diff --git a/README.md b/README.md
index bcd13d2..53bffc0 100644
--- a/README.md
+++ b/README.md
@@ -1,17 +1,90 @@
 # lab07-machine-learning
 
-1. Create a brief demo with Teachable Machine
-  - Go to https://teachablemachine.withgoogle.com/
-  - Follow the instrcutions for an Image Project (standard image model)
-  - Train a model to recognize something from your webcam
-  - Export your model, upload it!
-  - Work off off this base code: https://editor.p5js.org/kongsally/sketches/QlyiZmMNp
-  - Copy the base code into this web editor for [p5.js](https://editor.p5js.org/)
-  - Replace the trained model link in the base code with a link to your trained model.
-  - Code some kind of visual that reacts to the output of your classifier! See https://p5js.org/reference/ for reference
-
-2. Create a program that uses gesture recognition
-
-## Submission Instructions
-- Create a pull request against this repo
-- push your code and/or a link to a gif or video of your work
+![](lab07.gif)
+
+Model:
+```
+// Copyright (c) 2019 ml5
+//
+// This software is released under the MIT License.
+// https://opensource.org/licenses/MIT
+
+/* ===
+ml5 Example
+Webcam Image Classification using a pre-trained customized model and p5.js
+This example uses p5 preload function to create the classifier
+=== */
+
+// Classifier Variable
+let classifier;
+
+// TODO: Upate your model URL
+let imageModelURL = 'https://teachablemachine.withgoogle.com/models/vm-18A1lO/';
+
+// Video
+let video;
+let flippedVideo;
+// To store the classification
+let label = "";
+let peek_img;
+
+// Load the model first
+function preload() {
+  classifier = ml5.imageClassifier(imageModelURL + 'model.json');
+}
+
+function setup() {
+  createCanvas(320, 260);
+  
+  // Create the video
+  video = createCapture(VIDEO);
+  video.size(320, 240);
+  video.hide();
+  
+  peek_img = createImg("https://images-wixmp-ed30a86b8c4ca887773594c2.wixmp.com/f/844e0a8e-3ac6-49c6-b2d4-4f762e7605d9/d6qm7cx-846498fd-6e5c-41b3-9b41-b5f432cf3c1f.gif?token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJ1cm46YXBwOjdlMGQxODg5ODIyNjQzNzNhNWYwZDQxNWVhMGQyNmUwIiwiaXNzIjoidXJuOmFwcDo3ZTBkMTg4OTgyMjY0MzczYTVmMGQ0MTVlYTBkMjZlMCIsIm9iaiI6W1t7InBhdGgiOiJcL2ZcLzg0NGUwYThlLTNhYzYtNDljNi1iMmQ0LTRmNzYyZTc2MDVkOVwvZDZxbTdjeC04NDY0OThmZC02ZTVjLTQxYjMtOWI0MS1iNWY0MzJjZjNjMWYuZ2lmIn1dXSwiYXVkIjpbInVybjpzZXJ2aWNlOmZpbGUuZG93bmxvYWQiXX0.qK-H_DOU3WIFDiqaV3z7WGsH6m8PGkc-GAglOEjIJvI");
+  peek_img.hide();
+
+  flippedVideo = ml5.flipImage(video)
+  // Start classifying
+  classifyVideo();
+}
+
+function draw() {
+  background(0);
+  
+  // Draw the video
+  image(flippedVideo, 0, 0);
+
+  // Draw the label
+  fill(255);
+  textSize(16);
+  textAlign(CENTER);
+  
+  // TODO: Custom logic based on labels
+  if (label === "Class 2") {
+    image(peek_img, 0, 0, peek_img.width*0.75, peek_img.height*0.75);
+    text("Hello by Toothless", width / 2, height - 4);
+  } else if (label === "Class 1") {
+    text("Hello by Wenqing!", width / 2, height - 4);
+  }
+}
+
+// Get a prediction for the current video frame
+function classifyVideo() {
+  flippedVideo = ml5.flipImage(video)
+  classifier.classify(flippedVideo, gotResult);
+}
+
+// When we get a result
+function gotResult(error, results) {
+  // If there is an error
+  if (error) {
+    console.error(error);
+    return;
+  }
+  // The results are in an array ordered by confidence.
+  label = results[0].label;
+  // Classifiy again!
+  classifyVideo();
+}
+```
diff --git a/lab07.gif b/lab07.gif
new file mode 100644
index 0000000..1e711c3
Binary files /dev/null and b/lab07.gif differ