Custom Visualization With AI

Introduction

Custom visualization With AI allow you to embed custom code into your application, enabling the creation of specific visualizations such as charts, maps, tables, dashboards, filters, and more. By leveraging web technologies such as HTML, CSS, and JavaScript, along with powerful libraries like Chart.js, developers can build tailored, dynamic visualizations that clearly communicate complex datasets.

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Steps to Create Custom Visualization With AI

1. If you haven’t already created a dashboard, start by creating one using the Create Viz.
   

2. In dashboard canvas, go to the Widgets section and select Custom Visualization With AI.

   

3. After selecting Custom Visualization With AI, the custom visualization editor will appear, where you can start building your visualization using different code sections.

   

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Custom Visualization Editor

The custom visualization editor consists of the following sections:

• Datasets:
  Dynamically select data by choosing a dataset. Upon selection, the relevant columns associated with that dataset will be available for display.

• Available Columns:
  Displays all the columns tied to the selected dataset so you can map specific fields to your visualization.

• HTML:
  Defines the structure of your visualization. You can add elements like headings, paragraphs, images, links, or <canvas> elements for charts.

• JavaScript:
  Adds interactivity and functionality. Use it to bind data, implement dynamic behaviors, or create charts using libraries such as Chart.js.

• CSS:
  Styles your visualization by adjusting colors, fonts, spacing, and layouts, ensuring the design aligns with your branding and enhances readability.

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Dataset Binding in Custom Visualization With AI

There are two primary methods for connecting datasets to custom visualization: Dynamic Dataset Binding and Static Dataset Binding.

1. Dynamic Dataset Binding

This widget uses the swiggy dataset (injected via {{dataset::swiggy}}) to dynamically calculate the total number of restaurants. The dataset is bound in the dashboard editor through a visual dropdown, and the purpose of this setup is to generate a KPI card that displays the Total Restaurants count in real time.

HTML

<div class="kpi-card" id="swiggy-kpi">
  <h3>Total Restaurants</h3>
  <p id="swiggy-kpi-value">0</p>
</div>

JS

const swiggyData = {{dataset::swiggy}};  

function getTotalRestaurants() {
  return Array.isArray(swiggyData) ? swiggyData.length : 0;
}

document.addEventListener('DOMContentLoaded', () => {
  const count = getTotalRestaurants();
  const el = document.getElementById('swiggy-kpi-value');
  if (el) el.textContent = count;
});

CSS

.kpi-card {
  background: #fff;
  border-left: 5px solid #fc8019;
  padding: 20px;
  border-radius: 8px;
  font-family: Arial, sans-serif;
  width: 220px;
  box-shadow: 0 4px 8px rgba(0,0,0,0.05);
}

.kpi-card h3 {
  margin: 0 0 10px;
  color: #fc8019;
}

.kpi-card p {
  font-size: 24px;
  font-weight: bold;
  margin: 0;
  color: #fc8019;
}

Output

2. Static Dataset Binding

In static binding, the data is embedded directly in the code (usually as JSON). This approach is useful when the dataset does not change or does not require real-time updates. In the following example, a static JSON object (userData) is used to generate a User Information widget, and the output renders a styled component displaying key profile details such as Name, Email, Status, Roles, Balance, Theme, and Notifications.

HTML

<h1>User Information</h1>
<div id="user-info" class="user-info"></div>

JS

const userData = {
  "user": {
    "id": 1,
    "name": "John Doe",
    "email": "john.doe@example.com",
    "isActive": true,
    "roles": ["admin", "editor"]
  },
  "account": {
    "balance": 1000.50,
    "currency": "USD"
  },
  "preferences": {
    "theme": "dark",
    "notifications": true
  }
};

function displayUserInfo(data) {
  const userInfoDiv = document.getElementById('user-info');
  userInfoDiv.innerHTML = `
    <p><span>Name:</span> ${data.user.name}</p>
    <p><span>Email:</span> ${data.user.email}</p>
    <p><span>Status:</span> ${data.user.isActive ? 'Active' : 'Inactive'}</p>
    <p><span>Roles:</span> ${data.user.roles.join(', ')}</p>
    <p><span>Balance:</span> ${data.account.balance} ${data.account.currency}</p>
    <p><span>Theme:</span> ${data.preferences.theme}</p>
    <p><span>Notifications:</span> ${data.preferences.notifications ? 'Enabled' : 'Disabled'}</p>
  `;
}

displayUserInfo(userData);

CSS

.user-info {
  background-color: white;
  padding: 20px;
  border-radius: 8px;
  box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1);
  width: 300px;
  margin: 0 auto;
}

.user-info p {
  font-size: 16px;
  margin: 10px 0;
}

.user-info span {
  font-weight: bold;
}

Output

Using Chart.js for Custom Visualizations

Chart.js is a versatile and lightweight JavaScript library for creating responsive and interactive charts. It supports a wide range of chart types—Pie, Line, Bar, Radar—and provides:

• Customizable color schemes
• Tooltips
• Animations
• Responsive layouts
• Interactive legends

HTML

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>Chart.js Example</title>
  <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
  
</head>
<body>
  <div class="chart-container">
    <canvas id="myChart"></canvas>
  </div>

</body>
</html>

JS

 const ctx = document.getElementById('myChart');

    new Chart(ctx, {
      type: 'bar',
      data: {
        labels: ['Red', 'Blue', 'Yellow'],
        datasets: [{
          label: 'Votes',
          data: [12, 19, 3],
          backgroundColor: ['red', 'blue', 'yellow']
        }]
      },
      options: {
        responsive: true,
        plugins: {
          legend: { position: 'top' },
          tooltip: { enabled: true }
        }
      }
    });

CSS

      body {
      font-family: Arial, sans-serif;
      display: flex;
      justify-content: center;
      align-items: center;
      height: 100vh;
      background: #ffffff;
      overflow: hidden;
    }
    .chart-container {
      width: 500px;
      background: #fff;
      padding: 20px;
      border-radius: 12px;
      box-shadow: 0 4px 10px rgba(0,0,0,0.1);
    }

Output

Key Benefits

• Dynamic Data Rendering: Visualizations stay updated with the latest dataset values.

• Interactivity: Features like hover states, tooltips, and legends enhance usability.

• Customization: Colors, layouts, labels, and behaviors can be tailored.

• Responsive Design: Charts adapt seamlessly across devices.

By combining dynamic dataset binding, static dataset binding, and libraries like Chart.js, you can create rich, interactive, and responsive custom visualizations. These are ideal for dashboards, reporting systems, and analytics platforms, offering real-time insights in an engaging format.

Summary

The AIV Custom Visualization Buddy adds complete flexibility to dashboards by enabling custom-built visualizations.
With HTML, CSS, and JavaScript support, it helps teams deliver tailored, interactive, and branded visual experiences beyond standard widgets.

Follow the official AIV Custom Visualization Buddy Guide