# Use TensorFlow Lite on Nerves Livebook

```elixir
Mix.install([
  {:tflite_elixir, "~> 0.3.0"},
  {:req, "~> 0.4"},
  {:kino, "~> 0.13"}
])
```

## Introduction

TensorFlow Lite is a stripped-down version of
[TensorFlow](https://en.wikipedia.org/wiki/TensorFlow), a free and open-source
software library for machine learning and artificial intelligence.

In Elixir, we can use TensorFlow Lite through the
[`tflite_elixir`](https://github.com/cocoa-xu/tflite_elixir) package, which
does the TensorFlow Lite Elixir bindings with optional [Edge
TPU](https://en.wikipedia.org/wiki/Tensor_Processing_Unit#Edge_TPU) support.

In this notebook, we will perform image classification with pre-trained
[mobilenet_v2_1.0_224_inat_bird_quant.tflite](https://github.com/google-coral/edgetpu/blob/master/test_data/mobilenet_v2_1.0_224_inat_bird_quant.tflite)
model. The example code below is based on the instructions in the
[tflite_elixir
README](https://github.com/cocoa-xu/tflite_elixir/blob/main/README.md). For
more information, check out the [tflite_elixir API
reference](https://hexdocs.pm/tflite_elixir/api-reference.html).

## Decide on where downloaded files are saved

```elixir
downloads_dir = "/data/tmp"
File.mkdir_p!(downloads_dir)
```

## Download pre-trained model

```elixir
model_source =
  "https://raw.githubusercontent.com/google-coral/test_data/master/mobilenet_v2_1.0_224_inat_bird_quant.tflite"

model_file = Path.join(downloads_dir, "mobilenet_v2_1.0_224_inat_bird_quant.tflite")
Req.get!(model_source, into: File.stream!(model_file))
IO.puts("Model saved to #{model_file}")
```

## Download labels

```elixir
label_source =
  "https://raw.githubusercontent.com/google-coral/test_data/master/inat_bird_labels.txt"

labels = Req.get!(label_source).body |> String.split("\n", trim: true)
Kino.DataTable.new(Enum.with_index(labels, &%{class_name: &1, class_id: &2}), name: "Labels")
```

## Choose image to be classified

An input image can be uploaded here, or default parrot image will be used.

```elixir
image_input = Kino.Input.image("Image", size: {224, 224})
```

```elixir
uploaded_image = Kino.Input.read(image_input)

default_input_image_url =
  "https://raw.githubusercontent.com/google-coral/test_data/master/parrot.jpg"

input_image =
  if uploaded_image do
    # Build a tensor from the raw pixel data
    uploaded_image.file_ref
    |> Kino.Input.file_path()
    |> File.read!()
    |> Nx.from_binary(:u8)
    |> Nx.reshape({uploaded_image.height, uploaded_image.width, 3})
  else
    IO.puts("Loading default image from #{default_input_image_url}")

    Req.get!(default_input_image_url).body
    |> StbImage.read_binary!()
    |> StbImage.to_nx()
  end

Kino.Image.new(input_image)
```

## Classify image

```elixir
how_many_results = 3
labels = List.to_tuple(labels)

input_nx =
  input_image
  |> StbImage.from_nx()
  |> StbImage.resize(224, 224)
  |> StbImage.to_nx()

interpreter = TFLiteElixir.Interpreter.new!(model_file)
[output_tensor_0] = TFLiteElixir.Interpreter.predict(interpreter, input_nx[[.., .., 0..2]])
indices_nx = Nx.flatten(output_tensor_0)

class_ids =
  indices_nx
  |> Nx.argsort(direction: :desc)
  |> Nx.take(Nx.iota({how_many_results}))
  |> Nx.to_flat_list()

class_ids
|> Enum.map(fn class_id -> %{class_id: class_id, class_name: elem(labels, class_id)} end)
|> Kino.DataTable.new(name: "Inference results")
```

## Next steps

### Run other models

You can find a variety of pre-trained open-source models in [TensorFlow
Hub](https://tfhub.dev). For Elixir code, check out [example
notebooks](https://github.com/cocoa-xu/tflite_elixir/blob/bda47628e143c860e8cc796f491edd49260b787b/notebooks/README.md)
in `tflite_elixir` repository.

In case some example notebooks require the
[`evision`](https://github.com/cocoa-xu/evision) package for using
[OpenCV](https://opencv.org), add it to your Nerves project's `mix.exs` file
and rebuild Nerves firmware.

### Run inference on Edge TPU

You can speed up model inference time, running a TensorFlow Lite model on the
Edge TPU. Check out `tflite_elixir`'s ["Inference on TPU"
example](https://github.com/cocoa-xu/tflite_elixir/blob/main/notebooks/tpu.livemd).