Making a Crude ML Powered Chatbot in Swift using CoreML

+ +

A chatbot/virtual assistant, on paper, looks easy to build. +The user says something, the programs finds the best action, checks if additional input is required and sends back the output. +To do this in Swift, I used two separate ML Models created using Apple's Create ML App. +First is a Text Classifier to classify intent, and the other a word tagger for extracting input from the input message. +Disclaimer: This is a very crude proof-of-concept, but it does work.

+ +

Text Classifier

+ +

I opened a CSV file and added some sample entries, with a corresponding label.

+ +

Screenshot of Sample Dataset + Screenshot of Create ML Text Classifier

+ +

Word Tagging

+ +

This is useful to extract the required variables directly from the user's input. +This model will be only called if the intent from the classifier is a custom action. +I created a sample JSON with only 3 examples (I know, very less, but works for a crude PoC).

+ +

Screenshot of Sample Dataset + Screenshot of Create ML Text Classifier

+ +

Time to Get Swift-y

+ +

The initial part is easy, importing CoreML and NaturalLanguage and then initializing the models and the tagger.

+ +

Screenshot

+ +

import CoreML
+import NaturalLanguage
+
+let mlModelClassifier = try IntentDetection_1(configuration:  MLModelConfiguration()).model
+let mlModelTagger = try CompoundTagger(configuration: MLModelConfiguration()).model
+
+let intentPredictor = try NLModel(mlModel: mlModelClassifier)
+let tagPredictor = try NLModel(mlModel: mlModelTagger)
+
+let tagger = NLTagger(tagSchemes: [.nameType, NLTagScheme("Apple")])
+tagger.setModels([tagPredictor], forTagScheme: NLTagScheme("Apple"))
+

+ +

Now, we define a simple structure which the custom function(s) can use to access the provided input. +It can also be used to hold additional variables. +This custom action for our third label, uses the Word Tagger model to check for the compound in the user's message. +If it is present then it displays the name, otherwise it tells the user that they have not provided the input. +The latter can be replaced with a function which asks the user for the input.

+ +

Screenshot

+ +

struct User {
+    static var message = ""
+}
+
+func customAction() -> String {
+    let sampleMessage = User.message
+    var actionable_item = ""
+    tagger.string = sampleMessage
+    tagger.enumerateTags(in: sampleMessage.startIndex..<sampleMessage.endIndex, unit: .word,
+                             scheme: NLTagScheme("Apple"), options: .omitWhitespace) { tag, tokenRange  in
+            if let tag = tag {
+                if tag.rawValue == "COMPOUND" {
+                    actionable_item += sampleMessage[tokenRange]
+                }
+            }
+        return true
+    }
+    if actionable_item == "" {
+        return "You did not provide any input"
+    } else {
+        return "You provided input \(actionable_item) for performing custom action"
+    }
+
+}
+

+ +

Sometimes, no action needs to be performed, and the bot can use a predefined set of responses. +Otherwise, if an action is required, it can call the custom action.

+ +

Screenshot

+ +

let defaultResponses = [
+    "greetings": "Hello",
+    "banter": "no, plix no"
+]
+
+let customActions = [
+    "deez-drug": customAction
+]
+

+ +

In the sample input, the program is updating the User.message and checking if it has a default response. +Otherwise, it calls the custom action.

+ +

Screenshot

+ +

let defaultResponses = [
+    "greetings": "Hello",
+    "banter": "no, plix no"
+]
+
+let customActions = [
+    "deez-drug": customAction
+]
+

+ +

Output

+ +

So easy.

+ +

If I ever release a part-2, it will either be about implementing this in Tensorflow.JS or an iOS app using SwiftUI ;)

+ +