WebsiteAIAssistant 3.1.0

Website AI Assistant

Library built using ML .NET, Microsoft's machine learning platform

Create and your own bespoke AI model and make predictions

AI Assistant helps visitors to your website, narrow down which of the offered products or services suits their needs.

Packages	Version	Downloads
WebsiteAIAssistant.MinimalAPI
WebsiteAIAssistant.AzureFunction
WebsiteAIAssistant.AWSLambda

Overview

Websites usually have sections that contain information about the various products or services offered by the website.

It may contain a list of the different products or services offered, along with descriptions and more information in other pages.

This AI Assistant can help visitors narrow down which of the website's products or services suits their needs,

by classifying the visitor's natural language and/or numeric based input into one of the categories of products or services offered by the website.

You can then provide more information about that category.

The API provided by the library let you create your bespoke AI model based on your training data

and then load your model and make predictions.

Documentation

You can solve real-world AI problems using the library API.

Car Category Classification Model

In this scenario, the classification into a category is done based on text & numeric training data.

The Car categories

public enum CarCategory
{
    None = -1,
    TwoDoorBasic = 0,
    TwoDoorLuxury = 1,               
    FourDoorBasic = 2,
    FourDoorLuxury = 3
}

The training data

It comprises of 2 tab-separated columns.

First is the Label (ie category) column & second is the Feature (text) column.

-1	
0	2 door
0	basic
0	low price $ 20,000
0	mid price $ 25,000
0	high price $ 30,000
1	2 door
1	luxury
1	low price $ 40,000
1	mid price $ 45,000
1	high price $ 50,000
2	4 door
2	basic
2	low price $ 60,000
2	mid price $ 65,000
2	high price $ 70,000
3	4 door
3	luxury
3	low price $ 80,000
3	mid price $ 85,000
3	high price $ 90,000

The bespoke AI model is built using this training data using API provided by the library.

Then, that model is loaded & predictions are made.

The Unit tests on the model

An out of the box, ready to use helper service is provided to load the model & make predictions.

You can see the inputs to the model along with the predicted category.

[Theory]
[InlineData("price $ 42,000", CarCategory.TwoDoorLuxury)]
[InlineData("price $ 39,000", CarCategory.TwoDoorBasic)]
[InlineData("price $ 53,000", CarCategory.TwoDoorLuxury)]
[InlineData("4 door price $ 67,000", CarCategory.FourDoorBasic)]
[InlineData("luxury price $ 88,000", CarCategory.FourDoorLuxury)]
[InlineData("luxury price $ 62,000", CarCategory.TwoDoorLuxury)]
[InlineData("2 door price $ 29,000", CarCategory.TwoDoorBasic)]
[InlineData("low price $ 55,000", CarCategory.TwoDoorLuxury)]
[InlineData("high price $ 34,000", CarCategory.TwoDoorBasic)]
[InlineData("What is the colour of a rose?", CarCategory.None)]
public async Task Load_Predict_Service_CarCategory(string userInput, CarCategory expectedResult)
{
    // Arrange                      
    var aiAssistantService = _aiAssistantServiceProvider!.GetRequiredService<IWebsiteAIAssistantService>();

    var input = new ModelInput { Feature = userInput };

    // Act
    var prediction = await aiAssistantService.PredictAsync(input);

    // Assert
    Assert.NotNull(prediction);
    Assert.Equal(expectedResult, (CarCategory)prediction.PredictedLabel);
}

Read more here