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<h1>Making a Crude ML Powered Chatbot in Swift using CoreML</h1>
<p>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.</p>
<h2>Text Classifier</h2>
<p>I opened a CSV file and added some sample entries, with a corresponding label.</p>
<p><img src="/assets/posts/swift-chatbot/intent-csv.png" alt="Screenshot of Sample Dataset" />
<img src="/assets/posts/swift-chatbot/create-intent.png" alt="Screenshot of Create ML Text Classifier" /></p>
<h2>Word Tagging</h2>
<p>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).</p>
<p><img src="/assets/posts/swift-chatbot/drugs-json.png" alt="Screenshot of Sample Dataset" />
<img src="/assets/posts/swift-chatbot/create-tagger.png" alt="Screenshot of Create ML Text Classifier" /></p>
<h2>Time to Get Swift-y</h2>
<p>The initial part is easy, importing CoreML and NaturalLanguage and then initializing the models and the tagger.</p>
<p><img src="/assets/posts/swift-chatbot/carbon.png" alt="Screenshot" /></p>
<div class="codehilite"><pre><span></span><code><span class="kd">import</span> <span class="nc">CoreML</span>
<span class="kd">import</span> <span class="nc">NaturalLanguage</span>
<span class="kd">let</span> <span class="nv">mlModelClassifier</span> <span class="p">=</span> <span class="k">try</span> <span class="n">IntentDetection_1</span><span class="p">(</span><span class="n">configuration</span><span class="p">:</span> <span class="bp">MLModelConfiguration</span><span class="p">()).</span><span class="n">model</span>
<span class="kd">let</span> <span class="nv">mlModelTagger</span> <span class="p">=</span> <span class="k">try</span> <span class="n">CompoundTagger</span><span class="p">(</span><span class="n">configuration</span><span class="p">:</span> <span class="bp">MLModelConfiguration</span><span class="p">()).</span><span class="n">model</span>
<span class="kd">let</span> <span class="nv">intentPredictor</span> <span class="p">=</span> <span class="k">try</span> <span class="bp">NLModel</span><span class="p">(</span><span class="n">mlModel</span><span class="p">:</span> <span class="n">mlModelClassifier</span><span class="p">)</span>
<span class="kd">let</span> <span class="nv">tagPredictor</span> <span class="p">=</span> <span class="k">try</span> <span class="bp">NLModel</span><span class="p">(</span><span class="n">mlModel</span><span class="p">:</span> <span class="n">mlModelTagger</span><span class="p">)</span>
<span class="kd">let</span> <span class="nv">tagger</span> <span class="p">=</span> <span class="bp">NLTagger</span><span class="p">(</span><span class="n">tagSchemes</span><span class="p">:</span> <span class="p">[.</span><span class="n">nameType</span><span class="p">,</span> <span class="n">NLTagScheme</span><span class="p">(</span><span class="s">"Apple"</span><span class="p">)])</span>
<span class="n">tagger</span><span class="p">.</span><span class="n">setModels</span><span class="p">([</span><span class="n">tagPredictor</span><span class="p">],</span> <span class="n">forTagScheme</span><span class="p">:</span> <span class="n">NLTagScheme</span><span class="p">(</span><span class="s">"Apple"</span><span class="p">))</span>
</code></pre></div>
<p>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. </p>
<p><img src="/assets/posts/swift-chatbot/carbon-2.png" alt="Screenshot" /></p>
<div class="codehilite"><pre><span></span><code><span class="kd">struct</span> <span class="nc">User</span> <span class="p">{</span>
<span class="kd">static</span> <span class="kd">var</span> <span class="nv">message</span> <span class="p">=</span> <span class="s">""</span>
<span class="p">}</span>
<span class="kd">func</span> <span class="nf">customAction</span><span class="p">()</span> <span class="p">-></span> <span class="nb">String</span> <span class="p">{</span>
<span class="kd">let</span> <span class="nv">sampleMessage</span> <span class="p">=</span> <span class="n">User</span><span class="p">.</span><span class="n">message</span>
<span class="kd">var</span> <span class="nv">actionable_item</span> <span class="p">=</span> <span class="s">""</span>
<span class="n">tagger</span><span class="p">.</span><span class="n">string</span> <span class="p">=</span> <span class="n">sampleMessage</span>
<span class="n">tagger</span><span class="p">.</span><span class="n">enumerateTags</span><span class="p">(</span><span class="k">in</span><span class="p">:</span> <span class="n">sampleMessage</span><span class="p">.</span><span class="n">startIndex</span><span class="p">..<</span><span class="n">sampleMessage</span><span class="p">.</span><span class="n">endIndex</span><span class="p">,</span> <span class="n">unit</span><span class="p">:</span> <span class="p">.</span><span class="n">word</span><span class="p">,</span>
<span class="n">scheme</span><span class="p">:</span> <span class="n">NLTagScheme</span><span class="p">(</span><span class="s">"Apple"</span><span class="p">),</span> <span class="n">options</span><span class="p">:</span> <span class="p">.</span><span class="n">omitWhitespace</span><span class="p">)</span> <span class="p">{</span> <span class="n">tag</span><span class="p">,</span> <span class="n">tokenRange</span> <span class="k">in</span>
<span class="k">if</span> <span class="kd">let</span> <span class="nv">tag</span> <span class="p">=</span> <span class="n">tag</span> <span class="p">{</span>
<span class="k">if</span> <span class="n">tag</span><span class="p">.</span><span class="n">rawValue</span> <span class="p">==</span> <span class="s">"COMPOUND"</span> <span class="p">{</span>
<span class="n">actionable_item</span> <span class="o">+=</span> <span class="n">sampleMessage</span><span class="p">[</span><span class="n">tokenRange</span><span class="p">]</span>
<span class="p">}</span>
<span class="p">}</span>
<span class="k">return</span> <span class="kc">true</span>
<span class="p">}</span>
<span class="k">if</span> <span class="n">actionable_item</span> <span class="p">==</span> <span class="s">""</span> <span class="p">{</span>
<span class="k">return</span> <span class="s">"You did not provide any input"</span>
<span class="p">}</span> <span class="k">else</span> <span class="p">{</span>
<span class="k">return</span> <span class="s">"You provided input </span><span class="si">\(</span><span class="n">actionable_item</span><span class="si">)</span><span class="s"> for performing custom action"</span>
<span class="p">}</span>
<span class="p">}</span>
</code></pre></div>
<p>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.</p>
<p><img src="/assets/posts/swift-chatbot/carbon-3.png" alt="Screenshot" /></p>
<div class="codehilite"><pre><span></span><code><span class="kd">let</span> <span class="nv">defaultResponses</span> <span class="p">=</span> <span class="p">[</span>
<span class="s">"greetings"</span><span class="p">:</span> <span class="s">"Hello"</span><span class="p">,</span>
<span class="s">"banter"</span><span class="p">:</span> <span class="s">"no, plix no"</span>
<span class="p">]</span>
<span class="kd">let</span> <span class="nv">customActions</span> <span class="p">=</span> <span class="p">[</span>
<span class="s">"deez-drug"</span><span class="p">:</span> <span class="n">customAction</span>
<span class="p">]</span>
</code></pre></div>
<p>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.</p>
<p><img src="/assets/posts/swift-chatbot/carbon-4.png" alt="Screenshot" /></p>
<div class="codehilite"><pre><span></span><code><span class="kd">let</span> <span class="nv">defaultResponses</span> <span class="p">=</span> <span class="p">[</span>
<span class="s">"greetings"</span><span class="p">:</span> <span class="s">"Hello"</span><span class="p">,</span>
<span class="s">"banter"</span><span class="p">:</span> <span class="s">"no, plix no"</span>
<span class="p">]</span>
<span class="kd">let</span> <span class="nv">customActions</span> <span class="p">=</span> <span class="p">[</span>
<span class="s">"deez-drug"</span><span class="p">:</span> <span class="n">customAction</span>
<span class="p">]</span>
</code></pre></div>
<p><img src="/assets/posts/swift-chatbot/output.png" alt="Output" /></p>
<p>So easy.</p>
<p>If I ever release a part-2, it will either be about implementing this in Tensorflow.JS or an iOS app using SwiftUI ;)</p>
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