blazor-ml

Example application that shows how to integrate a Blazor server-side application with ML.NET

Users can upload images using the Blazor application, which are classified into one of 1000 labels using the ML.NET model.
The ML.NET model simply loads a pre-trained TensorFlow Google's inception model (available here)

The application is discussed in detail in the article Using Blazor, Tensorflow and ML.NET to Identify Images, published in the DotNetCurry magazine.

Installation

Make sure you have the .NET Core SDK 5.0 installed (download here) and clone this repo.

You will see a solution with 2 projects:

The ModelBuilder is used to generate the ML.NET model, which is then saved to a ZIP file for the Blazor app to classify uploaded images
```
cd ModelBuilder
dotnet build
dotnet run
# ML.NET model's zip saved to PredictionModel.zip
```
The BlazorClient provides the server-side Blazor app. Navigate the the Identify Image page (or /identify) to upload an image (using the InputFile component), which will be classified using the previously saved ML.NET model
```
  cd BlazorClient
  dotnet build
  dotnet run
  # navigate to https://localhost:5001/identify
```

There are sample images in the SampleImages folder (see wikimedia.md for attribution) which you can use to test the application

Mac/Linux users

The code relies on System.Drawing in order to convert an image into a bitmap. While System.Drawing is now part of .NET Core you will probably need to install its GDI+ dependencies:

# Linux
sudo apt install libc6-dev
sudo apt install libgdiplus
# Mac
brew install mono-libgdiplus

Feel free to try and use alternatives to System.Drawing such as ImageSharp.

Reference

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Example combining Blazor with ML.NET

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