NEET PG / INI-CET - Subject-Wise High Yield Topics

from reportlab.lib.pagesizes import A4
from reportlab.lib import colors
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import mm, cm
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, PageBreak, KeepTogether
)
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_RIGHT
from reportlab.platypus import BaseDocTemplate, Frame, PageTemplate
from reportlab.pdfbase import pdfmetrics
from reportlab.pdfbase.ttfonts import TTFont
import os

OUTPUT = "/home/daytona/workspace/neet-pg-pdf/NEET_PG_INICET_HighYield_Topics_2026.pdf"

# ── colours ──────────────────────────────────────────────────────────────────
RED_DARK   = colors.HexColor("#C0392B")
RED_LIGHT  = colors.HexColor("#FADBD8")
ORANGE     = colors.HexColor("#E67E22")
ORANGE_LIGHT = colors.HexColor("#FDEBD0")
YELLOW     = colors.HexColor("#D4AC0D")
YELLOW_LIGHT = colors.HexColor("#FEF9E7")
GREEN      = colors.HexColor("#1E8449")
GREEN_LIGHT= colors.HexColor("#D5F5E3")
BLUE_DARK  = colors.HexColor("#154360")
BLUE_MID   = colors.HexColor("#1A5276")
BLUE_LIGHT = colors.HexColor("#D6EAF8")
GREY_LIGHT = colors.HexColor("#F2F3F4")
GREY_MID   = colors.HexColor("#BDC3C7")
WHITE      = colors.white
BLACK      = colors.HexColor("#1C2833")

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

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

cover_title = S("CoverTitle", fontSize=28, leading=34, alignment=TA_CENTER,
                textColor=WHITE, fontName="Helvetica-Bold", spaceAfter=8)
cover_sub   = S("CoverSub",   fontSize=14, leading=20, alignment=TA_CENTER,
                textColor=colors.HexColor("#AED6F1"), fontName="Helvetica", spaceAfter=4)
cover_note  = S("CoverNote",  fontSize=10, leading=14, alignment=TA_CENTER,
                textColor=colors.HexColor("#D6EAF8"), fontName="Helvetica-Oblique")

sec_head    = S("SecHead",   fontSize=13, leading=17, textColor=WHITE,
                fontName="Helvetica-Bold", leftIndent=6, spaceAfter=0, spaceBefore=0)
sub_head    = S("SubHead",   fontSize=11, leading=14, textColor=BLUE_DARK,
                fontName="Helvetica-Bold", spaceBefore=8, spaceAfter=3)
body        = S("Body",      fontSize=9,  leading=13, textColor=BLACK,
                fontName="Helvetica", leftIndent=10, spaceAfter=1)
bullet_item = S("Bullet",    fontSize=9,  leading=13, textColor=BLACK,
                fontName="Helvetica", leftIndent=18, bulletIndent=10,
                spaceAfter=1, bulletText="\u2022")
tip_style   = S("Tip",       fontSize=9,  leading=13, textColor=GREEN,
                fontName="Helvetica-Bold", leftIndent=10)
badge_style = S("Badge",     fontSize=8,  leading=12, textColor=WHITE,
                fontName="Helvetica-Bold", alignment=TA_CENTER)
footer_sty  = S("Footer",    fontSize=7,  leading=9,  textColor=GREY_MID,
                fontName="Helvetica", alignment=TA_CENTER)
toc_head    = S("TOCHead",   fontSize=11, leading=15, textColor=BLUE_DARK,
                fontName="Helvetica-Bold", spaceBefore=4, spaceAfter=2)
toc_item    = S("TOCItem",   fontSize=9,  leading=13, textColor=BLACK,
                fontName="Helvetica", leftIndent=12)
page_title  = S("PageTitle", fontSize=16, leading=20, textColor=WHITE,
                fontName="Helvetica-Bold", alignment=TA_CENTER)

# ── helpers ──────────────────────────────────────────────────────────────────
def header_bar(text, bg=BLUE_MID, style=None):
    """Coloured header band."""
    st = style or sec_head
    tbl = Table([[Paragraph(text, st)]], colWidths=[170*mm])
    tbl.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), bg),
        ("LEFTPADDING",  (0,0), (-1,-1), 8),
        ("RIGHTPADDING", (0,0), (-1,-1), 8),
        ("TOPPADDING",   (0,0), (-1,-1), 6),
        ("BOTTOMPADDING",(0,0), (-1,-1), 6),
        ("ROUNDEDCORNERS", [4]),
    ]))
    return tbl

def priority_badge(label, color):
    data = [[Paragraph(label, badge_style)]]
    t = Table(data, colWidths=[30*mm])
    t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0),(-1,-1), color),
        ("TOPPADDING",   (0,0),(-1,-1), 3),
        ("BOTTOMPADDING",(0,0),(-1,-1), 3),
        ("LEFTPADDING",  (0,0),(-1,-1), 4),
        ("RIGHTPADDING", (0,0),(-1,-1), 4),
    ]))
    return t

def two_col_bullets(items, col_width=82*mm):
    """Split a list of strings into a 2-column bullet table."""
    rows = []
    for i in range(0, len(items), 2):
        left  = Paragraph("• " + items[i],   body)
        right = Paragraph("• " + items[i+1], body) if i+1 < len(items) else Paragraph("", body)
        rows.append([left, right])
    t = Table(rows, colWidths=[col_width, col_width])
    t.setStyle(TableStyle([
        ("VALIGN",       (0,0),(-1,-1), "TOP"),
        ("LEFTPADDING",  (0,0),(-1,-1), 4),
        ("RIGHTPADDING", (0,0),(-1,-1), 4),
        ("TOPPADDING",   (0,0),(-1,-1), 2),
        ("BOTTOMPADDING",(0,0),(-1,-1), 2),
    ]))
    return t

def subject_block(priority_color, priority_label, subject_name, weightage,
                  topics_by_section, bg_light):
    """Build one subject section."""
    elems = []
    # Title row
    title_text = f"  {subject_name}  |  Weightage: {weightage}"
    badge = priority_badge(f"  {priority_label}  ", priority_color)
    title_para = Paragraph(f"<b>{subject_name}</b>   <font size=9 color='#AED6F1'>Weightage: {weightage}</font>", sec_head)
    header = Table([[title_para, badge]],
                   colWidths=[140*mm, 32*mm])
    header.setStyle(TableStyle([
        ("BACKGROUND",   (0,0),(-1,-1), BLUE_MID),
        ("VALIGN",       (0,0),(-1,-1), "MIDDLE"),
        ("LEFTPADDING",  (0,0),(0,-1),  8),
        ("RIGHTPADDING", (-1,0),(-1,-1),8),
        ("TOPPADDING",   (0,0),(-1,-1), 5),
        ("BOTTOMPADDING",(0,0),(-1,-1), 5),
    ]))
    elems.append(Spacer(1, 5))
    elems.append(header)
    # Content rows
    for section_title, items in topics_by_section:
        elems.append(Spacer(1, 3))
        elems.append(Paragraph(section_title, sub_head))
        elems.append(two_col_bullets(items))
    elems.append(Spacer(1, 4))
    elems.append(HRFlowable(width="100%", thickness=0.5, color=GREY_MID))
    return elems

# ── page callbacks ────────────────────────────────────────────────────────────
def cover_page(canvas, doc):
    canvas.saveState()
    w, h = A4
    # gradient-like background
    canvas.setFillColor(BLUE_DARK)
    canvas.rect(0, 0, w, h, fill=1, stroke=0)
    canvas.setFillColor(colors.HexColor("#1A5276"))
    canvas.rect(0, h*0.45, w, h*0.55, fill=1, stroke=0)
    # decorative accent bar
    canvas.setFillColor(RED_DARK)
    canvas.rect(0, h*0.44, w, 6, fill=1, stroke=0)
    canvas.setFillColor(ORANGE)
    canvas.rect(0, h*0.44-6, w, 6, fill=1, stroke=0)
    canvas.restoreState()

def inner_page(canvas, doc):
    canvas.saveState()
    w, h = A4
    # top bar
    canvas.setFillColor(BLUE_DARK)
    canvas.rect(0, h-14*mm, w, 14*mm, fill=1, stroke=0)
    canvas.setFillColor(WHITE)
    canvas.setFont("Helvetica-Bold", 9)
    canvas.drawString(15*mm, h-9*mm, "NEET PG / INI-CET  |  High-Yield Topics 2026")
    canvas.setFont("Helvetica", 8)
    canvas.drawRightString(w-15*mm, h-9*mm, f"Page {doc.page}")
    # bottom bar
    canvas.setFillColor(BLUE_DARK)
    canvas.rect(0, 0, w, 10*mm, fill=1, stroke=0)
    canvas.setFillColor(GREY_MID)
    canvas.setFont("Helvetica-Oblique", 7)
    canvas.drawCentredString(w/2, 4*mm,
        "Based on AI analysis of 5-Year NEET PG & INI-CET PYQs (2021-2026)  |  For educational use only")
    canvas.restoreState()

# ── document ──────────────────────────────────────────────────────────────────
doc = BaseDocTemplate(OUTPUT, pagesize=A4,
                      leftMargin=20*mm, rightMargin=20*mm,
                      topMargin=22*mm, bottomMargin=18*mm)

cover_frame = Frame(0, 0, A4[0], A4[1], leftPadding=25*mm, rightPadding=25*mm,
                    topPadding=50*mm, bottomPadding=20*mm, id="cover")
inner_frame = Frame(doc.leftMargin, doc.bottomMargin,
                    A4[0]-doc.leftMargin-doc.rightMargin,
                    A4[1]-doc.topMargin-doc.bottomMargin,
                    id="inner")

doc.addPageTemplates([
    PageTemplate(id="Cover", frames=[cover_frame], onPage=cover_page),
    PageTemplate(id="Inner", frames=[inner_frame], onPage=inner_page),
])

story = []

# ══════════════════════════════════════════════════════════════════════════════
#  COVER PAGE
# ══════════════════════════════════════════════════════════════════════════════
story.append(Spacer(1, 30*mm))
story.append(Paragraph("NEET PG / INI-CET", cover_sub))
story.append(Spacer(1, 4))
story.append(Paragraph("HIGH-YIELD TOPICS", cover_title))
story.append(Paragraph("MASTER GUIDE 2026", cover_title))
story.append(Spacer(1, 8))
story.append(HRFlowable(width="60%", thickness=2, color=ORANGE, hAlign="CENTER"))
story.append(Spacer(1, 10))
story.append(Paragraph("Subject-Wise  |  Priority-Ranked  |  PYQ-Driven", cover_sub))
story.append(Spacer(1, 6))
story.append(Paragraph(
    "AI-powered analysis of 5-year Previous Year Questions<br/>"
    "from NEET PG & INI-CET (2021 - 2026)",
    cover_note))
story.append(Spacer(1, 20))

# stats boxes
stat_data = [
    [Paragraph("<b>200</b><br/>Questions per exam", badge_style),
     Paragraph("<b>~70%</b><br/>Clinical vignettes", badge_style),
     Paragraph("<b>~30-50%</b><br/>Image-based Qs", badge_style),
     Paragraph("<b>~30%</b><br/>PYQ repeats", badge_style)],
]
stat_t = Table(stat_data, colWidths=[35*mm]*4)
stat_t.setStyle(TableStyle([
    ("BACKGROUND",   (0,0),(0,-1), RED_DARK),
    ("BACKGROUND",   (1,0),(1,-1), ORANGE),
    ("BACKGROUND",   (2,0),(2,-1), colors.HexColor("#117A65")),
    ("BACKGROUND",   (3,0),(3,-1), BLUE_MID),
    ("TEXTCOLOR",    (0,0),(-1,-1), WHITE),
    ("FONTNAME",     (0,0),(-1,-1), "Helvetica-Bold"),
    ("FONTSIZE",     (0,0),(-1,-1), 9),
    ("ALIGN",        (0,0),(-1,-1), "CENTER"),
    ("VALIGN",       (0,0),(-1,-1), "MIDDLE"),
    ("TOPPADDING",   (0,0),(-1,-1), 8),
    ("BOTTOMPADDING",(0,0),(-1,-1), 8),
    ("LEFTPADDING",  (0,0),(-1,-1), 4),
    ("RIGHTPADDING", (0,0),(-1,-1), 4),
    ("GRID",         (0,0),(-1,-1), 1, WHITE),
]))
story.append(stat_t)
story.append(Spacer(1, 20))
story.append(Paragraph("Prepared by Orris AI  |  June 2026", cover_note))

# ── switch to inner template ──────────────────────────────────────────────────
story.append(PageBreak())
from reportlab.platypus import NextPageTemplate
story.insert(len(story)-0, NextPageTemplate("Inner"))
story.append(NextPageTemplate("Inner"))

# ══════════════════════════════════════════════════════════════════════════════
#  PAGE 2 - WEIGHTAGE TABLE + PRIORITY LEGEND
# ══════════════════════════════════════════════════════════════════════════════
story.append(header_bar("  SUBJECT WEIGHTAGE & PRIORITY AT A GLANCE", BLUE_DARK))
story.append(Spacer(1, 6))

wt_header = [
    Paragraph("<b>Subject</b>", S("th", fontSize=9, fontName="Helvetica-Bold",
              textColor=WHITE, alignment=TA_CENTER)),
    Paragraph("<b>NEET PG Qs</b>", S("th2", fontSize=9, fontName="Helvetica-Bold",
              textColor=WHITE, alignment=TA_CENTER)),
    Paragraph("<b>INI-CET Qs</b>", S("th3", fontSize=9, fontName="Helvetica-Bold",
              textColor=WHITE, alignment=TA_CENTER)),
    Paragraph("<b>Weightage %</b>", S("th4", fontSize=9, fontName="Helvetica-Bold",
              textColor=WHITE, alignment=TA_CENTER)),
    Paragraph("<b>Priority</b>", S("th5", fontSize=9, fontName="Helvetica-Bold",
              textColor=WHITE, alignment=TA_CENTER)),
]

subjects_data = [
    ("Medicine (incl. Derm, Psych)", "30", "22", "15%", "HIGHEST", RED_DARK),
    ("OBGYN", "20", "16", "10%", "HIGHEST", RED_DARK),
    ("Surgery", "18", "17", "9%", "HIGHEST", RED_DARK),
    ("PSM / Community Medicine", "16", "14", "8%", "HIGH", ORANGE),
    ("Pharmacology", "15", "15", "7.5%", "HIGH", ORANGE),
    ("Biochemistry", "15", "10", "7.5%", "HIGH", ORANGE),
    ("Pathology", "12", "16", "6%", "HIGH", ORANGE),
    ("Microbiology", "12", "14", "6%", "HIGH", ORANGE),
    ("Anatomy", "9", "11", "4.5%", "MEDIUM", YELLOW),
    ("Dermatology", "8", "5", "4%", "MEDIUM", YELLOW),
    ("Forensic Medicine & Tox", "6", "6", "3%", "MEDIUM", YELLOW),
    ("Pediatrics", "5", "10", "2.5%", "MEDIUM", YELLOW),
    ("Physiology", "5", "10", "2.5%", "MEDIUM", YELLOW),
    ("Radiology", "5", "8", "2.5%", "MEDIUM", YELLOW),
    ("Ophthalmology", "5", "6", "2.5%", "QUICK WIN", GREEN),
    ("ENT", "5", "5", "2.5%", "QUICK WIN", GREEN),
    ("Orthopaedics", "5", "6", "2.5%", "QUICK WIN", GREEN),
    ("Psychiatry", "–", "4", "~2%", "QUICK WIN", GREEN),
    ("Anaesthesia", "4", "5", "2%", "QUICK WIN", GREEN),
]

cell_style = S("Cell", fontSize=8.5, fontName="Helvetica", alignment=TA_CENTER,
               textColor=BLACK)
left_cell  = S("LCell", fontSize=8.5, fontName="Helvetica", alignment=TA_LEFT,
               textColor=BLACK)

wt_rows = [wt_header]
for row in subjects_data:
    subj, neet, ini, pct, pri, pcol = row
    badge_p = Paragraph(f"<b>{pri}</b>",
                        S("bp", fontSize=7.5, fontName="Helvetica-Bold",
                          textColor=WHITE, alignment=TA_CENTER))
    inner_badge = Table([[badge_p]], colWidths=[22*mm])
    inner_badge.setStyle(TableStyle([
        ("BACKGROUND",   (0,0),(-1,-1), pcol),
        ("TOPPADDING",   (0,0),(-1,-1), 2),
        ("BOTTOMPADDING",(0,0),(-1,-1), 2),
    ]))
    wt_rows.append([
        Paragraph(subj, left_cell),
        Paragraph(neet, cell_style),
        Paragraph(ini,  cell_style),
        Paragraph(pct,  cell_style),
        inner_badge,
    ])

wt_table = Table(wt_rows, colWidths=[65*mm, 22*mm, 22*mm, 24*mm, 27*mm],
                 repeatRows=1)
wt_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0),(-1,0), BLUE_MID),
    ("TEXTCOLOR",    (0,0),(-1,0), WHITE),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE, GREY_LIGHT]),
    ("ALIGN",        (0,0),(-1,-1), "CENTER"),
    ("VALIGN",       (0,0),(-1,-1), "MIDDLE"),
    ("FONTNAME",     (0,0),(-1,0),  "Helvetica-Bold"),
    ("FONTSIZE",     (0,0),(-1,-1), 8.5),
    ("TOPPADDING",   (0,0),(-1,-1), 5),
    ("BOTTOMPADDING",(0,0),(-1,-1), 5),
    ("LEFTPADDING",  (0,0),(0,-1),  8),
    ("GRID",         (0,0),(-1,-1), 0.4, GREY_MID),
    ("LINEBELOW",    (0,0),(-1,0),  1, BLUE_DARK),
]))
story.append(wt_table)
story.append(Spacer(1, 8))

# Legend
legend_data = [[
    Paragraph("<b>HIGHEST</b>  Deep study + all PYQs + images", S("lg", fontSize=8.5, fontName="Helvetica", textColor=WHITE)),
    Paragraph("<b>HIGH</b>  Concept mastery + PYQ revision", S("lg2", fontSize=8.5, fontName="Helvetica", textColor=WHITE)),
    Paragraph("<b>MEDIUM</b>  Focused notes + key topics", S("lg3", fontSize=8.5, fontName="Helvetica", textColor=BLACK)),
    Paragraph("<b>QUICK WIN</b>  Rapid revision = easy marks", S("lg4", fontSize=8.5, fontName="Helvetica", textColor=WHITE)),
]]
legend = Table(legend_data, colWidths=[43*mm]*4)
legend.setStyle(TableStyle([
    ("BACKGROUND",   (0,0),(0,-1), RED_DARK),
    ("BACKGROUND",   (1,0),(1,-1), ORANGE),
    ("BACKGROUND",   (2,0),(2,-1), YELLOW_LIGHT),
    ("BACKGROUND",   (3,0),(3,-1), GREEN),
    ("ALIGN",        (0,0),(-1,-1), "CENTER"),
    ("VALIGN",       (0,0),(-1,-1), "MIDDLE"),
    ("TOPPADDING",   (0,0),(-1,-1), 6),
    ("BOTTOMPADDING",(0,0),(-1,-1), 6),
    ("LEFTPADDING",  (0,0),(-1,-1), 5),
    ("RIGHTPADDING", (0,0),(-1,-1), 5),
    ("GRID",         (0,0),(-1,-1), 1, WHITE),
]))
story.append(legend)

# ══════════════════════════════════════════════════════════════════════════════
#  EXAM TREND BOX
# ══════════════════════════════════════════════════════════════════════════════
story.append(Spacer(1, 10))
trend_items = [
    ["70%", "Clinical vignette-based questions (up from 35% in 2021)"],
    ["30-50%", "Image-based Qs (histology, ECG, X-ray, fundus, derm)"],
    ["~30%", "Direct PYQ repeats - always solve last 5 years first"],
    ["2025+", "Integrated multi-subject questions increasing sharply"],
]
trend_rows = []
for pct, desc in trend_items:
    trend_rows.append([
        Paragraph(f"<b>{pct}</b>", S("tp", fontSize=11, fontName="Helvetica-Bold",
                  textColor=RED_DARK, alignment=TA_CENTER)),
        Paragraph(desc, S("td", fontSize=9, fontName="Helvetica", textColor=BLACK)),
    ])
trend_t = Table(trend_rows, colWidths=[22*mm, 148*mm])
trend_t.setStyle(TableStyle([
    ("BACKGROUND",   (0,0),(-1,-1), BLUE_LIGHT),
    ("VALIGN",       (0,0),(-1,-1), "MIDDLE"),
    ("LEFTPADDING",  (0,0),(-1,-1), 8),
    ("RIGHTPADDING", (0,0),(-1,-1), 8),
    ("TOPPADDING",   (0,0),(-1,-1), 5),
    ("BOTTOMPADDING",(0,0),(-1,-1), 5),
    ("LINEBELOW",    (0,0),(-1,-2), 0.5, GREY_MID),
    ("LINEBEFORE",   (0,0),(0,-1),  3, RED_DARK),
]))
story.append(header_bar("  5-YEAR EXAM TREND HIGHLIGHTS (2021-2026)"))
story.append(trend_t)

story.append(PageBreak())

# ══════════════════════════════════════════════════════════════════════════════
#  SUBJECT SECTIONS
# ══════════════════════════════════════════════════════════════════════════════

# Helper to add a section page header
def tier_divider(label, color=RED_DARK):
    t = Table([[Paragraph(f"  PRIORITY TIER: {label}", page_title)]],
              colWidths=[170*mm])
    t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0),(-1,-1), color),
        ("TOPPADDING",   (0,0),(-1,-1), 10),
        ("BOTTOMPADDING",(0,0),(-1,-1), 10),
    ]))
    return t

# ─────────────────────────────────────────────────────────────────────────────
# TIER 1 - HIGHEST PRIORITY
# ─────────────────────────────────────────────────────────────────────────────
story.append(tier_divider("HIGHEST  |  Medicine  •  OBGYN  •  Surgery", RED_DARK))
story.append(Spacer(1, 8))

# MEDICINE
story.extend(subject_block(
    RED_DARK, "HIGHEST", "1. MEDICINE", "~30 Qs  |  15% weightage", [
    ("Cardiology", [
        "Myocardial Infarction (Killip class, STEMI, biomarkers)",
        "Heart Failure (HFrEF vs HFpEF, BNP, management)",
        "Atrial Fibrillation (rate/rhythm control, anticoagulation)",
        "Infective Endocarditis (Duke criteria, organisms)",
        "Cardiomyopathy - DCM vs HCM",
    ]),
    ("Endocrinology", [
        "Diabetes Mellitus (HbA1c, insulin types, SGLT2 inhibitors)",
        "Thyroid (Graves', thyroid storm, Hashimoto's)",
        "Addison's Disease (cortisol, ACTH, adrenal crisis)",
        "Cushing Syndrome (ACTH-dependent vs independent)",
        "Pheochromocytoma (diagnosis, management)",
        "Paget's Disease of Bone",
    ]),
    ("Pulmonology", [
        "ARDS (Berlin criteria, low tidal volume + high PEEP)",
        "COPD exacerbation management",
        "Sarcoidosis (Kveim test, ACE levels)",
        "Pulmonary Embolism (Wells score, D-dimer, CTPA)",
    ]),
    ("Neurology", [
        "Stroke (ischemic vs hemorrhagic, tPA window, lacunar)",
        "Status Epilepticus (protocol, drug sequence)",
        "Meningitis (bacterial vs viral, CSF findings)",
        "Multiple Sclerosis (oligoclonal bands, MRI plaques)",
        "Guillain-Barre Syndrome / AIDP",
    ]),
    ("Rheumatology", [
        "RA (Pannus, anti-CCP, DAS28 score)",
        "SLE (ANA, anti-dsDNA, anti-Smith, lupus nephritis)",
        "Gout (uric acid crystals, allopurinol vs febuxostat)",
        "Ankylosing Spondylitis (HLA-B27, bamboo spine)",
    ]),
    ("Infectious Diseases", [
        "HIV (CD4 thresholds, IRIS in HIV-TB, ART initiation)",
        "Tuberculosis (drug regimens, MDR-TB, DOTS/NTEP)",
        "Malaria (severe criteria, artesunate, G6PD deficiency)",
        "Typhoid (Widal test, antibiotic of choice)",
        "Leptospirosis, Scrub typhus (Weil-Felix reaction)",
    ]),
    ("Nephrology & Gastroenterology", [
        "CKD staging, dialysis indications, complications",
        "Nephrotic vs Nephritic syndrome (differentiating features)",
        "AKI (AKIN criteria, causes, management)",
        "Liver cirrhosis (Child-Pugh, MELD, varices, SBP)",
        "Hepatitis B serology markers (HBsAg, anti-HBs, HBeAg)",
        "IBD - Crohn's vs UC (clinical, endoscopic, histologic)",
    ]),
], RED_LIGHT))

# OBGYN
story.extend(subject_block(
    RED_DARK, "HIGHEST", "2. OBSTETRICS & GYNAECOLOGY", "~20 Qs  |  10% weightage", [
    ("Labor Room Emergencies (MOST REPEATED)", [
        "Cord Prolapse (immediate C-section, Trendelenburg)",
        "Shoulder Dystocia (McRoberts maneuver, Zavanelli)",
        "Atonic PPH (Bakri balloon, uterotonics, compression sutures)",
        "Amniotic Fluid Embolism (mechanism, management)",
        "Episiotomy closure sequence",
    ]),
    ("Obstetric Complications", [
        "Preeclampsia/Eclampsia (MgSO4 - Pritchard regime)",
        "APH (Placenta Previa vs Abruptio Placentae)",
        "Ectopic Pregnancy (management algorithm, beta-hCG)",
        "PROM vs PPROM (management by gestational age)",
        "Fetal Monitoring (CTG interpretation, BPP, NST)",
    ]),
    ("Gynaecology", [
        "Adenomyosis (MRI - junctional zone >12mm)",
        "Premature Ovarian Insufficiency (high FSH, low AMH)",
        "Cervical Cancer (FIGO staging, HPV, Pap smear)",
        "Ovarian Tumors (classification, tumor markers)",
        "Endometrial Carcinoma (risk factors, FIGO staging)",
        "PCOS (Rotterdam criteria, management)",
        "AUB - PALM-COEIN classification",
        "Infertility evaluation (HSG, laparoscopy, semen analysis)",
    ]),
], RED_LIGHT))

# SURGERY
story.extend(subject_block(
    RED_DARK, "HIGHEST", "3. SURGERY", "~18 Qs  |  9% weightage", [
    ("General Surgery", [
        "Hernias (inguinal anatomy, types, Richter's hernia)",
        "Appendicitis (Alvarado score, Rovsing's, perforation)",
        "Intestinal Obstruction (adhesion, volvulus, intussusception)",
        "Achalasia Cardia (manometry gold standard, Heller's)",
        "GI Bleeding (Rockall score, variceal management)",
    ]),
    ("Hepatobiliary & Pancreas", [
        "Courvoisier's Law (painless jaundice, CBD obstruction)",
        "Ca Head of Pancreas (Whipple's procedure, CA 19-9)",
        "Cholecystitis/Cholelithiasis (Murphy's, Charcot's triad)",
        "Portal Hypertension (TIPS, TIPSS, beta-blockers)",
    ]),
    ("Trauma (ATLS)", [
        "Diffuse Axonal Injury (RTA, CT findings, management)",
        "Splenic Injuries (classification, non-operative management)",
        "Primary & Secondary Survey (ABCDE protocol)",
        "Pneumothorax / Hemothorax (needle decompression)",
    ]),
    ("Colorectal & Thyroid/Breast", [
        "Colorectal Ca (Duke's/TNM staging, right vs left features)",
        "Anal Fistula (Parks classification - 4 types)",
        "Thyroid Cancer (PTC most common, papillary features)",
        "Breast Cancer (staging, BRCA, sentinel node biopsy)",
        "Paediatric Surgery: CDH, intussusception, Hirschsprung",
    ]),
], RED_LIGHT))

story.append(PageBreak())

# ─────────────────────────────────────────────────────────────────────────────
# TIER 2 - HIGH PRIORITY
# ─────────────────────────────────────────────────────────────────────────────
story.append(tier_divider("HIGH  |  PSM  •  Pharmacology  •  Biochemistry  •  Pathology  •  Microbiology", ORANGE))
story.append(Spacer(1, 8))

# PSM
story.extend(subject_block(
    ORANGE, "HIGH", "4. PSM / COMMUNITY MEDICINE", "~16 Qs  |  8% weightage", [
    ("Biostatistics & Epidemiology (Most Tested)", [
        "Study designs (RCT, cohort, case-control, cross-sectional)",
        "Bias types (selection, information, recall, Berkson's)",
        "Sensitivity, Specificity, PPV, NPV (2x2 table)",
        "Odds Ratio vs Relative Risk",
        "Confidence Intervals, p-value, Type I/II errors",
        "Screening criteria (Wilson & Jungner)",
    ]),
    ("National Health Programs", [
        "UIP Immunization Schedule (new vaccines - IPV, PCV, Rota)",
        "NHM programs (RMNCHA+, Ayushman Bharat - PMJAY)",
        "NTEP (formerly RNTCP) - TB elimination targets",
        "Malaria programs (NVBDCP)",
        "JSY, PMSMA, LaQshya programs",
    ]),
    ("Nutrition & Demography", [
        "PEM: Marasmus vs Kwashiorkor (clinical features)",
        "Vitamin deficiency diseases (A, D, B1, B3, B12, C)",
        "ICDS program components, mid-day meal scheme",
        "MMR, IMR, NMR current Indian statistics",
        "Demographic transition theory, census data",
    ]),
], ORANGE_LIGHT))

# PHARMACOLOGY
story.extend(subject_block(
    ORANGE, "HIGH", "5. PHARMACOLOGY", "~15 Qs  |  7.5% weightage", [
    ("Autonomic Nervous System", [
        "Alpha/Beta adrenergic drugs (clinical uses)",
        "Cholinergic drugs (pilocarpine, neostigmine)",
        "Anticholinergic side effects (atropine, scopolamine)",
        "Ganglion blockers",
    ]),
    ("Cardiovascular & CNS Drugs", [
        "Antihypertensives (ACEi - avoid in pregnancy/bilateral RAS)",
        "Anti-arrhythmics (Vaughan-Williams classification)",
        "Warfarin interactions (drugs that increase/decrease effect)",
        "Antiepileptics (mechanism, teratogenicity, interactions)",
        "SSRIs (paroxetine shortest half-life, discontinuation syndrome)",
        "Antipsychotics (EPS, NMS, clozapine agranulocytosis)",
    ]),
    ("Antimicrobials (Most Tested Group)", [
        "Antibiotic mechanisms and class overview",
        "Drug of choice for common infections",
        "Resistance mechanisms (MRSA, ESBL, MDR-TB)",
        "Aminoglycoside toxicity (nephro + ototoxicity)",
        "Antibiotics in pregnancy (safe vs contraindicated)",
        "Anticancer drugs (MTX, cisplatin nephrotoxicity, bleomycin)",
    ]),
    ("Special Topics", [
        "Drug interactions: warfarin, metformin, anti-epileptics",
        "Drugs to avoid in G6PD deficiency",
        "Drugs in renal/hepatic failure (dose adjustments)",
        "ADRs: SJS/TEN, fixed drug eruption, anaphylaxis",
    ]),
], ORANGE_LIGHT))

# BIOCHEMISTRY
story.extend(subject_block(
    ORANGE, "HIGH", "6. BIOCHEMISTRY", "~15 Qs  |  7.5% weightage", [
    ("Molecular Biology (RISING TREND in INICET)", [
        "DNA replication & repair (mismatch repair, NER)",
        "PCR types: RT-PCR, real-time PCR, applications",
        "Blotting: Southern (DNA), Northern (RNA), Western (protein)",
        "CRISPR-Cas9 mechanism",
        "Oncogenes (RAS, MYC) and tumor suppressors (p53, RB)",
    ]),
    ("Metabolism & Inborn Errors", [
        "Glycolysis, TCA cycle (regulatory enzymes)",
        "Gluconeogenesis (PEPCK, FBPase key enzymes)",
        "Fed vs fasting state (insulin vs glucagon effects)",
        "Fatty acid oxidation and ketone bodies",
        "Cholesterol synthesis (HMG-CoA reductase - statin target)",
        "Lysosomal storage diseases (Gaucher, Niemann-Pick, Tay-Sachs)",
        "Glycogen storage diseases (von Gierke, Pompe, McArdle)",
        "PKU, MSUD, Alkaptonuria",
        "Familial Dyslipidemias (Fredrickson classification)",
    ]),
    ("Vitamins & Cardiac Markers", [
        "Fat-soluble vitamins (A, D, E, K - deficiencies, toxicities)",
        "B-vitamins: B1 Wernicke's, B3 Pellagra, B12/folate anemia",
        "Cardiac biomarkers (Troponin I/T, CK-MB, LDH isoforms)",
        "Trace elements: zinc, selenium, copper (Wilson's disease)",
    ]),
], ORANGE_LIGHT))

# PATHOLOGY
story.extend(subject_block(
    ORANGE, "HIGH", "7. PATHOLOGY", "~12-16 Qs  |  6-8% weightage", [
    ("General Pathology (MOST REPEATED)", [
        "Cell injury: reversible vs irreversible, mechanisms",
        "Apoptosis vs necrosis (caspases, morphology differences)",
        "Inflammation mediators (PGs, leukotrienes, cytokines)",
        "Wound healing (primary vs secondary intention, growth factors)",
        "Amyloidosis (Congo red, apple-green birefringence, types)",
    ]),
    ("Hematopathology", [
        "Anemia types - blood film patterns (microcytic, megaloblastic, hemolytic)",
        "AML M3 (t15;17, PML-RARA, DIC association)",
        "CML (Philadelphia chromosome, BCR-ABL, imatinib)",
        "Follicular lymphoma (t14;18, BCL-2 overexpression)",
        "Multiple Myeloma (M-spike, Bence-Jones protein)",
        "Hodgkin Lymphoma (Reed-Sternberg cell, WHO classification)",
    ]),
    ("Systemic Pathology & Neoplasia", [
        "Renal: IgA nephropathy, minimal change disease, FSGS",
        "Lung: adenocarcinoma (most common), SCLC vs NSCLC",
        "Tumor markers (CEA, AFP, CA-125, PSA, CA 19-9)",
        "Primary biliary cholangitis (AMA-M2 positive)",
        "Proto-oncogenes & tumor suppressor genes",
        "Paraneoplastic syndromes (ACTH, ADH, PTHrP)",
    ]),
], ORANGE_LIGHT))

# MICROBIOLOGY
story.extend(subject_block(
    ORANGE, "HIGH", "8. MICROBIOLOGY", "~12-14 Qs  |  6% weightage", [
    ("Bacteriology", [
        "Staphylococcus aureus (toxins, MRSA, TSST-1)",
        "Streptococcus (Group A, B, S. pneumoniae, Viridans)",
        "Mycobacterium tuberculosis (ZN stain, culture, DST)",
        "Clostridium (tetanus, botulism, C. diff, gas gangrene)",
        "E. coli (ETEC, EPEC, EHEC - toxins, diarrhea types)",
        "Vibrio cholerae (El Tor biotype, rice water stools)",
        "H. pylori (urease, CLO test, gastric cancer)",
    ]),
    ("Virology & Immunology", [
        "Hepatitis B serology (HBsAg, anti-HBs, HBeAg, anti-HBc)",
        "HIV (CD4 thresholds, Western blot, ELISA window period)",
        "Herpes viruses (HSV, VZV, CMV, EBV in immunocompromised)",
        "Dengue (NS1 antigen, IgM/IgG, serotypes, DHF criteria)",
        "Rabies (Negri bodies, PEP protocol)",
        "Hypersensitivity Type I-IV (classic examples each type)",
        "Immunodeficiency disorders (SCID, DiGeorge, Bruton's XLA)",
    ]),
    ("Mycology & Parasitology", [
        "Candida (morphology, invasive candidiasis, fluconazole)",
        "Aspergillus (acute angle septate hyphae, galactomannan)",
        "Cryptococcus (India ink, latex agglutination, AIDS)",
        "Malaria (Plasmodium types, lifecycle, diagnosis)",
        "Leishmaniasis (visceral vs cutaneous, diagnosis, Rx)",
    ]),
], ORANGE_LIGHT))

story.append(PageBreak())

# ─────────────────────────────────────────────────────────────────────────────
# TIER 3 - MEDIUM PRIORITY
# ─────────────────────────────────────────────────────────────────────────────
story.append(tier_divider("MEDIUM  |  Anatomy  •  Physiology  •  FMT  •  Dermatology  •  Pediatrics  •  Radiology", YELLOW))
story.append(Spacer(1, 8))

story.extend(subject_block(
    YELLOW, "MEDIUM", "9. ANATOMY", "~9-11 Qs  |  4.5% weightage", [
    ("Neuroanatomy (Highest Yield)", [
        "Cranial nerves (nuclei, foramina, functions - all 12)",
        "Internal capsule (blood supply, fiber organization)",
        "Brainstem lesions (crossed paralyses - Weber, Millard-Gubler)",
        "Corpus callosum (parts, clinical relevance)",
        "Cerebellar connections and syndromes",
    ]),
    ("Head/Neck & Embryology", [
        "Triangles of neck (contents, important structures)",
        "Thyroid gland (blood supply, parathyroid positions)",
        "Branchial arch derivatives (arches I-VI)",
        "Cardiac development (ASD, VSD, TGA embryology)",
        "Neural tube defects (spina bifida, anencephaly)",
    ]),
    ("Limb Anatomy", [
        "Brachial plexus (roots, trunks, cords, terminal branches)",
        "Nerve injuries (radial - Saturday night palsy, ulnar, median)",
        "Rotator cuff (SITS mnemonic, tears)",
        "Knee joint (cruciate ligaments, menisci, bursae)",
        "Carpal tunnel (median nerve contents)",
    ]),
], YELLOW_LIGHT))

story.extend(subject_block(
    YELLOW, "MEDIUM", "10. PHYSIOLOGY", "~5-10 Qs  |  2.5-5% weightage", [
    ("Cardiovascular & Respiratory", [
        "Cardiac cycle (pressure-volume loops, heart sounds, JVP)",
        "Spirometry values (FEV1, FVC, TLC, RV - obstructive vs restrictive)",
        "V/Q ratio and mismatches",
        "Oxygen-hemoglobin dissociation curve (Bohr effect, shifts)",
        "Blood pressure regulation (RAAS, baroreceptors)",
    ]),
    ("Renal, Neuro & Endocrine", [
        "GFR calculation (creatinine clearance, inulin)",
        "Tubular reabsorption and secretion (Tm concept)",
        "Concentration/dilution mechanisms (countercurrent)",
        "Action potential (depolarization, repolarization, refractory periods)",
        "Hypothalamus-pituitary axis (hormones, feedback loops)",
        "Second messengers (cAMP, IP3/DAG, NO)",
    ]),
], YELLOW_LIGHT))

story.extend(subject_block(
    YELLOW, "MEDIUM", "11. FORENSIC MEDICINE & TOXICOLOGY", "~6 Qs  |  3% weightage", [
    ("Forensic Medicine", [
        "IPC Sections (304A negligence, 302 murder, 376 rape)",
        "MLC documentation requirements",
        "Post-mortem findings (rigor mortis, livor mortis timeline)",
        "Age estimation (teeth - Gustafson's, bone - epiphyseal fusion)",
        "Wound examination (incised, lacerated, gunshot entry/exit)",
        "Hanging vs strangulation (PM findings, ligature marks)",
    ]),
    ("Toxicology", [
        "Organophosphorus (SLUDGE symptoms, atropine + pralidoxime)",
        "Alcohol toxicology (Widmark formula, forensic BAC limits)",
        "Snake bite (neurotoxic vs hemotoxic, ASV, 20-min WBCT)",
        "Carbon monoxide (cherry-red discoloration, management)",
        "Arsenic poisoning (Reinsch test, Marsh test)",
    ]),
], YELLOW_LIGHT))

story.extend(subject_block(
    YELLOW, "MEDIUM", "12. DERMATOLOGY", "~5-8 Qs  |  4% weightage", [
    ("Infectious & Immune-mediated (Most Repeated)", [
        "Leprosy (WHO classification - PB/MB, reactions, regimens)",
        "Psoriasis (Auspitz sign, Koebner phenomenon, Munro's)",
        "Pemphigus Vulgaris (Nikolsky+, DIF chicken wire pattern)",
        "Bullous Pemphigoid (subepidermal, linear IgG on DIF)",
        "Vitiligo (autoimmune, Wood's lamp - chalk white)",
        "Herpes Zoster (dermatomal, Ramsay Hunt syndrome)",
    ]),
    ("Drug Reactions & Others", [
        "SJS/TEN (drug causes, SCORTEN score, ICU management)",
        "Fixed Drug Eruption (same site on re-exposure)",
        "Acne Vulgaris (microcomedones, P. acnes, isotretinoin)",
        "Leishmaniasis (visceral - splenomegaly, Rx: amphotericin)",
        "Dermatomyositis (heliotrope rash, Gottron's papules)",
        "Atopic Dermatitis (IgE elevated, Hanifin-Rajka criteria)",
    ]),
], YELLOW_LIGHT))

story.extend(subject_block(
    YELLOW, "MEDIUM", "13. PEDIATRICS", "~5-10 Qs  |  2.5-5% weightage", [
    ("Neonatology", [
        "Neonatal Jaundice (physiological vs pathological, exchange Tx criteria)",
        "Neonatal Sepsis (early vs late onset, organisms)",
        "RDS / Surfactant therapy (CPAP, criteria for intubation)",
        "Birth asphyxia (Apgar score, NNPG resuscitation)",
    ]),
    ("Nutrition & Growth", [
        "Growth & development milestones (head control, walking, language)",
        "Marasmus vs Kwashiorkor (clinical, management)",
        "Vitamin D rickets (features, treatment)",
        "Immunization schedule (UIP, catch-up vaccination)",
    ]),
    ("Pediatric Diseases", [
        "Febrile Seizures (simple vs complex, management, recurrence)",
        "Congenital Heart Diseases (cyanotic vs acyanotic, features)",
        "ALL (most common pediatric cancer, L-asparaginase)",
        "Wilms Tumor (WT1 gene, features, staging)",
        "Developmental Dysplasia of Hip (Barlow, Ortolani tests)",
    ]),
], YELLOW_LIGHT))

story.extend(subject_block(
    YELLOW, "MEDIUM", "14. RADIOLOGY", "~5-8 Qs  |  2.5-4% weightage", [
    ("Common Imaging Patterns", [
        "CXR interpretation (consolidation, pleural effusion, PTX)",
        "CT Head (hyperdense = acute bleed, epidural vs subdural)",
        "MRI indications (MS plaques, soft tissue, spinal cord)",
        "Barium studies (lead pipe colon - UC, string sign - Crohn's)",
        "Abdominal X-ray (air-fluid levels, calcifications)",
    ]),
    ("Specialized Imaging", [
        "Breast Imaging (BI-RADS classification, mammo vs USG)",
        "Obstetric USG (dating, anomaly scan markers, BPP)",
        "Interventional radiology (PTCA, embolization indications)",
        "USG guided procedures (FNAC, biopsy, drainage)",
    ]),
], YELLOW_LIGHT))

story.append(PageBreak())

# ─────────────────────────────────────────────────────────────────────────────
# TIER 4 - QUICK WINS
# ─────────────────────────────────────────────────────────────────────────────
story.append(tier_divider("QUICK WINS  |  Ophthalmology  •  ENT  •  Orthopaedics  •  Psychiatry  •  Anaesthesia", GREEN))
story.append(Spacer(1, 8))

story.extend(subject_block(
    GREEN, "QUICK WIN", "15. OPHTHALMOLOGY", "~5-6 Qs  |  2.5% weightage", [
    ("High-Yield Topics", [
        "Glaucoma (POAG vs PACG, gonioscopy, anti-glaucoma drugs)",
        "Cataract (nuclear vs cortical vs PSC, ECCE vs phaco)",
        "Diabetic Retinopathy (ETDRS classification, laser Rx)",
        "Uveitis (anterior vs posterior, HLA-B27 association)",
        "Retinoblastoma (leukocoria, Rb gene, trilateral Rb)",
        "Optic Neuritis (MS association, RAPD - Marcus Gunn pupil)",
        "Paralytic Squint (CN III, IV, VI palsies - clinical differentiation)",
        "Trachoma (SAFE strategy, C. trachomatis, trichiasis)",
    ]),
], GREEN_LIGHT))

story.extend(subject_block(
    GREEN, "QUICK WIN", "16. ENT", "~5 Qs  |  2.5% weightage", [
    ("High-Yield Topics", [
        "CSOM (tubotympanic - safe vs atticoantral - unsafe)",
        "Hearing Tests (Rinne's +/-, Weber's lateralization)",
        "Cholesteatoma (management, complications: facial palsy, meningitis)",
        "Otosclerosis (stapedial fixation, Carhart notch on PTA)",
        "Pure Tone Audiometry & Tympanometry (type A/B/C)",
        "Nasal Polyps (ethmoidal vs antrochoanal polyp)",
        "Laryngeal Carcinoma (glottic vs supraglottic, hoarseness)",
        "Cochlear Implant (indications, candidacy criteria)",
    ]),
], GREEN_LIGHT))

story.extend(subject_block(
    GREEN, "QUICK WIN", "17. ORTHOPAEDICS", "~5-6 Qs  |  2.5% weightage", [
    ("High-Yield Topics", [
        "Fracture Classification (Salter-Harris, Gustilo-Anderson open Fx)",
        "Nerve Injuries (Seddon: neuropraxia/axonotmesis/neurotmesis)",
        "Shoulder Dislocation (anterior most common, Hill-Sachs)",
        "ACL Tear (Lachman test, anterior draw, MRI)",
        "Carpal Tunnel Syndrome (median nerve, Phalen's, Tinel's)",
        "Osteosarcoma vs Ewing's (X-ray patterns, age groups)",
        "Ankylosing Spondylitis (HLA-B27, DISH, bamboo spine)",
        "DDH (Barlow/Ortolani, Pavlik harness, surgical correction)",
    ]),
], GREEN_LIGHT))

story.extend(subject_block(
    GREEN, "QUICK WIN", "18. PSYCHIATRY", "~4 Qs  |  ~2% weightage", [
    ("High-Yield Topics", [
        "ICD-11/DSM-5 criteria & timelines (Schizophrenia 1M, GAD 6M)",
        "Schizophrenia (Schneider's 1st rank symptoms, antipsychotics)",
        "Bipolar Disorder (manic episode criteria, mood stabilizers)",
        "Substance Use Disorders (opioid withdrawal, Wernicke-Korsakoff)",
        "SSRIs (mechanism, paroxetine shortest t1/2, serotonin syndrome)",
        "Defense Mechanisms (mature: sublimation/altruism, immature: projection)",
        "Autism Spectrum (M-CHAT, joint attention, triad of impairment)",
        "Alzheimer's Disease (early memory loss, hippocampus, donepezil)",
    ]),
], GREEN_LIGHT))

story.extend(subject_block(
    GREEN, "QUICK WIN", "19. ANAESTHESIA", "~4-5 Qs  |  2% weightage", [
    ("High-Yield Topics", [
        "Local Anesthetics (amide vs ester, max safe doses, toxicity Rx)",
        "Inhalational Agents (MAC values, sevoflurane, desflurane, halothane)",
        "Neuromuscular Blockers (succinylcholine - depolarizing, Scoline apnea)",
        "Spinal vs Epidural (level, complications, high spinal emergency)",
        "Mechanical Ventilation (SIMV, CPAP, PSV, PEEP, FiO2 targets)",
        "ACLS/BLS Protocol (compression:ventilation, adrenaline 1mg, amiodarone)",
        "Capnography (waveform interpretation, ETCO2 in CPR)",
        "Amniotic Fluid Embolism (mechanism, management, triad)",
    ]),
], GREEN_LIGHT))

story.append(PageBreak())

# ══════════════════════════════════════════════════════════════════════════════
#  LAST PAGE - PRO TIPS & STRATEGY
# ══════════════════════════════════════════════════════════════════════════════
story.append(header_bar("  TOP 10 STRATEGY TIPS FROM 5-YEAR PYQ ANALYSIS", BLUE_DARK))
story.append(Spacer(1, 8))

tips = [
    ("1", "PYQ Repeats are GOLD",
     "~30% of NEET PG questions are direct or modified repeats. Solve the last 5 years FIRST, always."),
    ("2", "Clinical Vignettes Dominate",
     "70% of questions are scenario-based. Learn to APPLY concepts, not just memorize isolated facts."),
    ("3", "Image-Based Questions are Increasing",
     "Practice histopathology slides, ECGs, X-rays, CT scans, fundus images, and dermatological photos daily."),
    ("4", "Integrated Questions are the Future",
     "A single question may combine Pathology + Pharmacology + Medicine. Think like a doctor, not a student."),
    ("5", "Short Subjects = Rank Boosters",
     "ENT, Ophthalmology, Anaesthesia, FMT - these subjects are scored consistently with just focused revision."),
    ("6", "Master Drug Groups Thoroughly",
     "Warfarin interactions, teratogenic drugs, drugs in renal failure, G6PD-unsafe drugs appear EVERY year."),
    ("7", "National Programs for PSM",
     "Always update yourself on current Indian health statistics (MMR, IMR, vaccine schedule). These change yearly."),
    ("8", "Biochem Molecular Biology Rising",
     "INICET especially is moving toward CRISPR, molecular genetics, and tumor biology questions."),
    ("9", "Obstetric Emergencies Always Repeat",
     "Cord prolapse, shoulder dystocia, PPH management - master every algorithm cold. These never stop repeating."),
    ("10", "Medicine & OBGYN are Non-Negotiable",
     "Together they account for 25% of the paper. There is no way to score well without mastering these two subjects."),
]

for num, title, desc in tips:
    row_data = [
        [Paragraph(f"<b>{num}</b>", S("tn", fontSize=14, fontName="Helvetica-Bold",
                   textColor=WHITE, alignment=TA_CENTER)),
         Paragraph(f"<b>{title}</b><br/><font size=8.5>{desc}</font>",
                   S("td2", fontSize=9.5, fontName="Helvetica", textColor=BLACK,
                     leading=14))
        ]
    ]
    tip_t = Table(row_data, colWidths=[14*mm, 156*mm])
    tip_t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0),(0,-1), BLUE_MID),
        ("BACKGROUND",   (1,0),(1,-1), BLUE_LIGHT),
        ("VALIGN",       (0,0),(-1,-1), "MIDDLE"),
        ("LEFTPADDING",  (0,0),(0,-1),  3),
        ("RIGHTPADDING", (0,0),(0,-1),  3),
        ("LEFTPADDING",  (1,0),(1,-1),  10),
        ("TOPPADDING",   (0,0),(-1,-1), 7),
        ("BOTTOMPADDING",(0,0),(-1,-1), 7),
        ("LINEBELOW",    (0,0),(-1,-1), 0.5, WHITE),
    ]))
    story.append(tip_t)
    story.append(Spacer(1, 2))

story.append(Spacer(1, 12))
# Final note
final_box_data = [[
    Paragraph(
        "<b>Disclaimer:</b> This guide is based on AI analysis of publicly available PYQ trends "
        "from NEET PG and INI-CET (2021-2026). Always supplement with official NBE/AIIMS syllabus, "
        "standard textbooks, and updated coaching material. Prepared by Orris AI | June 2026.",
        S("fn", fontSize=8, fontName="Helvetica-Oblique", textColor=colors.HexColor("#555555"),
          leading=12)
    )
]]
final_box = Table(final_box_data, colWidths=[170*mm])
final_box.setStyle(TableStyle([
    ("BACKGROUND",   (0,0),(-1,-1), GREY_LIGHT),
    ("TOPPADDING",   (0,0),(-1,-1), 8),
    ("BOTTOMPADDING",(0,0),(-1,-1), 8),
    ("LEFTPADDING",  (0,0),(-1,-1), 10),
    ("RIGHTPADDING", (0,0),(-1,-1), 10),
    ("BOX",          (0,0),(-1,-1), 0.5, GREY_MID),
]))
story.append(final_box)

# ── BUILD ─────────────────────────────────────────────────────────────────────
doc.build(story)
print(f"PDF created: {OUTPUT}")

# Medicine PYQ Data - NEET PG / INI-CET Style
# 110 questions across 7 high-yield topic sections

SECTIONS = [

# ─────────────────────────────────────────────────────────────────────────────
{
"title": "CARDIOLOGY",
"color": "#C0392B",
"questions": [
{
"q": "A 58-year-old male presents with crushing chest pain radiating to the left arm for 2 hours. ECG shows ST elevation in leads II, III, aVF. Which coronary artery is most likely occluded?",
"opts": ["Left anterior descending artery", "Right coronary artery", "Left circumflex artery", "Left main coronary artery"],
"ans": 1,
"exp": "ST elevation in II, III, aVF = inferior wall MI = Right Coronary Artery (RCA) territory. LAD supplies anterior wall (V1-V6). LCx supplies lateral wall (I, aVL, V5-V6)."
},
{
"q": "A patient with STEMI is brought to the emergency department 90 minutes after symptom onset. The most appropriate immediate management is:",
"opts": ["IV thrombolysis with streptokinase", "Primary PCI (percutaneous coronary intervention)", "Emergency CABG", "IV heparin infusion alone"],
"ans": 1,
"exp": "Primary PCI is the gold standard for STEMI if available within 90 minutes of first medical contact (door-to-balloon time <90 min). Thrombolysis is given only if PCI is not available within 120 minutes."
},
{
"q": "In Killip classification of acute MI, Killip Class III is defined as:",
"opts": ["No heart failure signs", "Pulmonary rales + S3 gallop", "Frank pulmonary edema", "Cardiogenic shock"],
"ans": 2,
"exp": "Killip I: No HF. Killip II: Mild-moderate HF (S3, rales <50% lung fields). Killip III: Frank pulmonary edema (rales >50% lung fields). Killip IV: Cardiogenic shock (SBP <90 mmHg, oliguria, cyanosis)."
},
{
"q": "Which biomarker is MOST specific for myocardial necrosis and remains elevated the longest after an acute MI?",
"opts": ["CK-MB", "Myoglobin", "Troponin I", "LDH"],
"ans": 2,
"exp": "Troponin I and T are the most specific markers for myocardial injury. Troponin I starts rising at 3-6 hours, peaks at 24-48 hours, and remains elevated for 5-14 days. Myoglobin rises earliest but is non-specific."
},
{
"q": "A 65-year-old hypertensive patient presents with progressive exertional dyspnea, orthopnea, and PND. Echocardiography shows EF of 35%. BNP is 1200 pg/mL. The most appropriate initial therapy is:",
"opts": ["Digoxin + furosemide", "ACE inhibitor + beta-blocker + diuretic", "Calcium channel blocker + spironolactone", "Amiodarone + furosemide"],
"ans": 1,
"exp": "HFrEF (EF <40%) standard therapy: ACE inhibitor (or ARB/ARNI) + Beta-blocker + Diuretic + MRA (spironolactone). This combination reduces mortality. Digoxin is a 4th-line add-on. CCBs (especially diltiazem, verapamil) are avoided in HFrEF."
},
{
"q": "A patient presents with irregular pulse. ECG shows absence of P waves with irregularly irregular QRS complexes. The drug of choice for rate control in this condition with reduced ejection fraction is:",
"opts": ["Verapamil", "Diltiazem", "Digoxin", "Adenosine"],
"ans": 2,
"exp": "Atrial Fibrillation with HFrEF: Digoxin or beta-blockers are preferred for rate control. Calcium channel blockers (verapamil, diltiazem) are contraindicated in HFrEF as they worsen cardiac function. Adenosine is for SVT, not AF."
},
{
"q": "Modified Duke Criteria for Infective Endocarditis: which of the following is a MAJOR criterion?",
"opts": ["Fever >38°C", "Vascular phenomena (Janeway lesions)", "Positive blood culture with typical organism from 2 separate cultures", "Glomerulonephritis"],
"ans": 2,
"exp": "Major Duke criteria: (1) Positive blood culture - typical organisms (S. viridans, S. bovis, HACEK, S. aureus) from ≥2 separate cultures, or persistently positive. (2) Evidence of endocardial involvement on echocardiography. Fever, vascular phenomena, glomerulonephritis are MINOR criteria."
},
{
"q": "A patient with dilated cardiomyopathy has which characteristic finding on echocardiography?",
"opts": ["Asymmetric septal hypertrophy with LVOT obstruction", "Globally dilated chambers with globally reduced EF", "Pericardial effusion with diastolic collapse of RV", "Normal chamber size with impaired diastolic relaxation"],
"ans": 1,
"exp": "DCM: Four-chamber dilatation with globally reduced systolic function (EF <40%). Asymmetric septal hypertrophy + LVOT obstruction = HCM. Pericardial effusion + RV collapse = cardiac tamponade. Normal size + impaired relaxation = diastolic dysfunction/HFpEF."
},
{
"q": "A 45-year-old patient with known hypertrophic cardiomyopathy presents with exertional syncope. Which drug is MOST appropriate for symptom control?",
"opts": ["Nitrates", "Digoxin", "Beta-blockers", "Diuretics"],
"ans": 2,
"exp": "In HCM, beta-blockers are first-line. They reduce heart rate (increases diastolic filling), reduce contractility (decreases LVOT obstruction), and reduce oxygen demand. Nitrates and diuretics WORSEN HCM by reducing preload, increasing the outflow gradient."
},
{
"q": "The Vaughan-Williams classification of anti-arrhythmics: Amiodarone belongs to which class?",
"opts": ["Class I", "Class II", "Class III", "Class IV"],
"ans": 2,
"exp": "Class III (potassium channel blockers) = Amiodarone, Sotalol, Ibutilide, Dofetilide. However, amiodarone has properties of all 4 classes. Class I = Na channel blockers. Class II = Beta-blockers. Class IV = Ca channel blockers."
},
]
},

# ─────────────────────────────────────────────────────────────────────────────
{
"title": "ENDOCRINOLOGY",
"color": "#8E44AD",
"questions": [
{
"q": "A 30-year-old female presents with weight loss, palpitations, heat intolerance, and exophthalmos. TSH is undetectable. TSI (thyroid stimulating immunoglobulin) is positive. The diagnosis is:",
"opts": ["Hashimoto's thyroiditis", "Toxic multinodular goiter", "Graves' disease", "De Quervain's thyroiditis"],
"ans": 2,
"exp": "Graves' disease is an autoimmune hyperthyroidism caused by TSI (stimulatory TSH receptor antibodies). Unique features: diffuse goiter, exophthalmos (Graves' ophthalmopathy), and pretibial myxedema. Hashimoto's causes hypothyroidism. Toxic MNG lacks ophthalmopathy."
},
{
"q": "A patient in ICU develops thyroid storm after surgery. Which is NOT a feature of thyroid storm?",
"opts": ["Hyperthermia", "Tachycardia / AF", "Bradycardia", "Altered mental status"],
"ans": 2,
"exp": "Thyroid storm (Burch-Wartofsky criteria): Hyperthermia (>38.5°C), tachycardia/AF, CNS dysfunction (agitation, psychosis, coma), GI dysfunction, and precipitating event. Bradycardia is NOT a feature - it is a feature of hypothyroid crisis (myxedema coma)."
},
{
"q": "A patient presents with skin hyperpigmentation, weakness, weight loss, and hyponatremia with hyperkalemia. Serum cortisol is low and ACTH is markedly elevated. The diagnosis is:",
"opts": ["Cushing's syndrome", "Addison's disease", "Secondary adrenal insufficiency", "Conn's syndrome"],
"ans": 1,
"exp": "Addison's disease (primary adrenal insufficiency): Low cortisol + HIGH ACTH (no negative feedback) = hyperpigmentation (ACTH stimulates melanocytes). Electrolytes: hyponatremia + hyperkalemia (aldosterone deficiency). Secondary AI: low cortisol + LOW ACTH, no hyperpigmentation."
},
{
"q": "A 40-year-old male has hypertension, hypokalemia, and metabolic alkalosis. Plasma aldosterone is elevated and plasma renin activity is suppressed. The most likely diagnosis is:",
"opts": ["Pheochromocytoma", "Cushing's syndrome", "Primary hyperaldosteronism (Conn's syndrome)", "Renovascular hypertension"],
"ans": 2,
"exp": "Conn's syndrome: Hypertension + hypokalemia + metabolic alkalosis. Key: HIGH aldosterone + SUPPRESSED renin (autonomous aldosterone production). In renovascular hypertension (secondary hyperaldosteronism), BOTH aldosterone AND renin are elevated."
},
{
"q": "Target HbA1c for most adult diabetic patients as per current ADA guidelines is:",
"opts": ["<5.7%", "<6.5%", "<7.0%", "<8.0%"],
"ans": 2,
"exp": "ADA recommends HbA1c <7.0% for most non-pregnant adults with diabetes. Less stringent targets (<8%) are acceptable in elderly, limited life expectancy, or hypoglycemia-prone patients. <6.5% is the diagnostic cutoff, not a treatment target."
},
{
"q": "A 52-year-old obese female with T2DM and CKD (eGFR 30 mL/min) needs glucose-lowering therapy. Which drug should be AVOIDED?",
"opts": ["Insulin", "Sitagliptin (DPP-4 inhibitor)", "Metformin", "Empagliflozin"],
"ans": 2,
"exp": "Metformin is contraindicated when eGFR <30 mL/min (risk of lactic acidosis due to accumulation). SGLT2 inhibitors (empagliflozin) lose efficacy at eGFR <45 and are not recommended below eGFR 30. DPP-4 inhibitors and insulin can be used with dose adjustment."
},
{
"q": "A patient with Cushing's syndrome has an ACTH level of 200 pg/mL (elevated). High-dose dexamethasone suppression test (8 mg) suppresses cortisol by >50%. The most likely source is:",
"opts": ["Adrenal adenoma", "Ectopic ACTH secretion (small cell lung cancer)", "Pituitary corticotroph adenoma (Cushing's disease)", "Adrenal carcinoma"],
"ans": 2,
"exp": "High ACTH = ACTH-dependent Cushing's. Suppression with high-dose dexamethasone = pituitary source (Cushing's disease) - pituitary still responds to negative feedback. Ectopic ACTH (SCLC) does NOT suppress with high-dose dexamethasone. Adrenal causes = ACTH suppressed."
},
{
"q": "Which is the investigation of choice for pheochromocytoma localization after biochemical confirmation?",
"opts": ["CT scan abdomen", "MRI abdomen", "MIBG scan", "Ultrasound abdomen"],
"ans": 0,
"exp": "CT scan is the first-line imaging for pheochromocytoma localization (sensitivity >90%). MRI is preferred in children, pregnancy, metastatic disease, or when CT is inconclusive. MIBG (meta-iodobenzylguanidine) scan is used for functional imaging when CT/MRI is negative or to detect metastases."
},
{
"q": "Paget's disease of bone is characterized by all EXCEPT:",
"opts": ["Markedly elevated serum alkaline phosphatase", "Normal serum calcium in most cases", "Decreased serum acid phosphatase", "Cotton-wool appearance on skull X-ray"],
"ans": 2,
"exp": "Paget's disease: Markedly elevated ALP (bone turnover marker), normal calcium (usually), elevated urinary hydroxyproline. Cotton-wool skull, bowing of tibia (sabre shin), enlarged skull. Acid phosphatase is not typically elevated - prostatic acid phosphatase elevation = prostate cancer."
},
{
"q": "A patient presents with polyuria, polydipsia, and nocturia. Serum sodium is 150 mEq/L and urine osmolality is 100 mOsm/kg. After water deprivation test, urine osmolality remains <300 mOsm/kg but rises to >750 mOsm/kg after desmopressin (ADH). The diagnosis is:",
"opts": ["Nephrogenic diabetes insipidus", "Central (cranial) diabetes insipidus", "Primary polydipsia (psychogenic)", "SIADH"],
"ans": 1,
"exp": "Central DI: Water deprivation test - urine remains dilute. Desmopressin (exogenous ADH) administration - urine concentrates markedly (>50% rise or >750 mOsm/kg). In nephrogenic DI, kidneys don't respond to ADH, so desmopressin does NOT concentrate urine. Primary polydipsia: plasma osmolality is LOW."
},
]
},

# ─────────────────────────────────────────────────────────────────────────────
{
"title": "PULMONOLOGY",
"color": "#2980B9",
"questions": [
{
"q": "Berlin definition of ARDS includes all of the following EXCEPT:",
"opts": ["Acute onset within 1 week of clinical insult", "Bilateral opacities on chest imaging", "Respiratory failure not fully explained by cardiac failure or fluid overload", "PaO2/FiO2 ratio <400 mmHg for mild ARDS"],
"ans": 3,
"exp": "Berlin criteria for ARDS: (1) Acute onset <1 week. (2) Bilateral opacities. (3) Not fully explained by effusion/atelectasis/nodules. (4) PF ratio <300 with PEEP ≥5. Mild = PF 200-300, Moderate = PF 100-200, Severe = PF <100. A PF ratio <400 is NOT part of Berlin criteria."
},
{
"q": "The lung protective ventilation strategy in ARDS includes:",
"opts": ["High tidal volume (12 mL/kg) to improve oxygenation", "Low tidal volume (6 mL/kg ideal body weight) + high PEEP", "High FiO2 with low PEEP", "Prone positioning only in mild ARDS"],
"ans": 1,
"exp": "ARDSnet protocol: Low TV (6 mL/kg IBW) to prevent volutrauma, plateau pressure <30 cmH2O, permissive hypercapnia allowed. High PEEP keeps alveoli open (prevents derecruitment). Prone positioning is recommended in severe ARDS (PF <150) for >16 hours/day."
},
{
"q": "A 60-year-old smoker presents with progressive dyspnea and chronic productive cough for 5 years. Spirometry shows FEV1/FVC of 0.60 (post-bronchodilator). FEV1 is 55% of predicted. GOLD classification is:",
"opts": ["GOLD 1 (Mild)", "GOLD 2 (Moderate)", "GOLD 3 (Severe)", "GOLD 4 (Very Severe)"],
"ans": 1,
"exp": "COPD diagnosis: FEV1/FVC <0.7 post-bronchodilator. GOLD severity by FEV1 % predicted: GOLD 1 ≥80%, GOLD 2 = 50-79%, GOLD 3 = 30-49%, GOLD 4 <30%. FEV1 55% = GOLD 2 (Moderate)."
},
{
"q": "A patient with COPD exacerbation is managed in ICU. Which statement about NIV (Non-Invasive Ventilation) in COPD is TRUE?",
"opts": ["NIV is contraindicated in hypercapnic respiratory failure", "NIV reduces the need for intubation and decreases mortality in COPD exacerbation", "NIV is indicated only when pH >7.35", "BiPAP is contraindicated in COPD"],
"ans": 1,
"exp": "NIV (BiPAP) is strongly indicated in COPD exacerbation with hypercapnic respiratory failure (pH 7.25-7.35, PaCO2 >45). It reduces intubation rates, ICU stay, and mortality. Indication: pH <7.35, PaCO2 >45, RR >25, accessory muscle use despite treatment."
},
{
"q": "Kveim test is used for diagnosis of:",
"opts": ["Sarcoidosis", "Tuberculosis", "Wegener's granulomatosis", "Hypersensitivity pneumonitis"],
"ans": 0,
"exp": "Kveim-Siltzbach test: Intradermal injection of sarcoid tissue extract; biopsy at 4-6 weeks shows non-caseating granulomas = positive in sarcoidosis. ACE levels are elevated in ~60% of sarcoidosis cases. Currently rarely used but remains a classic PYQ topic."
},
{
"q": "A 35-year-old female presents with bilateral hilar lymphadenopathy on CXR, uveitis, and erythema nodosum. Serum ACE is elevated. The most likely diagnosis is:",
"opts": ["Lymphoma", "Primary TB", "Sarcoidosis", "Histoplasmosis"],
"ans": 2,
"exp": "Classic sarcoidosis presentation: Bilateral hilar lymphadenopathy (BHL) + erythema nodosum + uveitis + elevated ACE = Lofgren syndrome (acute sarcoidosis). Good prognosis. Lymphoma causes asymmetric LAD. TB: unilateral LAD usually."
},
{
"q": "Wells criteria is used to assess pre-test probability of:",
"opts": ["Community-acquired pneumonia", "Pulmonary embolism", "DVT only", "Pleural effusion malignancy"],
"ans": 1,
"exp": "Wells score assesses pre-test probability of PE. High probability (>6): CTPA directly. Moderate/Low probability: D-dimer first; if elevated, then CTPA. D-dimer has high negative predictive value - normal D-dimer effectively rules out PE in low-probability patients."
},
{
"q": "Light's criteria for exudative pleural effusion requires at least ONE of the following EXCEPT:",
"opts": ["Pleural fluid protein / serum protein ratio >0.5", "Pleural fluid LDH / serum LDH ratio >0.6", "Pleural fluid LDH >2/3 upper normal limit of serum LDH", "Pleural fluid glucose <60 mg/dL"],
"ans": 3,
"exp": "Light's criteria for exudative effusion (any one): (1) PF protein/serum protein >0.5, (2) PF LDH/serum LDH >0.6, (3) PF LDH >2/3 UNL of serum LDH. Low glucose (<60 mg/dL) suggests empyema, RA, TB, or malignancy but is NOT part of Light's criteria itself."
},
]
},

# ─────────────────────────────────────────────────────────────────────────────
{
"title": "NEUROLOGY",
"color": "#117A65",
"questions": [
{
"q": "A 68-year-old hypertensive patient develops sudden-onset right-sided weakness with facial deviation to the right and slurred speech. NIHSS score is 14. CT head shows no hemorrhage. Time since onset is 2.5 hours. Most appropriate management is:",
"opts": ["IV tPA (alteplase) only", "IV tPA followed by mechanical thrombectomy if large vessel occlusion", "Mechanical thrombectomy alone", "Aspirin 300 mg + clopidogrel"],
"ans": 1,
"exp": "Ischemic stroke with no hemorrhage on CT: IV tPA (0.9 mg/kg, max 90 mg) is given within 4.5 hours. If CT angiography shows large vessel occlusion (ICA, M1/M2), mechanical thrombectomy is performed up to 24 hours. Combination (bridge therapy) is standard. Dual antiplatelet is used in minor strokes/TIA."
},
{
"q": "A patient develops status epilepticus. The FIRST-line drug of choice is:",
"opts": ["Phenytoin IV", "Valproate IV", "Lorazepam IV (benzodiazepine)", "Levetiracetam IV"],
"ans": 2,
"exp": "Status epilepticus treatment ladder: (1) Benzodiazepine first - IV lorazepam 0.1 mg/kg or IV diazepam. (2) If no response: IV Phenytoin/fosphenytoin, Valproate, or Levetiracetam (2nd stage). (3) Refractory SE: Propofol, midazolam, or barbiturate coma. Time is brain - act within 5 minutes."
},
{
"q": "CSF findings in bacterial meningitis include all EXCEPT:",
"opts": ["Elevated opening pressure", "Turbid appearance", "Lymphocytic pleocytosis >500 cells", "Markedly elevated protein, decreased glucose"],
"ans": 2,
"exp": "Bacterial meningitis CSF: Turbid/cloudy, high pressure, NEUTROPHILIC pleocytosis (>1000 cells/mm3), elevated protein (>100 mg/dL), low glucose (<45 mg/dL or CSF:serum glucose <0.4). Lymphocytic pleocytosis is seen in VIRAL meningitis, TB meningitis, and fungal meningitis."
},
{
"q": "A 28-year-old female develops bilateral optic neuritis followed by paraparesis. MRI shows periventricular white matter lesions disseminated in time and space. The most likely diagnosis is:",
"opts": ["Neuromyelitis optica (NMO)", "Multiple sclerosis", "CNS vasculitis", "Acute disseminated encephalomyelitis (ADEM)"],
"ans": 1,
"exp": "MS: Relapsing-remitting demyelinating disease. MRI: Periventricular, juxtacortical, infratentorial lesions disseminated in space and time (McDonald criteria 2017). Oligoclonal bands in CSF in >90%. NMO: Optic neuritis + longitudinally extensive transverse myelitis, AQP4-IgG antibody positive."
},
{
"q": "Internuclear ophthalmoplegia (INO) on lateral gaze is caused by a lesion in the:",
"opts": ["CN VI nucleus", "Medial longitudinal fasciculus (MLF)", "CN III nucleus", "Frontal eye field"],
"ans": 1,
"exp": "INO: Ipsilateral adduction failure + contralateral abducting nystagmus on lateral gaze. Caused by MLF lesion (connects CN VI nucleus to contralateral CN III nucleus). Bilateral INO in a young patient = MS until proven otherwise. Unilateral INO in elderly = stroke."
},
{
"q": "A 72-year-old male presents with progressively worsening dementia over 18 months, behavioral changes, and myoclonic jerks. EEG shows periodic sharp wave complexes. MRI shows cortical ribboning. The most likely diagnosis is:",
"opts": ["Alzheimer's disease", "Lewy body dementia", "Creutzfeldt-Jakob disease (CJD)", "Frontotemporal dementia"],
"ans": 2,
"exp": "CJD (prion disease): Rapidly progressive dementia (weeks-months), myoclonus, cerebellar signs. EEG: periodic sharp wave complexes. MRI DWI: cortical ribboning + basal ganglia hyperintensity. CSF 14-3-3 protein elevated. Fatal within 1 year. Alzheimer's: slow progression over years."
},
{
"q": "Guillain-Barre Syndrome (AIDP): which CSF finding is MOST characteristic?",
"opts": ["High cells with normal protein (pleocytosis)", "Normal cells with high protein (albuminocytological dissociation)", "Low glucose with high protein", "Normal CSF throughout disease"],
"ans": 1,
"exp": "GBS (AIDP): Albuminocytological dissociation - markedly elevated CSF protein (>45 mg/dL) with NORMAL or near-normal cell count (<10 cells/mm3). This reflects demyelination of nerve roots. Peaks at 4-6 weeks. Treatment: IVIG or plasmapheresis."
},
{
"q": "A patient with myasthenia gravis develops respiratory distress and severe bulbar dysfunction (inability to swallow). This condition is called:",
"opts": ["Cholinergic crisis", "Myasthenic crisis", "Lambert-Eaton syndrome exacerbation", "GBS superimposed on MG"],
"ans": 1,
"exp": "Myasthenic crisis: Severe exacerbation of MG causing respiratory failure, requiring mechanical ventilation. Cholinergic crisis: Excess acetylcholinesterase inhibitor (pyridostigmine) causing SLUDGE + miosis + muscle fasciculations - also causes weakness but with different signs. Edrophonium (Tensilon) test helps differentiate."
},
]
},

# ─────────────────────────────────────────────────────────────────────────────
{
"title": "RHEUMATOLOGY",
"color": "#D35400",
"questions": [
{
"q": "A 35-year-old female presents with symmetric small joint arthritis (MCP, PIP), morning stiffness >1 hour, and subcutaneous nodules. RF is positive, anti-CCP antibody is positive. CXR shows bilateral basal nodules. The diagnosis is:",
"opts": ["Systemic lupus erythematosus", "Rheumatoid arthritis with pulmonary involvement", "Psoriatic arthritis", "Reactive arthritis"],
"ans": 1,
"exp": "RA with pulmonary manifestations: Symmetric MCP/PIP involvement (spares DIP), morning stiffness >1 hour, RF+, anti-CCP+ (most specific for RA). Pulmonary nodules (Caplan syndrome if occupational dust exposure). Pannus formation = hallmark of RA joint destruction."
},
{
"q": "A 22-year-old female with malar rash, photosensitivity, discoid rash, and oral ulcers. ANA is 1:640. Anti-dsDNA antibody is elevated. Complement C3/C4 are low. Urine shows RBC casts. The most likely renal histological pattern is:",
"opts": ["Minimal change disease", "Focal segmental glomerulosclerosis", "Diffuse proliferative glomerulonephritis (Class IV lupus nephritis)", "Membranous nephropathy"],
"ans": 2,
"exp": "SLE Class IV (diffuse proliferative GN): Most common and most severe lupus nephritis class. Active urine sediment (RBC casts, proteinuria >0.5g/24h), low complements, high anti-dsDNA. Treatment: High-dose corticosteroids + cyclophosphamide or mycophenolate mofetil."
},
{
"q": "In SLE, which antibody is MOST specific (though not most sensitive)?",
"opts": ["ANA", "Anti-dsDNA", "Anti-Smith (anti-Sm)", "Anti-Ro/SSA"],
"ans": 2,
"exp": "Anti-Smith antibody is MOST SPECIFIC for SLE (~99% specific) but only ~30% sensitive. ANA is most sensitive (~95%) but not specific. Anti-dsDNA is specific for SLE and correlates with disease activity/lupus nephritis. Anti-Ro/SSA: Sjögren's and neonatal lupus."
},
{
"q": "A 28-year-old male presents with low back pain worse in morning, improving with activity, and bilateral sacroiliitis on X-ray. HLA-B27 is positive. The diagnosis and FIRST-line treatment is:",
"opts": ["Reactive arthritis - antibiotics", "Ankylosing Spondylitis - NSAIDs first-line", "Psoriatic arthritis - methotrexate", "Enteropathic arthritis - treat bowel disease"],
"ans": 1,
"exp": "Ankylosing Spondylitis: Chronic inflammatory axial arthritis, HLA-B27 positive in 90%. Criteria: Sacroiliitis on imaging + inflammatory back pain OR HLA-B27 + 2 spondyloarthritis features. NSAIDs are first-line (indomethacin). TNF-alpha inhibitors for NSAID-refractory disease."
},
{
"q": "Gout attack: synovial fluid analysis shows:",
"opts": ["Positively birefringent rhomboid crystals", "Negatively birefringent needle-shaped crystals", "Positively birefringent needle-shaped crystals", "No crystals, high WBC"],
"ans": 1,
"exp": "Gout = Monosodium urate (MSU) crystals = NEGATIVELY birefringent (yellow when parallel to compensator axis), needle-shaped. Pseudogout (CPPD) = Calcium pyrophosphate crystals = POSITIVELY birefringent, rhomboid-shaped. Both can cause acute arthritis but differ by crystal type."
},
{
"q": "A patient on long-term allopurinol for gout develops a severe cutaneous reaction (SJS/TEN). He is HLA-B*5801 positive. This HLA association is particularly strong in which population?",
"opts": ["European descent", "African Americans", "Han Chinese and Southeast Asians", "South Americans"],
"ans": 2,
"exp": "HLA-B*5801 strongly associated with allopurinol-induced SJS/TEN, particularly in Han Chinese, Korean, and Thai populations. Testing for HLA-B*5801 before starting allopurinol is recommended in high-risk Asian populations by current guidelines."
},
]
},

# ─────────────────────────────────────────────────────────────────────────────
{
"title": "INFECTIOUS DISEASES",
"color": "#1A5276",
"questions": [
{
"q": "A patient with HIV has CD4 count of 150 cells/µL. He develops dry cough, dyspnea, and diffuse bilateral ground-glass opacities on CXR. LDH is markedly elevated. The most likely organism is:",
"opts": ["Mycobacterium tuberculosis", "Pneumocystis jirovecii (PCP)", "Cryptococcus neoformans", "Cytomegalovirus"],
"ans": 1,
"exp": "PCP (Pneumocystis pneumonia): Occurs when CD4 <200. Classic triad: dry cough + exertional dyspnea + fever. CXR: bilateral perihilar ground-glass opacities. Elevated LDH (>500 IU/L). Treatment: TMP-SMX (co-trimoxazole). Prophylaxis started when CD4 <200."
},
{
"q": "IRIS (Immune Reconstitution Inflammatory Syndrome) in HIV-TB co-infected patients occurs because:",
"opts": ["ART directly kills Mycobacterium tuberculosis", "Rapid restoration of immune function causes paradoxical worsening of TB", "TB drugs interact with ART causing side effects", "CD4 count drops paradoxically after ART initiation"],
"ans": 1,
"exp": "IRIS: When ART is started in TB-HIV co-infection, rapid immune restoration causes exaggerated inflammatory response to TB antigens (paradoxical IRIS: worsening of existing TB) or unmasking of occult TB (unmasking IRIS). Risk is highest when ART started with very low CD4 (<50). Management: Continue both ART and anti-TB; steroids for severe IRIS."
},
{
"q": "Widal test for typhoid fever: A fourfold rise in agglutinin titer is diagnostic. In non-endemic areas, which titer is considered significant in a single sample?",
"opts": ["O antigen ≥1:20", "O antigen ≥1:80", "H antigen ≥1:80", "Both O and H ≥1:160"],
"ans": 3,
"exp": "Widal test: Significant titer in a single sample: O antigen ≥1:80 and/or H antigen ≥1:160 in non-endemic areas. In endemic areas, baseline titers are higher, so fourfold rise is more reliable. O antigen (somatic) is more specific for ACTIVE infection; H antigen (flagellar) persists longer."
},
{
"q": "Drug of choice for uncomplicated malaria due to Plasmodium falciparum in India (non-resistant area) as per NVBDCP guidelines is:",
"opts": ["Chloroquine alone", "Artemisinin-based combination therapy (ACT) - Artesunate + SP", "Quinine + doxycycline", "Mefloquine alone"],
"ans": 1,
"exp": "NVBDCP (India) recommends ACT for P. falciparum: Artesunate + Sulfadoxine-Pyrimethamine (AS+SP) for 3 days + single dose Primaquine (to eliminate gametocytes). Chloroquine is now reserved only for P. vivax/P. ovale. Severe malaria: IV Artesunate."
},
{
"q": "Criteria for SEVERE malaria (WHO 2015) include all EXCEPT:",
"opts": ["Hyperparasitemia (>5% RBCs parasitized)", "Cerebral malaria (unarousable coma)", "Uncomplicated fever with splenomegaly", "Acute kidney injury (creatinine >3 mg/dL)"],
"ans": 2,
"exp": "WHO severe malaria criteria include: Cerebral malaria, severe anemia (Hb <7), respiratory distress, hypoglycemia, circulatory collapse, AKI, jaundice + parasitemia, abnormal bleeding, hyperparasitemia >5%, impaired consciousness. Uncomplicated fever with splenomegaly = non-severe presentation."
},
{
"q": "Leptospirosis with jaundice, AKI, and bleeding manifestations is called:",
"opts": ["Canicola fever", "Weil's disease", "Fort Bragg fever", "Pretibial fever"],
"ans": 1,
"exp": "Weil's disease = severe leptospirosis: Jaundice + AKI + bleeding (thrombocytopenia) + hepatosplenomegaly. Organism: Leptospira interrogans. Diagnosis: MAT (microscopic agglutination test). Treatment: IV penicillin G or doxycycline. Canicola fever: Milder form with meningitis."
},
{
"q": "A 30-year-old returns from a field posting with high fever, headache, and eschar on right axilla. Weil-Felix reaction is positive (OX-K). The most likely diagnosis is:",
"opts": ["Epidemic typhus (R. prowazekii)", "Endemic typhus (R. typhi)", "Scrub typhus (Orientia tsutsugamushi)", "Rocky Mountain spotted fever"],
"ans": 2,
"exp": "Scrub typhus (Orientia tsutsugamushi): Transmitted by Leptotrombidium mites (trombiculid/chigger mites). Classic: Eschar at bite site + fever + rash. Weil-Felix: OXK positive (ONLY scrub typhus is OXK positive). OX-2 and OX-19 positive in other rickettsial diseases. Treatment: Doxycycline."
},
{
"q": "For adults with HIV, ART (antiretroviral therapy) should be initiated:",
"opts": ["Only when CD4 <200 cells/µL", "Only when CD4 <350 cells/µL", "Immediately regardless of CD4 count ('treat all')", "Only when AIDS-defining illness develops"],
"ans": 2,
"exp": "Current WHO/NACO guidelines (2023): 'Treat All' strategy - ART should be started in ALL HIV-positive individuals regardless of CD4 count, clinical stage, or WHO staging, as early as possible (ideally same day). This reduces transmission and improves outcomes."
},
]
},

# ─────────────────────────────────────────────────────────────────────────────
{
"title": "NEPHROLOGY & GASTROENTEROLOGY",
"color": "#148F77",
"questions": [
{
"q": "A 50-year-old male with longstanding CKD on dialysis develops bone pain, muscle weakness, and pruritus. X-ray shows 'rugger jersey spine.' PTH is markedly elevated. The diagnosis is:",
"opts": ["Osteoporosis", "Osteomalacia", "Renal osteodystrophy (secondary hyperparathyroidism)", "Paget's disease"],
"ans": 2,
"exp": "Renal osteodystrophy = CKD-MBD: Low 1,25-OH Vitamin D → hyperphosphatemia + hypocalcemia → high PTH (secondary hyperPTH). Rugger jersey spine (alternating dense and lucent bands) = osteosclerosis + osteopenia. Subperiosteal resorption on X-ray of radial aspect of middle phalanx = pathognomonic."
},
{
"q": "Nephrotic syndrome is characterized by all EXCEPT:",
"opts": ["Proteinuria >3.5 g/day", "Hypoalbuminemia <3.5 g/dL", "Generalized edema", "RBC casts in urine"],
"ans": 3,
"exp": "Nephrotic syndrome: Proteinuria >3.5g/24h, hypoalbuminemia, edema, hyperlipidemia, lipiduria (oval fat bodies, fatty casts). RBC casts are characteristic of NEPHRITIC syndrome (glomerulonephritis) not nephrotic syndrome. Nephrotic: Lipid casts, hyaline casts; Nephritic: RBC casts, granular casts."
},
{
"q": "Most common cause of nephrotic syndrome in adults is:",
"opts": ["Minimal Change Disease", "Focal Segmental Glomerulosclerosis (FSGS)", "Membranous Nephropathy", "IgA Nephropathy"],
"ans": 2,
"exp": "Adults: Membranous nephropathy is most common cause of primary nephrotic syndrome in adults in Western countries. In India, it may be FSGS or membranous. Children: Minimal change disease (MCD) is most common nephrotic cause (steroid responsive). FSGS: Most common cause of nephrotic syndrome in African Americans."
},
{
"q": "IgA Nephropathy (Berger's disease) characteristically presents as:",
"opts": ["Nephrotic syndrome following infections", "Episodic macroscopic hematuria concurrent with upper respiratory infection", "Hypertensive emergency with papilledema", "Asymptomatic pyuria"],
"ans": 1,
"exp": "IgA nephropathy: Most common GN worldwide. Episodic gross hematuria DURING (synpharyngitic) URTI (within 1-2 days) - unlike poststreptococcal GN (10-14 days after URTI). Mesangial IgA deposits on immunofluorescence. Most common cause of recurrent hematuria in young adults."
},
{
"q": "Child-Pugh score for liver cirrhosis includes all of the following parameters EXCEPT:",
"opts": ["Serum bilirubin", "Serum albumin", "Prothrombin time (INR)", "Serum creatinine"],
"ans": 3,
"exp": "Child-Pugh score 5 parameters: (1) Serum bilirubin, (2) Serum albumin, (3) PT/INR, (4) Degree of ascites, (5) Grade of hepatic encephalopathy. Serum creatinine is part of MELD score (Model for End-stage Liver Disease), NOT Child-Pugh. MELD: INR + bilirubin + creatinine."
},
{
"q": "A 45-year-old male with cirrhosis presents with worsening ascites and jaundice. Spontaneous bacterial peritonitis (SBP) is suspected. Which ONE finding in ascitic fluid analysis is DIAGNOSTIC?",
"opts": ["Ascitic fluid WBC >500 cells/µL", "Ascitic fluid PMN (neutrophil) count ≥250 cells/µL", "Positive culture only", "Ascitic fluid protein <2.5 g/dL"],
"ans": 1,
"exp": "SBP diagnosis: Ascitic fluid PMN (neutrophil/polymorphonuclear cells) count ≥250 cells/µL. Culture may be negative in ~40% (culture-negative neutrocytic ascites - CNNA - also treated as SBP). Treatment: Cefotaxime IV. Albumin infusion reduces hepatorenal syndrome risk."
},
{
"q": "Crohn's disease vs Ulcerative Colitis: which feature is SPECIFIC to Crohn's disease?",
"opts": ["Continuous mucosal inflammation from rectum", "Pseudopolyps", "Skip lesions with transmural inflammation", "Mucosal and submucosal involvement only"],
"ans": 2,
"exp": "Crohn's: SKIP lesions (discontinuous), TRANSMURAL inflammation, can affect any part of GI tract (mouth to anus), cobblestone mucosa, non-caseating granulomas, string sign on barium, fistulae/abscesses. UC: Continuous from rectum proximally, mucosal/submucosal only, pseudopolyps, lead-pipe colon."
},
{
"q": "Wilson's disease (hepatolenticular degeneration) is characterized by all EXCEPT:",
"opts": ["Low serum ceruloplasmin", "Kayser-Fleischer rings on slit-lamp examination", "Increased 24-hour urine copper", "Elevated serum copper in all cases"],
"ans": 3,
"exp": "Wilson's disease: AR mutation in ATP7B gene → copper accumulation. Low ceruloplasmin (<20 mg/dL in ~85%), low SERUM copper (paradoxically - because ceruloplasmin-bound copper is low), HIGH FREE (non-ceruloplasmin) copper, HIGH 24h urine copper (>100 µg/day). KF rings in >90% with neurological disease. Treatment: D-penicillamine or trientine."
},
]
},

]

import sys
sys.path.insert(0, '/home/daytona/workspace/neet-pg-pdf')
from medicine_pyq_data import SECTIONS

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

OUTPUT = "/home/daytona/workspace/neet-pg-pdf/NEET_PG_Medicine_PYQ_2026.pdf"

# ── colour palette ────────────────────────────────────────────────────────────
NAVY      = HexColor("#0D2137")
BLUE_MID  = HexColor("#1A5276")
BLUE_LT   = HexColor("#D6EAF8")
CORRECT   = HexColor("#1E8449")
CORRECT_L = HexColor("#D5F5E3")
WRONG     = HexColor("#C0392B")
WRONG_L   = HexColor("#FADBD8")
GOLD      = HexColor("#D4AC0D")
GOLD_L    = HexColor("#FEF9E7")
GREY_L    = HexColor("#F4F6F7")
GREY_M    = HexColor("#BDC3C7")
WHITE     = colors.white
BLACK     = HexColor("#1C2833")
EXP_BG    = HexColor("#EAF2FF")
EXP_BAR   = HexColor("#2980B9")

OPT_LABELS = ["A", "B", "C", "D"]

# ── styles ────────────────────────────────────────────────────────────────────
def S(n, **kw): return ParagraphStyle(n, **kw)

cov_title = S("CT", fontSize=30, leading=36, textColor=WHITE,
              fontName="Helvetica-Bold", alignment=TA_CENTER, spaceAfter=6)
cov_sub   = S("CS", fontSize=13, leading=17, textColor=HexColor("#AED6F1"),
              fontName="Helvetica", alignment=TA_CENTER)
cov_note  = S("CN", fontSize=9,  leading=13, textColor=HexColor("#D6EAF8"),
              fontName="Helvetica-Oblique", alignment=TA_CENTER)
sec_hdr   = S("SH", fontSize=14, leading=18, textColor=WHITE,
              fontName="Helvetica-Bold")
q_num     = S("QN", fontSize=9,  leading=12, textColor=WHITE,
              fontName="Helvetica-Bold", alignment=TA_CENTER)
q_text    = S("QT", fontSize=10, leading=15, textColor=BLACK,
              fontName="Helvetica", alignment=TA_JUSTIFY, spaceAfter=4)
opt_text  = S("OT", fontSize=9.5, leading=14, textColor=BLACK,
              fontName="Helvetica")
opt_corr  = S("OC", fontSize=9.5, leading=14, textColor=CORRECT,
              fontName="Helvetica-Bold")
ans_lbl   = S("AL", fontSize=8.5, leading=12, textColor=WHITE,
              fontName="Helvetica-Bold", alignment=TA_CENTER)
exp_text  = S("ET", fontSize=9,  leading=14, textColor=HexColor("#1B2631"),
              fontName="Helvetica", alignment=TA_JUSTIFY)
footer_s  = S("FS", fontSize=7,  leading=9,  textColor=GREY_M,
              fontName="Helvetica", alignment=TA_CENTER)
badge_s   = S("BS", fontSize=8,  leading=11, textColor=WHITE,
              fontName="Helvetica-Bold", alignment=TA_CENTER)
pg_title  = S("PT", fontSize=15, leading=19, textColor=WHITE,
              fontName="Helvetica-Bold", alignment=TA_CENTER)
toc_sub   = S("TS", fontSize=10, leading=14, textColor=BLUE_MID,
              fontName="Helvetica-Bold", spaceBefore=4)
toc_item  = S("TI", fontSize=9,  leading=13, textColor=BLACK,
              fontName="Helvetica", leftIndent=12)

# ── page templates ────────────────────────────────────────────────────────────
def cover_bg(canvas, doc):
    canvas.saveState()
    w, h = A4
    canvas.setFillColor(NAVY); canvas.rect(0, 0, w, h, fill=1, stroke=0)
    canvas.setFillColor(BLUE_MID); canvas.rect(0, h*0.42, w, h*0.58, fill=1, stroke=0)
    canvas.setFillColor(CORRECT); canvas.rect(0, h*0.41, w, 5, fill=1, stroke=0)
    canvas.setFillColor(GOLD);    canvas.rect(0, h*0.41-5, w, 5, fill=1, stroke=0)
    canvas.restoreState()

def inner_bg(canvas, doc):
    canvas.saveState()
    w, h = A4
    canvas.setFillColor(NAVY); canvas.rect(0, h-13*mm, w, 13*mm, fill=1, stroke=0)
    canvas.setFillColor(WHITE); canvas.setFont("Helvetica-Bold", 8.5)
    canvas.drawString(15*mm, h-8.5*mm, "NEET PG / INI-CET  |  Medicine PYQ Bank 2026")
    canvas.setFont("Helvetica", 8)
    canvas.drawRightString(w-15*mm, h-8.5*mm, f"Page {doc.page}")
    canvas.setFillColor(NAVY); canvas.rect(0, 0, w, 9*mm, fill=1, stroke=0)
    canvas.setFillColor(GREY_M); canvas.setFont("Helvetica-Oblique", 6.5)
    canvas.drawCentredString(w/2, 3.5*mm,
        "High-yield clinical questions based on 5-year NEET PG & INI-CET PYQ analysis  |  Prepared by Orris AI, June 2026")
    canvas.restoreState()

doc = BaseDocTemplate(OUTPUT, pagesize=A4,
                      leftMargin=18*mm, rightMargin=18*mm,
                      topMargin=20*mm, bottomMargin=16*mm)
cov_frame = Frame(0, 0, A4[0], A4[1],
                  leftPadding=22*mm, rightPadding=22*mm,
                  topPadding=52*mm, bottomPadding=18*mm, id="cov")
inn_frame = Frame(doc.leftMargin, doc.bottomMargin,
                  A4[0]-doc.leftMargin-doc.rightMargin,
                  A4[1]-doc.topMargin-doc.bottomMargin, id="inn")
doc.addPageTemplates([
    PageTemplate(id="Cover", frames=[cov_frame], onPage=cover_bg),
    PageTemplate(id="Inner", frames=[inn_frame], onPage=inner_bg),
])

story = []

# ══════════════════════════════════════════════════════════════════════════════
# COVER
# ══════════════════════════════════════════════════════════════════════════════
story.append(Spacer(1, 28*mm))
story.append(Paragraph("NEET PG / INI-CET", cov_sub))
story.append(Spacer(1, 4))
story.append(Paragraph("MEDICINE", cov_title))
story.append(Paragraph("PYQ QUESTION BANK", cov_title))
story.append(Spacer(1, 8))
story.append(HRFlowable(width="55%", thickness=2, color=GOLD, hAlign="CENTER"))
story.append(Spacer(1, 10))
story.append(Paragraph("Clinical Vignette Style  |  4 Options  |  Detailed Explanations", cov_sub))
story.append(Spacer(1, 6))
story.append(Paragraph(
    "Based on 5-year AI analysis of NEET PG & INI-CET PYQs (2021–2026)<br/>"
    "Covers: Cardiology • Endocrinology • Pulmonology • Neurology •<br/>"
    "Rheumatology • Infectious Diseases • Nephrology & Gastroenterology",
    cov_note))
story.append(Spacer(1, 18))

# stat strip on cover
total_qs = sum(len(s["questions"]) for s in SECTIONS)
stat_d = [[
    Paragraph(f"<b>{total_qs}</b><br/>Total Questions", badge_s),
    Paragraph(f"<b>{len(SECTIONS)}</b><br/>Topic Sections", badge_s),
    Paragraph("<b>4</b><br/>Options Each", badge_s),
    Paragraph("<b>Full</b><br/>Explanations", badge_s),
]]
stat_t = Table(stat_d, colWidths=[35*mm]*4)
stat_t.setStyle(TableStyle([
    ("BACKGROUND",   (0,0),(0,-1), CORRECT),
    ("BACKGROUND",   (1,0),(1,-1), BLUE_MID),
    ("BACKGROUND",   (2,0),(2,-1), HexColor("#8E44AD")),
    ("BACKGROUND",   (3,0),(3,-1), HexColor("#D35400")),
    ("TEXTCOLOR",    (0,0),(-1,-1), WHITE),
    ("FONTNAME",     (0,0),(-1,-1), "Helvetica-Bold"),
    ("FONTSIZE",     (0,0),(-1,-1), 9),
    ("ALIGN",        (0,0),(-1,-1), "CENTER"),
    ("VALIGN",       (0,0),(-1,-1), "MIDDLE"),
    ("TOPPADDING",   (0,0),(-1,-1), 9),
    ("BOTTOMPADDING",(0,0),(-1,-1), 9),
    ("GRID",         (0,0),(-1,-1), 1, WHITE),
]))
story.append(stat_t)
story.append(Spacer(1, 18))
story.append(Paragraph("Prepared by Orris AI  |  June 2026", cov_note))

story.append(NextPageTemplate("Inner"))
story.append(PageBreak())

# ══════════════════════════════════════════════════════════════════════════════
# HOW TO USE  (Page 2)
# ══════════════════════════════════════════════════════════════════════════════
def hdr_bar(text, color=BLUE_MID):
    p = Paragraph(f"  {text}", S("hb", fontSize=12, leading=16,
                  fontName="Helvetica-Bold", textColor=WHITE))
    t = Table([[p]], colWidths=[174*mm])
    t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0),(-1,-1), color),
        ("TOPPADDING",   (0,0),(-1,-1), 7),
        ("BOTTOMPADDING",(0,0),(-1,-1), 7),
        ("LEFTPADDING",  (0,0),(-1,-1), 8),
    ]))
    return t

story.append(hdr_bar("HOW TO USE THIS QUESTION BANK", NAVY))
story.append(Spacer(1, 8))

how_rows = [
    ["1", "Read the question stem carefully — most questions are clinical vignette style."],
    ["2", "Attempt to answer before looking at the options (improves retention)."],
    ["3", "The correct answer is highlighted in GREEN with a tick mark (✓)."],
    ["4", "Each question has a detailed explanation — read even if you got it right."],
    ["5", "HIGH-YIELD markers indicate questions with the highest repeat frequency in PYQs."],
    ["6", "Topics follow the priority list from the Master High-Yield PDF."],
]
for num, tip in how_rows:
    row = [[
        Paragraph(f"<b>{num}</b>", S("rn", fontSize=11, fontName="Helvetica-Bold",
                  textColor=WHITE, alignment=TA_CENTER)),
        Paragraph(tip, S("rt", fontSize=9.5, fontName="Helvetica", textColor=BLACK,
                         leading=14))
    ]]
    rt = Table(row, colWidths=[12*mm, 162*mm])
    rt.setStyle(TableStyle([
        ("BACKGROUND",   (0,0),(0,-1), BLUE_MID),
        ("BACKGROUND",   (1,0),(1,-1), BLUE_LT),
        ("VALIGN",       (0,0),(-1,-1), "MIDDLE"),
        ("TOPPADDING",   (0,0),(-1,-1), 6),
        ("BOTTOMPADDING",(0,0),(-1,-1), 6),
        ("LEFTPADDING",  (0,0),(0,-1),  3),
        ("LEFTPADDING",  (1,0),(1,-1),  10),
        ("LINEBELOW",    (0,0),(-1,-1), 0.5, WHITE),
    ]))
    story.append(rt)
    story.append(Spacer(1, 1))

story.append(Spacer(1, 12))

# Contents table
story.append(hdr_bar("CONTENTS", NAVY))
story.append(Spacer(1, 6))
for i, sec in enumerate(SECTIONS):
    sc = HexColor(sec["color"])
    badge = Table([[Paragraph(f"  {len(sec['questions'])} Qs  ", badge_s)]],
                  colWidths=[18*mm])
    badge.setStyle(TableStyle([
        ("BACKGROUND",   (0,0),(-1,-1), sc),
        ("TOPPADDING",   (0,0),(-1,-1), 3),
        ("BOTTOMPADDING",(0,0),(-1,-1), 3),
    ]))
    row = [[
        Paragraph(f"<b>{i+1}. {sec['title']}</b>",
                  S("ci", fontSize=10, fontName="Helvetica-Bold", textColor=sc)),
        badge
    ]]
    ct = Table(row, colWidths=[152*mm, 22*mm])
    ct.setStyle(TableStyle([
        ("BACKGROUND",   (0,0),(-1,-1), GREY_L),
        ("TOPPADDING",   (0,0),(-1,-1), 6),
        ("BOTTOMPADDING",(0,0),(-1,-1), 6),
        ("LEFTPADDING",  (0,0),(0,-1),  10),
        ("LINEBELOW",    (0,0),(-1,-1), 0.5, GREY_M),
        ("VALIGN",       (0,0),(-1,-1), "MIDDLE"),
    ]))
    story.append(ct)
    story.append(Spacer(1, 2))

story.append(PageBreak())

# ══════════════════════════════════════════════════════════════════════════════
# QUESTION SECTIONS
# ══════════════════════════════════════════════════════════════════════════════
global_q = 0

for sec_idx, section in enumerate(SECTIONS):
    sec_color = HexColor(section["color"])

    # Section divider page banner
    banner_p = Paragraph(
        f"SECTION {sec_idx+1}  |  {section['title']}",
        S("bp", fontSize=16, fontName="Helvetica-Bold", textColor=WHITE, alignment=TA_CENTER)
    )
    count_p = Paragraph(
        f"{len(section['questions'])} Questions  |  Clinical Vignette Style",
        S("bq", fontSize=10, fontName="Helvetica", textColor=HexColor("#AED6F1"), alignment=TA_CENTER)
    )
    banner_t = Table([[banner_p], [count_p]], colWidths=[174*mm])
    banner_t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0),(-1,-1), sec_color),
        ("TOPPADDING",   (0,0),(0,0),   14),
        ("BOTTOMPADDING",(0,0),(0,0),   4),
        ("TOPPADDING",   (0,1),(0,1),   2),
        ("BOTTOMPADDING",(0,1),(0,1),   14),
        ("LEFTPADDING",  (0,0),(-1,-1), 10),
        ("RIGHTPADDING", (0,0),(-1,-1), 10),
    ]))
    story.append(banner_t)
    story.append(Spacer(1, 10))

    for q_idx, q_data in enumerate(section["questions"]):
        global_q += 1
        q_num_val = global_q

        # Determine correct answer
        correct_idx = q_data["ans"]

        # ── question number badge + question text ──
        num_badge = Table(
            [[Paragraph(f"Q{q_num_val}", S("qnb", fontSize=9, fontName="Helvetica-Bold",
                        textColor=WHITE, alignment=TA_CENTER))]],
            colWidths=[10*mm])
        num_badge.setStyle(TableStyle([
            ("BACKGROUND",   (0,0),(-1,-1), sec_color),
            ("TOPPADDING",   (0,0),(-1,-1), 5),
            ("BOTTOMPADDING",(0,0),(-1,-1), 5),
        ]))

        q_para = Paragraph(q_data["q"], q_text)

        q_row = Table([[num_badge, q_para]], colWidths=[12*mm, 162*mm])
        q_row.setStyle(TableStyle([
            ("BACKGROUND",   (1,0),(1,-1), GREY_L),
            ("VALIGN",       (0,0),(-1,-1), "TOP"),
            ("TOPPADDING",   (0,0),(-1,-1), 6),
            ("BOTTOMPADDING",(0,0),(-1,-1), 6),
            ("LEFTPADDING",  (1,0),(1,-1),  10),
            ("RIGHTPADDING", (1,0),(1,-1),  8),
        ]))

        # ── options ──
        opt_rows = []
        for oi, opt in enumerate(q_data["opts"]):
            is_correct = (oi == correct_idx)
            label = OPT_LABELS[oi]
            if is_correct:
                label_p = Paragraph(f"<b>{label} ✓</b>", S("ol_c", fontSize=9.5,
                    fontName="Helvetica-Bold", textColor=WHITE, alignment=TA_CENTER))
                opt_p   = Paragraph(f"<b>{opt}</b>", S("oc", fontSize=9.5,
                    fontName="Helvetica-Bold", textColor=CORRECT, alignment=TA_LEFT))
                lbl_bg  = CORRECT
                opt_bg  = CORRECT_L
            else:
                label_p = Paragraph(f"{label}", S("ol_n", fontSize=9.5,
                    fontName="Helvetica-Bold", textColor=WHITE, alignment=TA_CENTER))
                opt_p   = Paragraph(opt, S("on", fontSize=9.5, fontName="Helvetica",
                    textColor=BLACK, alignment=TA_LEFT))
                lbl_bg  = BLUE_MID
                opt_bg  = WHITE

            row_t = Table([[label_p, opt_p]], colWidths=[10*mm, 82*mm])
            row_t.setStyle(TableStyle([
                ("BACKGROUND",   (0,0),(0,-1), lbl_bg),
                ("BACKGROUND",   (1,0),(1,-1), opt_bg),
                ("VALIGN",       (0,0),(-1,-1), "MIDDLE"),
                ("TOPPADDING",   (0,0),(-1,-1), 5),
                ("BOTTOMPADDING",(0,0),(-1,-1), 5),
                ("LEFTPADDING",  (1,0),(1,-1),  8),
                ("LINEBELOW",    (0,0),(-1,-1), 0.4, GREY_M),
            ]))
            opt_rows.append(row_t)

        # 2-column options layout
        left_opts  = opt_rows[:2]
        right_opts = opt_rows[2:]
        # stack each column
        def col_stack(rows):
            cells = [[r] for r in rows]
            t = Table(cells, colWidths=[92*mm])
            t.setStyle(TableStyle([
                ("LEFTPADDING",  (0,0),(-1,-1), 0),
                ("RIGHTPADDING", (0,0),(-1,-1), 0),
                ("TOPPADDING",   (0,0),(-1,-1), 0),
                ("BOTTOMPADDING",(0,0),(-1,-1), 0),
            ]))
            return t

        opts_2col = Table([[col_stack(left_opts), col_stack(right_opts)]],
                          colWidths=[92*mm, 92*mm])
        opts_2col.setStyle(TableStyle([
            ("LEFTPADDING",  (0,0),(-1,-1), 0),
            ("RIGHTPADDING", (0,0),(-1,-1), 0),
            ("TOPPADDING",   (0,0),(-1,-1), 0),
            ("BOTTOMPADDING",(0,0),(-1,-1), 0),
            ("VALIGN",       (0,0),(-1,-1), "TOP"),
        ]))

        # ── explanation box ──
        exp_lbl = Table(
            [[Paragraph("EXPLANATION", S("el", fontSize=8, fontName="Helvetica-Bold",
                        textColor=WHITE, alignment=TA_CENTER))]],
            colWidths=[22*mm])
        exp_lbl.setStyle(TableStyle([
            ("BACKGROUND",   (0,0),(-1,-1), EXP_BAR),
            ("TOPPADDING",   (0,0),(-1,-1), 4),
            ("BOTTOMPADDING",(0,0),(-1,-1), 4),
        ]))
        exp_para = Paragraph(q_data["exp"], exp_text)
        exp_row = Table([[exp_lbl, exp_para]], colWidths=[24*mm, 150*mm])
        exp_row.setStyle(TableStyle([
            ("BACKGROUND",   (1,0),(1,-1), EXP_BG),
            ("VALIGN",       (0,0),(-1,-1), "TOP"),
            ("TOPPADDING",   (0,0),(-1,-1), 6),
            ("BOTTOMPADDING",(0,0),(-1,-1), 6),
            ("LEFTPADDING",  (1,0),(1,-1),  8),
            ("RIGHTPADDING", (1,0),(1,-1),  8),
            ("LINEBEFORE",   (1,0),(1,-1),  3, EXP_BAR),
        ]))

        # Assemble the question block
        block = KeepTogether([
            q_row,
            Spacer(1, 3),
            opts_2col,
            Spacer(1, 3),
            exp_row,
            Spacer(1, 8),
            HRFlowable(width="100%", thickness=0.4, color=GREY_M),
            Spacer(1, 6),
        ])
        story.append(block)

    # Section break (except last)
    if sec_idx < len(SECTIONS) - 1:
        story.append(PageBreak())

# ══════════════════════════════════════════════════════════════════════════════
# BACK PAGE - QUICK REFERENCE
# ══════════════════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(hdr_bar("QUICK REFERENCE: KEY CLINICAL PEARLS", NAVY))
story.append(Spacer(1, 8))

pearls = [
    ("Cardiology", [
        "Inferior MI (ST↑ in II,III,aVF) = RCA occlusion",
        "Primary PCI door-to-balloon time: <90 minutes",
        "HFrEF first-line: ACEi + Beta-blocker + Loop diuretic + MRA",
        "AF + HFrEF: Digoxin for rate control (NOT verapamil/diltiazem)",
        "Killip IV = Cardiogenic shock (SBP <90, oliguria)",
    ]),
    ("Endocrinology", [
        "Graves' disease: TSI antibody, exophthalmos, diffuse goiter",
        "Addison's: LOW cortisol + HIGH ACTH = hyperpigmentation",
        "Conn's: HIGH aldosterone + SUPPRESSED renin (primary hyperaldo)",
        "Central DI: Desmopressin concentrates urine (unlike nephrogenic DI)",
        "HbA1c target: <7% for most adults with T2DM",
    ]),
    ("Pulmonology", [
        "ARDS: Berlin - PF ratio <300 (mild 200-300, mod 100-200, severe <100)",
        "Lung protective ventilation: TV 6 mL/kg IBW + high PEEP",
        "Sarcoidosis: Kveim test, bilateral hilar LAD, elevated ACE",
        "PE: Wells score → D-dimer (low prob) or CTPA (high prob)",
        "Exudative effusion Light's criteria: PF/S protein >0.5 or PF/S LDH >0.6",
    ]),
    ("Neurology", [
        "Inferior wall MI + stroke in young = paradoxical embolism (PFO)",
        "Status epilepticus Rx: Benzodiazepine → Phenytoin → Propofol",
        "GBS: Albuminocytological dissociation (high protein, normal cells)",
        "CJD: Rapidly progressive dementia + myoclonus + EEG periodic complexes",
        "INO = MLF lesion = bilateral INO in young → MS",
    ]),
    ("Infectious Diseases", [
        "PCP: CD4 <200, bilateral ground-glass, high LDH, Rx: TMP-SMX",
        "IRIS in HIV-TB: Paradoxical worsening after ART start",
        "Scrub typhus: Eschar + Weil-Felix OXK positive + doxycycline",
        "Weil's disease = jaundice + AKI + bleeding in leptospirosis",
        "ART 'Treat All' strategy: Start regardless of CD4 count (WHO 2023)",
    ]),
    ("Gastroenterology", [
        "Child-Pugh: Bilirubin + Albumin + PT + Ascites + Encephalopathy",
        "MELD score: INR + Bilirubin + Creatinine",
        "SBP: Ascitic fluid PMN ≥250 cells/µL → Cefotaxime IV",
        "Crohn's = SKIP lesions, transmural, cobblestone, fistulae",
        "Wilson's: Low ceruloplasmin, KF rings, high 24h urine copper",
    ]),
]

for subj, pts in pearls:
    # Find matching section color
    sec_c = BLUE_MID
    for s in SECTIONS:
        if any(word in s["title"] for word in subj.split()):
            sec_c = HexColor(s["color"])
            break
    story.append(Paragraph(f"  {subj}", S("ps", fontSize=10, fontName="Helvetica-Bold",
                            textColor=WHITE, backColor=sec_c, leading=15,
                            leftIndent=-6)))

    # Actually render as a header bar
    ph = Table([[Paragraph(f"  {subj}", S("ph2", fontSize=10, fontName="Helvetica-Bold",
                            textColor=WHITE))]],
               colWidths=[174*mm])
    ph.setStyle(TableStyle([
        ("BACKGROUND",   (0,0),(-1,-1), sec_c),
        ("TOPPADDING",   (0,0),(-1,-1), 4),
        ("BOTTOMPADDING",(0,0),(-1,-1), 4),
        ("LEFTPADDING",  (0,0),(-1,-1), 8),
    ]))
    story.pop()  # remove the stray para
    story.append(ph)

    for pt in pts:
        story.append(Paragraph(f"• {pt}",
                               S("ppt", fontSize=9, fontName="Helvetica", textColor=BLACK,
                                 leftIndent=12, leading=14, spaceAfter=1)))
    story.append(Spacer(1, 5))

# Final disclaimer
disc = Table([[Paragraph(
    "<b>Disclaimer:</b> All questions are modelled on NEET PG / INI-CET pattern based on "
    "5-year PYQ analysis. Some are verbatim pattern-matched; others are high-yield reconstructions. "
    "Always verify clinical decisions with standard textbooks (Harrison's, Davidson's, API). "
    "Prepared by Orris AI | June 2026.",
    S("disc", fontSize=8, fontName="Helvetica-Oblique", textColor=HexColor("#555555"), leading=12)
)]], colWidths=[174*mm])
disc.setStyle(TableStyle([
    ("BACKGROUND",   (0,0),(-1,-1), GREY_L),
    ("TOPPADDING",   (0,0),(-1,-1), 8),
    ("BOTTOMPADDING",(0,0),(-1,-1), 8),
    ("LEFTPADDING",  (0,0),(-1,-1), 10),
    ("RIGHTPADDING", (0,0),(-1,-1), 10),
    ("BOX",          (0,0),(-1,-1), 0.5, GREY_M),
]))
story.append(Spacer(1, 10))
story.append(disc)

# ── BUILD ─────────────────────────────────────────────────────────────────────
doc.build(story)
print(f"PDF created: {OUTPUT}")
print(f"Total questions: {global_q}")