const fs = require('fs');
const { execSync } = require('child_process');
const {
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  Packer,
  Paragraph,
  TextRun,
  HeadingLevel,
  AlignmentType,
  WidthType,
  Table,
  TableRow,
  TableCell,
  BorderStyle,
  ImageRun,
  Footer,
  PageNumber,
  PageBreak,
  LevelFormat,
  Header,
} = require('docx');

const projectDir = '/home/daytona/workspace/estrus-detection-assignment';
const outPath = `${projectDir}/Automated_Estrus_Detection_Assignment_DVM.docx`;

const imageUrls = [
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  'https://cdn.orris.care/cdss_images/pmc_clinical_VQA_9f3e4b423c9e46d38e3277a870f28a2347fd83c10ff5651e2777371ef3072e79.jpg',
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  'https://cdn.orris.care/cdss_images/pmc_clinical_VQA_c5936b7d5ad621304063e745b66cecefbab0419cdc25bf7952c86092d30aa196.jpg'
];

const fetched = JSON.parse(execSync(
  `node /home/daytona/skills/shared/scripts/fetch_images.js ${imageUrls.map(u => `"${u}"`).join(' ')}`
).toString());

function imgRun(index, width, height, caption) {
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  if (!item || item.error || !item.base64) return null;
  const [, b64] = item.base64.split(',');
  return {
    image: new ImageRun({
      type: 'jpg',
      data: Buffer.from(b64, 'base64'),
      transformation: { width, height },
      altText: { title: caption, description: caption, name: caption },
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    url: item.url,
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}

const img1 = imgRun(0, 520, 300, 'Bovine hormonal profile and cyclicity concept');
const img2 = imgRun(1, 460, 300, 'Wearable monitor placement concept');
const img3 = imgRun(2, 320, 220, 'Pedometer hardware concept');
const img4 = imgRun(3, 300, 220, 'Thigh mounted accelerometer concept');

const para = (text, opts = {}) => new Paragraph({
  spacing: { after: 120, line: 320 },
  ...opts,
  children: [new TextRun({ text, size: 24 })],
});

const doc = new Document({
  creator: 'Orris',
  title: 'Automated Estrus Detection using Activity Monitors and Pedometers',
  description: 'Detailed DVM assignment with visual images and flowcharts',
  numbering: {
    config: [
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            children: [new TextRun({ text: 'DVM Assignment | Automated Estrus Detection', size: 18 })],
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        spacing: { after: 300 },
        children: [new TextRun({ text: 'ASSIGNMENT', bold: true, size: 36 })],
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        alignment: AlignmentType.CENTER,
        spacing: { after: 300 },
        children: [new TextRun({ text: 'Automated Estrus Detection using Activity Monitors and Pedometers', bold: true, size: 32 })],
      }),
      new Paragraph({
        alignment: AlignmentType.CENTER,
        spacing: { after: 220 },
        children: [new TextRun({ text: 'For Doctor of Veterinary Medicine (DVM) Students', italics: true, size: 24 })],
      }),
      para('Submission-ready detailed academic content with conceptual visuals and flowchart guidance.'),
      new Paragraph({ children: [new PageBreak()] }),

      new Paragraph({ heading: HeadingLevel.HEADING_1, children: [new TextRun('1. Introduction')] }),
      para('Efficient estrus detection is one of the most critical reproductive management steps in dairy and beef herds. Missed estrus directly causes delayed insemination, prolonged calving interval, and reduced farm profitability. Traditional visual observation is labor-intensive and often unreliable in modern intensive production systems, where estrus signs are short in duration or occur at night.'),
      para('Automated estrus detection systems based on activity monitors and pedometers provide a practical and objective approach by continuously recording behavioral and locomotor changes. The principle is that cows in estrus show a measurable rise in movement, mounting activity, restlessness, and altered lying-walking patterns. These changes are captured by wearable sensors and interpreted by software algorithms to produce timely heat alerts.'),

      new Paragraph({ heading: HeadingLevel.HEADING_1, children: [new TextRun('2. Physiological Basis of Estrus-Related Activity Changes')] }),
      para('Estrus behavior is governed by endocrine changes, mainly rising estrogen with low progesterone during the follicular phase. Increased estrogen stimulates standing heat behavior, mounting tendency, and higher locomotor activity. Therefore, biological hormonal events are translated into measurable activity signals used by digital monitoring systems.'),
      ...(img1 ? [
        new Paragraph({ alignment: AlignmentType.CENTER, children: [img1.image] }),
        new Paragraph({ alignment: AlignmentType.CENTER, spacing: { after: 240 }, children: [new TextRun({ text: `Figure 1. Conceptual hormonal cyclicity and activity relationship (source image: ${img1.url})`, italics: true, size: 18 })] }),
      ] : []),

      new Paragraph({ heading: HeadingLevel.HEADING_1, children: [new TextRun('3. Activity Monitors and Pedometers: Concepts and Components')] }),
      para('Pedometers are typically leg-mounted devices that count steps and movement frequency. Activity monitors (accelerometer-based) may be mounted on neck, leg, collar, ear, or body and capture multidimensional motion patterns. Data are transferred to a receiver or cloud platform where algorithms compare current activity to baseline activity and generate estrus probability alerts.'),
      para('Typical system components include: wearable sensor unit, data transmission network, management software, animal ID integration, and heat alert interface (SMS/app/dashboard).'),
      ...(img2 ? [
        new Paragraph({ alignment: AlignmentType.CENTER, children: [img2.image] }),
        new Paragraph({ alignment: AlignmentType.CENTER, spacing: { after: 160 }, children: [new TextRun({ text: `Figure 2. Conceptual body placement of activity-monitor devices (source image: ${img2.url})`, italics: true, size: 18 })] }),
      ] : []),

      new Paragraph({
        alignment: AlignmentType.CENTER,
        spacing: { after: 120 },
        children: [new TextRun({ text: 'Hardware examples (conceptual)', bold: true, size: 22 })],
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      ...(img3 && img4 ? [
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                new TableCell({ children: [new Paragraph({ alignment: AlignmentType.CENTER, children: [new TextRun({ text: 'Figure 3A. Pedometer concept', italics: true, size: 18 })] })] }),
                new TableCell({ children: [new Paragraph({ alignment: AlignmentType.CENTER, children: [new TextRun({ text: 'Figure 3B. Accelerometer concept', italics: true, size: 18 })] })] }),
              ],
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          ],
        }),
      ] : []),

      new Paragraph({ heading: HeadingLevel.HEADING_1, children: [new TextRun('4. Flowchart: Automated Estrus Detection Workflow')] }),
      para('The following flowchart-style sequence summarizes the data path from animal behavior to insemination decision:'),
      para('START → Continuous movement sensing (steps, acceleration, restlessness) → Data transmission to software platform → Baseline vs current activity comparison → Estrus probability score generation → Alert trigger (heat suspected) → Farmer/technician checks cow clinically (mucus, standing response, ovarian status if needed) → Decision node:'),
      new Paragraph({ numbering: { reference: 'num', level: 0 }, children: [new TextRun({ text: 'If true estrus confirmed: schedule AI at optimal time', size: 24 })] }),
      new Paragraph({ numbering: { reference: 'num', level: 0 }, children: [new TextRun({ text: 'If doubtful/false alert: continue monitoring and re-evaluate', size: 24 })] }),
      para('→ Record outcome (pregnant/non-pregnant) → Use data feedback to improve threshold settings and herd-specific model performance → END'),

      new Paragraph({ heading: HeadingLevel.HEADING_1, children: [new TextRun('5. Interpretation of Alerts in Field Conditions')] }),
      para('An activity spike alone should not be treated as absolute evidence of estrus. Practical veterinary decision-making requires combining sensor alerts with reproductive history, days in milk, postpartum status, body condition, and possible confounders (lameness, social stress, regrouping, high temperature, disease).'),
      para('Best practice: sensor alert + targeted physical confirmation + proper AI timing protocol gives the highest reproductive efficiency.'),

      new Paragraph({ heading: HeadingLevel.HEADING_1, children: [new TextRun('6. Advantages of Automated Estrus Detection')] }),
      new Paragraph({ numbering: { reference: 'bul', level: 0 }, children: [new TextRun({ text: '24-hour monitoring, including night-time heat events', size: 24 })] }),
      new Paragraph({ numbering: { reference: 'bul', level: 0 }, children: [new TextRun({ text: 'Reduced dependence on labor-intensive visual heat observation', size: 24 })] }),
      new Paragraph({ numbering: { reference: 'bul', level: 0 }, children: [new TextRun({ text: 'Early detection and improved insemination timing', size: 24 })] }),
      new Paragraph({ numbering: { reference: 'bul', level: 0 }, children: [new TextRun({ text: 'Potential improvement in conception rate and calving interval', size: 24 })] }),
      new Paragraph({ numbering: { reference: 'bul', level: 0 }, children: [new TextRun({ text: 'Objective digital records for herd-level reproductive analysis', size: 24 })] }),

      new Paragraph({ heading: HeadingLevel.HEADING_1, children: [new TextRun('7. Limitations and Sources of Error')] }),
      new Paragraph({ numbering: { reference: 'bul', level: 0 }, children: [new TextRun({ text: 'False positives due to excitement, hierarchy changes, or environmental disturbances', size: 24 })] }),
      new Paragraph({ numbering: { reference: 'bul', level: 0 }, children: [new TextRun({ text: 'False negatives in silent heats or low-expression estrus animals', size: 24 })] }),
      new Paragraph({ numbering: { reference: 'bul', level: 0 }, children: [new TextRun({ text: 'Device loss, battery issues, or data transfer failures', size: 24 })] }),
      new Paragraph({ numbering: { reference: 'bul', level: 0 }, children: [new TextRun({ text: 'Need for calibration to herd-specific baseline behavior', size: 24 })] }),
      new Paragraph({ numbering: { reference: 'bul', level: 0 }, children: [new TextRun({ text: 'Initial economic investment and training requirements', size: 24 })] }),

      new Paragraph({ heading: HeadingLevel.HEADING_1, children: [new TextRun('8. Comparative Summary Table')] }),
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        columnWidths: [2200, 2400, 2400, 2360],
        rows: [
          new TableRow({
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                children: [new Paragraph({ children: [new TextRun({ text: t, bold: true, size: 22 })] })],
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          ...[
            ['Labor requirement', 'High', 'Low to moderate', 'Low'],
            ['Night estrus detection', 'Poor', 'Good', 'Good to excellent'],
            ['Objectivity', 'Observer-dependent', 'Moderate', 'High'],
            ['Data recording', 'Manual', 'Automated step logs', 'Automated multi-parameter logs'],
            ['Initial cost', 'Low', 'Moderate', 'Moderate to high'],
            ['Field reliability', 'Variable', 'Good', 'Good (with proper maintenance)'],
          ].map(row => new TableRow({
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              children: [new Paragraph({ children: [new TextRun({ text: t, size: 21 })] })],
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      new Paragraph({ heading: HeadingLevel.HEADING_1, children: [new TextRun('9. Practical Veterinary Protocol (Applied DVM Perspective)')] }),
      new Paragraph({ numbering: { reference: 'num', level: 0 }, children: [new TextRun({ text: 'Register all eligible breeding animals in the software with accurate ID and reproductive history.', size: 24 })] }),
      new Paragraph({ numbering: { reference: 'num', level: 0 }, children: [new TextRun({ text: 'Check device attachment, battery status, and signal quality daily.', size: 24 })] }),
      new Paragraph({ numbering: { reference: 'num', level: 0 }, children: [new TextRun({ text: 'Respond to alerts by same-day examination and estrus confirmation.', size: 24 })] }),
      new Paragraph({ numbering: { reference: 'num', level: 0 }, children: [new TextRun({ text: 'Apply AI according to farm protocol and timing window.', size: 24 })] }),
      new Paragraph({ numbering: { reference: 'num', level: 0 }, children: [new TextRun({ text: 'Track conception outcomes and adjust alert threshold if false alerts are frequent.', size: 24 })] }),
      new Paragraph({ numbering: { reference: 'num', level: 0 }, children: [new TextRun({ text: 'Integrate with body condition scoring, lameness screening, and uterine health checks for better reproductive decisions.', size: 24 })] }),

      new Paragraph({ heading: HeadingLevel.HEADING_1, children: [new TextRun('10. Conclusion')] }),
      para('Automated estrus detection using activity monitors and pedometers is a highly practical and increasingly essential strategy in modern veterinary herd reproduction programs. It improves estrus detection efficiency, supports timely insemination, and contributes to better reproductive economics. However, the highest accuracy is achieved when digital alerts are interpreted clinically rather than used in isolation.'),
      para('From a DVM perspective, these tools should be treated as decision-support systems integrated with physiology, farm records, and field examination skills.'),

      new Paragraph({ heading: HeadingLevel.HEADING_1, children: [new TextRun('11. Suggested Reference Format for Submission')] }),
      new Paragraph({ numbering: { reference: 'bul', level: 0 }, children: [new TextRun({ text: 'Textbook chapters on bovine reproduction and estrus behavior', size: 24 })] }),
      new Paragraph({ numbering: { reference: 'bul', level: 0 }, children: [new TextRun({ text: 'Peer-reviewed research papers on pedometer- and accelerometer-based heat detection', size: 24 })] }),
      new Paragraph({ numbering: { reference: 'bul', level: 0 }, children: [new TextRun({ text: 'Farm-level reproductive monitoring guidelines and extension manuals', size: 24 })] }),
      para('Note: Replace this section with your college-required citation style (APA/Vancouver) before final submission if needed.'),
    ],
  }],
});

Packer.toBuffer(doc).then((buffer) => {
  fs.writeFileSync(outPath, buffer);
  console.log(outPath);
});