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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. + + + + +## 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). + + + + +## Time to Get Swift-y + +The initial part is easy, importing CoreML and NaturalLanguage and then initializing the models and the tagger. + + + +```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. + + +```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. + + +```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. + + + +```swift +let defaultResponses = [ + "greetings": "Hello", + "banter": "no, plix no" +] + +let customActions = [ + "deez-drug": customAction +] +``` + + + +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 ;) + diff --git 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type="application/rss+xml"/> @@ -2023,6 +2023,133 @@ Configure failed due to the errors above! <item> <guid isPermaLink="true"> + https://web.navan.dev/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.html + </guid> + <title> + Making a Crude ML Powered Chatbot in Swift using CoreML + </title> + <description> + Writing a simple Machine-Learning powered Chatbot (or, daresay virtual personal assistant ) in Swift using CoreML. + </description> + <link>https://web.navan.dev/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.html</link> + <pubDate>Sun, 27 Jun 2021 23:26:00 -0000</pubDate> + <content:encoded><![CDATA[<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> +]]></content:encoded> + </item> + + <item> + <guid isPermaLink="true"> https://web.navan.dev/posts/2019-12-10-TensorFlow-Model-Prediction.html </guid> <title> @@ -3342,24 +3469,21 @@ new Dics({ <pubDate>Sat, 26 Jun 2021 13:04:00 -0000</pubDate> <content:encoded><![CDATA[<h1>Cheminformatics on the Web (2021)</h1> -<p>Here, I have compiled a list of some tools and possible solutions. -The web is a nice platform, it is available anywhere and just requires an internet connection. -I, personally like static websites which don't require a server side application and can be hosted on platforms like GitHub Pages. -Or, just open the HTML file and run it in your browser. -No data is required to be sent to any server and your device's computational power is used. -Even our phones have a lot of computational power now, which allows the user to run tasks on the go without needing to worry about managing dependencies. -WebAssembly (Wasm) has made running code written for other platfroms on the web relativevly easier. +<p>Here, I have compiled a list of some libraries and possible ideas. +I, personally, like static websites which don't require a server side application and can be hosted on platforms like GitHub Pages. +Or, just by opening the HTML file and running it in your browser. +WebAssembly (Wasm) has made running code written for other platforms on the web relatively easier. Combine Wasm with some pure JavaScript libraries, and you get a platform to quickly amp up your speed in some common tasks.</p> <h2>RDKit</h2> <p>RDKit bundles a minimal JavaScript Wrapper in their core RDKit suite. -This is perfect for generating 2D Figures (HTML5 Canva/SVGs), Cannonical SMILES, Descriptors e.t.c</p> +This is perfect for generating 2D Figures (HTML5 Canva/SVGs), Canonical SMILES, Descriptors e.t.c</p> <h3>Substructure Matching</h3> <p>This can be used to flag undesirable functional groups in a given compound. -Create a simple key:value pair of name:SMARTS and use it to highlight substructure matches. +Create a simple key:value pairs of name:SMARTS and use it to highlight substructure matches. Thus, something like PostEra's Medicinal Chemistry Alert can be done with RDKit-JS alone.</p> <p><img src="/assets/posts/cheminformatics-web/postera-demo.png" alt="PostEra Demo" /></p> @@ -3379,22 +3503,22 @@ Thus, something like PostEra's Medicinal Chemistry Alert can be done with RDKit- <p>Obviously, it takes a few hits in the time to complete the docking because the code is transpiled from C++ to Wasm. But, the only major drawback (for now) is that it uses SharedArrayBuffer. Due to Spectre, this feature was disabled on all browsers. -Currently, only Chromium-based and Firefox browsers have reimplemented and renabled it. -Hopefully, soon this will be again supported by all major browsers.</p> +Currently, only Chromium-based and Firefox browsers have reimplemented and enabled it. +Hopefully, soon, this will be again supported by all major browsers.</p> <h2>Machine Learning</h2> <p>Frameworks have now evolved enough to allow exporting models to be able to run them through JavaScript/Wasm backend. An example task can be <strong>NER</strong> or Named-entity Recognition. -It can be used to extract compounds or diseases from a large blob of text and then matched with external refferences. +It can be used to extract compounds or diseases from a large blob of text and then matched with external references. Another example is target-prediction right in the browser: <a rel="noopener" target="_blank" href="http://chembl.blogspot.com/2021/03/target-predictions-in-browser-with.html">CHEMBL - Target Prediction in Browser</a></p> <p>CHEMBL Group is first training the model using PyTorch (A Python ML Library), then converting it to the ONNX runtime. -A model like this can be directly implemented in Tensorflow, and then exported to be able to run with TensorFlow.js</p> +A model like this can be directly implemented in TensorFlow, and then exported to be able to run with TensorFlow.js</p> <h2>Cheminfo-to-web</h2> -<p>The project aims to port chemoinformatics libraries into JavaScript via Emscripten. +<p>The project aims to port cheminformatics libraries into JavaScript via Emscripten. They have ported InChI, Indigo, OpenBabel, and OpenMD</p> <h3>Kekule.js</h3> @@ -3409,7 +3533,7 @@ They have ported InChI, Indigo, OpenBabel, and OpenMD</p> <p>The previous machine learning examples can be packaged as browser-extensions to perform tasks on the article you are reading. With iOS 15 bringing WebExtensions to iOS/iPadOS, the same browser extension source code can be now used on Desktop and Mobile Phones. -You can quickly create an extenison to convert PDB codes into links to RCSB, highlight SMILES, highlight output of NER models, e.t.c</p> +You can quickly create an extension to convert PDB codes into links to RCSB, highlight SMILES, highlight output of NER models, e.t.c</p> <h2>Conclusion</h2> diff --git a/docs/images/04d0580b-d347-476a-232d-9568839851cd.webPlatform.png b/docs/images/04d0580b-d347-476a-232d-9568839851cd.webPlatform.png Binary files differindex 53777c6..b515ebe 100644 --- a/docs/images/04d0580b-d347-476a-232d-9568839851cd.webPlatform.png +++ b/docs/images/04d0580b-d347-476a-232d-9568839851cd.webPlatform.png diff --git a/docs/images/82e24f17-2e71-90d8-67a7-587163282ebf.webPlatform.png b/docs/images/82e24f17-2e71-90d8-67a7-587163282ebf.webPlatform.png Binary files differindex 6fe876b..3f94c60 100644 --- 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differindex 18ba491..27272be 100644 --- a/docs/images/f400aaaa-861c-78c0-0919-07a886e57304.webPlatform.png +++ b/docs/images/f400aaaa-861c-78c0-0919-07a886e57304.webPlatform.png diff --git a/docs/images/f7842765-fff5-aa39-9f7f-fdd3024d4056.webPlatform.png b/docs/images/f7842765-fff5-aa39-9f7f-fdd3024d4056.webPlatform.png Binary files differindex 1b6ddb8..1bae893 100644 --- a/docs/images/f7842765-fff5-aa39-9f7f-fdd3024d4056.webPlatform.png +++ b/docs/images/f7842765-fff5-aa39-9f7f-fdd3024d4056.webPlatform.png diff --git a/docs/index.html b/docs/index.html index c220d2f..56e642e 100644 --- a/docs/index.html +++ b/docs/index.html @@ -45,6 +45,21 @@ <ul> + <li><a href="/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.html">Making a Crude ML Powered Chatbot in Swift using CoreML</a></li> + <ul> + <li>Writing a simple Machine-Learning powered Chatbot (or, daresay virtual personal assistant ) in Swift using CoreML.</li> + <li>Published On: 2021-06-27 23:26</li> + <li>Tags: + + Swift, + + CoreML, + + NLP, + + </ul> + + <li><a href="/posts/2021-06-26-Cheminformatics-On-The-Web-2021.html">Cheminformatics on the Web (2021)</a></li> <ul> <li>Summarising Cheminformatics on the web in 2021.</li> diff --git a/docs/posts/2021-06-26-Cheminformatics-On-The-Web-2021.html b/docs/posts/2021-06-26-Cheminformatics-On-The-Web-2021.html index 885c7b5..3324928 100644 --- a/docs/posts/2021-06-26-Cheminformatics-On-The-Web-2021.html +++ b/docs/posts/2021-06-26-Cheminformatics-On-The-Web-2021.html @@ -41,24 +41,21 @@ <main> <h1>Cheminformatics on the Web (2021)</h1> -<p>Here, I have compiled a list of some tools and possible solutions. -The web is a nice platform, it is available anywhere and just requires an internet connection. -I, personally like static websites which don't require a server side application and can be hosted on platforms like GitHub Pages. -Or, just open the HTML file and run it in your browser. -No data is required to be sent to any server and your device's computational power is used. -Even our phones have a lot of computational power now, which allows the user to run tasks on the go without needing to worry about managing dependencies. -WebAssembly (Wasm) has made running code written for other platfroms on the web relativevly easier. +<p>Here, I have compiled a list of some libraries and possible ideas. +I, personally, like static websites which don't require a server side application and can be hosted on platforms like GitHub Pages. +Or, just by opening the HTML file and running it in your browser. +WebAssembly (Wasm) has made running code written for other platforms on the web relatively easier. Combine Wasm with some pure JavaScript libraries, and you get a platform to quickly amp up your speed in some common tasks.</p> <h2>RDKit</h2> <p>RDKit bundles a minimal JavaScript Wrapper in their core RDKit suite. -This is perfect for generating 2D Figures (HTML5 Canva/SVGs), Cannonical SMILES, Descriptors e.t.c</p> +This is perfect for generating 2D Figures (HTML5 Canva/SVGs), Canonical SMILES, Descriptors e.t.c</p> <h3>Substructure Matching</h3> <p>This can be used to flag undesirable functional groups in a given compound. -Create a simple key:value pair of name:SMARTS and use it to highlight substructure matches. +Create a simple key:value pairs of name:SMARTS and use it to highlight substructure matches. Thus, something like PostEra's Medicinal Chemistry Alert can be done with RDKit-JS alone.</p> <p><img src="/assets/posts/cheminformatics-web/postera-demo.png" alt="PostEra Demo" /></p> @@ -78,22 +75,22 @@ Thus, something like PostEra's Medicinal Chemistry Alert can be done with RDKit- <p>Obviously, it takes a few hits in the time to complete the docking because the code is transpiled from C++ to Wasm. But, the only major drawback (for now) is that it uses SharedArrayBuffer. Due to Spectre, this feature was disabled on all browsers. -Currently, only Chromium-based and Firefox browsers have reimplemented and renabled it. -Hopefully, soon this will be again supported by all major browsers.</p> +Currently, only Chromium-based and Firefox browsers have reimplemented and enabled it. +Hopefully, soon, this will be again supported by all major browsers.</p> <h2>Machine Learning</h2> <p>Frameworks have now evolved enough to allow exporting models to be able to run them through JavaScript/Wasm backend. An example task can be <strong>NER</strong> or Named-entity Recognition. -It can be used to extract compounds or diseases from a large blob of text and then matched with external refferences. +It can be used to extract compounds or diseases from a large blob of text and then matched with external references. Another example is target-prediction right in the browser: <a rel="noopener" target="_blank" href="http://chembl.blogspot.com/2021/03/target-predictions-in-browser-with.html">CHEMBL - Target Prediction in Browser</a></p> <p>CHEMBL Group is first training the model using PyTorch (A Python ML Library), then converting it to the ONNX runtime. -A model like this can be directly implemented in Tensorflow, and then exported to be able to run with TensorFlow.js</p> +A model like this can be directly implemented in TensorFlow, and then exported to be able to run with TensorFlow.js</p> <h2>Cheminfo-to-web</h2> -<p>The project aims to port chemoinformatics libraries into JavaScript via Emscripten. +<p>The project aims to port cheminformatics libraries into JavaScript via Emscripten. They have ported InChI, Indigo, OpenBabel, and OpenMD</p> <h3>Kekule.js</h3> @@ -108,7 +105,7 @@ They have ported InChI, Indigo, OpenBabel, and OpenMD</p> <p>The previous machine learning examples can be packaged as browser-extensions to perform tasks on the article you are reading. With iOS 15 bringing WebExtensions to iOS/iPadOS, the same browser extension source code can be now used on Desktop and Mobile Phones. -You can quickly create an extenison to convert PDB codes into links to RCSB, highlight SMILES, highlight output of NER models, e.t.c</p> +You can quickly create an extension to convert PDB codes into links to RCSB, highlight SMILES, highlight output of NER models, e.t.c</p> <h2>Conclusion</h2> diff --git a/docs/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.html b/docs/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.html new file mode 100644 index 0000000..e4e4d1d --- /dev/null +++ b/docs/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.html @@ -0,0 +1,161 @@ +<!DOCTYPE html> +<html lang="en"> +<head> + + <link rel="stylesheet" href="/assets/main.css" /> + <link rel="stylesheet" href="/assets/sakura.css" /> + <meta charset="utf-8"> + <meta name="viewport" content="width=device-width, initial-scale=1.0"> + <title>Hey - Post</title> + <meta name="og:site_name" content="Navan Chauhan" /> + <link rel="canonical" href="https://navanchauhan.github.io/" /> + <meta name="twitter:url" content="https://navanchauhan.github.io/" /> + <meta name="og:url" content="https://navanchauhan.github.io/" /> + <meta name="twitter:title" content="Hey" /> + <meta name="og:title" content="Hey" /> + <meta name="description" content="Welcome to my personal fragment of the internet." /> + <meta name="twitter:description" content="Welcome to my personal fragment of the internet. Majority of the posts should be complete." /> + <meta name="og:description" content="Welcome to my personal fragment of the internet." /> + <meta name="twitter:card" content="summary" /> + <meta name="viewport" content="width=device-width, initial-scale=1.0" /> + <link rel="shortcut icon" href="/images/favicon.png" type="image/png" /> + <link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan" /> + <meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png" /> + <meta name="og:image" content="https://navanchauhan.github.io/images/logo.png" /> + <link rel="manifest" href="manifest.json" /> + <meta name="google-site-verification" content="LVeSZxz-QskhbEjHxOi7-BM5dDxTg53x2TwrjFxfL0k" /> + <script async src="//gc.zgo.at/count.js" data-goatcounter="https://navanchauhan.goatcounter.com/count"></script> + +</head> +<body> + <nav style="display: block;"> +| +<a href="/">home</a> | +<a href="/about/">about/links</a> | +<a href="/posts/">posts</a> | +<a href="/publications/">publications</a> | +<a href="/repo/">iOS repo</a> | +<a href="/feed.rss">RSS Feed</a> | +</nav> + +<main> + <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> + +</main> + + +<script src="assets/manup.min.js"></script> +<script src="/pwabuilder-sw-register.js"></script> +</body> +</html>
\ No newline at end of file diff --git a/docs/posts/index.html b/docs/posts/index.html index 119b7e6..6ee3224 100644 --- a/docs/posts/index.html +++ b/docs/posts/index.html @@ -48,6 +48,21 @@ <ul> + <li><a href="/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.html">Making a Crude ML Powered Chatbot in Swift using CoreML</a></li> + <ul> + <li>Writing a simple Machine-Learning powered Chatbot (or, daresay virtual personal assistant ) in Swift using CoreML.</li> + <li>Published On: 2021-06-27 23:26</li> + <li>Tags: + + Swift, + + CoreML, + + NLP, + + </ul> + + <li><a href="/posts/2021-06-26-Cheminformatics-On-The-Web-2021.html">Cheminformatics on the Web (2021)</a></li> <ul> <li>Summarising Cheminformatics on the web in 2021.</li> |
