From b5c4bdce27ca7bc75c91dc28223e12ec1be2ea47 Mon Sep 17 00:00:00 2001
From: navanchauhan <navanchauhan@gmail.com>
Date: Mon, 28 Jun 2021 00:48:18 +0530
Subject: added CoreML Chatbot

---
 ...2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.md | 123 +++++++++++++++++++++
 1 file changed, 123 insertions(+)
 create mode 100644 Content/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.md

(limited to 'Content')

diff --git a/Content/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.md b/Content/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.md
new file mode 100644
index 0000000..0dcabec
--- /dev/null
+++ b/Content/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.md
@@ -0,0 +1,123 @@
+---
+date: 2021-06-27 23:26
+description: Writing a simple Machine-Learning powered Chatbot (or, daresay virtual personal assistant ) in Swift using CoreML.
+tags: Swift, CoreML, NLP
+---
+
+# 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](/assets/posts/swift-chatbot/intent-csv.png)
+![Screenshot of Create ML Text Classifier](/assets/posts/swift-chatbot/create-intent.png)
+
+## 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](/assets/posts/swift-chatbot/drugs-json.png)
+![Screenshot of Create ML Text Classifier](/assets/posts/swift-chatbot/create-tagger.png)
+
+## Time to Get Swift-y
+
+The initial part is easy, importing CoreML and NaturalLanguage and then initializing the models and the tagger.
+
+![Screenshot](/assets/posts/swift-chatbot/carbon.png)
+
+```swift
+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](/assets/posts/swift-chatbot/carbon-2.png)
+```swift
+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](/assets/posts/swift-chatbot/carbon-3.png)
+```swift
+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](/assets/posts/swift-chatbot/carbon-4.png)
+
+```swift
+let defaultResponses = [
+    "greetings": "Hello",
+    "banter": "no, plix no"
+]
+
+let customActions = [
+    "deez-drug": customAction
+]
+```
+
+![Output](/assets/posts/swift-chatbot/output.png)
+
+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 ;)
+
-- 
cgit v1.2.3