Classification of Aharadravyas

import openpyxl
from openpyxl.styles import (
    Font, PatternFill, Alignment, Border, Side, GradientFill
)
from openpyxl.utils import get_column_letter
from openpyxl.worksheet.dimensions import ColumnDimension

wb = openpyxl.Workbook()

# ─────────────────────────────────────────────
# Helper styles
# ─────────────────────────────────────────────
def thick_border():
    s = Side(style='medium', color='4A4A4A')
    return Border(left=s, right=s, top=s, bottom=s)

def thin_border():
    s = Side(style='thin', color='AAAAAA')
    return Border(left=s, right=s, top=s, bottom=s)

def cell(ws, row, col, value, bold=False, font_size=10, font_color='000000',
         bg=None, wrap=True, align='center', valign='center', border='thin', italic=False):
    c = ws.cell(row=row, column=col, value=value)
    c.font = Font(name='Arial', bold=bold, size=font_size, color=font_color, italic=italic)
    c.alignment = Alignment(wrap_text=wrap, horizontal=align, vertical=valign)
    if bg:
        c.fill = PatternFill(fill_type='solid', fgColor=bg)
    if border == 'thick':
        c.border = thick_border()
    elif border == 'thin':
        c.border = thin_border()
    return c

# ═══════════════════════════════════════════════════════════
# SHEET 1 — The 12 Ahara Vargas (Main Comparative Chart)
# ═══════════════════════════════════════════════════════════
ws1 = wb.active
ws1.title = "12 Ahara Vargas"

# Column widths
col_widths = [4, 24, 32, 30, 36, 30, 28]
for i, w in enumerate(col_widths, 1):
    ws1.column_dimensions[get_column_letter(i)].width = w

# ── Row 1: Main Title ──
ws1.merge_cells('A1:G1')
c = cell(ws1, 1, 1,
         "CLASSIFICATION OF AHARADRAVYAS",
         bold=True, font_size=16, font_color='FFFFFF',
         bg='2E4057', wrap=True, align='center', border='thick')
ws1.row_dimensions[1].height = 36

# ── Row 2: Subtitle ──
ws1.merge_cells('A2:G2')
cell(ws1, 2, 1,
     "Classical Ayurveda (Charaka Samhita, Sutra Sthana 27)  ↔  Contemporary Nutritional Science",
     bold=True, font_size=11, font_color='FFFFFF',
     bg='048A81', wrap=True, align='center', border='thick')
ws1.row_dimensions[2].height = 24

# ── Row 3: blank spacer ──
ws1.row_dimensions[3].height = 6

# ── Row 4: Column headers ──
headers = ['#', 'Ayurvedic Varga\n(Sanskrit Name)', 'Classical Constituents\n(Examples)',
           'Ayurvedic Properties\n(Guna / Virya / Vipaka)', 'Modern Food Group\n& Category',
           'Key Nutrients\n(Contemporary Science)', 'Physiological\nCorrespondence']
header_bg = '1B4F72'
for col, h in enumerate(headers, 1):
    cell(ws1, 4, col, h, bold=True, font_size=10, font_color='FFFFFF',
         bg=header_bg, wrap=True, align='center', border='thick')
ws1.row_dimensions[4].height = 40

# ── Data rows ──
data = [
    # (No, Varga, Constituents, Ayurvedic Props, Modern Group, Key Nutrients, Physio)
    (1,
     "Shukadhanya Varga\n(Bristled/Husk Cereals)",
     "Shashtika shali (red rice), Yava (barley), Godhuma (wheat), Vrihi, Syamaka (foxtail millet), Koradusa (kodo millet)",
     "Madhura rasa, Sheeta/Ushna virya (varies), Laghu-Guru (varies); pacify Vata-Pitta; nourish Dhatus",
     "Cereals & Whole Grains\n(Monocotyledons)",
     "Complex carbohydrates, Dietary fiber, B-vitamins (B1, B3, folate), Iron, Magnesium, Moderate protein",
     "Primary energy source; supports Agni (digestive fire) ↔ Glycemic regulation, gut microbiome substrate"),

    (2,
     "Shamidhanya Varga\n(Legumes / Pulses)",
     "Mudga (green gram), Masura (lentil), Masha (black gram), Chanaka (chickpea), Kalaya (peas), Kulattha (horse gram)",
     "Kashaya-Madhura rasa, Laghu, Sheeta virya; Tridoshahara (Mudga); Ruksha, Vatakara; best protein source",
     "Legumes & Pulses\n(Dicotyledons)",
     "Plant protein (20-25%), Lysine, Dietary fiber, Folate, Iron, Zinc, Phytosterols, Isoflavones",
     "Tissue building (Dhatu Poshan) ↔ Muscle protein synthesis, LDL lowering, prebiotic effect"),

    (3,
     "Mamsa Varga\n(Meat / Animal Flesh)",
     "Jangala (dry land: deer, rabbit); Anupa (marshy/aquatic: buffalo, fish); Viskira (birds: hen, quail, pigeon)",
     "Madhura rasa, Snigdha, Guru, Ushna virya; Vata-shamaka; highly nourishing (Brumhana); Jangala = best",
     "Animal Proteins\n(Meat, Poultry, Seafood)",
     "Complete proteins (all EAAs), Vitamin B12, Heme iron, Zinc, Omega-3 FA (fish), Fat-soluble vitamins A/D",
     "Bala (strength) & Ojas building ↔ Muscle mass, immune function, neurological health"),

    (4,
     "Shaka Varga\n(Cooked Vegetables)",
     "Patola (pointed gourd), Karavellaka (bitter gourd), Vartaka (brinjal), Mulaka (radish), Palakya (spinach)",
     "Tikta-Katu dominant; Laghu, Ruksha; Kapha-Pitta shamaka; Agni-deepana; Vata-kara in excess",
     "Vegetables\n(Cooked/Leafy/Root)",
     "Vitamins A, C, K; Dietary fiber, Folate, Potassium, Phytonutrients, Antioxidants (carotenoids, flavonoids)",
     "Sroto-shodhana (channel cleansing) ↔ Antioxidant protection, gut motility, immune modulation"),

    (5,
     "Phala Varga\n(Fruits)",
     "Draksha (grapes), Dadima (pomegranate), Amalaki (Indian gooseberry), Bilva, Kapittha, Kola (jujube)",
     "Madhura-Amla rasa; Sheeta/Ushna varies; Laghu; Pitta-shamaka (sweet); Amalaki = best fruit",
     "Fruits\n(Fresh & Dried)",
     "Natural sugars (fructose), Vitamin C, Polyphenols, Quercetin, Resveratrol (grapes), Dietary fiber, Tannins",
     "Rasayana (rejuvenation) ↔ Antioxidant, anti-inflammatory, cardioprotective, immune-boosting"),

    (6,
     "Harita Varga\n(Raw Greens & Herbs)",
     "Lasuna (garlic), Ardraka (ginger), Palandu (onion), Methi (fenugreek), Tanduliya, Shatapushpa",
     "Katu-Tikta dominant; Ushna virya; Deepana-Pachana; Kapha-Vata shamaka; used raw (uncooked)",
     "Functional Foods\n(Herbs, Spices, Raw Greens)",
     "Allicin (garlic), Gingerols/Shogaols (ginger), Quercetin (onion), Fenugreekine, Volatile sulfur compounds, Chlorophyll",
     "Agni stimulation, antimicrobial ↔ Antimicrobial, anti-inflammatory, hepatoprotective, cardioprotective"),

    (7,
     "Madya Varga\n(Fermented Beverages)",
     "Sura (grain wine), Varuni (palm wine), Arishta, Asava (medicated ferments), Maireya, Medaka",
     "Amla rasa & Vipaka, Ushna virya; Laghu, Tikshna; Deepana; in moderation: Hridya, Balya; excess = toxic",
     "Fermented Foods\n& Alcoholic Beverages",
     "Probiotics (non-alcoholic ferments), Ethanol, Organic acids, B-vitamins, Polyphenols (wine), Short-chain fatty acids",
     "Agni-deepana, microbiome modulation ↔ Gut microbiota support, ethanol metabolism (liver)"),

    (8,
     "Jala / Ambu Varga\n(Water)",
     "Divya jala (rain water), Naadeya (river), Sarovara (lake), Kupa (well), Shita (cold), Ushna (boiled)",
     "No rasa/taste ideally; Laghu, Sheeta; Tridosha-shamaka; Divya jala = best; boiled water = Laghu",
     "Water & Hydration\n(Beverages)",
     "Water (H₂O), Trace minerals (Ca, Mg, K), Electrolytes, Zero macronutrients",
     "Kleda (moisture), Soma (fluidity of body) ↔ Cellular hydration, thermoregulation, nutrient transport"),

    (9,
     "Gorasa / Kshira Varga\n(Milk & Dairy Products)",
     "Go-kshira (cow milk), Mahishi (buffalo milk), Aja (goat milk), Dadhi (curd/yoghurt), Takra (buttermilk), Navanita (butter), Ghrita (ghee)",
     "Madhura rasa & Vipaka; Sheeta virya (milk/ghee); Snigdha, Guru; Ojas-vardhaka; Ghrita = best for Agni",
     "Dairy & Fermented Dairy\n(Milk, Cheese, Yoghurt)",
     "Calcium, Phosphorus, Casein & Whey protein, Vitamins A/D/K2/B12, Butyrate (ghee), Probiotics (yoghurt/buttermilk)",
     "Ojas (vital essence) nourishment ↔ Bone health, gut epithelial support (butyrate), microbiome (probiotics)"),

    (10,
     "Ikshu Varga\n(Sugarcane & Products)",
     "Ikshurasa (raw sugarcane juice), Guda (jaggery), Sarkara (sugar), Khandava, Sitopala (candy sugar)",
     "Madhura rasa; Sheeta virya (juice/guda); Snigdha; Balya, Hridya; Guda > Sarkara > refined sugar in quality",
     "Sugars & Natural Sweeteners\n(Caloric Sweeteners)",
     "Sucrose, Glucose, Fructose; Jaggery retains Iron, Potassium, Magnesium, Calcium; Molasses = B-vitamins",
     "Immediate energy (Bala) ↔ Rapid glucose for energy; jaggery = micronutrient-dense vs. empty calories (refined sugar)"),

    (11,
     "Kritanna Varga\n(Processed/Cooked Preparations)",
     "Manda (thin rice gruel), Peya (thin porridge), Yavagu (thick gruel), Vilepi (very thick), Yusha (lentil soup), Krishara (khichdi), Sattu (roasted flour)",
     "Properties depend on base ingredient; generally Laghu, Deepana, easy to digest; used in Samsarjana Krama (post-Panchakarma refeeding)",
     "Processed & Cooked Foods\n(Therapeutic & Functional Preparations)",
     "Variable macronutrient profile; enhanced bioavailability post-cooking; resistant starch (cooled rice/gruel); reduced anti-nutrients",
     "Samsarjana Krama = gradual dietary rehabilitation ↔ Modern post-surgical/post-illness graded refeeding protocol"),

    (12,
     "Ahara-Upayogi Varga\n(Accessory / Condiment Articles)",
     "Saindhava lavana (rock salt), Sauvarchala (black salt), Tila taila (sesame oil), Sarshapa taila (mustard oil), Madhu (honey), Takra",
     "Varies: Saindhava = best salt (Tridoshahara, Laghu); Madhu = best Yogavahi (carrier); Tila taila = Ushna, Snigdha",
     "Condiments, Oils, Salts\n& Flavor Enhancers",
     "Electrolytes (Na, K, Cl, Mg - rock salt); Unsaturated FA/MUFA/PUFA (oils); Methylglyoxal, Hydrogen peroxide (honey); Trace minerals",
     "Agni enhancement, bioavailability carrier ↔ Electrolyte balance, essential fatty acids, antimicrobial (honey)")
]

# Row colors (alternating)
row_colors = ['EBF5FB', 'FDFEFE']  # light blue / near white

for i, row_data in enumerate(data):
    r = 5 + i
    bg = row_colors[i % 2]
    ws1.row_dimensions[r].height = 72

    # No column
    cell(ws1, r, 1, str(row_data[0]), bold=True, font_size=11, font_color='FFFFFF',
         bg='2E4057', align='center', border='thin')

    # Varga name (col 2) — Sanskrit bold orange
    cell(ws1, r, 2, row_data[1], bold=True, font_size=10, font_color='884400',
         bg=bg, align='left', border='thin')

    # Constituents (col 3)
    cell(ws1, r, 3, row_data[2], bold=False, font_size=9, font_color='1A1A1A',
         bg=bg, align='left', border='thin')

    # Ayurvedic props (col 4)
    cell(ws1, r, 4, row_data[3], bold=False, font_size=9, font_color='1B4F72',
         bg=bg, align='left', border='thin', italic=True)

    # Modern group (col 5)
    cell(ws1, r, 5, row_data[4], bold=True, font_size=9, font_color='1D6A39',
         bg=bg, align='left', border='thin')

    # Key nutrients (col 6)
    cell(ws1, r, 6, row_data[5], bold=False, font_size=9, font_color='1A1A1A',
         bg=bg, align='left', border='thin')

    # Physiological correspondence (col 7)
    cell(ws1, r, 7, row_data[6], bold=False, font_size=9, font_color='4A235A',
         bg=bg, align='left', border='thin', italic=True)

# ── Footer row ──
r_foot = 5 + len(data)
ws1.row_dimensions[r_foot].height = 30
ws1.merge_cells(f'A{r_foot}:G{r_foot}')
cell(ws1, r_foot, 1,
     "Source: Charaka Samhita (Sutra Sthana 27) | Ashtanga Hridayam | Sushruta Samhita | WJPR 2025 (Jadhav et al.) | FAO/WHO Food Classification Framework",
     bold=False, font_size=8, font_color='666666', bg='E8E8E8',
     align='left', border='thin', italic=True)

# ═══════════════════════════════════════════════════════════
# SHEET 2 — Multi-Axis Ayurvedic Classification
# ═══════════════════════════════════════════════════════════
ws2 = wb.create_sheet("Ayurvedic Classification Axes")

col_widths2 = [22, 22, 28, 26, 26]
for i, w in enumerate(col_widths2, 1):
    ws2.column_dimensions[get_column_letter(i)].width = w

# Title
ws2.merge_cells('A1:E1')
cell(ws2, 1, 1, "AYURVEDIC CLASSIFICATION AXES FOR AHARADRAVYAS",
     bold=True, font_size=14, font_color='FFFFFF', bg='2E4057',
     align='center', border='thick')
ws2.row_dimensions[1].height = 32

ws2.merge_cells('A2:E2')
cell(ws2, 2, 1, "Multiple overlapping frameworks used to classify food substances in classical Ayurveda",
     bold=False, font_size=10, font_color='FFFFFF', bg='048A81',
     align='center', border='thick', italic=True)
ws2.row_dimensions[2].height = 22

# ── Section A: Rasa (6 Tastes) ──
ws2.row_dimensions[4].height = 22
ws2.merge_cells('A4:E4')
cell(ws2, 4, 1, "A. RASA (Taste) — 6 Primary Tastes & Dosha Effect",
     bold=True, font_size=11, font_color='FFFFFF', bg='1B4F72', align='left', border='thick')

headers_rasa = ['Rasa (Taste)', 'Sanskrit', 'Dosha Effect', 'Food Examples', 'Modern Compound Parallel']
for col, h in enumerate(headers_rasa, 1):
    cell(ws2, 5, col, h, bold=True, font_size=10, font_color='FFFFFF', bg='2980B9',
         align='center', border='thin')
ws2.row_dimensions[5].height = 22

rasa_data = [
    ('Madhura (Sweet)',    'मधुर', 'Pacifies Vata & Pitta; Increases Kapha',   'Rice, milk, ghee, sugar, dates',       'Carbohydrates, fats, amino acids'),
    ('Amla (Sour)',        'अम्ल', 'Pacifies Vata; Increases Pitta & Kapha',   'Citrus fruits, tamarind, yoghurt, vinegar', 'Citric acid, lactic acid, ascorbic acid'),
    ('Lavana (Salty)',     'लवण', 'Pacifies Vata; Increases Pitta & Kapha',   'Rock salt, sea salt, salty foods',      'NaCl, KCl, trace minerals'),
    ('Katu (Pungent)',     'कटु', 'Pacifies Kapha; Increases Vata & Pitta',   'Ginger, pepper, garlic, mustard, chili', 'Capsaicin, gingerols, allicin, piperine'),
    ('Tikta (Bitter)',     'तिक्त', 'Pacifies Pitta & Kapha; Increases Vata',  'Bitter gourd, turmeric, fenugreek, neem', 'Alkaloids, polyphenols, glycosides, terpenes'),
    ('Kashaya (Astringent)','कषाय','Pacifies Pitta & Kapha; Increases Vata',  'Pomegranate, banana flower, raw banana', 'Tannins, polyphenols, catechins'),
]
rasa_bgs = ['D6EAF8', 'FADBD8', 'FDEBD0', 'D5F5E3', 'F9EBEA', 'EBF5FB']
for i, rd in enumerate(rasa_data):
    r = 6 + i
    ws2.row_dimensions[r].height = 30
    bg = rasa_bgs[i]
    for col, val in enumerate(rd, 1):
        bold = (col == 1)
        cell(ws2, r, col, val, bold=bold, font_size=9, bg=bg, align='left', border='thin')

# ── Section B: Guna (Key Qualities) ──
r_start_b = 14
ws2.row_dimensions[r_start_b].height = 22
ws2.merge_cells(f'A{r_start_b}:E{r_start_b}')
cell(ws2, r_start_b, 1, "B. GUNA (Qualities) — Clinically Most Relevant Pairs (Gurvadi Guna)",
     bold=True, font_size=11, font_color='FFFFFF', bg='1B4F72', align='left', border='thick')

headers_guna = ['Guna Pair', 'Sanskrit', 'Effect on Body/Agni', 'Food Examples', 'Modern Science Parallel']
for col, h in enumerate(headers_guna, 1):
    cell(ws2, r_start_b+1, col, h, bold=True, font_size=10, font_color='FFFFFF', bg='2980B9',
         align='center', border='thin')
ws2.row_dimensions[r_start_b+1].height = 22

guna_data = [
    ('Guru (Heavy) / Laghu (Light)', 'गुरु / लघु', 'Guru → slows digestion, nourishes; Laghu → easy to digest, kindles Agni',
     'Guru: Milk, meat, wheat | Laghu: Mung dal, rice gruel, barley', 'High vs. low caloric density; High vs. low glycemic load'),
    ('Snigdha (Unctuous) / Ruksha (Dry)', 'स्निग्ध / रूक्ष', 'Snigdha → lubricates, Vata-shamaka; Ruksha → drying, Kapha-hara',
     'Snigdha: Ghee, sesame, avocado | Ruksha: Pulses, dry cereals', 'Lipid-rich vs. fiber-rich foods; SCFA vs. insoluble fiber'),
    ('Ushna (Hot) / Sheeta (Cold)', 'उष्ण / शीत', 'Ushna → enhances Agni, Vata/Kapha-hara; Sheeta → cools, Pitta-hara',
     'Ushna: Ginger, pepper, mustard oil | Sheeta: Coconut, milk, coriander', 'Thermogenic foods vs. anti-inflammatory/cooling foods'),
    ('Tikshna (Sharp) / Manda (Dull)', 'तीक्ष्ण / मन्द', 'Tikshna → penetrating, fast action; Manda → slow, gentle',
     'Tikshna: Alcohol, vinegar | Manda: Rice gruel, banana', 'Fast-absorbing vs. slow-digesting; high vs. low fermentability'),
    ('Sthira (Stable) / Sara (Mobile)', 'स्थिर / सर', 'Sthira → builds tissue, stabilizes; Sara → promotes elimination',
     'Sthira: Potatoes, root vegetables | Sara: Castor oil, prunes, figs', 'Anabolic vs. catabolic nutrients; constipating vs. laxative effects'),
]
guna_bg = ['EAF2FF', 'FFF3E0', 'E8F8E8', 'FFF0F0', 'F3E5F5']
for i, gd in enumerate(guna_data):
    r = r_start_b + 2 + i
    ws2.row_dimensions[r].height = 36
    for col, val in enumerate(gd, 1):
        bold = (col == 1)
        cell(ws2, r, col, val, bold=bold, font_size=9, bg=guna_bg[i], align='left', border='thin')

# ── Section C: Triguna / Mahagunas ──
r_start_c = r_start_b + 2 + len(guna_data) + 2
ws2.row_dimensions[r_start_c].height = 22
ws2.merge_cells(f'A{r_start_c}:E{r_start_c}')
cell(ws2, r_start_c, 1, "C. TRIGUNA (Mahaguna) Classification — Psychophysiological Impact of Food",
     bold=True, font_size=11, font_color='FFFFFF', bg='1B4F72', align='left', border='thick')

headers_triguna = ['Guna', 'Nature', 'Food Examples', 'Effect on Mind & Body', 'Modern Parallel']
for col, h in enumerate(headers_triguna, 1):
    cell(ws2, r_start_c+1, col, h, bold=True, font_size=10, font_color='FFFFFF', bg='2980B9',
         align='center', border='thin')
ws2.row_dimensions[r_start_c+1].height = 22

triguna_data = [
    ('Sattvic (सात्त्विक)', 'Pure, clarity, balance',
     'Fresh fruits, milk, ghee, honey, whole grains, leafy vegetables, nuts',
     'Promotes clarity (Satva), compassion, intelligence; supports Ojas; disease-preventive',
     'Anti-inflammatory diet; omega-3-rich, antioxidant-rich, low AGE foods; supports gut-brain axis'),
    ('Rajasic (राजसिक)', 'Stimulating, activating, restless',
     'Spicy/salty/sour foods, meat, coffee, alcohol (mild), garlic, onion in excess',
     'Increases activity, passion, aggression; disturbs mental equanimity; excess → Pitta aggravation',
     'Sympathomimetic foods; cortisol-stimulating; capsaicin, caffeine, high-protein diets'),
    ('Tamasic (तामसिक)', 'Dull, inertia, heaviness',
     'Stale/overcooked food, excessive fermented food, alcohol, processed/packaged food, leftovers > 3 hrs',
     'Promotes lethargy, dullness, ignorance; impairs Agni; Ama-generating; disease-promoting',
     'Ultra-processed foods; high AGE foods; proinflammatory; impairs gut microbiota diversity'),
]
triguna_bgs = ['E8F8F5', 'FDEBD0', 'FDEDEC']
for i, td in enumerate(triguna_data):
    r = r_start_c + 2 + i
    ws2.row_dimensions[r].height = 44
    for col, val in enumerate(td, 1):
        bold = (col == 1)
        cell(ws2, r, col, val, bold=bold, font_size=9, bg=triguna_bgs[i], align='left', border='thin')

# ── Section D: Virya/Vipaka ──
r_start_d = r_start_c + 2 + len(triguna_data) + 2
ws2.row_dimensions[r_start_d].height = 22
ws2.merge_cells(f'A{r_start_d}:E{r_start_d}')
cell(ws2, r_start_d, 1, "D. VIRYA & VIPAKA — Potency & Post-digestive Effect",
     bold=True, font_size=11, font_color='FFFFFF', bg='1B4F72', align='left', border='thick')

headers_vv = ['Parameter', 'Types', 'Meaning', 'Food Examples', 'Modern Parallel']
for col, h in enumerate(headers_vv, 1):
    cell(ws2, r_start_d+1, col, h, bold=True, font_size=10, font_color='FFFFFF', bg='2980B9',
         align='center', border='thin')

vv_data = [
    ('Virya (Potency / वीर्य)', 'Ushna (Hot) / Sheeta (Cold)',
     'The dominant pharmacological potency of a food during digestion',
     'Ushna: Garlic, ginger, pepper, mustard | Sheeta: Milk, coconut, coriander, fennel',
     'Thermogenic effect (TEF), pro-inflammatory vs. anti-inflammatory bioactive compounds'),
    ('Vipaka (Post-digestive effect / विपाक)', 'Madhura / Amla / Katu',
     'The final taste/effect after complete metabolism; governs long-term Dosha effect',
     'Madhura Vipaka: Rice, milk (nourishing, anabolic) | Katu Vipaka: Honey, turmeric (catabolic, Kapha-hara)',
     'End-products of digestion/fermentation: SCFAs (butyrate), glucose, amino acids, secondary bile acids'),
]
for i, vd in enumerate(vv_data):
    r = r_start_d + 2 + i
    ws2.row_dimensions[r].height = 44
    bg = 'F4F6F7' if i % 2 == 0 else 'EBF5FB'
    for col, val in enumerate(vd, 1):
        bold = (col == 1)
        cell(ws2, r, col, val, bold=bold, font_size=9, bg=bg, align='left', border='thin')

# ── Footer Sheet 2 ──
r_foot2 = r_start_d + 2 + len(vv_data) + 1
ws2.merge_cells(f'A{r_foot2}:E{r_foot2}')
cell(ws2, r_foot2, 1,
     "References: Charaka Samhita Sutra Sthana 1, 26-27 | Ashtanga Hridayam Sutra 6 | Jadhav et al. WJPR 2025 | Carakasamhitaonline.com",
     bold=False, font_size=8, font_color='888888', bg='EEEEEE', align='left', border='thin', italic=True)

# ═══════════════════════════════════════════════════════════
# SHEET 3 — Mode of Intake + Origin Classification
# ═══════════════════════════════════════════════════════════
ws3 = wb.create_sheet("Other Classifications")

col_widths3 = [25, 22, 26, 26, 26]
for i, w in enumerate(col_widths3, 1):
    ws3.column_dimensions[get_column_letter(i)].width = w

ws3.merge_cells('A1:E1')
cell(ws3, 1, 1, "OTHER AYURVEDIC CLASSIFICATION FRAMEWORKS FOR AHARADRAVYAS",
     bold=True, font_size=13, font_color='FFFFFF', bg='2E4057',
     align='center', border='thick')
ws3.row_dimensions[1].height = 30

# Mode of Intake
ws3.merge_cells('A3:E3')
cell(ws3, 3, 1, "A. MODE OF INTAKE (Physical State of Food)",
     bold=True, font_size=11, font_color='FFFFFF', bg='1B4F72', align='left', border='thick')

for col, h in enumerate(['Mode (Sanskrit)', 'Meaning', 'Examples', 'Modern Equivalent', 'Clinical Relevance'], 1):
    cell(ws3, 4, col, h, bold=True, font_size=10, font_color='FFFFFF', bg='2980B9',
         align='center', border='thin')

intake_data = [
    ('Ashita (आशित)', 'Solid eatables', 'Cooked rice, bread, rotis, baked foods', 'Solid foods', 'Standard meal; requires good Agni; contraindicated post-Panchakarma'),
    ('Peeta (पीत)', 'Drinkables (liquids)', 'Fruit juices, soups, milk, water, broths', 'Liquid foods / Beverages', 'Easily absorbed; prescribed in compromised Agni, febrile states'),
    ('Khaadita (खादित)', 'Chewables', 'Raw vegetables, sugarcane, hard fruits, nuts', 'Finger foods / Raw foods', 'Stimulates salivary amylase; mechanical digestion; high-fiber'),
    ('Lehya (लेह्य)', 'Lickable (semi-solid)', 'Honey, Chyawanprash, Avaleha, jams', 'Semi-solid / Spreads', 'Buccal absorption; concentrated nutrition; medicinal use'),
]
for i, row in enumerate(intake_data):
    r = 5 + i
    ws3.row_dimensions[r].height = 36
    bg = 'EBF5FB' if i % 2 == 0 else 'FDFEFE'
    for col, val in enumerate(row, 1):
        cell(ws3, r, col, val, bold=(col==1), font_size=9, bg=bg, align='left', border='thin')

# Origin Classification
ws3.merge_cells('A11:E11')
cell(ws3, 11, 1, "B. ORIGIN-BASED CLASSIFICATION (Mula/Prabhava)",
     bold=True, font_size=11, font_color='FFFFFF', bg='1B4F72', align='left', border='thick')

for col, h in enumerate(['Category', 'Sanskrit', 'Sub-categories', 'Examples', 'Modern Classification'], 1):
    cell(ws3, 12, col, h, bold=True, font_size=10, font_color='FFFFFF', bg='2980B9',
         align='center', border='thin')

origin_data = [
    ('Plant Origin', 'Sthavara (स्थावर)', 'Mula (roots), Kanda (tubers), Pushpa (flowers), Phala (fruits), Patra (leaves), Tvak (bark), Sara (heartwood), Niryasa (resin)',
     'Turmeric (rhizome), Lotus (flower), Mango (fruit), Spinach (leaf)', 'Plant-based foods; Whole foods diet; Phytotherapy'),
    ('Animal Origin', 'Jangama (जङ्गम)', 'Mamsa (meat), Kshira (milk), Madhu (honey), Mutra (urine - medicinal), Purisha (feces - therapeutic), Rakta (blood)',
     'Cow milk, beef, honey, goat meat, fish', 'Animal-based proteins; Omnivore diet classification'),
    ('Mineral/Inorganic', 'Parthiva (पार्थिव)', 'Lavana (salts), Jala (water varieties), Dhatu (metals - medicinal)',
     'Rock salt, limestone water, Shilajit', 'Minerals & trace elements; electrolytes'),
]
origin_bgs = ['D5F5E3', 'FDEBD0', 'EBF5FB']
for i, row in enumerate(origin_data):
    r = 13 + i
    ws3.row_dimensions[r].height = 50
    for col, val in enumerate(row, 1):
        cell(ws3, r, col, val, bold=(col==1), font_size=9, bg=origin_bgs[i], align='left', border='thin')

# Pathya/Apathya
ws3.merge_cells('A18:E18')
cell(ws3, 18, 1, "C. PATHYA-APATHYA CLASSIFICATION (Wholesomeness / Therapeutic Suitability)",
     bold=True, font_size=11, font_color='FFFFFF', bg='1B4F72', align='left', border='thick')

for col, h in enumerate(['Category', 'Definition', 'Criteria', 'Examples', 'Modern Equivalent'], 1):
    cell(ws3, 19, col, h, bold=True, font_size=10, font_color='FFFFFF', bg='2980B9',
         align='center', border='thin')

pp_data = [
    ('Pathya (पथ्य)', 'Wholesome / Beneficial food',
     'Suits Prakriti, Ritu, Agni, Vyadhi; supports Dhatus; disease-preventing',
     'Warm water, mung dal, ghee, Shashtika rice (for most constitutions)',
     'Evidence-based dietary recommendations; personalized nutrition (nutrigenomics)'),
    ('Apathya (अपथ्य)', 'Unwholesome / Harmful food',
     'Incompatible with Prakriti or disease state; causes Ama; Dosha-aggravating',
     'Curd at night, fish+milk combination, cold water after exercise',
     'Dietary risk factors for disease; food-drug interactions; allergenic foods'),
    ('Viruddha Ahara (विरुद्ध)', 'Incompatible food combinations',
     'Contradictory properties in combination cause Ama; processed together or eaten together',
     'Milk + fish; honey + ghee in equal proportions; fruit + dairy combinations',
     'Food incompatibilities; altered bioavailability; Maillard reaction concerns'),
]
for i, row in enumerate(pp_data):
    r = 20 + i
    ws3.row_dimensions[r].height = 44
    bg = 'D5F5E3' if i == 0 else ('FDEDEC' if i == 1 else 'FFF3CD')
    for col, val in enumerate(row, 1):
        cell(ws3, r, col, val, bold=(col==1), font_size=9, bg=bg, align='left', border='thin')

# Footer
ws3.merge_cells('A24:E24')
cell(ws3, 24, 1,
     "Sources: Charaka Samhita | Ashtanga Hridayam | Sushruta Samhita | IJPRA 2024 | WJPR 2025",
     bold=False, font_size=8, font_color='888888', bg='EEEEEE', align='left', italic=True, border='thin')

# ── Save ──
out_path = '/tmp/workspace/aharadravyas/Aharadravyas_Classification_Chart.xlsx'
wb.save(out_path)
print(f"Saved: {out_path}")