{"name":"Fitness Project with Teachable Machine","description":"","photo":"","view_count":5,"fork_count":2,"expires_at":null,"created_at":"2025-11-20T16:59:47.056312Z","updated_at":"2025-11-20T16:59:47.056313Z","user_id":166,"status":"PRIVATE","parent_id":null,"id":10673,"user":{"photo":null,"email":"markivlev@gmail.com","recess_id":"38a987b1-569d-47cc-ae61-0c84198aa65c","created_at":"2024-08-05T03:36:08.774307Z","reset_token":null,"stripe_customer_id":null,"seats":null,"username":"m_ivlev","id":166,"password_hash":"$2b$12$lM.SwMhsrEJrs7ZjNyj9e.FOyWqAZgNBSeOEkB8gCGEsXkmGPZLu6","updated_at":"2024-08-05T03:36:08.774314Z","reset_token_expiry":null,"subscription_status":null},"code":{"script.js":"const URL = \"https://teachablemachine.withgoogle.com/models/x3ziYzSrm/\";\n\nlet model, webcam, ctx, labelContainer, maxPredictions;\n\nfunction setup() {\n //createCanvas(280,280);\n //background(135, 206, 235); // Sky blue background\n}\n\nfunction draw() {\n // Game logic will go here\n}\n\nasync function predict() {\n const { pose, posenetOutput } = await model.estimatePose(webcam.canvas);\n const prediction = await model.predict(posenetOutput);\n\n for (let i = 0; i < maxPredictions; i++) {\n const classPrediction =\n prediction[i].className + \": \" + prediction[i].probability.toFixed(2);\n labelContainer.childNodes[i].innerHTML = classPrediction;\n }\n\n drawPose(pose);\n}\n\nfunction drawPose(pose) {\n if (webcam.canvas) {\n ctx.drawImage(webcam.canvas, 0, 0);\n if (pose) {\n const minPartConfidence = 0.5;\n tmPose.drawKeypoints(pose.keypoints, minPartConfidence, ctx);\n tmPose.drawSkeleton(pose.keypoints, minPartConfidence, ctx);\n }\n }\n}\n\nasync function poseLoop(timestamp) {\n webcam.update();\n await predict();\n window.requestAnimationFrame(poseLoop);\n}\n\nasync function init() {\n const modelURL = URL + \"model.json\";\n const metadataURL = URL + \"metadata.json\";\n\n model = await tmPose.load(modelURL, metadataURL);\n maxPredictions = model.getTotalClasses();\n const size = 400;\n const flip = true;\n webcam = new tmPose.Webcam(size, size, flip);\n await webcam.setup();\n await webcam.play();\n window.requestAnimationFrame(poseLoop); // Updated to use poseLoop\n\n const canvas = document.getElementById(\"canvas\");\n canvas.width = size; canvas.height = size;\n ctx = canvas.getContext(\"2d\");\n labelContainer = document.getElementById(\"label-container\");\n for (let i = 0; i < maxPredictions; i++) {\n labelContainer.appendChild(document.createElement(\"div\"));\n }\n}\n\n","style.css":"html,\r\nbody {\r\n margin: 20px;\r\n padding: 0;\r\n font-family: Arial, Helvetica, sans-serif;\r\n background-color: #EEE;\r\n}\r\n\r\ncanvas {\r\n display: block;\r\n margin-bottom: 20px;\r\n}","index.html":"\n\n\n\n \n \n FitAI: Pose Perfect\n \n \n \n \n \n \n\n\n\n

Teachable Machine Pose Model

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