classification of diets according to nutrients BSc nutrition

classification of diets based on nutrients macronutrients micronutrients energy forming protective body building

https://www.fao.org/4/w0078e/w0078e08.htm

mkdir -p /home/daytona/workspace/diet-classification && ls /home/daytona/workspa…

~/diet-classification/generate_pdf.py

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

OUTPUT = "/home/daytona/workspace/diet-classification/Classification_of_Diets_According_to_Nutrients.pdf"

# ── Colour palette ──────────────────────────────────────────────────────────
DEEP_GREEN   = colors.HexColor("#1B5E20")
MID_GREEN    = colors.HexColor("#2E7D32")
LIGHT_GREEN  = colors.HexColor("#A5D6A7")
PALE_GREEN   = colors.HexColor("#E8F5E9")
ACCENT_GOLD  = colors.HexColor("#F9A825")
ACCENT_LIGHT = colors.HexColor("#FFF8E1")
DARK_GREY    = colors.HexColor("#263238")
MID_GREY     = colors.HexColor("#546E7A")
LIGHT_GREY   = colors.HexColor("#ECEFF1")
WHITE        = colors.white
RED_DARK     = colors.HexColor("#B71C1C")
BLUE_DARK    = colors.HexColor("#0D47A1")
BLUE_MID     = colors.HexColor("#1976D2")
BLUE_LIGHT   = colors.HexColor("#E3F2FD")
ORANGE_DARK  = colors.HexColor("#E65100")
ORANGE_LIGHT = colors.HexColor("#FFF3E0")

PAGE_W, PAGE_H = A4

# ── Header / Footer ─────────────────────────────────────────────────────────
def on_page(canv, doc):
    canv.saveState()
    # top banner
    canv.setFillColor(DEEP_GREEN)
    canv.rect(0, PAGE_H - 22*mm, PAGE_W, 22*mm, fill=1, stroke=0)
    canv.setFillColor(WHITE)
    canv.setFont("Helvetica-Bold", 9)
    canv.drawCentredString(PAGE_W/2, PAGE_H - 13*mm,
        "CLASSIFICATION OF DIETS ACCORDING TO NUTRIENTS  |  BSc Final Year Nutrition")
    # thin gold line
    canv.setFillColor(ACCENT_GOLD)
    canv.rect(0, PAGE_H - 24*mm, PAGE_W, 2*mm, fill=1, stroke=0)
    # footer
    canv.setFillColor(LIGHT_GREY)
    canv.rect(0, 0, PAGE_W, 12*mm, fill=1, stroke=0)
    canv.setFillColor(MID_GREY)
    canv.setFont("Helvetica", 7.5)
    canv.drawString(15*mm, 4*mm, "BSc Nutrition — Dietary Sciences  |  Academic Study Notes")
    canv.drawRightString(PAGE_W - 15*mm, 4*mm, f"Page {doc.page}")
    canv.restoreState()

def on_first_page(canv, doc):
    on_page(canv, doc)

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

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

title_s = S("TitleS", fontName="Helvetica-Bold", fontSize=22,
            textColor=WHITE, alignment=TA_CENTER, leading=28)
sub_s   = S("SubS",   fontName="Helvetica",      fontSize=11,
            textColor=LIGHT_GREEN, alignment=TA_CENTER, leading=16)

h1 = S("H1", fontName="Helvetica-Bold", fontSize=14,
        textColor=WHITE, leading=18,
        backColor=MID_GREEN, borderPad=6,
        leftIndent=0, rightIndent=0, spaceBefore=14, spaceAfter=6)
h2 = S("H2", fontName="Helvetica-Bold", fontSize=12,
        textColor=DEEP_GREEN, leading=16, spaceBefore=10, spaceAfter=4)
h3 = S("H3", fontName="Helvetica-BoldOblique", fontSize=10.5,
        textColor=MID_GREEN, leading=14, spaceBefore=8, spaceAfter=3)
body = S("Body", fontName="Helvetica", fontSize=10,
         textColor=DARK_GREY, leading=15, spaceBefore=3, spaceAfter=3,
         alignment=TA_JUSTIFY)
bullet = S("Bullet", fontName="Helvetica", fontSize=10,
           textColor=DARK_GREY, leading=14, spaceBefore=2, spaceAfter=2,
           leftIndent=14, firstLineIndent=-10)
note = S("Note", fontName="Helvetica-Oblique", fontSize=9,
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def_key = S("DefKey", fontName="Helvetica-Bold", fontSize=10,
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callout = S("Callout", fontName="Helvetica-Bold", fontSize=10.5,
            textColor=ORANGE_DARK, leading=15, alignment=TA_CENTER)

# ── Helper builders ──────────────────────────────────────────────────────────
def section_bar(text, bg=MID_GREEN, fg=WHITE, fs=13):
    style = S("_sb", fontName="Helvetica-Bold", fontSize=fs,
              textColor=fg, backColor=bg, leading=fs+5,
              leftIndent=6, rightIndent=6, spaceBefore=14, spaceAfter=6,
              borderPad=5)
    return Paragraph(text, style)

def sub_bar(text):
    style = S("_ssb", fontName="Helvetica-Bold", fontSize=11,
              textColor=DEEP_GREEN, backColor=PALE_GREEN, leading=15,
              leftIndent=4, borderPad=4, spaceBefore=8, spaceAfter=4)
    return Paragraph(text, style)

def bp(text):
    return Paragraph(f"•  {text}", bullet)

def body_p(text):
    return Paragraph(text, body)

def key_value(key, value):
    style = S("_kv", fontName="Helvetica", fontSize=10,
              textColor=DARK_GREY, leading=14, spaceBefore=2,
              leftIndent=14)
    return Paragraph(f"<b>{key}:</b> {value}", style)

def info_box(lines, bg=BLUE_LIGHT, border=BLUE_MID):
    data = [[Paragraph(l, body)] for l in lines]
    t = Table(data, colWidths=[16*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), bg),
        ("BOX",        (0,0), (-1,-1), 0.8, border),
        ("LEFTPADDING",(0,0), (-1,-1), 10),
        ("RIGHTPADDING",(0,0),(-1,-1), 10),
        ("TOPPADDING", (0,0), (-1,-1), 6),
        ("BOTTOMPADDING",(0,0),(-1,-1), 6),
    ]))
    return t

def gold_box(lines):
    return info_box(lines, bg=ACCENT_LIGHT, border=ACCENT_GOLD)

def orange_box(lines):
    return info_box(lines, bg=ORANGE_LIGHT, border=ORANGE_DARK)

def simple_table(headers, rows, col_widths=None, hdr_bg=MID_GREEN):
    if col_widths is None:
        n = len(headers)
        col_widths = [16*cm/n]*n
    hdr_cells = [Paragraph(f"<b>{h}</b>",
                    S("_th", fontName="Helvetica-Bold", fontSize=9.5,
                      textColor=WHITE, leading=13)) for h in headers]
    table_data = [hdr_cells]
    for i, row in enumerate(rows):
        cells = [Paragraph(str(c),
                    S("_td", fontName="Helvetica", fontSize=9,
                      textColor=DARK_GREY, leading=13)) for c in row]
        table_data.append(cells)
    t = Table(table_data, colWidths=col_widths, repeatRows=1)
    row_bg = [
        ("BACKGROUND", (0,0), (-1,0), hdr_bg),
        ("GRID", (0,0), (-1,-1), 0.4, colors.HexColor("#B0BEC5")),
        ("ROWBACKGROUNDS", (0,1), (-1,-1), [WHITE, LIGHT_GREY]),
        ("LEFTPADDING",(0,0),(-1,-1), 7),
        ("RIGHTPADDING",(0,0),(-1,-1), 7),
        ("TOPPADDING",(0,0),(-1,-1), 5),
        ("BOTTOMPADDING",(0,0),(-1,-1), 5),
        ("VALIGN",(0,0),(-1,-1),"MIDDLE"),
    ]
    t.setStyle(TableStyle(row_bg))
    return t

# ── Build story ──────────────────────────────────────────────────────────────
story = []
SP = Spacer(1, 0.35*cm)
LP = Spacer(1, 0.7*cm)

# ════════════════════════════════════════════════════════════════════════════
#  COVER PAGE
# ════════════════════════════════════════════════════════════════════════════
story.append(Spacer(1, 2.8*cm))

# cover background box
cover_data = [[
    Paragraph("CLASSIFICATION OF DIETS<br/>ACCORDING TO NUTRIENTS",
        S("_ct", fontName="Helvetica-Bold", fontSize=24,
          textColor=WHITE, alignment=TA_CENTER, leading=32)),
]]
cover_t = Table(cover_data, colWidths=[17*cm])
cover_t.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(-1,-1), DEEP_GREEN),
    ("BOX",(0,0),(-1,-1), 3, ACCENT_GOLD),
    ("TOPPADDING",(0,0),(-1,-1), 20),
    ("BOTTOMPADDING",(0,0),(-1,-1), 20),
    ("LEFTPADDING",(0,0),(-1,-1), 18),
    ("RIGHTPADDING",(0,0),(-1,-1), 18),
]))
story.append(cover_t)
story.append(Spacer(1, 0.5*cm))
story.append(Paragraph("Comprehensive Academic Study Notes", sub_s))
story.append(Spacer(1, 0.3*cm))
story.append(HRFlowable(width="80%", thickness=2, color=ACCENT_GOLD,
                        hAlign="CENTER"))
story.append(Spacer(1, 0.4*cm))
story.append(Paragraph("BSc Final Year — Nutrition &amp; Dietetics", sub_s))
story.append(Spacer(1, 0.5*cm))

# quick-reference badge
badge_data = [
    [Paragraph("TOPICS COVERED", S("_bt", fontName="Helvetica-Bold",
        fontSize=10, textColor=WHITE, alignment=TA_CENTER))],
    [Paragraph(
        "• Nutrient Classification Framework<br/>"
        "• Macronutrients in Detail (Carbohydrates, Proteins, Lipids)<br/>"
        "• Micronutrients (Vitamins &amp; Minerals)<br/>"
        "• Water &amp; Dietary Fibre<br/>"
        "• Functional Food Classification<br/>"
        "• Dietary Patterns &amp; Therapeutic Diets<br/>"
        "• Energy Values &amp; RDA at a Glance",
        S("_bc", fontName="Helvetica", fontSize=9.5, textColor=DARK_GREY,
          leading=16, leftIndent=6))
    ],
]
badge_t = Table(badge_data, colWidths=[14*cm])
badge_t.setStyle(TableStyle([
    ("BACKGROUND",(0,0),(0,0), MID_GREEN),
    ("BACKGROUND",(0,1),(0,1), PALE_GREEN),
    ("BOX",(0,0),(-1,-1), 1, MID_GREEN),
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    ("BOTTOMPADDING",(0,0),(-1,-1), 7),
    ("LEFTPADDING",(0,0),(-1,-1), 12),
    ("RIGHTPADDING",(0,0),(-1,-1), 12),
]))
story.append(badge_t)
story.append(PageBreak())

# ════════════════════════════════════════════════════════════════════════════
#  SECTION 1 — INTRODUCTION
# ════════════════════════════════════════════════════════════════════════════
story.append(section_bar("1.  INTRODUCTION TO NUTRIENTS AND DIETS"))
story.append(body_p(
    "A <b>nutrient</b> is any chemical substance in food that the body uses to sustain "
    "physiological processes — growth, energy production, repair, and regulation of "
    "metabolism. The study of how food and its components affect health is the foundation "
    "of nutrition science. For a BSc Nutrition student, understanding the classification "
    "of diets and nutrients is the first step toward clinical and community nutrition practice."
))
story.append(SP)
story.append(body_p(
    "Diets can be classified in several ways. The most academically important classification "
    "is <b>according to nutrient composition</b> — that is, by the type, quantity, and "
    "proportion of nutrients they contain. This document covers that classification "
    "systematically, from the broadest categories (macronutrients vs. micronutrients) "
    "down to individual nutrient classes, dietary patterns, and therapeutic diet modifications."
))
story.append(SP)
story.append(gold_box([
    "<b>Definition (WHO/FAO):</b> A diet is the sum of food consumed by a person, "
    "characterised by the types and amounts of food, beverages, and nutrients it provides. "
    "Classifying diets by nutrients means categorising them based on which nutrient groups "
    "are dominant, absent, or modified."
]))
story.append(LP)

# ════════════════════════════════════════════════════════════════════════════
#  SECTION 2 — PRIMARY CLASSIFICATION FRAMEWORK
# ════════════════════════════════════════════════════════════════════════════
story.append(section_bar("2.  PRIMARY CLASSIFICATION: MACRONUTRIENTS vs. MICRONUTRIENTS"))
story.append(body_p(
    "The most fundamental classification divides all nutrients into two major groups based "
    "on the quantity in which they are required by the body:"
))
story.append(SP)

# Two-column overview table
story.append(simple_table(
    ["Category", "Required Amount", "Primary Nutrients", "Main Functions"],
    [
        ["Macronutrients", "Grams per day\n(large amounts)",
         "Carbohydrates, Proteins, Lipids (Fats)", "Energy supply, structural building, metabolic regulation"],
        ["Micronutrients", "Milligrams or micrograms\n(small amounts)",
         "Vitamins, Minerals", "Enzyme cofactors, antioxidants, tissue development"],
        ["Water", "~2–3 litres/day", "Water (H₂O)", "Solvent, transport medium, thermoregulation"],
        ["Dietary Fibre", "25–38 g/day", "Non-starch polysaccharides", "Gut health, cholesterol regulation, elimination"],
    ],
    col_widths=[3.5*cm, 3.2*cm, 5*cm, 5*cm],
))
story.append(LP)

# ════════════════════════════════════════════════════════════════════════════
#  SECTION 3 — MACRONUTRIENTS
# ════════════════════════════════════════════════════════════════════════════
story.append(section_bar("3.  MACRONUTRIENTS"))

# 3.1 Carbohydrates
story.append(sub_bar("3.1  Carbohydrates"))
story.append(body_p(
    "Carbohydrates are organic molecules composed of carbon, hydrogen, and oxygen (CHO). "
    "They are the <b>primary and preferred source of energy</b> for the body, particularly "
    "for the brain and central nervous system. Each gram of carbohydrate yields <b>4 kcal</b>."
))
story.append(SP)
story.append(Paragraph("Classification of Carbohydrates:", h3))
story.append(simple_table(
    ["Class", "Degree of Polymerisation", "Examples", "Digestion"],
    [
        ["Monosaccharides", "1", "Glucose, Fructose, Galactose", "Directly absorbed"],
        ["Disaccharides", "2", "Sucrose, Lactose, Maltose", "Rapidly digested"],
        ["Oligosaccharides", "3–10", "Maltodextrins, Raffinose, FOS", "Partially digested"],
        ["Polysaccharides\n(Starch)", ">10", "Amylose, Amylopectin", "Slowly digested → glucose"],
        ["Non-starch\nPolysaccharides (NSP)", ">10", "Cellulose, Pectin, Inulin", "Not digested (dietary fibre)"],
    ],
    col_widths=[3.8*cm, 3.5*cm, 4.5*cm, 4.5*cm],
))
story.append(SP)
story.append(Paragraph("Dietary Significance:", h3))
for t in [
    "Simple/refined carbohydrates (sugar, white flour) → rapid glycaemic response, linked to obesity and T2DM.",
    "Complex carbohydrates (wholegrains, legumes) → slow glucose release, high satiety, fibre-rich.",
    "Recommended intake: <b>45–65% of total daily energy (AMDR).</b>",
    "Minimum for brain function: ~130 g/day (IOM recommendation).",
]:
    story.append(bp(t))
story.append(SP)

# 3.2 Proteins
story.append(sub_bar("3.2  Proteins"))
story.append(body_p(
    "Proteins are complex macromolecules made of amino acids linked by peptide bonds. "
    "They are primarily <b>body-building nutrients</b> — essential for growth, repair, "
    "enzyme synthesis, hormone production, immune function, and transport. "
    "Each gram yields <b>4 kcal</b>, but protein is used for energy only when carbohydrate "
    "and fat stores are depleted."
))
story.append(SP)
story.append(Paragraph("Classification of Dietary Proteins:", h3))
story.append(simple_table(
    ["Type", "Description", "Examples"],
    [
        ["Complete (High biological value)", "Contains all 9 essential amino acids in adequate proportions",
         "Eggs, meat, fish, poultry, dairy"],
        ["Incomplete (Low biological value)", "Deficient in one or more essential amino acids",
         "Legumes, cereals, nuts, seeds"],
        ["Complementary proteins", "Two incomplete proteins together provide all EAAs",
         "Rice + lentils, maize + beans"],
    ],
    col_widths=[4.5*cm, 6*cm, 5.5*cm],
))
story.append(SP)
story.append(Paragraph("Essential Amino Acids (9):", h3))
story.append(body_p(
    "Histidine, Isoleucine, Leucine, Lysine, Methionine, Phenylalanine, Threonine, "
    "Tryptophan, Valine — must be obtained from the diet as the body cannot synthesise them."
))
story.append(SP)
for t in [
    "Recommended intake: <b>10–35% of total energy (AMDR); ~0.8 g/kg/day (RDA adults).</b>",
    "Athletes/growing children may need 1.2–2.0 g/kg/day.",
    "Protein quality is assessed by PDCAAS (Protein Digestibility Corrected Amino Acid Score) or DIAAS.",
]:
    story.append(bp(t))
story.append(SP)

# 3.3 Lipids
story.append(sub_bar("3.3  Lipids (Fats and Oils)"))
story.append(body_p(
    "Lipids are a diverse group of hydrophobic organic molecules. They are the most "
    "energy-dense macronutrient, yielding <b>9 kcal per gram</b>. They serve as stored "
    "energy, insulate the body, protect organs, and transport fat-soluble vitamins (A, D, E, K). "
    "Cell membranes are composed of phospholipids."
))
story.append(SP)
story.append(Paragraph("Classification of Dietary Lipids:", h3))
story.append(simple_table(
    ["Type", "Chemical Nature", "Food Sources", "Health Effect"],
    [
        ["Saturated fatty acids (SFA)", "No double bonds; solid at room temperature",
         "Butter, ghee, coconut oil, red meat", "↑ LDL cholesterol — limit intake"],
        ["Monounsaturated fatty acids (MUFA)", "One double bond",
         "Olive oil, avocado, almonds", "↓ LDL, ↑ HDL — cardioprotective"],
        ["Polyunsaturated fatty acids (PUFA)", "Two or more double bonds",
         "Fish, flaxseed, sunflower oil", "Essential; include omega-3 and omega-6"],
        ["Trans fatty acids (TFA)", "Artificially hydrogenated; uncommon in nature",
         "Margarine, processed snacks, fried food", "↑ LDL, ↓ HDL — harmful; limit <1% energy"],
        ["Cholesterol", "Sterol; not a fatty acid",
         "Egg yolk, liver, shellfish", "Essential for hormones/bile; excess raises CVD risk"],
    ],
    col_widths=[3.8*cm, 3.5*cm, 3.8*cm, 4.7*cm],
))
story.append(SP)
story.append(orange_box([
    "<b>Essential Fatty Acids:</b> Linoleic acid (omega-6) and alpha-linolenic acid (omega-3) "
    "cannot be synthesised by the body. From these, the body derives arachidonic acid (AA), "
    "EPA (eicosapentaenoic acid), and DHA (docosahexaenoic acid) — critical for brain "
    "development, inflammation regulation, and cardiovascular health."
]))
story.append(LP)

# ════════════════════════════════════════════════════════════════════════════
#  SECTION 4 — MICRONUTRIENTS
# ════════════════════════════════════════════════════════════════════════════
story.append(section_bar("4.  MICRONUTRIENTS"))
story.append(body_p(
    "Micronutrients are required in milligram or microgram quantities. Though needed in "
    "small amounts, they are indispensable for biochemical reactions, immune function, "
    "bone development, antioxidant defence, and gene expression. They are classified "
    "into <b>vitamins</b> and <b>minerals</b>."
))
story.append(SP)

# 4.1 Vitamins
story.append(sub_bar("4.1  Vitamins"))
story.append(body_p(
    "Vitamins are organic micronutrients classified primarily by their <b>solubility</b>:"
))
story.append(SP)
story.append(Paragraph("A. Fat-Soluble Vitamins (A, D, E, K)", h3))
story.append(body_p(
    "Stored in adipose tissue and liver. Can accumulate to toxic levels if consumed "
    "in excess (hypervitaminosis). Absorbed along with dietary fat."
))
story.append(simple_table(
    ["Vitamin", "Key Functions", "Deficiency Disease", "Food Sources"],
    [
        ["A (Retinol)", "Vision, epithelial integrity, immune function", "Night blindness, Xerophthalmia", "Liver, egg yolk, dairy, orange vegetables"],
        ["D (Calciferol)", "Ca²⁺ & PO₄ absorption, bone mineralisation, immune modulation", "Rickets (children), Osteomalacia (adults)", "Sunlight (skin synthesis), fatty fish, fortified milk"],
        ["E (Tocopherol)", "Antioxidant, membrane protection, immune function", "Haemolytic anaemia (rare)", "Nuts, seeds, vegetable oils, green leafy vegetables"],
        ["K (Phylloquinone/Menaquinone)", "Blood clotting (clotting factors II, VII, IX, X), bone metabolism", "Bleeding disorders, haemorrhage", "Green leafy vegetables, fermented foods, liver"],
    ],
    col_widths=[2.5*cm, 4.5*cm, 3.5*cm, 5*cm],
))
story.append(SP)
story.append(Paragraph("B. Water-Soluble Vitamins (B-complex + Vitamin C)", h3))
story.append(body_p(
    "Not stored significantly; excreted in urine when in excess. Regular dietary intake required. "
    "Rarely toxic in food form."
))
story.append(simple_table(
    ["Vitamin", "Key Functions", "Deficiency Disease", "Food Sources"],
    [
        ["B1 (Thiamin)", "Carbohydrate metabolism (pyruvate dehydrogenase)", "Beriberi, Wernicke's encephalopathy", "Whole grains, legumes, pork"],
        ["B2 (Riboflavin)", "FAD/FMN coenzyme in oxidative phosphorylation", "Ariboflavinosis (glossitis, cheilosis)", "Dairy, eggs, meat, leafy greens"],
        ["B3 (Niacin)", "NAD/NADP — energy metabolism", "Pellagra (3Ds: dermatitis, diarrhoea, dementia)", "Meat, fish, peanuts, fortified cereals"],
        ["B5 (Pantothenic acid)", "Coenzyme A synthesis", "Rare — burning feet syndrome", "Widespread in all foods"],
        ["B6 (Pyridoxine)", "Amino acid metabolism, neurotransmitter synthesis", "Peripheral neuropathy, seborrhoeic dermatitis", "Meat, fish, potatoes, bananas"],
        ["B7 (Biotin)", "Carboxylation reactions (fatty acid synthesis, gluconeogenesis)", "Rare — dermatitis, alopecia", "Egg yolk, liver, nuts"],
        ["B9 (Folate)", "DNA synthesis, cell division; critical in pregnancy", "Megaloblastic anaemia, Neural tube defects", "Green leafy veg, legumes, fortified cereals"],
        ["B12 (Cobalamin)", "Myelin synthesis, DNA metabolism, homocysteine conversion", "Pernicious anaemia, subacute combined degeneration of cord", "Animal products ONLY (meat, fish, dairy, eggs)"],
        ["C (Ascorbic acid)", "Collagen synthesis, antioxidant, enhances Fe absorption", "Scurvy (bleeding gums, impaired wound healing)", "Citrus fruit, bell peppers, berries, tomatoes"],
    ],
    col_widths=[2.5*cm, 4.5*cm, 3.5*cm, 4.8*cm],
))
story.append(LP)

# 4.2 Minerals
story.append(sub_bar("4.2  Minerals"))
story.append(body_p(
    "Minerals are inorganic elements that cannot be synthesised by the body. They are "
    "classified by the quantity required:"
))
story.append(SP)
story.append(Paragraph("A. Macrominerals (>100 mg/day)", h3))
story.append(simple_table(
    ["Mineral", "Functions", "Deficiency", "Sources"],
    [
        ["Calcium (Ca)", "Bone/teeth structure, muscle contraction, nerve signalling, blood clotting",
         "Osteoporosis, Tetany, Rickets", "Dairy, tofu, leafy greens, fortified foods"],
        ["Phosphorus (P)", "Bone/teeth, ATP, phospholipids, acid-base balance",
         "Rare — weakness, bone pain", "Meat, dairy, whole grains, legumes"],
        ["Magnesium (Mg)", ">300 enzyme reactions, protein synthesis, neuromuscular function",
         "Muscle cramps, arrhythmia, hypomagnesaemia", "Nuts, seeds, whole grains, green veg"],
        ["Sodium (Na)", "Fluid balance, nerve impulse transmission, BP regulation",
         "Hyponatraemia (nausea, confusion)", "Salt, processed food, bread, cheese"],
        ["Potassium (K)", "Intracellular fluid, heart rhythm, muscle contraction",
         "Hypokalaemia (weakness, arrhythmia)", "Bananas, potatoes, legumes, tomatoes"],
        ["Chloride (Cl)", "Gastric acid (HCl), fluid balance (with Na)",
         "Rare — metabolic alkalosis", "Table salt, seaweed"],
        ["Sulphur (S)", "Component of amino acids (Met, Cys), glutathione, CoA",
         "Rare — low protein intake", "Eggs, meat, legumes, garlic"],
    ],
    col_widths=[2.5*cm, 5*cm, 3.5*cm, 4.8*cm],
))
story.append(SP)
story.append(Paragraph("B. Microminerals / Trace Elements (<100 mg/day)", h3))
story.append(simple_table(
    ["Mineral", "Functions", "Deficiency", "Sources"],
    [
        ["Iron (Fe)", "Haemoglobin/myoglobin synthesis, oxygen transport, electron transport chain",
         "Iron-deficiency anaemia (most common nutritional deficiency worldwide)", "Red meat, legumes, dark leafy veg, fortified cereals"],
        ["Zinc (Zn)", "100+ enzyme reactions, wound healing, immune function, DNA synthesis",
         "Growth retardation, impaired immunity, hypogonadism", "Oysters, meat, legumes, seeds"],
        ["Iodine (I)", "Thyroid hormone synthesis (T3, T4) — metabolism regulation",
         "Goitre, cretinism (congenital)", "Iodised salt, seafood, dairy"],
        ["Selenium (Se)", "Antioxidant (glutathione peroxidase), thyroid hormone metabolism",
         "Keshan disease (cardiomyopathy), Kashin-Beck disease", "Brazil nuts, seafood, meat, whole grains"],
        ["Copper (Cu)", "Iron metabolism (ceruloplasmin), collagen cross-linking, antioxidant",
         "Anaemia, neurological symptoms (rare)", "Liver, shellfish, nuts, seeds"],
        ["Fluoride (F)", "Enamel mineralisation, dental caries prevention",
         "Dental caries, osteoporosis", "Fluoridated water, tea, seafood"],
        ["Chromium (Cr)", "Insulin potentiation, glucose tolerance factor",
         "Glucose intolerance, neuropathy (rare)", "Broccoli, whole grains, meat"],
        ["Manganese (Mn)", "Antioxidant enzyme (MnSOD), bone formation, carbohydrate metabolism",
         "Rare — skeletal abnormalities", "Whole grains, nuts, leafy vegetables, tea"],
    ],
    col_widths=[2.5*cm, 4.8*cm, 3.5*cm, 4.5*cm],
))
story.append(LP)

# ════════════════════════════════════════════════════════════════════════════
#  SECTION 5 — WATER AND DIETARY FIBRE
# ════════════════════════════════════════════════════════════════════════════
story.append(section_bar("5.  WATER AND DIETARY FIBRE"))
story.append(sub_bar("5.1  Water"))
story.append(body_p(
    "Water is the most abundant component of the human body (~60% of body weight). "
    "Although it provides no calories, water is essential for every physiological process."
))
for t in [
    "<b>Functions:</b> Solvent for biochemical reactions; transport medium for nutrients, gases, and waste; thermoregulation through sweating; joint lubrication; maintenance of blood pressure.",
    "<b>Requirement:</b> ~2.5–3.7 L/day for men; ~2.0–2.7 L/day for women (total from food + beverages).",
    "<b>Sources:</b> Beverages (water, milk, juices), and from food (~20% of daily intake from fruits and vegetables).",
    "<b>Dehydration consequences:</b> >2% body weight loss → impaired cognitive performance and exercise capacity; >5% → heat stroke risk.",
]:
    story.append(bp(t))
story.append(SP)

story.append(sub_bar("5.2  Dietary Fibre"))
story.append(body_p(
    "Dietary fibre includes all non-digestible carbohydrates and lignin found in plant foods. "
    "It is fermented to varying degrees by the gut microbiota."
))
story.append(simple_table(
    ["Type", "Chemical Nature", "Properties", "Health Effects", "Sources"],
    [
        ["Soluble fibre", "Pectin, beta-glucan, guar gum, inulin",
         "Dissolves in water → forms gel", "↓ LDL cholesterol, glycaemic control, prebiotic effect",
         "Oats, barley, apples, legumes, psyllium"],
        ["Insoluble fibre", "Cellulose, hemicellulose, lignin",
         "Does not dissolve; increases bulk", "Prevents constipation, reduces colorectal cancer risk",
         "Wheat bran, whole grains, vegetables, nuts"],
    ],
    col_widths=[2.5*cm, 3.5*cm, 3*cm, 4*cm, 3*cm],
))
story.append(SP)
story.append(body_p(
    "<b>Recommended intake:</b> 25 g/day (women) to 38 g/day (men) (Dietary Reference Intakes, IOM). "
    "Most populations consume well below this."
))
story.append(LP)

# ════════════════════════════════════════════════════════════════════════════
#  SECTION 6 — CLASSIFICATION BY FUNCTIONAL ROLE (TRADITIONAL)
# ════════════════════════════════════════════════════════════════════════════
story.append(section_bar("6.  TRADITIONAL FUNCTIONAL CLASSIFICATION OF FOODS/DIETS"))
story.append(body_p(
    "A classic nutritional classification groups foods — and therefore diets — by their "
    "<b>functional role</b> in the body. Originally described as the 'Three Food Groups' "
    "by early nutritionists (FAO/WHO nutrition education materials), this system classifies "
    "nutrients and the diets containing them into:"
))
story.append(SP)
story.append(simple_table(
    ["Group", "Dominant Nutrient", "Primary Function", "Examples of Foods"],
    [
        ["Energy-giving foods", "Carbohydrates, Fats",
         "Fuel for body heat, physical activity, and metabolic processes",
         "Cereals, rice, wheat, sugar, oils, ghee, nuts"],
        ["Body-building foods", "Proteins, Calcium, Iron",
         "Growth, repair and maintenance of body tissues; haemoglobin synthesis",
         "Meat, fish, eggs, milk, legumes, dals"],
        ["Protective foods", "Vitamins, Minerals, Antioxidants",
         "Regulation of metabolic processes, immune defence, protection from disease",
         "Fruits, vegetables, dairy, eggs, organ meats"],
    ],
    col_widths=[3.5*cm, 3.5*cm, 5*cm, 5*cm],
))
story.append(SP)
story.append(orange_box([
    "<b>Academic Note:</b> The three-food-group framework is now considered oversimplified "
    "because most foods contribute to more than one category. For example, milk is "
    "body-building (protein/calcium) AND protective (riboflavin/vitamin A) AND energy-giving (fat/lactose). "
    "Modern nutrition education uses food-based dietary guidelines and MyPlate-style models. "
    "However, this classification remains standard exam content for BSc Nutrition."
]))
story.append(LP)

# ════════════════════════════════════════════════════════════════════════════
#  SECTION 7 — CLASSIFICATION BY DIETARY PATTERN
# ════════════════════════════════════════════════════════════════════════════
story.append(section_bar("7.  CLASSIFICATION OF DIETS BY PATTERN"))
story.append(body_p(
    "Beyond individual nutrients, complete diets are classified by their overall "
    "nutrient composition and food-inclusion patterns:"
))
story.append(SP)
story.append(simple_table(
    ["Diet Pattern", "Nutrient Profile", "Characteristic Features"],
    [
        ["Omnivorous diet", "All macronutrients, all vitamins, complete amino acids",
         "Includes animal and plant foods; nutritionally complete if varied"],
        ["Vegetarian diet (lacto-ovo)", "Lower saturated fat; adequate protein from eggs + dairy",
         "Excludes meat/fish; includes dairy and eggs"],
        ["Vegan diet", "No cholesterol; high fibre; may be low in B12, heme-Fe, zinc, DHA, Ca",
         "All plant-based; requires supplementation or fortified foods"],
        ["Mediterranean diet", "High MUFA (olive oil), omega-3, fibre, antioxidants; low SFA",
         "Plant-forward with fish; reduces CVD and T2DM risk"],
        ["DASH diet", "High K, Ca, Mg, fibre; low sodium, saturated fat",
         "Designed to reduce blood pressure; rich in fruits, vegetables, low-fat dairy"],
        ["Ketogenic diet", "Very low carbohydrate (<50g/day), high fat, moderate protein",
         "Induces ketosis; used for epilepsy, weight loss, T2DM management"],
        ["Low-carbohydrate diet", "Reduced CHO (<130g/day); increased fat and protein",
         "Used for weight management and glycaemic control"],
        ["High-protein diet", "Protein >25% of energy intake",
         "Popular for muscle gain; concern for renal load in at-risk individuals"],
        ["Low-fat diet", "Fat <30% of energy; often <20% in therapeutic settings",
         "Used for weight loss and cardiovascular risk reduction"],
    ],
    col_widths=[4*cm, 5*cm, 7*cm],
))
story.append(LP)

# ════════════════════════════════════════════════════════════════════════════
#  SECTION 8 — THERAPEUTIC DIET CLASSIFICATIONS
# ════════════════════════════════════════════════════════════════════════════
story.append(section_bar("8.  THERAPEUTIC DIET CLASSIFICATIONS BY NUTRIENT MODIFICATION"))
story.append(body_p(
    "Therapeutic or clinical diets are prescribed to manage medical conditions by "
    "modifying one or more nutrient parameters:"
))
story.append(SP)
story.append(simple_table(
    ["Therapeutic Diet", "Nutrient Modified", "Clinical Indication"],
    [
        ["Calorie-restricted diet", "↓ Total energy (500–1000 kcal deficit)", "Obesity, metabolic syndrome, type 2 diabetes"],
        ["Protein-restricted diet", "↓ Protein (0.6 g/kg/day or less)", "Chronic kidney disease (non-dialysis), hepatic encephalopathy"],
        ["High-protein diet", "↑ Protein (1.2–1.5 g/kg/day)", "Malnutrition, post-surgery, burns, cancer cachexia"],
        ["Fat-restricted diet", "↓ Fat (<40–50 g/day)", "Gallbladder disease, malabsorption (steatorrhoea), pancreatitis"],
        ["Low-sodium diet", "↓ Na (1.5–2 g/day)", "Hypertension, heart failure, oedema, liver cirrhosis"],
        ["Low-potassium diet", "↓ K (<2 g/day)", "Renal failure with hyperkalaemia"],
        ["High-iron diet", "↑ Haem iron + enhancers (vitamin C)", "Iron-deficiency anaemia"],
        ["Calcium-modified diet", "↑ or ↓ Ca depending on condition", "↑ for osteoporosis; ↓ for hypercalciuria/kidney stones"],
        ["Gluten-free diet", "Elimination of gluten (wheat, rye, barley)", "Coeliac disease, non-coeliac gluten sensitivity"],
        ["Low-FODMAP diet", "↓ Fermentable oligo-, di-, mono-saccharides and polyols", "Irritable bowel syndrome (IBS)"],
        ["Diabetic diet", "Controlled CHO (45–60 g/meal); low GI foods; ↓ SFA", "Type 1 and Type 2 diabetes mellitus"],
        ["Dysphagia diet", "Texture modified (IDDSI framework: Levels 0–7)", "Swallowing difficulties (stroke, neurological disease)"],
    ],
    col_widths=[4.5*cm, 4*cm, 7.5*cm],
))
story.append(LP)

# ════════════════════════════════════════════════════════════════════════════
#  SECTION 9 — ENERGY VALUES AT A GLANCE
# ════════════════════════════════════════════════════════════════════════════
story.append(section_bar("9.  ENERGY VALUES AND RECOMMENDED DAILY ALLOWANCES"))
story.append(sub_bar("9.1  Atwater Factors — Energy per Gram"))
story.append(simple_table(
    ["Nutrient", "Energy (kcal/g)", "Energy (kJ/g)", "Notes"],
    [
        ["Carbohydrates", "4", "17", "Including sugars and starch"],
        ["Proteins", "4", "17", "Amino acid catabolism"],
        ["Lipids (fats/oils)", "9", "37", "Most energy-dense macronutrient"],
        ["Dietary fibre (soluble)", "2", "8", "Partial fermentation by gut bacteria"],
        ["Alcohol (ethanol)", "7", "29", "NOT a recommended energy source"],
        ["Water", "0", "0", "No caloric value"],
    ],
    col_widths=[4.5*cm, 3.5*cm, 3.5*cm, 4.5*cm],
))
story.append(SP)

story.append(sub_bar("9.2  AMDR — Acceptable Macronutrient Distribution Ranges (IOM, Adults)"))
story.append(simple_table(
    ["Macronutrient", "AMDR (% of Total Energy)", "Example for 2000 kcal diet"],
    [
        ["Carbohydrates", "45–65%", "225–325 g/day"],
        ["Proteins", "10–35%", "50–175 g/day"],
        ["Fats (Total)", "20–35%", "44–78 g/day"],
        ["  Saturated fats", "<10%", "<22 g/day"],
        ["  Trans fats", "<1%", "<2 g/day"],
        ["  Omega-6 PUFA (linoleic acid)", "5–10%", "11–22 g/day"],
        ["  Omega-3 PUFA (ALA)", "0.6–1.2%", "1.3–2.7 g/day"],
    ],
    col_widths=[5.5*cm, 5.5*cm, 5*cm],
))
story.append(LP)

# ════════════════════════════════════════════════════════════════════════════
#  SECTION 10 — QUICK SUMMARY / EXAM REVISION TABLE
# ════════════════════════════════════════════════════════════════════════════
story.append(section_bar("10.  EXAM QUICK REFERENCE SUMMARY"))
story.append(simple_table(
    ["Classification Basis", "Categories", "Key Point"],
    [
        ["Quantity required", "Macronutrients vs. Micronutrients", "Macro = grams; Micro = mg/mcg"],
        ["Solubility (vitamins)", "Fat-soluble (A, D, E, K) vs. Water-soluble (B-complex, C)", "Fat-soluble stored; water-soluble excreted"],
        ["Mineral quantity", "Macrominerals (>100mg) vs. Trace elements (<100mg)", "Ca, P, Mg, Na, K = macro; Fe, Zn, I = trace"],
        ["Carbohydrate structure", "Mono, Di, Oligo, Polysaccharides, NSP", "Classified by degree of polymerisation"],
        ["Fatty acid saturation", "SFA, MUFA, PUFA, TFA", "Trans fats most harmful; omega-3 PUFA most beneficial"],
        ["Protein quality", "Complete (HBV) vs. Incomplete (LBV)", "Animal proteins = complete; most plants = incomplete"],
        ["Functional role (classic)", "Energy-giving, Body-building, Protective", "Oversimplified but still examined"],
        ["Dietary pattern", "Omnivorous, Vegetarian, Vegan, Mediterranean, DASH, Ketogenic", "Classified by food inclusion/exclusion and nutrient profile"],
        ["Therapeutic purpose", "Calorie-restricted, Low-protein, Low-Na, Diabetic, Gluten-free, etc.", "Nutrient modification to manage disease"],
        ["Fibre type", "Soluble vs. Insoluble", "Soluble: ↓ cholesterol; Insoluble: ↓ constipation"],
    ],
    col_widths=[4.5*cm, 5.5*cm, 6*cm],
))
story.append(LP)

story.append(gold_box([
    "<b>Key Exam Points to Remember:</b>",
    "1. Macronutrients provide energy; micronutrients regulate metabolism — neither group can substitute the other.",
    "2. The Atwater factors: CHO = 4 kcal/g, Protein = 4 kcal/g, Fat = 9 kcal/g, Alcohol = 7 kcal/g.",
    "3. Only vitamin B12 is found exclusively in animal products — vegans must supplement.",
    "4. EFAs (linoleic acid and alpha-linolenic acid) must come from diet; all other fatty acids can be synthesised.",
    "5. The three food groups (energy, body-building, protective) are historically important but nutritionally oversimplified.",
    "6. AMDR for CHO: 45-65%; Protein: 10-35%; Fat: 20-35% of total energy."
]))
story.append(LP)

# ════════════════════════════════════════════════════════════════════════════
#  REFERENCES
# ════════════════════════════════════════════════════════════════════════════
story.append(HRFlowable(width="100%", thickness=1, color=LIGHT_GREEN))
story.append(SP)
story.append(Paragraph("References", h2))
for ref in [
    "FAO/WHO. (1997). <i>Agriculture, Food and Nutrition for Africa — A Resource Book for Teachers of Agriculture.</i> Chapter 7: Food, Nutrients and Diets. FAO, Rome.",
    "Institute of Medicine. (2005). <i>Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids.</i> National Academies Press.",
    "StatPearls. (2024). <i>Biochemistry, Nutrients.</i> NBK554545. NCBI Bookshelf, NIH.",
    "StatPearls. (2023). <i>Nutrition: Macronutrient Intake, Imbalances, and Interventions.</i> NBK594226. NCBI Bookshelf, NIH.",
    "Centre for Health Protection, Hong Kong. (2023). <i>Nutrient Classifications.</i> CHP, Department of Health.",
    "WHO. (2020). <i>Healthy Diet Fact Sheet.</i> World Health Organization, Geneva.",
    "Mahan, L.K. & Raymond, J.L. (2017). <i>Krause's Food & the Nutrition Care Process</i> (14th ed.). Elsevier Saunders.",
]:
    story.append(Paragraph(ref, note))
    story.append(Spacer(1, 2*mm))

# ── Build PDF ────────────────────────────────────────────────────────────────
doc = BaseDocTemplate(
    OUTPUT,
    pagesize=A4,
    leftMargin=2*cm,
    rightMargin=2*cm,
    topMargin=3.2*cm,
    bottomMargin=2.2*cm,
)
frame = Frame(doc.leftMargin, doc.bottomMargin,
              doc.width, doc.height, id='normal')
doc.addPageTemplates([
    PageTemplate(id='all', frames=frame,
                 onPage=on_page, onPageEnd=None)
])
doc.build(story)
print(f"PDF created: {OUTPUT}")

cd /home/daytona/workspace/diet-classification && python generate_pdf.py 2>&1

~/diet-classification/generate_pdf.py

Successfully replaced 1 instance(s) of the string in '/home/daytona/workspace/diet-classification/generate_pdf.py'

cd /home/daytona/workspace/diet-classification && python generate_pdf.py 2>&1

~/diet-classification/generate_pdf.py

Successfully replaced 1 instance(s) of the string in '/home/daytona/workspace/diet-classification/generate_pdf.py'

cd /home/daytona/workspace/diet-classification && python generate_pdf.py 2>&1

~/diet-classification/generate_pdf.py

Successfully replaced 1 instance(s) of the string in '/home/daytona/workspace/diet-classification/generate_pdf.py'

cd /home/daytona/workspace/diet-classification && python generate_pdf.py 2>&1

ls -lh /home/daytona/workspace/diet-classification/Classification_of_Diets_Accor…