instruction: Create a Dockerfile for fundamentals
output: ```dockerfile
  from the webcam video feed.
  It incorporates several technologies and libraries to facilitate machine
  learning directly in the browser. It uses several TensorFlow.js libraries,
  including:tfjs-core and tfjs-converter for core TensorFlow.js functionality and model
  conversion.tfjs-backend-webgl, tfjs-backend-cpu, and the tf-backend-wasm script
  for different computational backend options that TensorFlow.js can use for
  processing. These backends allow the application to perform machine learning
  tasks efficiently by leveraging the user's hardware capabilities.The BlazeFace library, a TensorFlow model for face detection.It also uses the following additional libraries:dat.GUI for creating a graphical interface to interact with the application's
  settings in real-time, such as switching between TensorFlow.js backends.Stats.min.js for displaying performance metrics (like FPS) to monitor the
  application's efficiency during operation.index.html
  
  <style>
   body {
   margin: 25px;
   }
  
   .true {
   color: green;
   }
  
   .false {
   color: red;
   }
  
   #main {
   position: relative;
   margin: 50px 0;
   }
  
   canvas {
   position: absolute;
   top: 0;
   left: 0;
   }
  
   #description {
   margin-top: 20px;
   width: 600px;
   }
  
   #description-title {
   font-weight: bold;
   font-size: 18px;
   }
  </style>
  
  <body>
   <div id="main">
   <video
   id="video"
   playsinline
   style="
   -webkit-transform: scaleX(-1);
   transform: scaleX(-1);
   width: auto;
   height: auto;
   "
   ></video>
   <canvas id="output"></canvas>
   <video
   id="video"
   playsinline
   style="
   -webkit-transform: scaleX(-1);
   transform: scaleX(-1);
   visibility: hidden;
   width: auto;
   height: auto;
   "
   ></video>
   </div>
  </body>
  <script src="https://unpkg.com/@tensorflow/tfjs-core@2.1.0/dist/tf-core.js"></script>
  <script src="https://unpkg.com/@tensorflow/tfjs-converter@2.1.0/dist/tf-converter.js"></script>
  
  <script src="https://unpkg.com/@tensorflow/tfjs-backend-webgl@2.1.0/dist/tf-backend-webgl.js"></script>
  <script src="https://unpkg.com/@tensorflow/tfjs-backend-cpu@2.1.0/dist/tf-backend-cpu.js"></script>
  <script src="./tf-backend-wasm.js"></script>
  
  <script src="https://unpkg.com/@tensorflow-models/blazeface@0.0.5/dist/blazeface.js"></script>
  <script src="https://cdnjs.cloudflare.com/ajax/libs/dat-gui/0.7.6/dat.gui.min.js"></script>
  <script src="https://cdnjs.cloudflare.com/ajax/libs/stats.js/r16/Stats.min.js"></script>
  <script src="./index.js"></script>The index.js fileThe index.js file conducts the facial detection logic. It demonstrates several
  advanced concepts in web development and machine learning integration. Here's a
  breakdown of some of its key components and functionalities:Stats.js: The script starts by creating a Stats instance to monitor and
  display the frame rate (FPS) of the application in real time. This is helpful
  for performance analysis, especially when testing the impact of different
  TensorFlow.js backends on the application's speed.TensorFlow.js: The application allows users to switch between different
  computation backends (wasm, webgl, and cpu) for TensorFlow.js through a
  graphical interface provided by dat.GUI. Changing the backend can affect
  performance and compatibility depending on the device and browser. The
  addFlagLabels function dynamically checks and displays whether SIMD (Single
  Instruction, Multiple Data) and multithreading are supported, which are
  relevant for performance optimization in the wasm backend.setupCamera function: Initializes the user's webcam using the MediaDevices Web
  API. It configures the video stream to not include audio and to use the
  front-facing camera (facingMode: 'user'). Once the video metadata is loaded,
  it resolves a promise with the video element, which is then used for face
  detection.BlazeFace: The core of this application is the renderPrediction function,
  which performs real-time face detection using the BlazeFace model, a
  lightweight model for detecting faces in images. The function calls
  model.estimateFaces on each animation frame to detect faces
  ```