from reportlab.lib.pagesizes import A4
from reportlab.lib.units import mm, pt
from reportlab.lib import colors
from reportlab.lib.colors import HexColor
from reportlab.pdfgen import canvas
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, KeepTogether, PageBreak
)
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_RIGHT
from reportlab.platypus.flowables import Flowable
import textwrap

# ── Color Palette ──────────────────────────────────────────────
C_ACCENT1   = HexColor('#5B21B6')   # Indigo
C_ACCENT2   = HexColor('#BE185D')   # Deep Rose
C_ACCENT3   = HexColor('#0F766E')   # Teal
C_ACCENT4   = HexColor('#B45309')   # Amber
C_BGLIGHT   = HexColor('#F8FAFC')
C_BORDER    = HexColor('#E2E8F0')
C_TEXTDARK  = HexColor('#0F172A')
C_TEXTMUTED = HexColor('#475569')
C_WHITE     = colors.white

PAGE_W, PAGE_H = A4
MARGIN_L = 18*mm
MARGIN_R = 18*mm
MARGIN_T = 22*mm
MARGIN_B = 22*mm
CONTENT_W = PAGE_W - MARGIN_L - MARGIN_R


# ── Helper Flowables ───────────────────────────────────────────
class RoundedCard(Flowable):
    """A rounded rectangle card with optional top or left accent stripe."""
    def __init__(self, width, height, content_fn, radius=6,
                 bg=C_BGLIGHT, border=C_BORDER, accent_color=None,
                 accent_side='top', accent_w=2.5):
        super().__init__()
        self.card_w = width
        self.card_h = height
        self.content_fn = content_fn
        self.radius = radius
        self.bg = bg
        self.border = border
        self.accent_color = accent_color
        self.accent_side = accent_side
        self.accent_w = accent_w
        self.width = width
        self.height = height

    def draw(self):
        c = self.canv
        r = self.radius
        w, h = self.card_w, self.card_h
        # Shadow-like background
        c.setFillColor(self.bg)
        c.setStrokeColor(self.border)
        c.setLineWidth(0.8)
        c.roundRect(0, 0, w, h, r, stroke=1, fill=1)
        # Accent stripe
        if self.accent_color:
            c.setFillColor(self.accent_color)
            if self.accent_side == 'top':
                # top rounded stripe
                c.setStrokeColor(self.accent_color)
                c.setLineWidth(0)
                c.roundRect(0, h - self.accent_w, w, self.accent_w, r, stroke=0, fill=1)
                c.rect(0, h - self.accent_w - r, w, r + 1, stroke=0, fill=1)
            elif self.accent_side == 'left':
                c.setStrokeColor(self.accent_color)
                c.setLineWidth(0)
                c.roundRect(0, 0, self.accent_w, h, min(r, self.accent_w//2+1), stroke=0, fill=1)
                c.rect(r, 0, self.accent_w - r + 1, h, stroke=0, fill=1)
        # Call content drawing function
        self.content_fn(c, w, h)


class ProgressBar(Flowable):
    def __init__(self, width, height, fraction, color, label_left, label_right, font_size=9):
        super().__init__()
        self.bar_w = width
        self.bar_h = height
        self.fraction = fraction
        self.color = color
        self.label_left = label_left
        self.label_right = label_right
        self.font_size = font_size
        self.width = width
        self.height = height + 18

    def draw(self):
        c = self.canv
        y_bar = 2
        label_h = 14
        # Labels
        c.setFont('Helvetica-Bold', self.font_size)
        c.setFillColor(C_TEXTDARK)
        c.drawString(0, y_bar + self.bar_h + 4, self.label_left)
        c.setFillColor(self.color)
        c.drawRightString(self.bar_w, y_bar + self.bar_h + 4, self.label_right)
        # Track
        c.setFillColor(C_BORDER)
        c.roundRect(0, y_bar, self.bar_w, self.bar_h, self.bar_h/2, stroke=0, fill=1)
        # Fill
        fill_w = max(self.bar_w * self.fraction, self.bar_h)
        c.setFillColor(self.color)
        c.roundRect(0, y_bar, fill_w, self.bar_h, self.bar_h/2, stroke=0, fill=1)


class SectionHeader(Flowable):
    def __init__(self, title, color=C_ACCENT1, width=CONTENT_W):
        super().__init__()
        self.title = title
        self.color = color
        self.width = width
        self.height = 22

    def draw(self):
        c = self.canv
        c.setFont('Helvetica-Bold', 9.5)
        c.setFillColor(self.color)
        c.drawString(0, 9, self.title.upper())
        c.setStrokeColor(C_BORDER)
        c.setLineWidth(0.5)
        c.line(0, 3, self.width, 3)


# ── Build PDF ─────────────────────────────────────────────────
def build():
    out = '/home/daytona/workspace/chatgpt-report/ChatGPT_Report.pdf'
    doc = SimpleDocTemplate(
        out,
        pagesize=A4,
        leftMargin=MARGIN_L,
        rightMargin=MARGIN_R,
        topMargin=MARGIN_T,
        bottomMargin=MARGIN_B,
    )
    story = []
    W = CONTENT_W

    # ── HEADER ────────────────────────────────────────────────
    # Decorative subtitle line
    story.append(Paragraph(
        '<font color="#0F766E"><b>─── CHATGPT EXPORT ANALYSIS ───</b></font>',
        ParagraphStyle('sub', fontName='Helvetica-Bold', fontSize=8.5,
                       textColor=C_ACCENT3, alignment=TA_CENTER, spaceAfter=6)
    ))

    story.append(Paragraph(
        '<font color="#0F172A"><b>Your AI Conversation Report</b></font>',
        ParagraphStyle('h1', fontName='Helvetica-Bold', fontSize=26,
                       textColor=C_TEXTDARK, alignment=TA_CENTER, spaceAfter=4)
    ))

    story.append(Paragraph(
        '<font color="#475569">Dec 2025 – Apr 2026 &nbsp;&bull;&nbsp; Full Export Analysis</font>',
        ParagraphStyle('sub2', fontName='Helvetica', fontSize=10,
                       textColor=C_TEXTMUTED, alignment=TA_CENTER, spaceAfter=10)
    ))

    # Profile badge
    badge_data = [[
        Paragraph(
            '<font color="#5B21B6"><b>S</b></font>'
            '&nbsp;&nbsp;<font color="#475569">•</font>&nbsp;&nbsp;'
            '<font color="#0F172A">User born <b>2006</b></font>'
            '&nbsp;&nbsp;<font color="#475569">•</font>&nbsp;&nbsp;'
            '<font color="#475569">Free Plan</font>'
            '&nbsp;&nbsp;<font color="#475569">•</font>&nbsp;&nbsp;'
            '<font color="#0F172A"><b>syamaeib@gmail.com</b></font>',
            ParagraphStyle('badge', fontName='Helvetica', fontSize=9.5,
                           alignment=TA_CENTER, textColor=C_TEXTDARK)
        )
    ]]
    badge_w = W * 0.72
    badge_table = Table(badge_data, colWidths=[badge_w])
    badge_table.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), C_BGLIGHT),
        ('BOX',        (0,0), (-1,-1), 0.8, C_BORDER),
        ('ROUNDEDCORNERS', [12]),
        ('TOPPADDING',    (0,0), (-1,-1), 8),
        ('BOTTOMPADDING', (0,0), (-1,-1), 8),
        ('LEFTPADDING',   (0,0), (-1,-1), 16),
        ('RIGHTPADDING',  (0,0), (-1,-1), 16),
    ]))
    # Center the badge
    badge_outer = Table([[badge_table]], colWidths=[W])
    badge_outer.setStyle(TableStyle([
        ('ALIGN',  (0,0), (-1,-1), 'CENTER'),
        ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
    ]))
    story.append(badge_outer)
    story.append(Spacer(1, 16))

    # ── KEY STATS (4 cards) ────────────────────────────────────
    def make_stat_cell(value, label, accent):
        return Paragraph(
            f'<font color="{accent.hexval()}"><b><font size="22">{value}</font></b></font>'
            f'<br/><font color="#475569" size="7.5">{label.upper()}</font>',
            ParagraphStyle('sc', fontName='Helvetica', fontSize=9,
                           alignment=TA_CENTER, leading=18)
        )

    stat_data = [[
        make_stat_cell('166',   'Conversations', C_ACCENT1),
        make_stat_cell('1,902', 'Your Messages', C_ACCENT2),
        make_stat_cell('2,363', 'AI Replies',    C_ACCENT3),
        make_stat_cell('142',   'Days Active',   C_ACCENT4),
    ]]
    cw = (W - 3*6) / 4
    stat_table = Table(stat_data, colWidths=[cw]*4, hAlign='LEFT')
    stat_table.setStyle(TableStyle([
        ('BACKGROUND',    (0,0), (-1,-1), C_BGLIGHT),
        ('BOX',           (0,0), (0,0),  0.8, C_BORDER),
        ('BOX',           (1,0), (1,0),  0.8, C_BORDER),
        ('BOX',           (2,0), (2,0),  0.8, C_BORDER),
        ('BOX',           (3,0), (3,0),  0.8, C_BORDER),
        ('LINEABOVE',     (0,0), (0,0),  2.5, C_ACCENT1),
        ('LINEABOVE',     (1,0), (1,0),  2.5, C_ACCENT2),
        ('LINEABOVE',     (2,0), (2,0),  2.5, C_ACCENT3),
        ('LINEABOVE',     (3,0), (3,0),  2.5, C_ACCENT4),
        ('TOPPADDING',    (0,0), (-1,-1), 14),
        ('BOTTOMPADDING', (0,0), (-1,-1), 14),
        ('ALIGN',         (0,0), (-1,-1), 'CENTER'),
        ('VALIGN',        (0,0), (-1,-1), 'MIDDLE'),
        ('ROUNDEDCORNERS', [6]),
    ]))
    story.append(stat_table)
    story.append(Spacer(1, 18))

    # ── MONTHLY ACTIVITY ──────────────────────────────────────
    story.append(SectionHeader('Monthly Activity'))
    story.append(Spacer(1, 6))

    months = [
        ("DEC '25", C_ACCENT1, 0.52, '21'),
        ("JAN '26", C_ACCENT2, 0.98, '39'),
        ("FEB '26", C_ACCENT3, 0.70, '28'),
        ("MAR '26", C_ACCENT4, 0.95, '38'),
        ("APR '26", C_ACCENT1, 1.00, '40'),
    ]
    BAR_H = 9
    bar_col_w = W - 60 - 30 - 8  # label col + count col + padding

    def month_rows():
        rows = []
        for label, color, frac, count in months:
            lbl = Paragraph(f'<b><font size="9">{label}</font></b>',
                            ParagraphStyle('ml', fontName='Helvetica-Bold',
                                           fontSize=9, textColor=C_TEXTDARK,
                                           alignment=TA_LEFT))
            # bar as a colored rectangle in a nested table
            track_w = bar_col_w
            bar_fill_w = max(track_w * frac, BAR_H)

            class InlineBar(Flowable):
                def __init__(self, fill, total, clr, bh=BAR_H):
                    super().__init__()
                    self.fill = fill
                    self.total = total
                    self.clr = clr
                    self.bh = bh
                    self.width = total
                    self.height = bh + 4
                def draw(self):
                    cv = self.canv
                    cv.setFillColor(C_BORDER)
                    cv.roundRect(0, 2, self.total, self.bh, self.bh/2, stroke=0, fill=1)
                    cv.setFillColor(self.clr)
                    cv.roundRect(0, 2, max(self.fill, self.bh), self.bh, self.bh/2, stroke=0, fill=1)

            bar_flow = InlineBar(bar_fill_w, track_w, color)
            cnt = Paragraph(f'<b><font size="10">{count}</font></b>',
                            ParagraphStyle('mc', fontName='Helvetica-Bold',
                                           fontSize=10, textColor=C_TEXTDARK,
                                           alignment=TA_RIGHT))
            rows.append([lbl, bar_flow, cnt])
        return rows

    month_table = Table(month_rows(), colWidths=[60, bar_col_w, 30],
                        hAlign='LEFT', rowHeights=22)
    month_table.setStyle(TableStyle([
        ('BACKGROUND',    (0,0), (-1,-1), C_BGLIGHT),
        ('BOX',           (0,0), (-1,-1), 0.8, C_BORDER),
        ('TOPPADDING',    (0,0), (-1,-1), 5),
        ('BOTTOMPADDING', (0,0), (-1,-1), 5),
        ('LEFTPADDING',   (0,0), (-1,-1), 12),
        ('RIGHTPADDING',  (-1,0), (-1,-1), 12),
        ('VALIGN',        (0,0), (-1,-1), 'MIDDLE'),
        ('ROUNDEDCORNERS', [6]),
    ]))
    story.append(month_table)
    story.append(Spacer(1, 18))

    # ── TOPIC BREAKDOWN ───────────────────────────────────────
    story.append(SectionHeader('Topic Breakdown'))
    story.append(Spacer(1, 6))

    topics = [
        ('Nutrition & Diet',       C_ACCENT3, 0.72, '≈ 62'),
        ('Health & Medical',       C_ACCENT2, 0.30, '≈ 22'),
        ('Fitness & Gym',          C_ACCENT1, 0.25, '≈ 20'),
        ('Education & College',    C_ACCENT4, 0.18, '≈ 15'),
        ('Anime & Entertainment',  C_ACCENT2, 0.15, '≈ 12'),
        ('Tech & Gaming',          C_ACCENT1, 0.14, '≈ 12'),
        ('Supplements',            C_ACCENT3, 0.12, '≈ 10'),
        ('Coding & Math',          C_ACCENT1, 0.09, '≈ 8'),
        ('Personal / Lifestyle',   C_ACCENT4, 0.09, '≈ 5'),
    ]
    topic_label_w = 110
    topic_bar_w   = W - topic_label_w - 40 - 8
    topic_count_w = 40

    def topic_rows():
        rows = []
        for label, color, frac, count in topics:
            lbl = Paragraph(f'<b><font size="9">{label}</font></b>',
                            ParagraphStyle('tl', fontName='Helvetica-Bold',
                                           fontSize=9, textColor=C_TEXTDARK,
                                           alignment=TA_LEFT))
            class TBar(Flowable):
                def __init__(self, fill_f, total, clr, bh=8):
                    super().__init__()
                    self.fill_f = fill_f
                    self.total = total
                    self.clr = clr
                    self.bh = bh
                    self.width = total
                    self.height = bh + 4
                def draw(self):
                    cv = self.canv
                    cv.setFillColor(C_BORDER)
                    cv.roundRect(0, 2, self.total, self.bh, self.bh/2, stroke=0, fill=1)
                    cv.setFillColor(self.clr)
                    fill_px = max(self.total * self.fill_f, self.bh)
                    cv.roundRect(0, 2, fill_px, self.bh, self.bh/2, stroke=0, fill=1)

            bar_flow = TBar(frac, topic_bar_w, color)
            cnt = Paragraph(
                f'<font color="{color.hexval()}"><b><font size="9">{count}</font></b></font>',
                ParagraphStyle('tc', fontName='Helvetica-Bold', fontSize=9,
                               alignment=TA_RIGHT))
            rows.append([lbl, bar_flow, cnt])
        return rows

    topic_table = Table(topic_rows(),
                        colWidths=[topic_label_w, topic_bar_w, topic_count_w],
                        hAlign='LEFT', rowHeights=21)
    topic_table.setStyle(TableStyle([
        ('BACKGROUND',    (0,0), (-1,-1), C_BGLIGHT),
        ('BOX',           (0,0), (-1,-1), 0.8, C_BORDER),
        ('TOPPADDING',    (0,0), (-1,-1), 4),
        ('BOTTOMPADDING', (0,0), (-1,-1), 4),
        ('LEFTPADDING',   (0,0), (-1,-1), 12),
        ('RIGHTPADDING',  (-1,0), (-1,-1), 12),
        ('VALIGN',        (0,0), (-1,-1), 'MIDDLE'),
        ('ROWBACKGROUNDS',(0,0), (-1,-1), [C_BGLIGHT, HexColor('#F1F5F9')]),
        ('ROUNDEDCORNERS', [6]),
    ]))
    story.append(topic_table)

    # ── PAGE 2 ────────────────────────────────────────────────
    story.append(PageBreak())

    # ── MOST ACTIVE CONVERSATIONS ─────────────────────────────
    story.append(SectionHeader('Most Active Conversations'))
    story.append(Spacer(1, 6))

    convos = [
        ('01', 'Body Fat Percentage Estimate',  '1,473 messages  •  Health',    'EPIC',   C_ACCENT2),
        ('02', 'Gym Tips for Beginners',         '645 messages  •  Fitness',     'LONG',   C_ACCENT1),
        ('03', 'Lab Report Summary',             '394 messages  •  Medical',     'DEEP',   C_ACCENT3),
        ('04', 'Blood Sugar Post-Meal',          '84 messages  •  Nutrition',    'ACTIVE', C_ACCENT4),
        ('05', 'Dying Light Steam Price',        '77 messages  •  Gaming',       'ACTIVE', C_ACCENT3),
    ]

    def convo_rows():
        rows = []
        for num, title, meta, badge_text, badge_color in convos:
            num_p = Paragraph(
                f'<font color="{C_ACCENT1.hexval()}"><b>{num}</b></font>',
                ParagraphStyle('cn', fontName='Helvetica-Bold', fontSize=11,
                               alignment=TA_CENTER, textColor=C_ACCENT1))
            detail_p = Paragraph(
                f'<b><font size="10">{title}</font></b><br/>'
                f'<font color="#475569" size="8">{meta}</font>',
                ParagraphStyle('cd', fontName='Helvetica', fontSize=9,
                               leading=14, textColor=C_TEXTDARK))

            # Badge pill
            r, g, b = badge_color.red, badge_color.green, badge_color.blue
            badge_bg = HexColor('#{:02X}{:02X}{:02X}'.format(
                int(r*255*0.15 + 255*0.85),
                int(g*255*0.15 + 255*0.85),
                int(b*255*0.15 + 255*0.85)
            ))
            badge_p = Paragraph(
                f'<font color="{badge_color.hexval()}"><b><font size="7.5">{badge_text}</font></b></font>',
                ParagraphStyle('cb', fontName='Helvetica-Bold', fontSize=7.5,
                               alignment=TA_CENTER))
            badge_cell = Table([[badge_p]], colWidths=[46])
            badge_cell.setStyle(TableStyle([
                ('BACKGROUND',    (0,0), (-1,-1), badge_bg),
                ('TOPPADDING',    (0,0), (-1,-1), 3),
                ('BOTTOMPADDING', (0,0), (-1,-1), 3),
                ('LEFTPADDING',   (0,0), (-1,-1), 5),
                ('RIGHTPADDING',  (0,0), (-1,-1), 5),
                ('ROUNDEDCORNERS', [4]),
            ]))
            rows.append([num_p, detail_p, badge_cell])
        return rows

    convo_table = Table(convo_rows(), colWidths=[30, W-30-60, 60],
                        hAlign='LEFT')
    line_cmds = []
    for i in range(1, len(convos)):
        line_cmds.append(('LINEABOVE', (0,i), (-1,i), 0.5, C_BORDER))
    convo_table.setStyle(TableStyle([
        ('BACKGROUND',    (0,0), (-1,-1), C_BGLIGHT),
        ('BOX',           (0,0), (-1,-1), 0.8, C_BORDER),
        ('TOPPADDING',    (0,0), (-1,-1), 10),
        ('BOTTOMPADDING', (0,0), (-1,-1), 10),
        ('LEFTPADDING',   (0,0), (-1,-1), 12),
        ('RIGHTPADDING',  (-1,0), (-1,-1), 12),
        ('VALIGN',        (0,0), (-1,-1), 'MIDDLE'),
        ('ALIGN',         (0,0), (0,-1),  'CENTER'),
        ('ALIGN',         (2,0), (2,-1),  'CENTER'),
        ('ROUNDEDCORNERS', [6]),
    ] + line_cmds))
    story.append(convo_table)
    story.append(Spacer(1, 18))

    # ── KEY INSIGHTS ──────────────────────────────────────────
    story.append(SectionHeader('Key Insights'))
    story.append(Spacer(1, 6))

    insights = [
        ('Managing a Health Condition', C_ACCENT2,
         'You have many conversations about prediabetes, blood sugar, metformin, and lab reports - '
         "suggesting you're actively monitoring and managing a health condition. You asked very "
         'detailed, specific questions about glucose, insulin, and organ effects.'),
        ('Serious About Fitness', C_ACCENT1,
         'Gym tips, workout splits, body fat %, incline walking, creatine, whey protein - '
         "you're deeply invested in fitness and bodybuilding. Your longest single conversation "
         '(1,473 msgs!) was about body fat estimation.'),
        ('Making a Big Life Decision', C_ACCENT4,
         'Multiple conversations about Aakash, Physics Wallah, Narayana PU College, PCMC/PCMB '
         "switch, and Presidency College exams show you're in the middle of choosing your academic "
         'path - likely preparing for competitive entrance exams.'),
        ('Passionate Anime Fan', C_ACCENT2,
         'You debated Naruto vs AOT vs One Piece multiple times, discussed Gojo vs Yuji, and '
         "Minato's absence. One Piece vs Naruto appeared 3 separate times - clearly a topic "
         "you're very passionate about!"),
        ("Tech-Savvy Gamer", C_ACCENT3,
         'You researched KLIM laptops, Lenovo LOQ, DX11 vs DX12, Dying Light pricing, and game '
         'difficulty tips (Hunter Build, Master Mode). You compare specs carefully before buying.'),
        ('Nutrition Obsessed', C_ACCENT3,
         'Rice, tofu, soya chunks, sweet potato, stevia, chicken liver - you asked about almost '
         "every food category. You're carefully tracking macros, GI index, and cooking methods, "
         'likely tied to managing your health condition.'),
        ('Sleep Struggles', C_ACCENT4,
         'Multiple chats about trouble sleeping, sleep timing impact, sleep rebound effects, nap '
         'impact, and sleep duration calculation. Sleep quality is a recurring concern for you.'),
        ('Growing Usage Over Time', C_ACCENT1,
         'You started with 21 conversations in Dec 2025 and peaked at 40 in April 2026 - nearly '
         "doubling your usage. You're relying on ChatGPT more and more each month."),
    ]

    def insight_cell(title, accent, text):
        inner = Table([
            [Paragraph(f'<b><font size="9.5" color="#0F172A">{title}</font></b>',
                       ParagraphStyle('it', fontName='Helvetica-Bold', fontSize=9.5,
                                      textColor=C_TEXTDARK))],
            [Paragraph(f'<font color="#475569" size="9">{text}</font>',
                       ParagraphStyle('ib', fontName='Helvetica', fontSize=9,
                                      textColor=C_TEXTMUTED, leading=13))],
        ], colWidths=[W/2 - 24 - 5])
        inner.setStyle(TableStyle([
            ('TOPPADDING',    (0,0), (-1,-1), 2),
            ('BOTTOMPADDING', (0,0), (-1,-1), 2),
            ('LEFTPADDING',   (0,0), (-1,-1), 0),
            ('RIGHTPADDING',  (0,0), (-1,-1), 0),
        ]))
        outer = Table([[inner]], colWidths=[W/2 - 10])
        outer.setStyle(TableStyle([
            ('BACKGROUND',    (0,0), (-1,-1), C_BGLIGHT),
            ('BOX',           (0,0), (-1,-1), 0.8, C_BORDER),
            ('LINEBEFORE',    (0,0), (0,-1),  3,   accent),
            ('TOPPADDING',    (0,0), (-1,-1), 10),
            ('BOTTOMPADDING', (0,0), (-1,-1), 10),
            ('LEFTPADDING',   (0,0), (-1,-1), 10),
            ('RIGHTPADDING',  (0,0), (-1,-1), 10),
            ('ROUNDEDCORNERS', [4]),
        ]))
        return outer

    # 2-column grid
    for i in range(0, len(insights), 2):
        left  = insight_cell(*insights[i])
        right = insight_cell(*insights[i+1]) if i+1 < len(insights) else ''
        row = Table([[left, right]], colWidths=[W/2 - 5, W/2 - 5],
                    hAlign='LEFT')
        row.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'),
        ]))
        story.append(row)
        story.append(Spacer(1, 7))

    # ── PAGE 3 ────────────────────────────────────────────────
    story.append(PageBreak())

    # ── FUN FACTS ─────────────────────────────────────────────
    story.append(SectionHeader('Fun Facts'))
    story.append(Spacer(1, 6))

    fun_facts = [
        ('Most Debated Topic', C_ACCENT2,
         'Naruto vs One Piece - you started this debate at least '
         '<font color="#BE185D"><b>4 separate times</b></font>! '
         "You clearly can't make up your mind."),
        ('Most Focused Topic', C_ACCENT3,
         'Nutrition makes up over <font color="#0F766E"><b>37%</b></font> of all your '
         "conversations - you're practically a food scientist at this point!"),
        ('Deepest Dive', C_ACCENT1,
         'The "Body Fat Percentage Estimate" conversation had '
         '<font color="#5B21B6"><b>1,473 messages</b></font> - '
         "that's almost a full book's worth of conversation in one thread!"),
        ('Young & Curious', C_ACCENT4,
         'Born in <font color="#B45309"><b>2006</b></font>, '
         "you're around 19 years old and using AI to navigate everything from college choices "
         'to health and gaming. That\'s impressive!'),
    ]

    for i in range(0, len(fun_facts), 2):
        left  = insight_cell(fun_facts[i][0],  fun_facts[i][1],  fun_facts[i][2])
        right = insight_cell(fun_facts[i+1][0], fun_facts[i+1][1], fun_facts[i+1][2]) if i+1 < len(fun_facts) else ''
        row = Table([[left, right]], colWidths=[W/2 - 5, W/2 - 5], hAlign='LEFT')
        row.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'),
        ]))
        story.append(row)
        story.append(Spacer(1, 7))

    # ── FOOTER (via onLaterPages / custom) ────────────────────
    story.append(Spacer(1, 40))
    story.append(Paragraph(
        '<font color="#475569" size="8">GENERATED FROM CHATGPT EXPORT ANALYSIS  •  JULY 2026</font>',
        ParagraphStyle('footer', fontName='Helvetica', fontSize=8,
                       textColor=C_TEXTMUTED, alignment=TA_CENTER)
    ))

    doc.build(story)
    print(f'PDF saved to {out}')

build()

Successfully replaced 1 instance(s) of the string in '/home/daytona/workspace/chatgpt-report/build_report.py'

<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>ChatGPT Conversation Analysis</title>
<link href="https://fonts.googleapis.com/css2?family=Syne:wght@400;600;700;800&family=DM+Mono:wght@300;400;500&display=swap" rel="stylesheet">
<style>
  :root {
    --bg: #0a0a0f;
    --surface: #12121a;
    --card: #1a1a26;
    --border: #2a2a40;
    --accent1: #7c6af7;
    --accent2: #f76a8c;
    --accent3: #6af7c2;
    --accent4: #f7c26a;
    --text: #e8e8f0;
    --muted: #8888aa;
  }

  * { margin: 0; padding: 0; box-sizing: border-box; }

  body {
    background: var(--bg);
    color: var(--text);
    font-family: 'DM Mono', monospace;
    min-height: 100vh;
    overflow-x: hidden;
  }

  body::before {
    content: '';
    position: fixed;
    top: 0; left: 0; right: 0; bottom: 0;
    background:
      radial-gradient(ellipse 600px 400px at 10% 20%, rgba(124,106,247,0.08) 0%, transparent 70%),
      radial-gradient(ellipse 500px 300px at 90% 80%, rgba(247,106,140,0.06) 0%, transparent 70%),
      radial-gradient(ellipse 400px 400px at 50% 50%, rgba(106,247,194,0.04) 0%, transparent 70%);
    pointer-events: none;
    z-index: 0;
  }

  .container {
    max-width: 900px;
    margin: 0 auto;
    padding: 40px 20px;
    position: relative;
    z-index: 1;
  }

  /* Header */
  .header {
    text-align: center;
    margin-bottom: 60px;
    animation: fadeDown 0.8s ease both;
  }

  .header .tag {
    display: inline-block;
    font-size: 10px;
    letter-spacing: 4px;
    text-transform: uppercase;
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    margin-bottom: 20px;
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  .header h1 {
    font-family: 'Syne', sans-serif;
    font-size: clamp(32px, 6vw, 56px);
    font-weight: 800;
    line-height: 1.1;
    background: linear-gradient(135deg, var(--text) 0%, var(--accent1) 50%, var(--accent2) 100%);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    background-clip: text;
    margin-bottom: 12px;
  }

  .header p {
    color: var(--muted);
    font-size: 13px;
    letter-spacing: 1px;
  }

  /* Stats grid */
  .stats-grid {
    display: grid;
    grid-template-columns: repeat(2, 1fr);
    gap: 16px;
    margin-bottom: 40px;
    animation: fadeUp 0.8s 0.2s ease both;
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  @media (min-width: 600px) {
    .stats-grid { grid-template-columns: repeat(4, 1fr); }
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  .stat-card {
    background: var(--card);
    border: 1px solid var(--border);
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    padding: 20px 16px;
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    position: relative;
    overflow: hidden;
    transition: transform 0.2s, border-color 0.2s;
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  .stat-card:hover {
    transform: translateY(-3px);
    border-color: var(--accent1);
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  .stat-card::before {
    content: '';
    position: absolute;
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    height: 2px;
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  .stat-card:nth-child(1)::before { background: var(--accent1); }
  .stat-card:nth-child(2)::before { background: var(--accent2); }
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  .stat-number {
    font-family: 'Syne', sans-serif;
    font-size: 30px;
    font-weight: 800;
    line-height: 1;
    margin-bottom: 6px;
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  .stat-card:nth-child(1) .stat-number { color: var(--accent1); }
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  .stat-card:nth-child(4) .stat-number { color: var(--accent4); }

  .stat-label {
    font-size: 10px;
    color: var(--muted);
    letter-spacing: 2px;
    text-transform: uppercase;
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  /* Section */
  .section {
    margin-bottom: 40px;
    animation: fadeUp 0.8s 0.4s ease both;
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  .section-title {
    font-family: 'Syne', sans-serif;
    font-size: 11px;
    font-weight: 700;
    letter-spacing: 4px;
    text-transform: uppercase;
    color: var(--accent1);
    margin-bottom: 20px;
    display: flex;
    align-items: center;
    gap: 12px;
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  .section-title::after {
    content: '';
    flex: 1;
    height: 1px;
    background: var(--border);
  }

  /* Category bars */
  .category-list { display: flex; flex-direction: column; gap: 12px; }

  .cat-item {
    background: var(--card);
    border: 1px solid var(--border);
    border-radius: 10px;
    padding: 14px 16px;
    display: grid;
    grid-template-columns: 180px 1fr 44px;
    align-items: center;
    gap: 16px;
    transition: border-color 0.2s;
  }

  .cat-item:hover { border-color: var(--accent1); }

  .cat-name {
    font-size: 12px;
    color: var(--text);
    white-space: nowrap;
  }

  .cat-bar-bg {
    height: 6px;
    background: var(--border);
    border-radius: 3px;
    overflow: hidden;
  }

  .cat-bar {
    height: 100%;
    border-radius: 3px;
    transition: width 1.2s cubic-bezier(0.16, 1, 0.3, 1);
    animation: barGrow 1.5s cubic-bezier(0.16, 1, 0.3, 1) both;
  }

  @keyframes barGrow {
    from { width: 0% !important; }
  }

  .cat-count {
    font-family: 'Syne', sans-serif;
    font-size: 14px;
    font-weight: 700;
    text-align: right;
  }

  /* Monthly chart */
  .monthly-chart {
    background: var(--card);
    border: 1px solid var(--border);
    border-radius: 12px;
    padding: 24px;
  }

  .chart-bars {
    display: flex;
    align-items: flex-end;
    gap: 16px;
    height: 120px;
    margin-bottom: 12px;
  }

  .chart-bar-wrap {
    flex: 1;
    display: flex;
    flex-direction: column;
    align-items: center;
    gap: 6px;
    height: 100%;
    justify-content: flex-end;
  }

  .chart-bar-value {
    font-size: 11px;
    color: var(--muted);
    font-family: 'Syne', sans-serif;
    font-weight: 700;
  }

  .chart-bar-inner {
    width: 100%;
    border-radius: 6px 6px 0 0;
    animation: barUp 1.2s cubic-bezier(0.16,1,0.3,1) both;
    min-height: 4px;
  }

  @keyframes barUp {
    from { height: 0 !important; }
  }

  .chart-labels {
    display: flex;
    gap: 16px;
  }

  .chart-label {
    flex: 1;
    text-align: center;
    font-size: 10px;
    color: var(--muted);
    letter-spacing: 1px;
  }

  /* Top conversations */
  .top-list { display: flex; flex-direction: column; gap: 8px; }

  .top-item {
    background: var(--card);
    border: 1px solid var(--border);
    border-radius: 10px;
    padding: 14px 18px;
    display: flex;
    align-items: center;
    gap: 14px;
    transition: transform 0.2s, border-color 0.2s;
  }

  .top-item:hover {
    transform: translateX(4px);
    border-color: var(--accent2);
  }

  .top-rank {
    font-family: 'Syne', sans-serif;
    font-size: 20px;
    font-weight: 800;
    color: var(--border);
    min-width: 28px;
  }

  .top-info { flex: 1; }

  .top-title {
    font-size: 13px;
    color: var(--text);
    margin-bottom: 3px;
  }

  .top-msgs {
    font-size: 11px;
    color: var(--muted);
  }

  .top-badge {
    font-size: 10px;
    padding: 4px 10px;
    border-radius: 20px;
    letter-spacing: 1px;
  }

  /* Insights */
  .insights-grid {
    display: grid;
    grid-template-columns: 1fr;
    gap: 12px;
  }

  @media (min-width: 600px) {
    .insights-grid { grid-template-columns: 1fr 1fr; }
  }

  .insight-card {
    background: var(--card);
    border: 1px solid var(--border);
    border-radius: 12px;
    padding: 20px;
    transition: border-color 0.2s;
  }

  .insight-card:hover { border-color: var(--accent3); }

  .insight-icon {
    font-size: 24px;
    margin-bottom: 10px;
  }

  .insight-title {
    font-family: 'Syne', sans-serif;
    font-size: 13px;
    font-weight: 700;
    color: var(--text);
    margin-bottom: 8px;
  }

  .insight-text {
    font-size: 12px;
    color: var(--muted);
    line-height: 1.8;
  }

  /* Fun Facts */
  .funfacts-grid {
    display: grid;
    grid-template-columns: 1fr 1fr;
    gap: 12px;
  }

  @media (max-width: 599px) {
    .funfacts-grid { grid-template-columns: 1fr; }
  }

  .funfact-card {
    background: var(--card);
    border: 1px solid var(--border);
    border-radius: 12px;
    padding: 20px;
    transition: border-color 0.2s;
  }

  .funfact-card:hover { border-color: var(--accent4); }

  .funfact-icon {
    font-size: 28px;
    margin-bottom: 10px;
  }

  .funfact-title {
    font-family: 'Syne', sans-serif;
    font-size: 13px;
    font-weight: 700;
    color: var(--text);
    margin-bottom: 8px;
  }

  .funfact-text {
    font-size: 12px;
    color: var(--muted);
    line-height: 1.8;
  }

  .highlight-accent2 { color: var(--accent2); font-weight: 700; }
  .highlight-accent3 { color: var(--accent3); font-weight: 700; }
  .highlight-accent1 { color: var(--accent1); font-weight: 700; }
  .highlight-accent4 { color: var(--accent4); font-weight: 700; }

  /* Footer */
  .footer {
    text-align: center;
    padding: 40px 0 20px;
    color: var(--muted);
    font-size: 11px;
    letter-spacing: 2px;
    animation: fadeUp 0.8s 1s ease both;
  }

  /* Animations */
  @keyframes fadeDown {
    from { opacity: 0; transform: translateY(-20px); }
    to { opacity: 1; transform: translateY(0); }
  }

  @keyframes fadeUp {
    from { opacity: 0; transform: translateY(20px); }
    to { opacity: 1; transform: translateY(0); }
  }

  .delay-1 { animation-delay: 0.1s; }
  .delay-2 { animation-delay: 0.3s; }
  .delay-3 { animation-delay: 0.5s; }
  .delay-4 { animation-delay: 0.7s; }
  .delay-5 { animation-delay: 0.9s; }

  /* Profile chip */
  .profile-chip {
    display: inline-flex;
    align-items: center;
    gap: 10px;
    background: var(--card);
    border: 1px solid var(--border);
    border-radius: 100px;
    padding: 8px 20px 8px 8px;
    margin-bottom: 30px;
  }

  .profile-avatar {
    width: 32px;
    height: 32px;
    background: linear-gradient(135deg, var(--accent1), var(--accent2));
    border-radius: 50%;
    display: flex;
    align-items: center;
    justify-content: center;
    font-family: 'Syne', sans-serif;
    font-weight: 800;
    font-size: 14px;
    color: white;
  }

  .profile-text { font-size: 12px; color: var(--muted); }
  .profile-text span { color: var(--text); }
</style>
</head>
<body>
<div class="container">

  <!-- Header -->
  <div class="header">
    <div class="tag">ChatGPT Export Analysis</div>
    <h1>Your AI Conversation<br>Report</h1>
    <p>Dec 2025 &nbsp;·&nbsp; Apr 2026 &nbsp;·&nbsp; Full Export Analysis</p>
    <br>
    <div class="profile-chip">
      <div class="profile-avatar">S</div>
      <div class="profile-text">User born <span>2006</span> &nbsp;·&nbsp; Free Plan &nbsp;·&nbsp; <span>syamaeib@gmail.com</span></div>
    </div>
  </div>

  <!-- Key Stats -->
  <div class="stats-grid">
    <div class="stat-card">
      <div class="stat-number">166</div>
      <div class="stat-label">Conversations</div>
    </div>
    <div class="stat-card">
      <div class="stat-number">1,902</div>
      <div class="stat-label">Your Messages</div>
    </div>
    <div class="stat-card">
      <div class="stat-number">2,363</div>
      <div class="stat-label">AI Replies</div>
    </div>
    <div class="stat-card">
      <div class="stat-number">142</div>
      <div class="stat-label">Days Active</div>
    </div>
  </div>

  <!-- Monthly Activity -->
  <div class="section delay-2">
    <div class="section-title">Monthly Activity</div>
    <div class="monthly-chart">
      <div class="chart-bars">
        <div class="chart-bar-wrap">
          <div class="chart-bar-value">21</div>
          <div class="chart-bar-inner" style="height:52px; background: linear-gradient(180deg, var(--accent1), rgba(124,106,247,0.5));"></div>
        </div>
        <div class="chart-bar-wrap">
          <div class="chart-bar-value">39</div>
          <div class="chart-bar-inner" style="height:98px; background: linear-gradient(180deg, var(--accent2), rgba(247,106,140,0.5));"></div>
        </div>
        <div class="chart-bar-wrap">
          <div class="chart-bar-value">28</div>
          <div class="chart-bar-inner" style="height:70px; background: linear-gradient(180deg, var(--accent3), rgba(106,247,194,0.5));"></div>
        </div>
        <div class="chart-bar-wrap">
          <div class="chart-bar-value">38</div>
          <div class="chart-bar-inner" style="height:95px; background: linear-gradient(180deg, var(--accent4), rgba(247,194,106,0.5));"></div>
        </div>
        <div class="chart-bar-wrap">
          <div class="chart-bar-value">40</div>
          <div class="chart-bar-inner" style="height:100px; background: linear-gradient(180deg, var(--accent1), rgba(124,106,247,0.5));"></div>
        </div>
      </div>
      <div class="chart-labels">
        <div class="chart-label">DEC '25</div>
        <div class="chart-label">JAN '26</div>
        <div class="chart-label">FEB '26</div>
        <div class="chart-label">MAR '26</div>
        <div class="chart-label">APR '26</div>
      </div>
    </div>
  </div>

  <!-- Topic Breakdown -->
  <div class="section delay-3">
    <div class="section-title">Topic Breakdown</div>
    <div class="category-list">
      <div class="cat-item">
        <div class="cat-name">🥗 Nutrition &amp; Diet</div>
        <div class="cat-bar-bg"><div class="cat-bar" style="width:72%; background: linear-gradient(90deg, var(--accent3), rgba(106,247,194,0.5));"></div></div>
        <div class="cat-count" style="color:var(--accent3)">~62</div>
      </div>
      <div class="cat-item">
        <div class="cat-name">🩺 Health &amp; Medical</div>
        <div class="cat-bar-bg"><div class="cat-bar" style="width:30%; background: linear-gradient(90deg, var(--accent2), rgba(247,106,140,0.5));"></div></div>
        <div class="cat-count" style="color:var(--accent2)">~22</div>
      </div>
      <div class="cat-item">
        <div class="cat-name">💪 Fitness &amp; Gym</div>
        <div class="cat-bar-bg"><div class="cat-bar" style="width:25%; background: linear-gradient(90deg, var(--accent1), rgba(124,106,247,0.5));"></div></div>
        <div class="cat-count" style="color:var(--accent1)">~20</div>
      </div>
      <div class="cat-item">
        <div class="cat-name">🎓 Education &amp; College</div>
        <div class="cat-bar-bg"><div class="cat-bar" style="width:18%; background: linear-gradient(90deg, var(--accent4), rgba(247,194,106,0.5));"></div></div>
        <div class="cat-count" style="color:var(--accent4)">~15</div>
      </div>
      <div class="cat-item">
        <div class="cat-name">🎌 Anime &amp; Entertainment</div>
        <div class="cat-bar-bg"><div class="cat-bar" style="width:15%; background: linear-gradient(90deg, #f76af0, rgba(247,106,240,0.5));"></div></div>
        <div class="cat-count" style="color:#f76af0">~12</div>
      </div>
      <div class="cat-item">
        <div class="cat-name">🖥️ Tech &amp; Gaming</div>
        <div class="cat-bar-bg"><div class="cat-bar" style="width:14%; background: linear-gradient(90deg, #6ab4f7, rgba(106,180,247,0.5));"></div></div>
        <div class="cat-count" style="color:#6ab4f7">~12</div>
      </div>
      <div class="cat-item">
        <div class="cat-name">🧬 Supplements</div>
        <div class="cat-bar-bg"><div class="cat-bar" style="width:12%; background: linear-gradient(90deg, var(--accent3), rgba(106,247,194,0.3));"></div></div>
        <div class="cat-count" style="color:var(--accent3)">~10</div>
      </div>
      <div class="cat-item">
        <div class="cat-name">🧮 Coding &amp; Math</div>
        <div class="cat-bar-bg"><div class="cat-bar" style="width:9%; background: linear-gradient(90deg, var(--accent1), rgba(124,106,247,0.3));"></div></div>
        <div class="cat-count" style="color:var(--accent1)">~8</div>
      </div>
      <div class="cat-item">
        <div class="cat-name">🙋 Personal / Lifestyle</div>
        <div class="cat-bar-bg"><div class="cat-bar" style="width:9%; background: linear-gradient(90deg, var(--accent4), rgba(247,194,106,0.3));"></div></div>
        <div class="cat-count" style="color:var(--accent4)">~5</div>
      </div>
    </div>
  </div>

  <!-- Top Conversations -->
  <div class="section delay-4">
    <div class="section-title">Most Active Conversations</div>
    <div class="top-list">
      <div class="top-item">
        <div class="top-rank">01</div>
        <div class="top-info">
          <div class="top-title">Body Fat Percentage Estimate</div>
          <div class="top-msgs">1,473 messages · Health</div>
        </div>
        <div class="top-badge" style="background:rgba(247,106,140,0.15);color:var(--accent2)">EPIC</div>
      </div>
      <div class="top-item">
        <div class="top-rank">02</div>
        <div class="top-info">
          <div class="top-title">Gym Tips for Beginners</div>
          <div class="top-msgs">645 messages · Fitness</div>
        </div>
        <div class="top-badge" style="background:rgba(124,106,247,0.15);color:var(--accent1)">LONG</div>
      </div>
      <div class="top-item">
        <div class="top-rank">03</div>
        <div class="top-info">
          <div class="top-title">Lab Report Summary</div>
          <div class="top-msgs">394 messages · Medical</div>
        </div>
        <div class="top-badge" style="background:rgba(106,247,194,0.15);color:var(--accent3)">DEEP</div>
      </div>
      <div class="top-item">
        <div class="top-rank">04</div>
        <div class="top-info">
          <div class="top-title">Blood Sugar Post-Meal</div>
          <div class="top-msgs">84 messages · Nutrition</div>
        </div>
        <div class="top-badge" style="background:rgba(247,194,106,0.15);color:var(--accent4)">ACTIVE</div>
      </div>
      <div class="top-item">
        <div class="top-rank">05</div>
        <div class="top-info">
          <div class="top-title">Dying Light Steam Price</div>
          <div class="top-msgs">77 messages · Gaming</div>
        </div>
        <div class="top-badge" style="background:rgba(106,180,247,0.15);color:#6ab4f7">ACTIVE</div>
      </div>
    </div>
  </div>

  <!-- Key Insights -->
  <div class="section delay-5">
    <div class="section-title">Key Insights</div>
    <div class="insights-grid">
      <div class="insight-card">
        <div class="insight-icon">🩸</div>
        <div class="insight-title">Managing a Health Condition</div>
        <div class="insight-text">You have many conversations about prediabetes, blood sugar, metformin, and lab reports — suggesting you're actively monitoring and managing a health condition. You asked very detailed, specific questions about glucose, insulin, and organ effects.</div>
      </div>
      <div class="insight-card">
        <div class="insight-icon">💪</div>
        <div class="insight-title">Serious About Fitness</div>
        <div class="insight-text">Gym tips, workout splits, body fat %, incline walking, creatine, whey protein — you're deeply invested in fitness and bodybuilding. Your longest single conversation (1,473 msgs!) was about body fat estimation.</div>
      </div>
      <div class="insight-card">
        <div class="insight-icon">🎓</div>
        <div class="insight-title">Making a Big Life Decision</div>
        <div class="insight-text">Multiple conversations about Aakash, Physics Wallah, Narayana PU College, PCMC/PCMB switch, and Presidency College exams show you're in the middle of choosing your academic path — likely preparing for competitive entrance exams.</div>
      </div>
      <div class="insight-card">
        <div class="insight-icon">🎌</div>
        <div class="insight-title">Passionate Anime Fan</div>
        <div class="insight-text">You debated Naruto vs AOT vs One Piece multiple times, discussed Gojo vs Yuji, and Minato's absence. One Piece vs Naruto appeared 3 separate times — clearly a topic you're very passionate about!</div>
      </div>
      <div class="insight-card">
        <div class="insight-icon">🖥️</div>
        <div class="insight-title">Tech-Savvy Gamer</div>
        <div class="insight-text">You researched KLIM laptops, Lenovo LOQ, DX11 vs DX12, Dying Light pricing, and game difficulty tips (Hunter Build, Master Mode). You compare specs carefully before buying.</div>
      </div>
      <div class="insight-card">
        <div class="insight-icon">🥗</div>
        <div class="insight-title">Nutrition Obsessed</div>
        <div class="insight-text">Rice, tofu, soya chunks, sweet potato, stevia, chicken liver — you asked about almost every food category. You're carefully tracking macros, GI index, and cooking methods, likely tied to managing your health condition.</div>
      </div>
      <div class="insight-card">
        <div class="insight-icon">😴</div>
        <div class="insight-title">Sleep Struggles</div>
        <div class="insight-text">Multiple chats about trouble sleeping, sleep timing impact, sleep rebound effects, nap impact, and sleep duration calculation. Sleep quality is a recurring concern for you.</div>
      </div>
      <div class="insight-card">
        <div class="insight-icon">📈</div>
        <div class="insight-title">Growing Usage Over Time</div>
        <div class="insight-text">You started with 21 conversations in Dec 2025 and peaked at 40 in April 2026 — nearly doubling your usage. You're relying on ChatGPT more and more each month.</div>
      </div>
    </div>
  </div>

  <!-- Fun Facts -->
  <div class="section delay-5">
    <div class="section-title">Fun Facts</div>
    <div class="funfacts-grid">
      <div class="funfact-card">
        <div class="funfact-icon">⚔️</div>
        <div class="funfact-title">Most Debated Topic</div>
        <div class="funfact-text"><span class="highlight-accent2">Naruto vs One Piece</span> — you started this debate at least <span class="highlight-accent2">4 separate times</span>! You clearly can't make up your mind.</div>
      </div>
      <div class="funfact-card">
        <div class="funfact-icon">🔬</div>
        <div class="funfact-title">Most Focused Topic</div>
        <div class="funfact-text"><span class="highlight-accent3">Nutrition</span> makes up over <span class="highlight-accent3">37%</span> of all your conversations — you're practically a food scientist at this point!</div>
      </div>
      <div class="funfact-card">
        <div class="funfact-icon">📚</div>
        <div class="funfact-title">Deepest Dive</div>
        <div class="funfact-text">The "Body Fat Percentage Estimate" conversation had <span class="highlight-accent1">1,473 messages</span> — that's almost a full book's worth of conversation in one thread!</div>
      </div>
      <div class="funfact-card">
        <div class="funfact-icon">🌟</div>
        <div class="funfact-title">Young &amp; Curious</div>
        <div class="funfact-text">Born in <span class="highlight-accent4">2006</span>, you're around 19 years old and using AI to navigate everything from college choices to health and gaming. That's impressive!</div>
      </div>
    </div>
  </div>

  <!-- Footer -->
  <div class="footer">
    GENERATED FROM CHATGPT EXPORT ANALYSIS &nbsp;·&nbsp; JULY 2026
  </div>

</div>
</body>
</html>

"""
SYAM's FULL DAILY NUTRITION CALCULATOR
======================================
All raw weights. All values per day.
Organ rotation averaged over 3-day cycle:
  Day 1: mutton kidney 35g
  Day 2: chicken liver 25g
  Day 3: mutton karpura (testes) 35g
  Average/day used for all nutrients.

Sources: USDA FoodData Central values + Indian food composition tables
"""

# ─── FOOD DATABASE (per 100g raw edible) ──────────────────────────────────────
# Format: {cal, prot, fat, carb, fiber, vitamins/minerals}
# All mineral values in mg unless noted (mcg for B12, D, folate, K, selenium)

foods = {
    # ── MEATS ─────────────────────────────────────────────────────────────────
    "mutton_thigh_skinless": {  # boneless, lean with some fat, goat leg
        "g": 100,
        "cal": 143, "prot": 27.1, "fat": 3.7, "carb": 0, "fiber": 0,
        "vit_a_mcg": 0, "vit_c": 0, "vit_d_mcg": 0, "vit_e": 0.1,
        "vit_k_mcg": 0, "vit_b1": 0.15, "vit_b2": 0.27, "vit_b3": 6.2,
        "vit_b5": 0.8, "vit_b6": 0.4, "vit_b9_mcg": 5, "vit_b12_mcg": 2.6,
        "calcium": 17, "phosphorus": 230, "magnesium": 25, "potassium": 385,
        "sodium": 72, "iron": 2.3, "zinc": 5.3, "copper": 0.14,
        "manganese": 0.03, "selenium_mcg": 9, "chromium_mcg": 1.5,
        "iodine_mcg": 4, "boron_mcg": 0,
        "choline": 70,
    },
    "fish_basa": {  # pangasius, raw
        "g": 100,
        "cal": 89, "prot": 17.2, "fat": 2.0, "carb": 0, "fiber": 0,
        "vit_a_mcg": 12, "vit_c": 0, "vit_d_mcg": 1.5, "vit_e": 0.6,
        "vit_k_mcg": 0.1, "vit_b1": 0.08, "vit_b2": 0.07, "vit_b3": 3.9,
        "vit_b5": 0.5, "vit_b6": 0.28, "vit_b9_mcg": 12, "vit_b12_mcg": 1.5,
        "calcium": 35, "phosphorus": 210, "magnesium": 22, "potassium": 310,
        "sodium": 55, "iron": 0.4, "zinc": 0.5, "copper": 0.05,
        "manganese": 0.01, "selenium_mcg": 26, "chromium_mcg": 0.5,
        "iodine_mcg": 40, "boron_mcg": 0,
        "choline": 65,
    },
    # ── ORGAN ROTATION (averaged over 3 days) ────────────────────────────────
    # Day1: mutton kidney 35g | Day2: chicken liver 25g | Day3: mutton karpura 35g
    # Per 100g values:
    "mutton_kidney": {
        "g": 100,
        "cal": 112, "prot": 18.5, "fat": 3.6, "carb": 0.8, "fiber": 0,
        "vit_a_mcg": 118, "vit_c": 10, "vit_d_mcg": 0.5, "vit_e": 0.5,
        "vit_k_mcg": 0, "vit_b1": 0.36, "vit_b2": 2.2, "vit_b3": 7.5,
        "vit_b5": 3.8, "vit_b6": 0.45, "vit_b9_mcg": 88, "vit_b12_mcg": 79,
        "calcium": 14, "phosphorus": 265, "magnesium": 17, "potassium": 295,
        "sodium": 180, "iron": 6.2, "zinc": 2.9, "copper": 0.45,
        "manganese": 0.18, "selenium_mcg": 141, "chromium_mcg": 8,
        "iodine_mcg": 14, "boron_mcg": 0,
        "choline": 350,
    },
    "chicken_liver": {
        "g": 100,
        "cal": 119, "prot": 17.1, "fat": 4.8, "carb": 0.7, "fiber": 0,
        "vit_a_mcg": 3296, "vit_c": 17.9, "vit_d_mcg": 0.3, "vit_e": 0.7,
        "vit_k_mcg": 0.7, "vit_b1": 0.29, "vit_b2": 2.31, "vit_b3": 9.7,
        "vit_b5": 6.24, "vit_b6": 0.81, "vit_b9_mcg": 578, "vit_b12_mcg": 16.6,
        "calcium": 8, "phosphorus": 297, "magnesium": 20, "potassium": 230,
        "sodium": 71, "iron": 9.0, "zinc": 3.9, "copper": 0.49,
        "manganese": 0.36, "selenium_mcg": 54.6, "chromium_mcg": 5,
        "iodine_mcg": 15, "boron_mcg": 0,
        "choline": 290,
    },
    "mutton_karpura": {  # goat testes, similar to lamb testes
        "g": 100,
        "cal": 106, "prot": 17.8, "fat": 3.4, "carb": 0.5, "fiber": 0,
        "vit_a_mcg": 5, "vit_c": 3, "vit_d_mcg": 0.2, "vit_e": 0.4,
        "vit_k_mcg": 0, "vit_b1": 0.25, "vit_b2": 0.45, "vit_b3": 5.8,
        "vit_b5": 1.5, "vit_b6": 0.38, "vit_b9_mcg": 22, "vit_b12_mcg": 3.5,
        "calcium": 12, "phosphorus": 240, "magnesium": 20, "potassium": 330,
        "sodium": 85, "iron": 3.0, "zinc": 4.5, "copper": 0.20,
        "manganese": 0.08, "selenium_mcg": 28, "chromium_mcg": 2,
        "iodine_mcg": 8, "boron_mcg": 0,
        "choline": 80,
    },
    # ── PLANT FOODS ──────────────────────────────────────────────────────────
    "white_basmati_rice_cooked": {  # 50g raw = ~145g cooked; nutrients per 100g RAW
        "g": 100,
        "cal": 357, "prot": 6.7, "fat": 0.5, "carb": 79, "fiber": 0.6,
        "vit_a_mcg": 0, "vit_c": 0, "vit_d_mcg": 0, "vit_e": 0.1,
        "vit_k_mcg": 0, "vit_b1": 0.06, "vit_b2": 0.03, "vit_b3": 1.6,
        "vit_b5": 0.5, "vit_b6": 0.12, "vit_b9_mcg": 8, "vit_b12_mcg": 0,
        "calcium": 10, "phosphorus": 96, "magnesium": 12, "potassium": 85,
        "sodium": 1, "iron": 0.4, "zinc": 0.5, "copper": 0.1,
        "manganese": 0.5, "selenium_mcg": 7.5, "chromium_mcg": 0.5,
        "iodine_mcg": 2, "boron_mcg": 20,
        "choline": 2,
    },
    "banana": {  # raw
        "g": 100,
        "cal": 89, "prot": 1.1, "fat": 0.3, "carb": 22.8, "fiber": 2.6,
        "vit_a_mcg": 3, "vit_c": 8.7, "vit_d_mcg": 0, "vit_e": 0.1,
        "vit_k_mcg": 0.5, "vit_b1": 0.03, "vit_b2": 0.07, "vit_b3": 0.67,
        "vit_b5": 0.33, "vit_b6": 0.37, "vit_b9_mcg": 20, "vit_b12_mcg": 0,
        "calcium": 5, "phosphorus": 22, "magnesium": 27, "potassium": 358,
        "sodium": 1, "iron": 0.26, "zinc": 0.15, "copper": 0.08,
        "manganese": 0.27, "selenium_mcg": 1, "chromium_mcg": 1,
        "iodine_mcg": 3, "boron_mcg": 180,
        "choline": 9.8,
    },
    "tempeh": {  # raw
        "g": 100,
        "cal": 193, "prot": 18.5, "fat": 10.8, "carb": 9.4, "fiber": 4.1,
        "vit_a_mcg": 0, "vit_c": 0, "vit_d_mcg": 0, "vit_e": 0.1,
        "vit_k_mcg": 0, "vit_b1": 0.08, "vit_b2": 0.36, "vit_b3": 2.6,
        "vit_b5": 0.28, "vit_b6": 0.22, "vit_b9_mcg": 24, "vit_b12_mcg": 0,
        "calcium": 111, "phosphorus": 266, "magnesium": 81, "potassium": 412,
        "sodium": 9, "iron": 2.7, "zinc": 1.14, "copper": 0.56,
        "manganese": 1.30, "selenium_mcg": 0.3, "chromium_mcg": 0,
        "iodine_mcg": 0, "boron_mcg": 450,
        "choline": 87,
    },
    "cabbage": {  # raw
        "g": 100,
        "cal": 25, "prot": 1.3, "fat": 0.1, "carb": 5.8, "fiber": 2.5,
        "vit_a_mcg": 5, "vit_c": 36.6, "vit_d_mcg": 0, "vit_e": 0.15,
        "vit_k_mcg": 76, "vit_b1": 0.06, "vit_b2": 0.04, "vit_b3": 0.23,
        "vit_b5": 0.21, "vit_b6": 0.12, "vit_b9_mcg": 43, "vit_b12_mcg": 0,
        "calcium": 40, "phosphorus": 26, "magnesium": 12, "potassium": 170,
        "sodium": 18, "iron": 0.47, "zinc": 0.18, "copper": 0.02,
        "manganese": 0.16, "selenium_mcg": 0.3, "chromium_mcg": 0.5,
        "iodine_mcg": 0, "boron_mcg": 730,
        "choline": 10.7,
    },
    "eggs_whole": {  # large egg ~50g each, raw
        "g": 100,  # per 100g
        "cal": 143, "prot": 12.6, "fat": 9.5, "carb": 0.7, "fiber": 0,
        "vit_a_mcg": 149, "vit_c": 0, "vit_d_mcg": 2.0, "vit_e": 1.05,
        "vit_k_mcg": 0.3, "vit_b1": 0.04, "vit_b2": 0.46, "vit_b3": 0.07,
        "vit_b5": 1.53, "vit_b6": 0.17, "vit_b9_mcg": 47, "vit_b12_mcg": 1.11,
        "calcium": 56, "phosphorus": 198, "magnesium": 12, "potassium": 138,
        "sodium": 142, "iron": 1.75, "zinc": 1.29, "copper": 0.07,
        "manganese": 0.03, "selenium_mcg": 30, "chromium_mcg": 1,
        "iodine_mcg": 53, "boron_mcg": 0,
        "choline": 294,
    },
    "ghee": {  # per 100g
        "g": 100,
        "cal": 900, "prot": 0, "fat": 99.5, "carb": 0, "fiber": 0,
        "vit_a_mcg": 840, "vit_c": 0, "vit_d_mcg": 1.5, "vit_e": 2.8,
        "vit_k_mcg": 8.5, "vit_b1": 0, "vit_b2": 0, "vit_b3": 0,
        "vit_b5": 0, "vit_b6": 0, "vit_b9_mcg": 0, "vit_b12_mcg": 0,
        "calcium": 0, "phosphorus": 3, "magnesium": 0, "potassium": 5,
        "sodium": 2, "iron": 0, "zinc": 0.05, "copper": 0,
        "manganese": 0, "selenium_mcg": 0.5, "chromium_mcg": 0,
        "iodine_mcg": 0, "boron_mcg": 0,
        "choline": 0,
    },
    "nutritional_yeast": {  # fortified, Urban Platter (standard nooch values)
        "g": 100,
        "cal": 360, "prot": 45, "fat": 5, "carb": 35, "fiber": 14,
        "vit_a_mcg": 0, "vit_c": 0, "vit_d_mcg": 0, "vit_e": 0,
        "vit_k_mcg": 0, "vit_b1": 10, "vit_b2": 10, "vit_b3": 50,
        "vit_b5": 10, "vit_b6": 10, "vit_b9_mcg": 600, "vit_b12_mcg": 50,
        "calcium": 50, "phosphorus": 1100, "magnesium": 250, "potassium": 1500,
        "sodium": 10, "iron": 5, "zinc": 3.5, "copper": 0.5,
        "manganese": 0.5, "selenium_mcg": 5, "chromium_mcg": 100,
        "iodine_mcg": 0, "boron_mcg": 0,
        "choline": 30,
    },
    # ── AROMATICS / SPICES ────────────────────────────────────────────────────
    "onion_medium": {  # ~110g per medium onion
        "g": 100,
        "cal": 40, "prot": 1.1, "fat": 0.1, "carb": 9.3, "fiber": 1.7,
        "vit_a_mcg": 0, "vit_c": 7.4, "vit_d_mcg": 0, "vit_e": 0.02,
        "vit_k_mcg": 0.4, "vit_b1": 0.05, "vit_b2": 0.03, "vit_b3": 0.12,
        "vit_b5": 0.12, "vit_b6": 0.12, "vit_b9_mcg": 19, "vit_b12_mcg": 0,
        "calcium": 23, "phosphorus": 29, "magnesium": 10, "potassium": 166,
        "sodium": 4, "iron": 0.21, "zinc": 0.17, "copper": 0.04,
        "manganese": 0.13, "selenium_mcg": 0.5, "chromium_mcg": 0,
        "iodine_mcg": 3, "boron_mcg": 390,
        "choline": 6.1,
    },
    "garlic": {
        "g": 100,
        "cal": 149, "prot": 6.4, "fat": 0.5, "carb": 33.1, "fiber": 2.1,
        "vit_a_mcg": 0, "vit_c": 31.2, "vit_d_mcg": 0, "vit_e": 0.08,
        "vit_k_mcg": 1.7, "vit_b1": 0.2, "vit_b2": 0.11, "vit_b3": 0.7,
        "vit_b5": 0.6, "vit_b6": 1.24, "vit_b9_mcg": 3, "vit_b12_mcg": 0,
        "calcium": 181, "phosphorus": 153, "magnesium": 25, "potassium": 401,
        "sodium": 17, "iron": 1.7, "zinc": 1.16, "copper": 0.3,
        "manganese": 1.67, "selenium_mcg": 14.2, "chromium_mcg": 0,
        "iodine_mcg": 0, "boron_mcg": 0,
        "choline": 23.2,
    },
    "ginger": {
        "g": 100,
        "cal": 80, "prot": 1.8, "fat": 0.8, "carb": 17.8, "fiber": 2.0,
        "vit_a_mcg": 0, "vit_c": 5.0, "vit_d_mcg": 0, "vit_e": 0.26,
        "vit_k_mcg": 0.1, "vit_b1": 0.03, "vit_b2": 0.03, "vit_b3": 0.75,
        "vit_b5": 0.2, "vit_b6": 0.16, "vit_b9_mcg": 11, "vit_b12_mcg": 0,
        "calcium": 16, "phosphorus": 34, "magnesium": 43, "potassium": 415,
        "sodium": 13, "iron": 0.6, "zinc": 0.34, "copper": 0.23,
        "manganese": 0.23, "selenium_mcg": 0.7, "chromium_mcg": 0,
        "iodine_mcg": 0, "boron_mcg": 0,
        "choline": 28.8,
    },
    "coriander_seed": {
        "g": 100,
        "cal": 298, "prot": 12.4, "fat": 17.8, "carb": 54.99, "fiber": 41.9,
        "vit_a_mcg": 0, "vit_c": 21, "vit_d_mcg": 0, "vit_e": 2.5,
        "vit_k_mcg": 0, "vit_b1": 0.24, "vit_b2": 0.29, "vit_b3": 2.13,
        "vit_b5": 1.8, "vit_b6": 0.43, "vit_b9_mcg": 0, "vit_b12_mcg": 0,
        "calcium": 709, "phosphorus": 409, "magnesium": 330, "potassium": 1267,
        "sodium": 35, "iron": 16.3, "zinc": 4.7, "copper": 0.98,
        "manganese": 1.9, "selenium_mcg": 26.2, "chromium_mcg": 0,
        "iodine_mcg": 0, "boron_mcg": 0,
        "choline": 0,
    },
    "iodised_salt": {  # iodised: ~77mcg iodine per gram
        "g": 100,
        "cal": 0, "prot": 0, "fat": 0, "carb": 0, "fiber": 0,
        "vit_a_mcg": 0, "vit_c": 0, "vit_d_mcg": 0, "vit_e": 0,
        "vit_k_mcg": 0, "vit_b1": 0, "vit_b2": 0, "vit_b3": 0,
        "vit_b5": 0, "vit_b6": 0, "vit_b9_mcg": 0, "vit_b12_mcg": 0,
        "calcium": 24, "phosphorus": 0, "magnesium": 0, "potassium": 8,
        "sodium": 38758, "iron": 0.3, "zinc": 0.1, "copper": 0,
        "manganese": 0, "selenium_mcg": 0, "chromium_mcg": 0,
        "iodine_mcg": 7700, "boron_mcg": 0,
        "choline": 0,
    },
    "isabgol": {  # psyllium husk
        "g": 100,
        "cal": 200, "prot": 3, "fat": 0.5, "carb": 85, "fiber": 70,
        "vit_a_mcg": 0, "vit_c": 0, "vit_d_mcg": 0, "vit_e": 0,
        "vit_k_mcg": 0, "vit_b1": 0, "vit_b2": 0, "vit_b3": 0,
        "vit_b5": 0, "vit_b6": 0, "vit_b9_mcg": 0, "vit_b12_mcg": 0,
        "calcium": 70, "phosphorus": 30, "magnesium": 30, "potassium": 80,
        "sodium": 10, "iron": 15, "zinc": 0, "copper": 0,
        "manganese": 0, "selenium_mcg": 0, "chromium_mcg": 0,
        "iodine_mcg": 0, "boron_mcg": 0,
        "choline": 0,
    },
}

# ─── DAILY AMOUNTS ────────────────────────────────────────────────────────────
daily_amounts = {
    "mutton_thigh_skinless":     100,   # g
    "fish_basa":                 150,   # g
    # Organ rotation average per day:
    # Kidney day: 35g × (1/3) + Liver day: 25g × (1/3) + Karpura day: 35g × (1/3)
    # computed per food below
    "mutton_kidney":             35/3,  # average g/day
    "chicken_liver":             25/3,  # average g/day
    "mutton_karpura":            35/3,  # average g/day
    "white_basmati_rice_cooked": 50,    # g raw
    "banana":                    200,   # g
    "tempeh":                    200,   # g
    "cabbage":                   200,   # g
    "eggs_whole":                250,   # 5 eggs × 50g each
    "ghee":                       10,   # ml ≈ 10g (ghee density ~0.9, close enough)
    "nutritional_yeast":           2,   # g
    "onion_medium":              110,   # 1 medium onion ≈ 110g
    "garlic":                     10,   # g
    "ginger":                      5,   # g
    "coriander_seed":             16,   # g
    "iodised_salt":                5,   # g
    "isabgol":                    10,   # g
    # Spices (turmeric, chilli, cinnamon) — minor contributors, ~3g total
    # included as approximate 1g each below for completeness
}

# ─── SUPPLEMENTS ──────────────────────────────────────────────────────────────
supplements = {
    # Naturaltein Calcium Citrate × 2 tablets
    "calcium_supp":       700,   # mg calcium (2 × 315mg elemental) [NOTE: image says 315mg but user said 350mg — using image value 315mg × 2 = 630mg; will flag]
    "vit_d3_calcium_supp": 1200, # IU (2 × 600 IU)
    "k2_mk7_calcium_supp": 55,   # mcg (2 × 27.5 mcg)

    # Tata 1mg Vitamin D3 + K2 MK-7 × 1 tablet
    "vit_d3_tata":        600,   # IU (100% RDA India = 600 IU)
    "k2_mk7_tata":         55,   # mcg per tablet

    # Magnesium glycinate — 150mg elemental Mg
    "magnesium_supp":     150,   # mg

    # Tata 1mg Salmon Omega × 2 capsules
    "epa":               1440,   # mg (2 × 720)
    "dha":                960,   # mg (2 × 480)
    "fish_oil_total":    4000,   # mg (2 × 2000)
    "vit_e_omega":         20,   # mg (2 × 10mg)

    # Nutritional yeast B12 contribution included in food above
}

# ─── CALCULATION ENGINE ───────────────────────────────────────────────────────
nutrients = {
    "cal": 0, "prot": 0, "fat": 0, "carb": 0, "fiber": 0,
    "vit_a_mcg": 0, "vit_c": 0, "vit_d_mcg": 0, "vit_e": 0,
    "vit_k_mcg": 0, "vit_b1": 0, "vit_b2": 0, "vit_b3": 0,
    "vit_b5": 0, "vit_b6": 0, "vit_b9_mcg": 0, "vit_b12_mcg": 0,
    "calcium": 0, "phosphorus": 0, "magnesium": 0, "potassium": 0,
    "sodium": 0, "iron": 0, "zinc": 0, "copper": 0,
    "manganese": 0, "selenium_mcg": 0, "chromium_mcg": 0,
    "iodine_mcg": 0, "boron_mcg": 0, "choline": 0,
}

breakdown = {}
for food, grams in daily_amounts.items():
    data = foods[food]
    ratio = grams / 100.0
    row = {}
    for k in nutrients:
        val = data.get(k, 0) * ratio
        nutrients[k] += val
        row[k] = round(val, 2)
    row["grams"] = round(grams, 1)
    breakdown[food] = row

# Add supplements
nutrients["calcium"]    += supplements["calcium_supp"]
nutrients["vit_d_mcg"]  += (supplements["vit_d3_calcium_supp"] + supplements["vit_d3_tata"]) / 40  # IU to mcg (1 mcg = 40 IU)
nutrients["vit_k_mcg"]  += supplements["k2_mk7_calcium_supp"] + supplements["k2_mk7_tata"]
nutrients["magnesium"]  += supplements["magnesium_supp"]
nutrients["vit_e"]      += supplements["vit_e_omega"]  # mg alpha-tocopherol

# EPA/DHA added for reference
nutrients["epa_dha_mg"] = supplements["epa"] + supplements["dha"]

# ─── RDA / AI VALUES (19-year-old male, Indian ICMR / WHO standards) ──────────
rda = {
    "cal":          2400,   # kcal (active 81kg male, estimated TDEE moderate)
    "prot":           60,   # g (ICMR 0.8g/kg × 75kg ideal; higher for muscle building)
    "fat":            80,   # g (30% of 2400 kcal)
    "carb":          300,   # g (50% of 2400 kcal)
    "fiber":          38,   # g
    "vit_a_mcg":    900,    # mcg RAE
    "vit_c":         90,    # mg
    "vit_d_mcg":     15,    # mcg (600 IU)
    "vit_e":         15,    # mg
    "vit_k_mcg":    120,    # mcg
    "vit_b1":         1.2,  # mg
    "vit_b2":         1.3,  # mg
    "vit_b3":        16,    # mg NE
    "vit_b5":         5,    # mg AI
    "vit_b6":         1.3,  # mg
    "vit_b9_mcg":   400,    # mcg DFE
    "vit_b12_mcg":    2.4,  # mcg
    "calcium":      1000,   # mg (19yo male)
    "phosphorus":   700,    # mg
    "magnesium":    400,    # mg
    "potassium":   3500,    # mg AI
    "sodium":      2300,    # mg (UL)
    "iron":           8,    # mg (adult male)
    "zinc":          11,    # mg
    "copper":         0.9,  # mg
    "manganese":      2.3,  # mg AI
    "selenium_mcg":  55,    # mcg
    "chromium_mcg":  35,    # mcg AI
    "iodine_mcg":   150,    # mcg
    "boron_mcg":   1000,    # mcg (no RDA; suggested AI ~1-3mg/day)
    "choline":      550,    # mg AI
    "epa_dha_mg":   500,    # mg combined (WHO minimum; optimal 1g+)
}

# Upper Tolerable Intake Limits (UL)
ul = {
    "vit_a_mcg":  3000,
    "vit_d_mcg":   100,  # mcg (4000 IU)
    "vit_e":      1000,
    "vit_k_mcg":  None,
    "vit_c":      2000,
    "vit_b6":      100,
    "vit_b9_mcg": 1000,
    "calcium":    2500,
    "phosphorus": 4000,
    "magnesium":   350,  # supplemental only
    "iron":         45,
    "zinc":         40,
    "copper":       10,
    "selenium_mcg": 400,
    "iodine_mcg":  1100,
    "sodium":      2300,
    "manganese":      11,
    "choline":     3500,
}

# ─── PRINT REPORT ─────────────────────────────────────────────────────────────
print("=" * 72)
print("  SYAM'S COMPLETE DAILY NUTRITION ANALYSIS")
print("  Profile: Male, 19yr, 81kg, 179cm, 20.8% BF, BMR 1794 kcal")
print("=" * 72)

print("\n📦 DAILY FOOD BREAKDOWN (grams/day)")
print("-" * 50)
foods_display = {
    "mutton_thigh_skinless": "Mutton (goat thigh, skinless boneless)",
    "fish_basa": "Fish (Basa, boneless)",
    "mutton_kidney": "Mutton Kidney (avg/day from rotation)",
    "chicken_liver": "Chicken Liver (avg/day from rotation)",
    "mutton_karpura": "Mutton Karpura (avg/day from rotation)",
    "white_basmati_rice_cooked": "White Basmati Rice (raw weight)",
    "banana": "Banana",
    "tempeh": "Tempeh",
    "cabbage": "Cabbage",
    "eggs_whole": "Eggs (5 × 50g)",
    "ghee": "Ghee",
    "nutritional_yeast": "Nutritional Yeast (fortified)",
    "onion_medium": "Onion (1 medium, ~110g)",
    "garlic": "Garlic",
    "ginger": "Ginger",
    "coriander_seed": "Coriander Seeds",
    "iodised_salt": "Iodised Salt",
    "isabgol": "Isabgol (Psyllium Husk)",
}
for k, label in foods_display.items():
    print(f"  {label:<45} {daily_amounts[k]:.1f}g")

print("\n📊 MACRONUTRIENTS")
print("-" * 50)
macros = ["cal", "prot", "fat", "carb", "fiber"]
macro_names = {
    "cal": "Energy (kcal)", "prot": "Protein (g)", "fat": "Total Fat (g)",
    "carb": "Carbohydrates (g)", "fiber": "Dietary Fiber (g)"
}
for m in macros:
    val = nutrients[m]
    r = rda[m]
    pct = (val / r * 100) if r else 0
    flag = "⚠️ LOW" if pct < 70 else ("🔴 HIGH" if pct > 150 else "✅")
    print(f"  {macro_names[m]:<30} {val:>8.1f}  |  RDA {r}  |  {pct:.0f}%  {flag}")

print("\n🧪 FAT BREAKDOWN (estimated)")
total_fat = nutrients["fat"]
# Estimate SFA/MUFA/PUFA from food composition
# Omega-3 from supplements: EPA 1440 + DHA 960 = 2400mg from caps + Basa fish
print(f"  Total Fat:           {total_fat:.1f}g")
print(f"  EPA (from omega-3 caps):   1440 mg")
print(f"  DHA (from omega-3 caps):    960 mg")
print(f"  Basa fish omega-3 (est):    ~450 mg")
print(f"  TOTAL EPA+DHA:              ~{nutrients['epa_dha_mg'] + 450} mg  |  RDA min 500mg  ✅ EXCELLENT")

print("\n🌈 VITAMINS")
print("-" * 72)
vitamins = [
    ("vit_a_mcg",  "Vitamin A (mcg RAE)"),
    ("vit_c",      "Vitamin C (mg)"),
    ("vit_d_mcg",  "Vitamin D (mcg)"),
    ("vit_e",      "Vitamin E (mg)"),
    ("vit_k_mcg",  "Vitamin K total (mcg)"),
    ("vit_b1",     "Vitamin B1 Thiamine (mg)"),
    ("vit_b2",     "Vitamin B2 Riboflavin (mg)"),
    ("vit_b3",     "Vitamin B3 Niacin (mg)"),
    ("vit_b5",     "Vitamin B5 Pantothenic (mg)"),
    ("vit_b6",     "Vitamin B6 (mg)"),
    ("vit_b9_mcg", "Vitamin B9 Folate (mcg)"),
    ("vit_b12_mcg","Vitamin B12 (mcg)"),
    ("choline",    "Choline (mg)"),
]
for key, name in vitamins:
    val = nutrients[key]
    r = rda[key]
    pct = (val / r * 100) if r else 0
    flag = "⚠️ LOW" if pct < 70 else ("🔴 EXCESS" if pct > 300 else "✅")
    ul_val = ul.get(key)
    ul_str = f"  UL={ul_val}" if ul_val else ""
    print(f"  {name:<35} {val:>8.1f}  |  RDA {r}{ul_str}  |  {pct:.0f}%  {flag}")

print("\n⚙️  MINERALS")
print("-" * 72)
minerals = [
    ("calcium",      "Calcium (mg)"),
    ("phosphorus",   "Phosphorus (mg)"),
    ("magnesium",    "Magnesium (mg)"),
    ("potassium",    "Potassium (mg)"),
    ("sodium",       "Sodium (mg)"),
    ("iron",         "Iron (mg)"),
    ("zinc",         "Zinc (mg)"),
    ("copper",       "Copper (mg)"),
    ("manganese",    "Manganese (mg)"),
    ("selenium_mcg", "Selenium (mcg)"),
    ("chromium_mcg", "Chromium (mcg)"),
    ("iodine_mcg",   "Iodine (mcg)"),
    ("boron_mcg",    "Boron (mcg)"),
]
for key, name in minerals:
    val = nutrients[key]
    r = rda[key]
    pct = (val / r * 100) if r else 0
    flag = "⚠️ LOW" if pct < 70 else ("🔴 EXCESS" if pct > 300 else "✅")
    ul_val = ul.get(key)
    ul_str = f"  UL={ul_val}" if ul_val else ""
    if key == "sodium":
        flag = "🔴 HIGH" if val > 2300 else "✅"
    print(f"  {name:<35} {val:>8.1f}  |  RDA {r}{ul_str}  |  {pct:.0f}%  {flag}")

print("\n💊 SUPPLEMENTS SUMMARY")
print("-" * 50)
print(f"  Calcium Citrate ×2:         +630mg Ca, +1200 IU D3, +55mcg K2(MK-7)")
print(f"  Tata 1mg D3+K2 ×1:          +600 IU D3, +55mcg K2(MK-7)")
print(f"  Total D3 from supplements:   {(1200+600)/40:.1f} mcg ({1200+600} IU)")
print(f"  Total K2(MK-7) from supps:   {55+55}mcg")
print(f"  Magnesium Glycinate:         +150mg Mg elemental")
print(f"  Salmon Omega ×2 caps:        EPA 1440mg + DHA 960mg + Vit E 20mg")

print("\n🔬 ORGAN ROTATION NOTE")
print("-" * 50)
print("  Values above represent 3-day averages:")
print(f"  Day 1: Mutton Kidney 35g   — Massive B12, selenium, folate")
print(f"  Day 2: Chicken Liver 25g   — Vitamin A overdose risk! (see below)")
print(f"  Day 3: Mutton Karpura 35g  — Zinc, B12, moderate nutrients")
print()
print("  ⚠️  ON CHICKEN LIVER DAY:")
vit_a_liver_day = (nutrients["vit_a_mcg"]
                   - foods["chicken_liver"]["vit_a_mcg"] * (25/3/100)
                   + foods["chicken_liver"]["vit_a_mcg"] * (25/100))
print(f"     Vitamin A = {vit_a_liver_day:.0f} mcg RAE  (UL = 3000 mcg)  🔴 EXCEEDS UL!")

print("\n📈 CALORIC SUMMARY")
print("-" * 50)
cal = nutrients["cal"]
bmr = 1794
tdee_moderate = bmr * 1.55  # moderate activity
tdee_light = bmr * 1.375
print(f"  Diet calories:          {cal:.0f} kcal")
print(f"  Your BMR:               1794 kcal")
print(f"  TDEE (light activity):  {tdee_light:.0f} kcal")
print(f"  TDEE (moderate):        {tdee_moderate:.0f} kcal")
surplus_mod = cal - tdee_moderate
print(f"  Surplus/deficit (mod):  {surplus_mod:+.0f} kcal  ({'deficit — will lose weight' if surplus_mod < 0 else 'surplus — will gain weight'})")

print("\n" + "=" * 72)
print("  END OF CALCULATION")
print("=" * 72)