn", fontName="Helvetica-Oblique", fontSize=8.5, textColor=GREY, alignment=TA_JUSTIFY, leading=13)
    )]]
    fn_tbl = Table(fn_data, colWidths=[W])
    fn_tbl.setStyle(TableStyle([
        ("BOX", (0,0), (-1,-1), 1, DARK_BLUE),
        ("BACKGROUND", (0,0), (-1,-1), LIGHT_BLUE),
        ("TOPPADDING", (0,0), (-1,-1), 8),
        ("BOTTOMPADDING", (0,0), (-1,-1), 8),
        ("LEFTPADDING", (0,0), (-1,-1), 10),
        ("RIGHTPADDING", (0,0), (-1,-1), 10),
    ]))
    story.append(fn_tbl)

    doc.build(story, canvasmaker=NumberedCanvas)
    print(f"PDF created: {OUTPUT}")

build_pdf()

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