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machinelearning.html
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<DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta http-equiv="X-UA-Compatible" content="ie=edge" />
<title>Zeebo 🤖</title>
<link rel="stylesheet" href="machine.learning.css" />
<!-- Add web page logo -->
<link rel="icon" href="webpage-logo/zeebo.jpg" type="image/jpg">
</head>
<body>
<nav>
<ul class="nav">
<li class="nav1"><a href="index.html">Home Page</a></li>
<li class="nav1"><a href="takeaction.html"> Take action </a></li>
<li class="nav1">
<a id="ACTIVE" href="machinelearning.html"> Our Solution </a>
</li>
<li class="nav1"><a href="contactus.html"> Support </a></li>
</ul>
</nav>
<div class="intro">
<center>
<h1 id="heading-top">
Zeebo
</h1>
</center>
<center>
<p id="machine_desc">
This machine learning algorithm will be used to sort between organic
and recyclable trash, where a camera can be placed on the overhead
of a conveyor belt, which can automatically detect whether the item
is organic or recyclable, which then moves the article of trash to
the proper mode of waste management, which can increase the amount
of recyclable material made from each bottle or bag, and streamline
the process from the recycling bin all the way to being another
product in a store.
</p>
</center>
</div>
<div class="logo">
<img src="images/logo.jpg" width="75" height="75" />
</div>
<p id="mission_s">
Originally founded as GreenBot, our mission is to help reduce global
environmental pollution and make the world a better place by increasing
the amount of material that can be recycled, and making the process of
doing so much more efficient, via grouping materials categorically.
</p>
<div id="TIM">Try it out !</div>
<button id="TIMBTN" type="button" onclick="init()">Start</button>
<div id="webcam-container"></div>
<div id="label-container"></div>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@teachablemachine/[email protected]/dist/teachablemachine-image.min.js"></script>
<script type="text/javascript">
// More API functions here:
// https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/image
// the link to your model provided by Teachable Machine export panel
const URL = "https://teachablemachine.withgoogle.com/models/BjdA3e2S2/";
let model, webcam, labelContainer, maxPredictions;
// Load the image model and setup the webcam
async function init() {
const modelURL = URL + "model.json";
const metadataURL = URL + "metadata.json";
// load the model and metadata
// Refer to tmImage.loadFromFiles() in the API to support files from a file picker
// or files from your local hard drive
// Note: the pose library adds "tmImage" object to your window (window.tmImage)
model = await tmImage.load(modelURL, metadataURL);
maxPredictions = model.getTotalClasses();
// Convenience function to setup a webcam
const flip = true; // whether to flip the webcam
webcam = new tmImage.Webcam(400, 400, flip); // width, height, flip
await webcam.setup(); // request access to the webcam
await webcam.play();
window.requestAnimationFrame(loop);
// append elements to the DOM
document
.getElementById("webcam-container")
.appendChild(webcam.canvas);
labelContainer = document.getElementById("label-container");
for (let i = 0; i < maxPredictions; i++) {
// and class labels
labelContainer.appendChild(document.createElement("div"));
}
}
async function loop() {
webcam.update(); // update the webcam frame
await predict();
window.requestAnimationFrame(loop);
}
// run the webcam image through the image model
async function predict() {
// predict can take in an image, video or canvas html element
const prediction = await model.predict(webcam.canvas);
for (let i = 0; i < maxPredictions; i++) {
const classPrediction =
prediction[i].className +
": " +
prediction[i].probability.toFixed(2);
labelContainer.childNodes[i].innerHTML = classPrediction;
}
}
</script>
<h1 id="what_is">What is Machine Learning ?</h1>
<p id="what_answer">
Machine learning is a subset of Artificial Intelligence (AI),
where models(machines) are train on a large amount data to learn
and identify the patterns of the data through an algorithm, and
place it under a specific category (in Teachable Machine). Over
time, as the algorithm is trained, it “learns” this repetitive
data. Using this knowledge, it makes complex connections, which
then can be used to look at a new file and then categorize it.
</p>
</body>
</html>
</DOCTYPE>