From be97988c30c526746c5411709768efec74105dad Mon Sep 17 00:00:00 2001
From: Mathieu Dejean-Servieres <mdejeans@proton.me>
Date: Mon, 2 Sep 2024 19:37:03 +0200
Subject: [PATCH] chore(melos): update readme and add melos for FFI code
 generation

---
 README.md  | 44 ++++++++++++++++++++++++++++++--------------
 melos.yaml |  6 ++++++
 2 files changed, 36 insertions(+), 14 deletions(-)

diff --git a/README.md b/README.md
index a0a3144..0d3bd90 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@
     </br>
 </p>
 <p align="center">
- 
+
    <a href="https://flutter.dev">
      <img src="https://img.shields.io/badge/Platform-Flutter-02569B?logo=flutter"
        alt="Platform" />
@@ -18,7 +18,6 @@
     </a>
     <a href="https://opensource.org/licenses/Apache-2.0"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg"></a>
 
-
 </a>
 </p>
 
@@ -40,16 +39,14 @@ Thanks!
 
 TensorFlow Lite Flutter plugin provides a flexible and fast solution for accessing TensorFlow Lite interpreter and performing inference. The API is similar to the TFLite Java and Swift APIs. It directly binds to TFLite C API making it efficient (low-latency). Offers acceleration support using NNAPI, GPU delegates on Android, Metal and CoreML delegates on iOS, and XNNPack delegate on Desktop platforms.
 
-
 ## Key Features
 
-* Multi-platform Support for Android and iOS
-* Flexibility to use any TFLite Model.
-* Acceleration using multi-threading.
-* Similar structure as TensorFlow Lite Java API.
-* Inference speeds close to native Android Apps built using the Java API.
-* Run inference in different isolates to prevent jank in UI thread.
-
+- Multi-platform Support for Android and iOS
+- Flexibility to use any TFLite Model.
+- Acceleration using multi-threading.
+- Similar structure as TensorFlow Lite Java API.
+- Inference speeds close to native Android Apps built using the Java API.
+- Run inference in different isolates to prevent jank in UI thread.
 
 ## (Important) Initial setup : Add dynamic libraries to your app
 
@@ -133,7 +130,7 @@ install(
 
 ## TFLite Flutter Helper Library
 
-The helper library has been deprecated. New development underway for a replacement at https://github.com/google/flutter-mediapipe. Current timeline is to have wide support by the end of August, 2023.
+The helper library has been deprecated. New development underway for a replacement at <https://github.com/google/flutter-mediapipe>. Current timeline is to have wide support by the end of August, 2023.
 
 ## Import
 
@@ -144,11 +141,12 @@ import 'package:tflite_flutter/tflite_flutter.dart';
 ## Usage instructions
 
 ### Import the libraries
+
 In the dependency section of `pubspec.yaml` file, add `tflite_flutter: ^0.10.1` (adjust the version accordingly based on the latest release)
 
 ### Creating the Interpreter
 
-* **From asset**
+- **From asset**
 
     Place `your_model.tflite` in `assets` directory. Make sure to include assets in `pubspec.yaml`.
 
@@ -160,9 +158,10 @@ Refer to the documentation for info on creating interpreter from buffer or file.
 
 ### Performing inference
 
-* **For single input and output**
+- **For single input and output**
 
     Use `void run(Object input, Object output)`.
+
     ```dart
     // For ex: if input tensor shape [1,5] and type is float32
     var input = [[1.23, 6.54, 7.81, 3.21, 2.22]];
@@ -177,7 +176,7 @@ Refer to the documentation for info on creating interpreter from buffer or file.
     print(output);
     ```
   
-* **For multiple inputs and outputs**
+- **For multiple inputs and outputs**
 
     Use `void runForMultipleInputs(List<Object> inputs, Map<int, Object> outputs)`.
 
@@ -225,3 +224,20 @@ await isolateInterpreter.runForMultipleInputs(inputs, outputs);
 ```
 
 By using `IsolateInterpreter`, the inference runs in a separate isolate. This ensures that the main isolate, responsible for UI tasks, remains unblocked and responsive.
+
+## Contribute to this package
+
+This package is managed using [melos](https://pub.dev/packages/melos). Before starting to work on the project, make sure to run the bootstrap command.
+
+```sh
+dart pub global activate melos # Install or activate melos globally
+melos bootstrap # Initialize the workspace and bootstrap the package
+```
+
+### Generated code
+
+This package uses [ffigen](https://pub.dev/packages/ffigen) to generate FFI bindings. To run code generation, you can use the following melos command:
+
+```sh
+melos run ffigen 
+```
diff --git a/melos.yaml b/melos.yaml
index 4242060..df79663 100644
--- a/melos.yaml
+++ b/melos.yaml
@@ -27,3 +27,9 @@ command:
     branch: main
     linkToCommits: true
     workspaceChangelog: true
+
+scripts:
+  ffigen:
+    name: ffigen
+    description: Call ffigen and generate bindings for `src/tensorflow_lite/c_api.h`.
+    run: flutter pub run ffigen --config ffigen_tensorflow_lite.yaml
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