const fs = require('fs');
const path = require('path');
const pptxgen = require('pptxgenjs');

const imagesPath = '/home/daytona/workspace/estrus-detection-assignment/src/images.json';
let imageData = [];
try {
  imageData = JSON.parse(fs.readFileSync(imagesPath, 'utf8'));
} catch (e) {
  imageData = [];
}

const img1 = imageData[0]?.base64 || null;
const img2 = imageData[1]?.base64 || null;
const img3 = imageData[2]?.base64 || null;

const pptx = new pptxgen();
pptx.layout = 'LAYOUT_16x9';
pptx.author = 'Orris';
pptx.subject = 'DVM Assignment - Automated Estrus Detection';
pptx.title = 'Automated Estrus Detection using Activity Monitors and Pedometers';
pptx.company = 'Veterinary Reproduction';
pptx.lang = 'en-US';

autoTheme();

function autoTheme() {
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    headFontFace: 'Calibri',
    bodyFontFace: 'Calibri',
    lang: 'en-US'
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}

function addHeader(slide, title) {
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    y: 0,
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function addFooter(slide, page) {
  slide.addText(`DVM Assignment | Automated Estrus Detection | ${page}`, {
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}

// Slide 1: Title
let slide = pptx.addSlide();
slide.background = { color: '0B3C5D' };
slide.addText('Automated Estrus Detection', {
  x: 0.7, y: 1.3, w: 8.6, h: 0.8,
  fontSize: 42, bold: true, color: 'FFFFFF', align: 'center'
});
slide.addText('Using Activity Monitors and Pedometers', {
  x: 0.7, y: 2.2, w: 8.6, h: 0.5,
  fontSize: 24, color: 'D9EAF7', align: 'center'
});
slide.addText('Assignment for DVM Students', {
  x: 0.7, y: 3.0, w: 8.6, h: 0.4,
  fontSize: 18, color: 'FFFFFF', align: 'center', italic: true
});
slide.addText('Prepared by: ____________________    Roll No: ________    Date: __________', {
  x: 0.8, y: 4.5, w: 8.4, h: 0.4,
  fontSize: 14, color: 'FFFFFF', align: 'center'
});

// Slide 2: Learning objectives
slide = pptx.addSlide();
addHeader(slide, 'Learning Objectives');
slide.addText('At the end of this assignment, the student should be able to:', {
  x: 0.5, y: 1.0, w: 9.0, h: 0.4,
  fontSize: 18, bold: true, color: '0B3C5D'
});
const objectives = [
  'Explain limitations of visual estrus detection in dairy and beef herds',
  'Describe principles of pedometer- and accelerometer-based heat detection',
  'Interpret alert patterns, estrus intensity, and timing for AI decisions',
  'Compare strengths and weaknesses of ear, neck, leg, and rumen sensors',
  'Design a practical estrus detection workflow for a farm'
];
slide.addText(objectives.map(o => ({ text: o, options: { bullet: true, breakLine: true } })), {
  x: 0.8, y: 1.6, w: 8.8, h: 2.7, fontSize: 20, color: '1E1E1E'
});
addFooter(slide, '2');

// Slide 3: Why important
slide = pptx.addSlide();
addHeader(slide, 'Why Automated Estrus Detection Matters');
slide.addShape(pptx.ShapeType.roundRect, {
  x: 0.5, y: 1.0, w: 4.4, h: 3.9,
  fill: { color: 'E8F1F8' }, line: { color: 'B7D1E6' }, radius: 0.08
});
slide.addText('Problem with conventional practice', {
  x: 0.8, y: 1.2, w: 3.8, h: 0.3,
  fontSize: 16, bold: true, color: '0B3C5D'
});
slide.addText([
  { text: 'Silent estrus and short heat duration', options: { bullet: true, breakLine: true } },
  { text: 'Poor observation in large herds', options: { bullet: true, breakLine: true } },
  { text: 'Missed heat causes delayed conception', options: { bullet: true, breakLine: true } },
  { text: 'Longer calving interval reduces profitability', options: { bullet: true } }
], {
  x: 0.8, y: 1.7, w: 3.9, h: 2.8, fontSize: 14, color: '1E1E1E'
});

slide.addShape(pptx.ShapeType.roundRect, {
  x: 5.1, y: 1.0, w: 4.4, h: 3.9,
  fill: { color: 'EAF7ED' }, line: { color: 'B9DEBF' }, radius: 0.08
});
slide.addText('How automation helps', {
  x: 5.4, y: 1.2, w: 3.8, h: 0.3,
  fontSize: 16, bold: true, color: '1D5E2C'
});
slide.addText([
  { text: '24/7 objective behavior monitoring', options: { bullet: true, breakLine: true } },
  { text: 'Early heat alerts for timely insemination', options: { bullet: true, breakLine: true } },
  { text: 'Integration with herd software', options: { bullet: true, breakLine: true } },
  { text: 'Better service rate and pregnancy outcomes', options: { bullet: true } }
], {
  x: 5.4, y: 1.7, w: 3.8, h: 2.8, fontSize: 14, color: '1E1E1E'
});
addFooter(slide, '3');

// Slide 4: Flowchart
slide = pptx.addSlide();
addHeader(slide, 'Flowchart: Automated Estrus Detection Pipeline');

const y = 2.2;
const boxW = 1.7;
const boxH = 0.9;
const xs = [0.4, 2.3, 4.2, 6.1, 8.0];
const labels = ['Sensor Data\nCollection', 'Activity\nProcessing', 'Estrus Index\nGeneration', 'Alert to\nFarmer/Vet', 'AI Timing\nDecision'];
for (let i = 0; i < xs.length; i++) {
  slide.addShape(pptx.ShapeType.roundRect, {
    x: xs[i], y, w: boxW, h: boxH,
    fill: { color: i % 2 === 0 ? 'DCECF9' : 'EAF7ED' },
    line: { color: '7CA5C4', pt: 1.2 },
    radius: 0.08
  });
  slide.addText(labels[i], {
    x: xs[i] + 0.05, y: y + 0.15, w: boxW - 0.1, h: boxH - 0.1,
    fontSize: 12, bold: true, color: '0B3C5D', align: 'center', valign: 'mid'
  });
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    slide.addShape(pptx.ShapeType.chevron, {
      x: xs[i] + boxW + 0.05, y: y + 0.28, w: 0.2, h: 0.35,
      fill: { color: '4B83B0' }, line: { color: '4B83B0' }
    });
  }
}
slide.addText('Key inputs: steps, restlessness, mounting proxies, rumination drop, circadian activity deviation', {
  x: 0.5, y: 3.8, w: 9.1, h: 0.5, fontSize: 13, color: '1E1E1E', italic: true
});
addFooter(slide, '4');

// Slide 5: Devices and placement diagram
slide = pptx.addSlide();
addHeader(slide, 'Common Devices and Placement');
slide.addShape(pptx.ShapeType.rect, {
  x: 0.4, y: 1.0, w: 4.8, h: 4.1,
  fill: { color: 'FFFFFF' }, line: { color: 'C7D7E5' }
});
slide.addText('Device types used in bovine estrus detection', {
  x: 0.6, y: 1.2, w: 4.3, h: 0.3,
  fontSize: 15, bold: true, color: '0B3C5D'
});
slide.addText([
  { text: 'Leg pedometer: ', options: { bold: true } }, { text: 'step count spikes during estrus', options: { breakLine: true } },
  { text: 'Neck collar accelerometer: ', options: { bold: true } }, { text: 'activity + rumination trends', options: { breakLine: true } },
  { text: 'Ear tag accelerometer: ', options: { bold: true } }, { text: 'head/ear movement behavior', options: { breakLine: true } },
  { text: 'Rumen bolus: ', options: { bold: true } }, { text: 'temperature + activity-related signals', options: { breakLine: true } },
  { text: 'Tail patch/sensor (adjunct): ', options: { bold: true } }, { text: 'mounting pressure events' }
], {
  x: 0.7, y: 1.7, w: 4.2, h: 3.1, fontSize: 13, color: '222222'
});

slide.addShape(pptx.ShapeType.rect, {
  x: 5.4, y: 1.0, w: 4.2, h: 4.1,
  fill: { color: 'FFFFFF' }, line: { color: 'C7D7E5' }
});
if (img2) {
  slide.addImage({ data: img2, x: 5.55, y: 1.25, w: 3.9, h: 3.25 });
}
slide.addText('Representative wearable monitor placements (illustrative)', {
  x: 5.6, y: 4.6, w: 3.8, h: 0.3, fontSize: 10, color: '555555', align: 'center', italic: true
});
addFooter(slide, '5');

// Slide 6: Algorithm logic diagram
slide = pptx.addSlide();
addHeader(slide, 'Algorithm Logic for Estrus Alert Generation');
slide.addShape(pptx.ShapeType.roundRect, { x: 0.6, y: 1.2, w: 2.0, h: 0.9, fill: { color: 'E8F1F8' }, line: { color: '6C9BC0' }, radius: 0.08 });
slide.addText('Baseline\n(7-14 days)', { x: 0.7, y: 1.45, w: 1.8, h: 0.5, align: 'center', fontSize: 12, bold: true, color: '0B3C5D' });
slide.addShape(pptx.ShapeType.roundRect, { x: 2.9, y: 1.2, w: 2.0, h: 0.9, fill: { color: 'EAF7ED' }, line: { color: '7FAF87' }, radius: 0.08 });
slide.addText('Real-time\nactivity input', { x: 3.0, y: 1.45, w: 1.8, h: 0.5, align: 'center', fontSize: 12, bold: true, color: '1D5E2C' });
slide.addShape(pptx.ShapeType.roundRect, { x: 5.2, y: 1.2, w: 2.0, h: 0.9, fill: { color: 'FFF2E6' }, line: { color: 'D9A676' }, radius: 0.08 });
slide.addText('Deviation score\n(z-score/index)', { x: 5.3, y: 1.45, w: 1.8, h: 0.5, align: 'center', fontSize: 12, bold: true, color: '8A4E0F' });
slide.addShape(pptx.ShapeType.roundRect, { x: 7.5, y: 1.2, w: 2.0, h: 0.9, fill: { color: 'FDECEC' }, line: { color: 'D49292' }, radius: 0.08 });
slide.addText('Alert class\nLow/Med/High', { x: 7.6, y: 1.45, w: 1.8, h: 0.5, align: 'center', fontSize: 12, bold: true, color: '8D2D2D' });
for (let i = 0; i < 3; i++) {
  slide.addShape(pptx.ShapeType.chevron, { x: 2.55 + i * 2.3, y: 1.47, w: 0.25, h: 0.3, fill: { color: '4B83B0' }, line: { color: '4B83B0' } });
}
slide.addText('Decision rule example:', { x: 0.8, y: 2.55, w: 2.5, h: 0.3, fontSize: 13, bold: true, color: '0B3C5D' });
slide.addShape(pptx.ShapeType.rect, { x: 0.8, y: 2.9, w: 8.6, h: 1.6, fill: { color: 'FFFFFF' }, line: { color: 'D2DCE5' } });
slide.addText('If activity > baseline by threshold for sustained period\n+ rumination decline or mounting proxy\n=> generate estrus alert and predicted ovulation window', {
  x: 1.0, y: 3.15, w: 8.2, h: 1.1, fontSize: 16, color: '202020', align: 'center', valign: 'mid'
});
addFooter(slide, '6');

// Slide 7: Interpreting alerts
slide = pptx.addSlide();
addHeader(slide, 'How to Interpret Automated Alerts in Practice');
slide.addShape(pptx.ShapeType.table, { x: 0.6, y: 1.2, w: 8.8, h: 3.8, line: { color: 'CCCCCC' } });
slide.addText('Alert Level', { x: 0.7, y: 1.3, w: 1.8, h: 0.3, fontSize: 13, bold: true, color: 'FFFFFF', fill: { color: '0B3C5D' } });
slide.addText('Typical Pattern', { x: 2.6, y: 1.3, w: 2.8, h: 0.3, fontSize: 13, bold: true, color: 'FFFFFF', fill: { color: '0B3C5D' } });
slide.addText('Action', { x: 5.5, y: 1.3, w: 1.8, h: 0.3, fontSize: 13, bold: true, color: 'FFFFFF', fill: { color: '0B3C5D' } });
slide.addText('Note', { x: 7.4, y: 1.3, w: 1.8, h: 0.3, fontSize: 13, bold: true, color: 'FFFFFF', fill: { color: '0B3C5D' } });

const rows = [
  ['High', 'Sharp rise in activity, repeated movement events', 'Plan AI in system-recommended window', 'Best PPV'],
  ['Medium', 'Moderate rise, partial behavioral signs', 'Confirm with visual/vaginal mucus/USG', 'Avoid false positives'],
  ['Low', 'Mild isolated spike', 'Observe and recheck next cycle', 'Could be social/exercise effect']
];
for (let i = 0; i < rows.length; i++) {
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  slide.addShape(pptx.ShapeType.rect, { x: 0.7, y: yy, w: 8.5, h: 0.9, fill: { color: i % 2 ? 'F7FBFF' : 'EEF5FB' }, line: { color: 'D0DCE7' } });
  slide.addText(rows[i][0], { x: 0.85, y: yy + 0.2, w: 1.5, h: 0.4, fontSize: 12, bold: true, color: '0B3C5D' });
  slide.addText(rows[i][1], { x: 2.55, y: yy + 0.15, w: 2.8, h: 0.6, fontSize: 11, color: '222222' });
  slide.addText(rows[i][2], { x: 5.45, y: yy + 0.15, w: 1.8, h: 0.6, fontSize: 11, color: '222222' });
  slide.addText(rows[i][3], { x: 7.35, y: yy + 0.15, w: 1.6, h: 0.6, fontSize: 11, color: '222222' });
}
addFooter(slide, '7');

// Slide 8: Evidence slide
slide = pptx.addSlide();
addHeader(slide, 'Recent Scientific Evidence');
slide.addText('Selected findings relevant to DVM practice:', {
  x: 0.6, y: 1.0, w: 8.8, h: 0.35, fontSize: 16, bold: true, color: '0B3C5D'
});
slide.addText([
  { text: 'Schilkowsky et al., 2021 (PMID: 33685699): ', options: { bold: true } },
  { text: 'Ear-attached accelerometer system showed high estrus detection sensitivity (~91.6%) compared with reference methods; alert-to-ovulation interval was around 24 h.', options: { breakLine: true } },
  { text: 'Rocha et al., 2025 (PMID: 39793472): ', options: { bold: true } },
  { text: 'In grazing beef cows, accelerometer algorithm showed lower estrus detection accuracy than estrus patches in this study (91% vs 98%), highlighting need for validation per farm context.', options: { breakLine: true } },
  { text: 'Merkelyte et al., 2025 review (PMID: 40805102): ', options: { bold: true } },
  { text: 'Sensor technologies (pedometers, accelerometers, rumen sensors, infrared thermography) are increasingly used to improve estrus identification and insemination timing.' }
], {
  x: 0.8, y: 1.5, w: 8.6, h: 2.8, fontSize: 13, color: '1E1E1E'
});
if (img3) {
  slide.addImage({ data: img3, x: 6.2, y: 3.45, w: 3.0, h: 1.6 });
  slide.addText('Example monitoring dashboard style visualization', {
    x: 6.2, y: 5.08, w: 3.0, h: 0.2, fontSize: 9, color: '666666', align: 'center', italic: true
  });
}
addFooter(slide, '8');

// Slide 9: Advantages and limitations
slide = pptx.addSlide();
addHeader(slide, 'Advantages, Limitations, and Field Challenges');
slide.addShape(pptx.ShapeType.rect, { x: 0.5, y: 1.0, w: 4.4, h: 4.0, fill: { color: 'EAF7ED' }, line: { color: 'B9DEBF' } });
slide.addText('Advantages', { x: 0.8, y: 1.2, w: 3.8, h: 0.3, fontSize: 16, bold: true, color: '1D5E2C' });
slide.addText([
  { text: 'Continuous and objective monitoring', options: { bullet: true, breakLine: true } },
  { text: 'Reduced labor for heat observation', options: { bullet: true, breakLine: true } },
  { text: 'Early intervention and improved timing', options: { bullet: true, breakLine: true } },
  { text: 'Data-driven herd reproductive management', options: { bullet: true } }
], { x: 0.8, y: 1.7, w: 3.8, h: 2.9, fontSize: 13, color: '222222' });

slide.addShape(pptx.ShapeType.rect, { x: 5.1, y: 1.0, w: 4.4, h: 4.0, fill: { color: 'FDECEC' }, line: { color: 'E3BABA' } });
slide.addText('Limitations', { x: 5.4, y: 1.2, w: 3.8, h: 0.3, fontSize: 16, bold: true, color: '8D2D2D' });
slide.addText([
  { text: 'Initial equipment and subscription cost', options: { bullet: true, breakLine: true } },
  { text: 'False positives from non-estrus activity', options: { bullet: true, breakLine: true } },
  { text: 'Device loss, battery, or connectivity issues', options: { bullet: true, breakLine: true } },
  { text: 'Needs species/breed/farm-specific calibration', options: { bullet: true } }
], { x: 5.4, y: 1.7, w: 3.8, h: 2.9, fontSize: 13, color: '222222' });
addFooter(slide, '9');

// Slide 10: Case assignment
slide = pptx.addSlide();
addHeader(slide, 'Case-Based Assignment (For Submission)');
slide.addText('Farm scenario:', { x: 0.6, y: 1.0, w: 2.0, h: 0.3, fontSize: 16, bold: true, color: '0B3C5D' });
slide.addText('A 150-cow dairy farm reports poor heat detection and increased days open. The farm plans to adopt an automated estrus detection system.', {
  x: 0.6, y: 1.35, w: 8.8, h: 0.7, fontSize: 14, color: '1E1E1E'
});
slide.addText('Tasks for DVM student:', { x: 0.6, y: 2.2, w: 3.0, h: 0.3, fontSize: 16, bold: true, color: '0B3C5D' });
slide.addText([
  { text: 'Select one device type and justify your choice', options: { bullet: true, breakLine: true } },
  { text: 'Draw a farm-level implementation flowchart', options: { bullet: true, breakLine: true } },
  { text: 'Define AI decision timing based on alert intensity', options: { bullet: true, breakLine: true } },
  { text: 'List at least three possible false-alert causes and mitigation', options: { bullet: true, breakLine: true } },
  { text: 'Propose KPIs to track in 3 months (service rate, conception rate, days open)', options: { bullet: true }
  }
], { x: 0.8, y: 2.65, w: 8.5, h: 2.2, fontSize: 14, color: '222222' });
addFooter(slide, '10');

// Slide 11: Reference and figure credits
slide = pptx.addSlide();
addHeader(slide, 'References and Figure Credits');
slide.addText('Key references', { x: 0.6, y: 1.0, w: 3, h: 0.3, fontSize: 16, bold: true, color: '0B3C5D' });
slide.addText([
  { text: '1. Schilkowsky EM, et al. J Dairy Sci. 2021;104(5):5618-5632. PMID: 33685699\n', options: { breakLine: true } },
  { text: '2. Rocha CC, et al. Theriogenology. 2025;235:93-102. PMID: 39793472\n', options: { breakLine: true } },
  { text: '3. Merkelyte I, et al. Animals (Basel). 2025;15(15):2313. PMID: 40805102\n', options: { breakLine: true } },
  { text: '4. Alvarez J, et al. Sensors (Basel). 2025. PMID: 40942675\n', options: { breakLine: true } }
], { x: 0.8, y: 1.45, w: 8.6, h: 2.0, fontSize: 12, color: '1E1E1E' });
slide.addText('Image credits (illustrative educational use):', { x: 0.6, y: 3.65, w: 4.5, h: 0.3, fontSize: 13, bold: true, color: '0B3C5D' });
slide.addText('Images sourced via medical image search index (PMCID-linked clinical VQA derivatives). Replace with institution-approved figures if required by your college policy.', {
  x: 0.8, y: 4.0, w: 8.5, h: 0.8, fontSize: 11, color: '2A2A2A'
});
addFooter(slide, '11');

// Slide 12: Thank you
slide = pptx.addSlide();
slide.background = { color: '0B3C5D' };
slide.addText('Thank You', { x: 2.8, y: 2.0, w: 4.4, h: 0.8, fontSize: 44, bold: true, color: 'FFFFFF', align: 'center' });
slide.addText('Questions & Discussion', { x: 2.8, y: 2.9, w: 4.4, h: 0.4, fontSize: 20, color: 'D9EAF7', align: 'center' });

const outputPath = '/home/daytona/workspace/estrus-detection-assignment/output/Automated_Estrus_Detection_DVM_Assignment.pptx';
pptx.writeFile({ fileName: outputPath });
console.log(outputPath);