├── A Guide to CoreML.ipynb ├── MessageClassifier.mlmodel ├── README.md ├── SpamStopper ├── SpamStopper MessagesExtension │ ├── Assets.xcassets │ │ ├── Contents.json │ │ └── iMessage App Icon.stickersiconset │ │ │ └── Contents.json │ ├── Base.lproj │ │ └── MainInterface.storyboard │ ├── Info.plist │ ├── MessageClassifier.mlmodel │ ├── MessagesViewController.swift │ ├── SMSSpamCollection.txt │ └── words_ordered.txt ├── SpamStopper.xcodeproj │ ├── project.pbxproj │ ├── project.xcworkspace │ │ ├── contents.xcworkspacedata │ │ └── xcuserdata │ │ │ └── gokulswamy.xcuserdatad │ │ │ └── UserInterfaceState.xcuserstate │ └── xcuserdata │ │ └── gokulswamy.xcuserdatad │ │ └── xcschemes │ │ └── xcschememanagement.plist └── SpamStopper │ ├── Assets.xcassets │ └── AppIcon.appiconset │ │ └── Contents.json │ └── Info.plist ├── spam_detection.py └── words_ordered.txt /A Guide to CoreML.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# A Guide to CoreML on iOS\n", 8 | "\n", 9 | "![alt text](https://www.cygnismedia.com/images/post-images/wwdc17-for-ios-app-developers/apple-core-ml.jpg \"Logo Title Text 1\")\n", 10 | "\n", 11 | "## Our Demo\n", 12 | "\n", 13 | "We're going to build a simple iMessage app that can detect whether or not a message is spam. With the Messages framework, you can build a standalone iMessage app, or an extension to an iOS app that lets users interact with your iOS app without leaving Messages. We'll build an extension. The Model trained using the SMS Spam Collection Dataset in sklearn and converted using coremltools into .mlmodel format.\n", 14 | "\n", 15 | "![alt text](http://i.imgur.com/a3XkZJR.png \"Logo Title Text 1\")\n", 16 | "\n", 17 | "## The Stack\n", 18 | "\n", 19 | "### First, some History... \n", 20 | "\n", 21 | "#### The previous ML frameworks for iOS\n", 22 | "Last year, Apple Announced the Metal Performance Shader Convolutional Neural Network (MPSCNN). This implement CNNs using convolutionss, pooling, normalization, and other matrix multiplication operations. They also announced Basic Neural Network Subroutines (BNNS) a collection of math functions that take full advantage of the CPU’s fast vector instructions (also used for CNNs). \n", 23 | "\n", 24 | "#### Links to documentation\n", 25 | "https://developer.apple.com/documentation/accelerate/bnns (BNNs)\n", 26 | "https://developer.apple.com/documentation/metalperformanceshaders (MPSCNN)\n", 27 | "\n", 28 | "#### Features\n", 29 | "- Both let developers perform inference on convolutional neural networks on iOS devices.\n", 30 | "- BNNS runs on the CPU\n", 31 | "- MPSCNN runs on the GPU. \n", 32 | "- Sometimes using the CPU is faster, sometimes using the GPU is faster.\n", 33 | "- For training GPU always wins,but for inference sometimes CPU can be faster\n", 34 | "\n", 35 | "#### Where we are today\n", 36 | "This year we get lots of additions to Metal (the framework that allows direct access to on-device GPUs), a new computer vision framework, and Core ML: a toolkit that makes it really easy to put ML models into your app.\n", 37 | "\n", 38 | "![alt text](https://docs-assets.developer.apple.com/published/bc34b3e6c2/db81e861-1e06-4d14-8915-90707d9b114c.png \"Logo Title Text 1\")\n", 39 | "\n", 40 | "- CoreML is built on its previous 2 released ML libraries\n", 41 | "- CoreML is the foundation for domain-specific frameworks and functionality.\n", 42 | "- The 3 domains are Computer vision (image analysis), Foundation aka Natural Language Processing, and GamePlayKit (evaluating learned decison trees)\n", 43 | "- So you can take a CoreML model and pass it to any of these 3 APIs (yay modularity)\n", 44 | "- ML primitives > ML framework > domain specific ML frameworks > your app\n", 45 | "\n", 46 | "## The Development Workflow\n", 47 | "\n", 48 | "![alt text](https://docs-assets.developer.apple.com/published/72e22672fd/c35ebf2d-ee94-4448-8fae-16420e7cc4ed.png \"Logo Title Text 1\")\n", 49 | "\n", 50 | "#### This can be a 2 step process if you use a CoreML model from Apple\n", 51 | "1. loading a trained model\n", 52 | "2. making predictions\n", 53 | "\n", 54 | "![alt text](https://cdn-images-1.medium.com/max/1600/1*HhVGMZCEtPN-lfwjSQpkRw.png \"Logo Title Text 1\")\n", 55 | "\n", 56 | "#### Or it can be a 3 step process if you need to convert a third party model into a CoreML model\n", 57 | "1. Convert Model (via coremltools,a a Python package designed to help generate an .mlmodel file that Xcode can use)\n", 58 | "2. Load the Model\n", 59 | "3. Make Predictions\n", 60 | "\n", 61 | "#### The .mlmodel file is like the PDF of Machine Learning Models. Apple has created a standard format for its devices.\n", 62 | "\n", 63 | "- You can find popular models that apple has already converted into it's format here https://developer.apple.com/machine-learning/ \n", 64 | "- Else, here is an amazing list https://github.com/likedan/Awesome-CoreML-Models\n", 65 | "- The file format describes the layers in your model, the input and outputs, the class labels, any preprocessing that needs to happen on the data, and the learned parameters (the weights and biases).\n", 66 | "\n", 67 | "![alt text](https://docs-assets.developer.apple.com/published/bc34b3e6c2/6b4f8b26-1cd0-4d9e-8b54-54dac16a808c.png \"Logo Title Text 1\")\n", 68 | "\n", 69 | "#### Sample \n", 70 | "\n", 71 | "```swift\n", 72 | "let model = ResNet50()\n", 73 | "let pixelBuffer: CVPixelBuffer = /* your image */\n", 74 | "\n", 75 | "if let prediction = try? model.prediction(image: pixelBuffer) {\n", 76 | " print(prediction.classLabel)\n", 77 | "}\n", 78 | "```\n", 79 | "That's it!\n", 80 | "\n", 81 | "## Pros vs Cons of CoreML\n", 82 | "\n", 83 | "\n", 84 | "![alt text](https://cdn-images-1.medium.com/max/1600/1*7wyTjs8euvFaqiAC8NWMzA.png \"Logo Title Text 1\")\n", 85 | "\n", 86 | "\n", 87 | "### Pros\n", 88 | "\n", 89 | "- Optimized for on-device performance, which minimizes memory footprint and power consumption. \n", 90 | "- On-device means privacy of user data \n", 91 | "- On-Device means functional predictions even without an internet connection\n", 92 | "- It decides for itself whether to run the model on the CPU or the GPU (or both)\n", 93 | "- Because it can use CPU, you can run it from the iOS simulator (which doesn't support GPU)\n", 94 | "- It supports many models\n", 95 | " * support vector machines (SVM)\n", 96 | " * tree ensembles such as random forests and boosted trees\n", 97 | " * linear regression and logistic regression\n", 98 | " * neural networks: feed-forward, convolutional, recurrent\n", 99 | "\n", 100 | "![alt text](http://upload-images.jianshu.io/upload_images/519579-01796e57a93fd837.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240 \"Logo Title Text 1\")\n", 101 | "\n", 102 | "\n", 103 | "#### Cons\n", 104 | "\n", 105 | "- Native support for Supervised models only (no unsupervised or reinforcement learning, but could use 'generic' NN type potentially)\n", 106 | "- No training on device, only inference \n", 107 | "- If CoreML does not support a certain layer type, you can’t use it. Currently impossible to extend Core ML with your own layer types\n", 108 | "- Core ML conversion tools only support specific versions of a limited number of training tools (no tensorflow)\n", 109 | "- You cannot look at the output produced by intermediate layers, you only get the prediction\n", 110 | "- Only supports regression & classification. No clustering, ranking, dimensionality reduction, etc.)\n", 111 | "- no federated learning\n", 112 | "\n", 113 | "### My biggest wish for CoreML - add federated learning!\n", 114 | "\n", 115 | "![alt text](https://1.bp.blogspot.com/-K65Ed68KGXk/WOa9jaRWC6I/AAAAAAAABsM/gglycD_anuQSp-i67fxER1FOlVTulvV2gCLcB/s640/FederatedLearning_FinalFiles_Flow%2BChart1.png \"Logo Title Text 1\")\n", 116 | "\n", 117 | "\n", 118 | "A Your phone personalizes the model locally, based on your usage \n", 119 | "B Many users' updates are aggregated \n", 120 | "C to form a consensus change to the shared model, after which the procedure is repeated.\n", 121 | "\n", 122 | "\n", 123 | "### TL;DR CoreML is super simple to use but limited in its functionality. If you want full control, you’re going to have to DIY with Metal Performance Shaders or the Accelerate framework — or both!\n", 124 | "\n", 125 | "\n", 126 | "### Don't worry, there are other ways to do ML on iOS\n", 127 | "\n", 128 | "http://alexsosn.github.io/ml/2015/11/05/iOS-ML.html#cv\n", 129 | "\n", 130 | "\n", 131 | "# Our steps \n", 132 | "\n", 133 | "First, in Python....\n", 134 | "\n", 135 | "1. Import our dataset\n", 136 | "2. Train several models using Scikit-learn\n", 137 | "3. Convert the best model into an .mlmodel file\n", 138 | "\n", 139 | "Then, in Swift\n", 140 | "\n", 141 | "1. Drag and drop our dataset and trained .mlmodel into our project\n", 142 | "2. Write the basic prediction code\n", 143 | "3. Write the TF-IDF code (short for term frequency–inverse document frequency, a a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus)\n", 144 | "4. Demo!\n" 145 | ] 146 | }, 147 | { 148 | "cell_type": "markdown", 149 | "metadata": {}, 150 | "source": [ 151 | "## Dependencies" 152 | ] 153 | }, 154 | { 155 | "cell_type": "code", 156 | "execution_count": 1, 157 | "metadata": { 158 | "collapsed": true 159 | }, 160 | "outputs": [], 161 | "source": [ 162 | "#Sklearn and numpy\n", 163 | "import numpy as np\n", 164 | "import sklearn\n", 165 | "from sklearn.model_selection import train_test_split\n", 166 | "from sklearn.pipeline import Pipeline\n", 167 | "from sklearn.feature_extraction.text import CountVectorizer\n", 168 | "from sklearn.feature_extraction.text import TfidfTransformer\n", 169 | "from sklearn.feature_extraction.text import TfidfVectorizer\n", 170 | "from sklearn.naive_bayes import MultinomialNB\n", 171 | "from sklearn.svm import LinearSVC\n", 172 | "from sklearn.ensemble import RandomForestClassifier\n", 173 | "from sklearn.metrics import classification_report" 174 | ] 175 | }, 176 | { 177 | "cell_type": "markdown", 178 | "metadata": {}, 179 | "source": [ 180 | "### CoreMLtools doesnt run on python 3 so well need to install it via python 2\n", 181 | "```\n", 182 | "brew install pyenv\n", 183 | "pyenv install 2.7.12\n", 184 | "pyenv global 2.7.12\n", 185 | "pyenv rehash\n", 186 | "pip install coremltools\n", 187 | "```" 188 | ] 189 | }, 190 | { 191 | "cell_type": "code", 192 | "execution_count": null, 193 | "metadata": { 194 | "collapsed": false 195 | }, 196 | "outputs": [], 197 | "source": [ 198 | "import coremltools" 199 | ] 200 | }, 201 | { 202 | "cell_type": "markdown", 203 | "metadata": {}, 204 | "source": [ 205 | "## Import Data" 206 | ] 207 | }, 208 | { 209 | "cell_type": "code", 210 | "execution_count": null, 211 | "metadata": { 212 | "collapsed": false 213 | }, 214 | "outputs": [], 215 | "source": [ 216 | "#labeled data http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/\n", 217 | "\n", 218 | "# Reading in and parsing data\n", 219 | "raw_data = open('SMSSpamCollection.txt', 'r')\n", 220 | "sms_data = []\n", 221 | "for line in raw_data:\n", 222 | " split_line = line.split(\"\\t\")\n", 223 | " sms_data.append(split_line)\n", 224 | "\n", 225 | "# Splitting data into messages and labels and training and test\n", 226 | "sms_data = np.array(sms_data)\n", 227 | "X = sms_data[:, 1]\n", 228 | "y = sms_data[:, 0]\n", 229 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=22)\n", 230 | "\n", 231 | "print X_train" 232 | ] 233 | }, 234 | { 235 | "cell_type": "markdown", 236 | "metadata": {}, 237 | "source": [ 238 | "## Train Models for classification\n", 239 | "\n", 240 | "3 models\n", 241 | "- Multinomial Naive Bayes (A specialized version of Naive Bayes that is designed more for text documents. Whereas simple naive Bayes would model a document as the presence and absence of particular words, multinomial naive bayes explicitly models the word counts and adjusts the underlying calculations to deal with in)\n", 242 | "![alt text](https://nlp.stanford.edu/IR-book/html/htmledition/img897.png \"Logo Title Text 1\")\n", 243 | "- Support Vector Machine\n", 244 | "![alt text](http://docs.opencv.org/2.4/_images/optimal-hyperplane.png \"Logo Title Text 1\")\n", 245 | "- Random Forest \n", 246 | "![alt text](https://i.ytimg.com/vi/ajTc5y3OqSQ/hqdefault.jpg \"Logo Title Text 1\")\n", 247 | "\n", 248 | "\n", 249 | "2 vectoriziation strategies\n", 250 | "- count vectorizer (Convert a collection of text documents to a matrix of token counts) \n", 251 | "- TF-IDF vectorizers (Convert a collection of raw documents to a matrix of TF-IDF features.) \n", 252 | "\n", 253 | "![alt text](http://dovgalecs.com/blog/wp-content/uploads/2012/03/img131.gif \"Logo Title Text 1\")" 254 | ] 255 | }, 256 | { 257 | "cell_type": "code", 258 | "execution_count": null, 259 | "metadata": { 260 | "collapsed": false 261 | }, 262 | "outputs": [], 263 | "source": [ 264 | "# Building Pipelines\n", 265 | "pipeline_1 = Pipeline([('vect', CountVectorizer()),('clf', MultinomialNB())])\n", 266 | "pipeline_2 = Pipeline([('vect', TfidfVectorizer()),('clf', MultinomialNB())])\n", 267 | "pipeline_3 = Pipeline([('vect', CountVectorizer()),('clf', LinearSVC())])\n", 268 | "pipeline_4 = Pipeline([('vect', TfidfVectorizer()),('clf', LinearSVC())])\n", 269 | "pipeline_5 = Pipeline([('vect', CountVectorizer()),('clf', RandomForestClassifier())])\n", 270 | "pipeline_6 = Pipeline([('vect', TfidfVectorizer()),('clf', RandomForestClassifier())])\n", 271 | "pipelines = [pipeline_1, pipeline_2, pipeline_3, pipeline_4, pipeline_5, pipeline_6]\n", 272 | "\n", 273 | "# Performing classification and calculating accuracy\n", 274 | "for pipeline in pipelines:\n", 275 | " pipeline.fit(X_train, y_train)\n", 276 | " y_pred = pipeline.predict(X_test)\n", 277 | " print(classification_report(y_test, y_pred, target_names=[\"ham\", \"spam\"]))" 278 | ] 279 | }, 280 | { 281 | "cell_type": "markdown", 282 | "metadata": {}, 283 | "source": [ 284 | "## SVC + tfidf wins! Lets Save trained model as .mlmodel" 285 | ] 286 | }, 287 | { 288 | "cell_type": "code", 289 | "execution_count": 9, 290 | "metadata": { 291 | "collapsed": true 292 | }, 293 | "outputs": [], 294 | "source": [ 295 | "# Creating and saving an .mlmodel file and a list of words\n", 296 | "vectorizer = TfidfVectorizer()\n", 297 | "vectorized = vectorizer.fit_transform(X)\n", 298 | "words = open('words_ordered.txt', 'w')\n", 299 | "for feature in vectorizer.get_feature_names():\n", 300 | " words.write(feature.encode('utf-8') + '\\n')\n", 301 | "words.close()\n", 302 | "model = LinearSVC()\n", 303 | "model.fit(vectorized, y)\n", 304 | "coreml_model = coremltools.converters.sklearn.convert(model, \"message\", 'label')\n", 305 | "coreml_model.save('MessageClassifier.mlmodel')" 306 | ] 307 | }, 308 | { 309 | "cell_type": "code", 310 | "execution_count": null, 311 | "metadata": { 312 | "collapsed": true 313 | }, 314 | "outputs": [], 315 | "source": [] 316 | } 317 | ], 318 | "metadata": { 319 | "kernelspec": { 320 | "display_name": "Python 2", 321 | "language": "python", 322 | "name": "python2" 323 | }, 324 | "language_info": { 325 | "codemirror_mode": { 326 | "name": "ipython", 327 | "version": 2 328 | }, 329 | "file_extension": ".py", 330 | "mimetype": "text/x-python", 331 | "name": "python", 332 | "nbconvert_exporter": "python", 333 | "pygments_lexer": "ipython2", 334 | "version": "2.7.12" 335 | } 336 | }, 337 | "nbformat": 4, 338 | "nbformat_minor": 2 339 | } 340 | -------------------------------------------------------------------------------- /MessageClassifier.mlmodel: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/llSourcell/A_guide_to_coreML/38dcd5f71dabc80ef567a80688564030828cb0a8/MessageClassifier.mlmodel -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # A_guide_to_coreML 2 | This is the code for "A Guide to CoreML for iOS" by Siraj Raval on Youtube 3 | 4 | ## Overview 5 | 6 | This is the code for [this](https://youtu.be/T4t73CXB7CU) video on Youtube by Siraj Raval. The SMS dataset is [here](http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/). This is a guide to Core ML, the new ML framework from Apple. The notebook explains it and the Xcode project implements a spam classifier. 7 | 8 | ## Dependencies 9 | 10 | Install these with [pip](https://pip.pypa.io/en/stable/) 11 | * scikit-learn 12 | * numpy 13 | * coremltools 14 | 15 | These are Apple specific 16 | * Xcode 9.0+ 17 | * iOS 11+ to build on device 18 | 19 | ## Usage 20 | 21 | Run the [notebook](http://jupyter.readthedocs.io/en/latest/install.html) using `jupyter notebook` in terminal. You can run open the iOS code by double clicking on the .xcodeproj file from [Xcode](https://developer.apple.com/xcode/downloads/). 22 | 23 | ## Credits 24 | 25 | The credits for this code go to [Gokul](https://github.com/gkswamy98). I've merely created a wrapper to get people started. 26 | -------------------------------------------------------------------------------- /SpamStopper/SpamStopper MessagesExtension/Assets.xcassets/Contents.json: -------------------------------------------------------------------------------- 1 | { 2 | "info" : { 3 | "version" : 1, 4 | "author" : "xcode" 5 | } 6 | } -------------------------------------------------------------------------------- /SpamStopper/SpamStopper MessagesExtension/Assets.xcassets/iMessage App Icon.stickersiconset/Contents.json: -------------------------------------------------------------------------------- 1 | { 2 | "images" : [ 3 | { 4 | "idiom" : "iphone", 5 | "size" : "60x45", 6 | "scale" : "2x" 7 | }, 8 | { 9 | "idiom" : "iphone", 10 | "size" : "60x45", 11 | "scale" : "3x" 12 | }, 13 | { 14 | "idiom" : "ipad", 15 | "size" : "67x50", 16 | "scale" : "2x" 17 | }, 18 | { 19 | "idiom" : "ipad", 20 | "size" : "74x55", 21 | "scale" : "2x" 22 | }, 23 | { 24 | "size" : "27x20", 25 | "idiom" : "universal", 26 | "scale" : "2x", 27 | "platform" : "ios" 28 | }, 29 | { 30 | "size" : "27x20", 31 | "idiom" : "universal", 32 | "scale" : "3x", 33 | "platform" : "ios" 34 | }, 35 | { 36 | "size" : "32x24", 37 | "idiom" : "universal", 38 | "scale" : "2x", 39 | "platform" : "ios" 40 | }, 41 | { 42 | "size" : "32x24", 43 | "idiom" : "universal", 44 | "scale" : "3x", 45 | "platform" : "ios" 46 | }, 47 | { 48 | "size" : "1024x768", 49 | "idiom" : "ios-marketing", 50 | "scale" : "1x", 51 | "platform" : "ios" 52 | } 53 | ], 54 | "info" : { 55 | "version" : 1, 56 | "author" : "xcode" 57 | } 58 | } -------------------------------------------------------------------------------- /SpamStopper/SpamStopper MessagesExtension/Base.lproj/MainInterface.storyboard: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 29 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | -------------------------------------------------------------------------------- /SpamStopper/SpamStopper MessagesExtension/Info.plist: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | CFBundleDevelopmentRegion 6 | $(DEVELOPMENT_LANGUAGE) 7 | CFBundleDisplayName 8 | SpamStopper 9 | CFBundleExecutable 10 | $(EXECUTABLE_NAME) 11 | CFBundleIdentifier 12 | $(PRODUCT_BUNDLE_IDENTIFIER) 13 | CFBundleInfoDictionaryVersion 14 | 6.0 15 | CFBundleName 16 | $(PRODUCT_NAME) 17 | CFBundlePackageType 18 | XPC! 19 | CFBundleShortVersionString 20 | 1.0 21 | CFBundleVersion 22 | 1 23 | NSExtension 24 | 25 | NSExtensionMainStoryboard 26 | MainInterface 27 | NSExtensionPointIdentifier 28 | com.apple.message-payload-provider 29 | 30 | 31 | 32 | -------------------------------------------------------------------------------- /SpamStopper/SpamStopper MessagesExtension/MessageClassifier.mlmodel: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/llSourcell/A_guide_to_coreML/38dcd5f71dabc80ef567a80688564030828cb0a8/SpamStopper/SpamStopper MessagesExtension/MessageClassifier.mlmodel -------------------------------------------------------------------------------- /SpamStopper/SpamStopper MessagesExtension/MessagesViewController.swift: -------------------------------------------------------------------------------- 1 | // 2 | // MessagesViewController.swift 3 | // SpamStopper MessagesExtension 4 | // 5 | // Created by Gokul Swamy on 6/25/17. 6 | // Copyright © 2017 Gokul Swamy. All rights reserved. 7 | // 8 | 9 | import UIKit 10 | import Messages 11 | import CoreML 12 | 13 | class MessagesViewController: MSMessagesAppViewController { 14 | @IBOutlet weak var label: UILabel! 15 | @IBOutlet weak var button: UIButton! 16 | 17 | @IBAction func pushed(_ sender: Any) { 18 | let copied = UIPasteboard.general.string 19 | if let text = copied { 20 | let vec = tfidf(sms: text) 21 | do { 22 | let prediction = try MessageClassifier().prediction(message: vec).label 23 | print(prediction) 24 | label.text = prediction 25 | } catch { 26 | label.text = "No Prediction" 27 | } 28 | } 29 | } 30 | override func viewDidLoad() { 31 | super.viewDidLoad() 32 | } 33 | 34 | func tfidf(sms: String) -> MLMultiArray{ 35 | let wordsFile = Bundle.main.path(forResource: "words_ordered", ofType: "txt") 36 | let smsFile = Bundle.main.path(forResource: "SMSSpamCollection", ofType: "txt") 37 | do { 38 | let wordsFileText = try String(contentsOfFile: wordsFile!, encoding: String.Encoding.utf8) 39 | var wordsData = wordsFileText.components(separatedBy: .newlines) 40 | wordsData.removeLast() // Trailing newline. 41 | let smsFileText = try String(contentsOfFile: smsFile!, encoding: String.Encoding.utf8) 42 | var smsData = smsFileText.components(separatedBy: .newlines) 43 | smsData.removeLast() // Trailing newline. 44 | let wordsInMessage = sms.split(separator: " ") 45 | var vectorized = try MLMultiArray(shape: [NSNumber(integerLiteral: wordsData.count)], dataType: MLMultiArrayDataType.double) 46 | for i in 0.. 2 | 4 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /SpamStopper/SpamStopper.xcodeproj/project.xcworkspace/xcuserdata/gokulswamy.xcuserdatad/UserInterfaceState.xcuserstate: -------------------------------------------------------------------------------- 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"size" : "20x20", 11 | "scale" : "3x" 12 | }, 13 | { 14 | "idiom" : "iphone", 15 | "size" : "29x29", 16 | "scale" : "2x" 17 | }, 18 | { 19 | "idiom" : "iphone", 20 | "size" : "29x29", 21 | "scale" : "3x" 22 | }, 23 | { 24 | "idiom" : "iphone", 25 | "size" : "40x40", 26 | "scale" : "2x" 27 | }, 28 | { 29 | "idiom" : "iphone", 30 | "size" : "40x40", 31 | "scale" : "3x" 32 | }, 33 | { 34 | "idiom" : "iphone", 35 | "size" : "60x60", 36 | "scale" : "2x" 37 | }, 38 | { 39 | "idiom" : "iphone", 40 | "size" : "60x60", 41 | "scale" : "3x" 42 | }, 43 | { 44 | "idiom" : "ipad", 45 | "size" : "20x20", 46 | "scale" : "1x" 47 | }, 48 | { 49 | "idiom" : "ipad", 50 | "size" : "20x20", 51 | "scale" : "2x" 52 | }, 53 | { 54 | "idiom" : "ipad", 55 | "size" : "29x29", 56 | "scale" : "1x" 57 | }, 58 | { 59 | "idiom" : "ipad", 60 | "size" : "29x29", 61 | "scale" : "2x" 62 | }, 63 | { 64 | "idiom" : "ipad", 65 | "size" : "40x40", 66 | "scale" : "1x" 67 | }, 68 | { 69 | "idiom" : "ipad", 70 | "size" : "40x40", 71 | "scale" : "2x" 72 | }, 73 | { 74 | "idiom" : "ipad", 75 | "size" : "76x76", 76 | "scale" : "1x" 77 | }, 78 | { 79 | "idiom" : "ipad", 80 | "size" : "76x76", 81 | "scale" : "2x" 82 | }, 83 | { 84 | "idiom" : "ipad", 85 | "size" : "83.5x83.5", 86 | "scale" : "2x" 87 | } 88 | ], 89 | "info" : { 90 | "version" : 1, 91 | "author" : "xcode" 92 | } 93 | } -------------------------------------------------------------------------------- /SpamStopper/SpamStopper/Info.plist: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | CFBundleDevelopmentRegion 6 | $(DEVELOPMENT_LANGUAGE) 7 | CFBundleExecutable 8 | $(EXECUTABLE_NAME) 9 | CFBundleIdentifier 10 | $(PRODUCT_BUNDLE_IDENTIFIER) 11 | CFBundleInfoDictionaryVersion 12 | 6.0 13 | CFBundleName 14 | $(PRODUCT_NAME) 15 | CFBundlePackageType 16 | APPL 17 | CFBundleShortVersionString 18 | 1.0 19 | CFBundleVersion 20 | 1 21 | LSRequiresIPhoneOS 22 | 23 | UILaunchStoryboardName 24 | LaunchScreen 25 | UIRequiredDeviceCapabilities 26 | 27 | armv7 28 | 29 | UISupportedInterfaceOrientations 30 | 31 | UIInterfaceOrientationPortrait 32 | UIInterfaceOrientationLandscapeLeft 33 | UIInterfaceOrientationLandscapeRight 34 | 35 | UISupportedInterfaceOrientations~ipad 36 | 37 | UIInterfaceOrientationPortrait 38 | UIInterfaceOrientationPortraitUpsideDown 39 | UIInterfaceOrientationLandscapeLeft 40 | UIInterfaceOrientationLandscapeRight 41 | 42 | 43 | 44 | -------------------------------------------------------------------------------- /spam_detection.py: -------------------------------------------------------------------------------- 1 | # Required imports 2 | import numpy as np 3 | import sklearn 4 | from sklearn.model_selection import train_test_split 5 | from sklearn.pipeline import Pipeline 6 | from sklearn.feature_extraction.text import CountVectorizer 7 | from sklearn.feature_extraction.text import TfidfTransformer 8 | from sklearn.feature_extraction.text import TfidfVectorizer 9 | from sklearn.naive_bayes import MultinomialNB 10 | from sklearn.svm import LinearSVC 11 | from sklearn.ensemble import RandomForestClassifier 12 | from sklearn.metrics import classification_report 13 | 14 | import coremltools 15 | 16 | # Reading in and parsing data 17 | raw_data = open('SMSSpamCollection.txt', 'r') 18 | sms_data = [] 19 | for line in raw_data: 20 | split_line = line.split("\t") 21 | sms_data.append(split_line) 22 | 23 | # Splitting data into messages and labels and training and test 24 | sms_data = np.array(sms_data) 25 | X = sms_data[:, 1] 26 | y = sms_data[:, 0] 27 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=22) 28 | 29 | # Building Pipelines 30 | pipeline_1 = Pipeline([('vect', CountVectorizer()),('clf', MultinomialNB())]) 31 | pipeline_2 = Pipeline([('vect', TfidfVectorizer()),('clf', MultinomialNB())]) 32 | pipeline_3 = Pipeline([('vect', CountVectorizer()),('clf', LinearSVC())]) 33 | pipeline_4 = Pipeline([('vect', TfidfVectorizer()),('clf', LinearSVC())]) 34 | pipeline_5 = Pipeline([('vect', CountVectorizer()),('clf', RandomForestClassifier())]) 35 | pipeline_6 = Pipeline([('vect', TfidfVectorizer()),('clf', RandomForestClassifier())]) 36 | pipelines = [pipeline_1, pipeline_2, pipeline_3, pipeline_4, pipeline_5, pipeline_6] 37 | 38 | # Performing classification and calculating accuracy 39 | for pipeline in pipelines: 40 | pipeline.fit(X_train, y_train) 41 | y_pred = pipeline.predict(X_test) 42 | print(classification_report(y_test, y_pred, target_names=["ham", "spam"])) 43 | 44 | # Creating and saving an .mlmodel file and a list of words 45 | vectorizer = TfidfVectorizer() 46 | vectorized = vectorizer.fit_transform(X) 47 | words = open('words_ordered.txt', 'w') 48 | for feature in vectorizer.get_feature_names(): 49 | words.write(feature.encode('utf-8') + '\n') 50 | words.close() 51 | model = LinearSVC() 52 | model.fit(vectorized, y) 53 | coreml_model = coremltools.converters.sklearn.convert(model, "message", 'label') 54 | coreml_model.save('MessageClassifier.mlmodel') 55 | -------------------------------------------------------------------------------- /words_ordered.txt: -------------------------------------------------------------------------------- 1 | 00 2 | 000 3 | 000pes 4 | 008704050406 5 | 0089 6 | 0121 7 | 01223585236 8 | 01223585334 9 | 0125698789 10 | 02 11 | 0207 12 | 02072069400 13 | 02073162414 14 | 02085076972 15 | 021 16 | 03 17 | 04 18 | 0430 19 | 05 20 | 050703 21 | 0578 22 | 06 23 | 07 24 | 07008009200 25 | 07046744435 26 | 07090201529 27 | 07090298926 28 | 07099833605 29 | 07123456789 30 | 0721072 31 | 07732584351 32 | 07734396839 33 | 07742676969 34 | 07753741225 35 | 0776xxxxxxx 36 | 07781482378 37 | 07786200117 38 | 077xxx 39 | 078 40 | 07801543489 41 | 07808 42 | 07808247860 43 | 07808726822 44 | 07815296484 45 | 07821230901 46 | 078498 47 | 07880867867 48 | 0789xxxxxxx 49 | 07946746291 50 | 0796xxxxxx 51 | 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| attractive 1295 | attracts 1296 | attributed 1297 | atural 1298 | auction 1299 | audiitions 1300 | audition 1301 | audrey 1302 | audrie 1303 | august 1304 | aunt 1305 | auntie 1306 | aunties 1307 | aunts 1308 | aunty 1309 | aust 1310 | australia 1311 | authorise 1312 | auto 1313 | autocorrect 1314 | av 1315 | ava 1316 | availa 1317 | available 1318 | avalarr 1319 | avatar 1320 | avble 1321 | ave 1322 | avenge 1323 | avent 1324 | avenue 1325 | avin 1326 | avo 1327 | avoid 1328 | avoiding 1329 | avoids 1330 | await 1331 | awaiting 1332 | awake 1333 | award 1334 | awarded 1335 | away 1336 | awesome 1337 | awkward 1338 | aww 1339 | awww 1340 | ax 1341 | axel 1342 | axis 1343 | ay 1344 | ayn 1345 | ayo 1346 | b4 1347 | b4190604 1348 | b4280703 1349 | b4u 1350 | b4utele 1351 | ba 1352 | ba128nnfwfly150ppm 1353 | baaaaaaaabe 1354 | baaaaabe 1355 | babe 1356 | babes 1357 | babies 1358 | baby 1359 | babygoodbye 1360 | babyjontet 1361 | babysit 1362 | babysitting 1363 | bac 1364 | back 1365 | 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1657 | bowls 1658 | box 1659 | box1146 1660 | box139 1661 | box177 1662 | box245c2150pm 1663 | box326 1664 | box334 1665 | box334sk38ch 1666 | box385 1667 | box39822 1668 | box403 1669 | box420 1670 | box42wr29c 1671 | box434sk38wp150ppm18 1672 | box61 1673 | box95qu 1674 | box97n7qp 1675 | boy 1676 | boye 1677 | boyf 1678 | boyfriend 1679 | boys 1680 | boytoy 1681 | bpo 1682 | brah 1683 | brain 1684 | braindance 1685 | brainless 1686 | brains 1687 | brainy 1688 | brand 1689 | brandy 1690 | bras 1691 | brats 1692 | braved 1693 | bray 1694 | brb 1695 | brdget 1696 | bread 1697 | breadstick 1698 | break 1699 | breaker 1700 | breakfast 1701 | breakin 1702 | breaking 1703 | breaks 1704 | breath 1705 | breathe 1706 | breathe1 1707 | breather 1708 | breathing 1709 | breeze 1710 | breezy 1711 | bribe 1712 | bridal 1713 | bridge 1714 | bridgwater 1715 | brief 1716 | bright 1717 | brighten 1718 | brilliant 1719 | brilliantly 1720 | brin 1721 | bring 1722 | bringing 1723 | brings 1724 | brisk 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butting 1799 | buttons 1800 | buy 1801 | buyer 1802 | buyers 1803 | buying 1804 | buz 1805 | buzy 1806 | buzz 1807 | buzzzz 1808 | bw 1809 | bx 1810 | bx420 1811 | bx526 1812 | by 1813 | byatch 1814 | bye 1815 | c52 1816 | cab 1817 | cabin 1818 | cable 1819 | cafe 1820 | cage 1821 | cake 1822 | cakes 1823 | cal 1824 | calculated 1825 | calculation 1826 | cali 1827 | calicut 1828 | california 1829 | call 1830 | call09050000327 1831 | call2optout 1832 | callback 1833 | callcost 1834 | callcost150ppmmobilesvary 1835 | calld 1836 | called 1837 | caller 1838 | callers 1839 | callertune 1840 | callfreefone 1841 | callin 1842 | calling 1843 | callon 1844 | calls 1845 | calls1 1846 | calm 1847 | cam 1848 | camcorder 1849 | came 1850 | camera 1851 | camp 1852 | campus 1853 | camry 1854 | can 1855 | canada 1856 | canal 1857 | canary 1858 | cancel 1859 | canceled 1860 | cancelled 1861 | cancer 1862 | canlove 1863 | cann 1864 | canname 1865 | cannot 1866 | cant 1867 | cantdo 1868 | canteen 1869 | capacity 1870 | capital 1871 | cappuccino 1872 | caps 1873 | captain 1874 | captaining 1875 | car 1876 | card 1877 | cardiff 1878 | cardin 1879 | cards 1880 | care 1881 | careabout 1882 | cared 1883 | career 1884 | careers 1885 | careful 1886 | carefully 1887 | careless 1888 | cares 1889 | caring 1890 | carlie 1891 | carlin 1892 | carlos 1893 | carly 1894 | carolina 1895 | caroline 1896 | carpark 1897 | carry 1898 | carryin 1899 | cars 1900 | cartons 1901 | cartoon 1902 | case 1903 | cash 1904 | cashbin 1905 | cashed 1906 | cashto 1907 | casing 1908 | cast 1909 | casting 1910 | castor 1911 | casualty 1912 | cat 1913 | catch 1914 | catches 1915 | catching 1916 | categories 1917 | caught 1918 | cause 1919 | causes 1920 | causing 1921 | cave 1922 | caveboy 1923 | cbe 1924 | cc 1925 | cc100p 1926 | ccna 1927 | cd 1928 | cdgt 1929 | cds 1930 | cedar 1931 | ceiling 1932 | celeb 1933 | celebrate 1934 | celebrated 1935 | celebration 1936 | celebrations 1937 | cell 1938 | census 1939 | center 1940 | centre 1941 | century 1942 | cer 1943 | cereals 1944 | ceri 1945 | certainly 1946 | certificate 1947 | cha 1948 | chachi 1949 | chad 1950 | chain 1951 | challenge 1952 | challenging 1953 | champ 1954 | champlaxigating 1955 | champneys 1956 | chance 1957 | chances 1958 | change 1959 | changed 1960 | changes 1961 | changing 1962 | channel 1963 | chapel 1964 | chaps 1965 | chapter 1966 | character 1967 | characters 1968 | charge 1969 | charged 1970 | charges 1971 | charity 1972 | charles 1973 | charlie 1974 | charming 1975 | chart 1976 | charts 1977 | chase 1978 | chasing 1979 | chastity 1980 | chat 1981 | chat80155 1982 | chatlines 1983 | chatter 1984 | chatting 1985 | cheap 1986 | cheaper 1987 | cheat 1988 | cheating 1989 | chechi 1990 | check 1991 | checkboxes 1992 | checked 1993 | checkin 1994 | checking 1995 | checkmate 1996 | checkup 1997 | cheek 1998 | cheer 1999 | cheered 2000 | cheers 2001 | cheery 2002 | cheese 2003 | cheesy 2004 | cheetos 2005 | chef 2006 | chennai 2007 | cheque 2008 | cherish 2009 | cherthala 2010 | chess 2011 | chest 2012 | chex 2013 | cheyyamo 2014 | chez 2015 | chg 2016 | chgs 2017 | chic 2018 | chick 2019 | chicken 2020 | chickened 2021 | chief 2022 | chik 2023 | chikku 2024 | child 2025 | childish 2026 | childporn 2027 | children 2028 | childrens 2029 | chile 2030 | chill 2031 | chillaxin 2032 | chillin 2033 | china 2034 | chinatown 2035 | chinchillas 2036 | chinese 2037 | chinky 2038 | chinnu 2039 | chiong 2040 | chip 2041 | chit 2042 | chk 2043 | chloe 2044 | chocolate 2045 | choice 2046 | choices 2047 | choose 2048 | choosing 2049 | chop 2050 | chords 2051 | chores 2052 | chosen 2053 | chrgd 2054 | christ 2055 | christians 2056 | christmas 2057 | christmassy 2058 | chuck 2059 | chuckin 2060 | church 2061 | ciao 2062 | cine 2063 | cinema 2064 | citizen 2065 | city 2066 | citylink 2067 | cl 2068 | claim 2069 | claimcode 2070 | claims 2071 | claire 2072 | clarification 2073 | clarify 2074 | clark 2075 | clas 2076 | clash 2077 | class 2078 | classes 2079 | classic 2080 | classmates 2081 | claypot 2082 | cld 2083 | clean 2084 | cleaning 2085 | clear 2086 | cleared 2087 | clearer 2088 | clearing 2089 | clearly 2090 | clever 2091 | click 2092 | cliff 2093 | cliffs 2094 | clip 2095 | clock 2096 | clocks 2097 | clos1 2098 | close 2099 | closeby 2100 | closed 2101 | closer 2102 | closes 2103 | closingdate04 2104 | cloth 2105 | clothes 2106 | cloud 2107 | clover 2108 | club 2109 | club4 2110 | club4mobiles 2111 | clubmoby 2112 | clubsaisai 2113 | clubzed 2114 | clue 2115 | cm 2116 | cm2 2117 | cme 2118 | cmon 2119 | cn 2120 | cnl 2121 | cnn 2122 | cnupdates 2123 | co 2124 | coach 2125 | coast 2126 | coat 2127 | coaxing 2128 | coca 2129 | coccooning 2130 | cochin 2131 | cock 2132 | cocksuckers 2133 | coco 2134 | code 2135 | coffee 2136 | coherently 2137 | coimbatore 2138 | coin 2139 | coincidence 2140 | coins 2141 | cola 2142 | colany 2143 | cold 2144 | colin 2145 | collages 2146 | collapsed 2147 | colleagues 2148 | collect 2149 | collected 2150 | collecting 2151 | collection 2152 | colleg 2153 | college 2154 | color 2155 | colour 2156 | colourful 2157 | colours 2158 | com 2159 | com1win150ppmx3age16 2160 | com1win150ppmx3age16subscription 2161 | comb 2162 | combination 2163 | combine 2164 | come 2165 | comedy 2166 | comes 2167 | comfey 2168 | comfort 2169 | comin 2170 | coming 2171 | comingdown 2172 | command 2173 | comment 2174 | commercial 2175 | commit 2176 | common 2177 | community 2178 | como 2179 | comp 2180 | companies 2181 | companion 2182 | company 2183 | compare 2184 | compass 2185 | compensation 2186 | competition 2187 | complacent 2188 | complain 2189 | complaining 2190 | complaint 2191 | complementary 2192 | complete 2193 | completed 2194 | completely 2195 | completes 2196 | completing 2197 | complexities 2198 | complimentary 2199 | compliments 2200 | comprehensive 2201 | compromised 2202 | compulsory 2203 | computational 2204 | computer 2205 | computerless 2206 | computers 2207 | comuk 2208 | conacted 2209 | concentrate 2210 | concentrating 2211 | concentration 2212 | concern 2213 | concerned 2214 | concert 2215 | conclusion 2216 | condition 2217 | conditions 2218 | conducts 2219 | conected 2220 | conference 2221 | confidence 2222 | configure 2223 | confirm 2224 | confirmd 2225 | confirmed 2226 | conform 2227 | confused 2228 | confuses 2229 | congrats 2230 | congratulation 2231 | congratulations 2232 | connect 2233 | connected 2234 | connection 2235 | connections 2236 | cons 2237 | consensus 2238 | consent 2239 | conserve 2240 | consider 2241 | considering 2242 | consistently 2243 | console 2244 | constant 2245 | constantly 2246 | contact 2247 | contacted 2248 | contacts 2249 | contains 2250 | content 2251 | contented 2252 | contention 2253 | contents 2254 | continent 2255 | continue 2256 | continued 2257 | contract 2258 | contribute 2259 | control 2260 | convenience 2261 | conversations 2262 | converted 2263 | converter 2264 | convey 2265 | conveying 2266 | 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2336 | crave 2337 | craving 2338 | craziest 2339 | crazy 2340 | crazyin 2341 | crckt 2342 | cream 2343 | created 2344 | creative 2345 | creativity 2346 | credit 2347 | credited 2348 | credits 2349 | creep 2350 | creepy 2351 | cres 2352 | cribbs 2353 | cricket 2354 | cricketer 2355 | crickiting 2356 | cried 2357 | crisis 2358 | cro1327 2359 | crore 2360 | cross 2361 | crossing 2362 | crowd 2363 | croydon 2364 | crucial 2365 | crucify 2366 | cruel 2367 | cruise 2368 | cruisin 2369 | crushes 2370 | cry 2371 | crying 2372 | cs 2373 | csbcm4235wc1n3xx 2374 | csc 2375 | csh11 2376 | cst 2377 | cstore 2378 | ctagg 2379 | ctargg 2380 | cthen 2381 | ctla 2382 | cts 2383 | cttargg 2384 | ctter 2385 | cttergg 2386 | ctxt 2387 | cu 2388 | cuck 2389 | cud 2390 | cuddle 2391 | cuddled 2392 | cuddling 2393 | cudnt 2394 | culdnt 2395 | cultures 2396 | cum 2397 | cumin 2398 | cumming 2399 | cup 2400 | cupboard 2401 | cuppa 2402 | curfew 2403 | curious 2404 | current 2405 | currently 2406 | curry 2407 | curtsey 2408 | cusoon 2409 | cust 2410 | custcare 2411 | custom 2412 | customer 2413 | customercare 2414 | customers 2415 | customersqueries 2416 | cut 2417 | cute 2418 | cutefrnd 2419 | cutest 2420 | cutie 2421 | cutter 2422 | cutting 2423 | cuz 2424 | cw25wx 2425 | cya 2426 | cyclists 2427 | cysts 2428 | d3wv 2429 | da 2430 | daaaaa 2431 | dabbles 2432 | dabooks 2433 | dad 2434 | daddy 2435 | dads 2436 | dai 2437 | daily 2438 | dammit 2439 | damn 2440 | dan 2441 | dancce 2442 | dance 2443 | dancin 2444 | dancing 2445 | dane 2446 | dang 2447 | danger 2448 | dangerous 2449 | dao 2450 | dare 2451 | dark 2452 | darker 2453 | darkest 2454 | darkness 2455 | darlin 2456 | darling 2457 | darlings 2458 | darren 2459 | dartboard 2460 | das 2461 | dasara 2462 | dat 2463 | data 2464 | date 2465 | datebox1282essexcm61xn 2466 | dates 2467 | dating 2468 | dats 2469 | datz 2470 | dave 2471 | dawns 2472 | day 2473 | days 2474 | daytime 2475 | daywith 2476 | db 2477 | dbuk 2478 | dd 2479 | de 2480 | dead 2481 | deal 2482 | dealer 2483 | dealing 2484 | deals 2485 | dear 2486 | dear1 2487 | dearer 2488 | dearly 2489 | death 2490 | debating 2491 | dec 2492 | decades 2493 | december 2494 | decent 2495 | decide 2496 | decided 2497 | deciding 2498 | decimal 2499 | decision 2500 | decisions 2501 | deck 2502 | decking 2503 | declare 2504 | decorating 2505 | dedicate 2506 | dedicated 2507 | deduct 2508 | deep 2509 | deepak 2510 | deepest 2511 | deer 2512 | deeraj 2513 | def 2514 | defeat 2515 | defer 2516 | deficient 2517 | definite 2518 | definitely 2519 | definitly 2520 | defo 2521 | degree 2522 | degrees 2523 | dehydrated 2524 | dehydration 2525 | del 2526 | delay 2527 | delayed 2528 | delete 2529 | deleted 2530 | delhi 2531 | delicious 2532 | deliver 2533 | delivered 2534 | deliveredtomorrow 2535 | delivery 2536 | deltomorrow 2537 | deluxe 2538 | dem 2539 | demand 2540 | den 2541 | dena 2542 | dengra 2543 | denis 2544 | dent 2545 | dental 2546 | dentist 2547 | dentists 2548 | deny 2549 | denying 2550 | department 2551 | dependable 2552 | dependents 2553 | depends 2554 | deposit 2555 | deposited 2556 | depressed 2557 | depression 2558 | dept 2559 | der 2560 | derek 2561 | derp 2562 | describe 2563 | description 2564 | desert 2565 | deserve 2566 | designation 2567 | desires 2568 | desk 2569 | desparate 2570 | desparately 2571 | desperate 2572 | despite 2573 | dessert 2574 | destination 2575 | destiny 2576 | detail 2577 | detailed 2578 | details 2579 | determine 2580 | determined 2581 | detroit 2582 | deus 2583 | develop 2584 | developed 2585 | developer 2586 | device 2587 | devils 2588 | deviousbitch 2589 | devouring 2590 | dey 2591 | dha 2592 | dhanush 2593 | dhina 2594 | dhoni 2595 | dhorte 2596 | di 2597 | dial 2598 | dialling 2599 | dialogue 2600 | diamond 2601 | diamonds 2602 | diapers 2603 | dice 2604 | dick 2605 | dict 2606 | dictionary 2607 | did 2608 | diddy 2609 | didn 2610 | didnt 2611 | didntgive 2612 | die 2613 | died 2614 | diesel 2615 | diet 2616 | 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