Fix deleted WasmEdge tensorflow plug-in. (#159)

Signed-off-by: YiYing He <yiying@secondstate.io>

Author: q82419

docs/develop/rust/wasinn/mediapipe.md

@@ -63,7 +63,7 @@ DetectionResult:
 
 ## Understand the code
 
-The [main.rs](https://github.com/juntao/demo-object-detection/blob/main/src/main.rs) is the complete example Rust source. 
+The [main.rs](https://github.com/juntao/demo-object-detection/blob/main/src/main.rs) is the complete example Rust source.
 All `mediapipe-rs` APIs follow a common pattern. A Rust struct is designed to work with a model. It contains functions
 required to pre- and post-process data for the model. For example, we can create an `detector` instance
 using the builder pattern, which can build from any "object detection" model in the Mediapipe model library.

docs/develop/rust/wasinn/pytorch.md

@@ -74,7 +74,7 @@ wasmedge --dir .:. out.wasm mobilenet.pt input.jpg
 
 ## Understand the code
 
-The [main.rs](https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/pytorch-mobilenet-image/rust/src/main.rs) is the complete example Rust source. First, read the image file and PyTorch model file names from the command line. 
+The [main.rs](https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/pytorch-mobilenet-image/rust/src/main.rs) is the complete example Rust source. First, read the image file and PyTorch model file names from the command line.
 
 ```rust
 let args: Vec<String> = env::args().collect();

docs/develop/rust/wasinn/tensorflow_lite.md

@@ -74,7 +74,7 @@ wasmedge --dir .:. out.wasm lite-model_aiy_vision_classifier_birds_V1_3.tflite b
 
 ## Understand the code
 
-The [main.rs](https://github.com/second-state/WasmEdge-WASINN-examples/blob/master/tflite-birds_v1-image/rust/tflite-bird/src/main.rs) is the complete example Rust source. First, read the image file and Tensorflow Lite (tflite) model file names from the command line. 
+The [main.rs](https://github.com/second-state/WasmEdge-WASINN-examples/blob/master/tflite-birds_v1-image/rust/tflite-bird/src/main.rs) is the complete example Rust source. First, read the image file and Tensorflow Lite (tflite) model file names from the command line.
 
 ```rust
 let args: Vec<String> = env::args().collect();

i18n/zh/docusaurus-plugin-content-docs/current/develop/rust/tensorflow.md renamed to docs/develop/rust/wasinn/tf_plugin.md

@@ -1,14 +1,19 @@
 ---
-sidebar_position: 7
+sidebar_position: 5
 ---
 
-# TensorFlow Interface
+# TensorFlow Plug-in For WasmEdge
 
-Developers can use [WASI-NN](/category/neural-networks-for-wasi) to inference the models. However, for the TensorFlow and TensorFlow-Lite users, the WASI-NN APIs could be more friendly to retrieve the input and output tensors. Therefore WasmEdge provides the TensorFlow-related plug-in and rust SDK for inferencing models in WASM.
+Developers can use [WASI-NN](https://github.com/WebAssembly/wasi-nn) to inference the models. However, for the TensorFlow and TensorFlow-Lite users, the WASI-NN APIs could be more friendly to retrieve the input and output tensors. Therefore WasmEdge provides the TensorFlow-related plug-in and rust SDK for inferencing models in WASM.
+
+<!-- prettier-ignore -->
+:::info
+This is not a WASI-NN compatible plug-in. If you are finding the plug-ins working with the [WASI-NN crate](https://crates.io/crates/wasi-nn), please follow the [tensorflow-lite backend](tensorflow_lite.md) instead.
+:::
 
 ## Prerequisite
 
-Please ensure that you [Rust and WasmEdge installed](setup.md).
+Please ensure that you [Rust and WasmEdge installed](../setup.md).
 
 Developers will add the [`wasmedge_tensorflow_interface` crate](https://crates.io/crates/wasmedge_tensorflow_interface) as a dependency to their `Rust -> Wasm` applications. For example, add the following line to the application's `Cargo.toml` file.
 
@@ -27,7 +32,7 @@ use wasmedge_tensorflow_interface;
 
 In this crate, we provide several functions to decode and convert images into tensors using the `WasmEdge-Image` host functions.
 
-To use these functions in WASM and execute in WasmEdge, users should [install WasmEdge with WasmEdge-Image plug-in](../../start/install.md#wasmedge-image-plug-in).
+To use these functions in WASM and execute in WasmEdge, users should [install WasmEdge with WasmEdge-Image plug-in](../../../start/install.md#wasmedge-image-plug-in).
 
 For decoding the `JPEG` images, there are:
 
@@ -67,9 +72,9 @@ let flat_img = wasmedge_tensorflow_interface::load_jpg_image_to_rgb32f(&img_buf,
 
 ## Inferring TensorFlow And TensorFlow-Lite Models
 
-For using the `TFSession` struct to inference the TensorFlow models and executing in WasmEdge, users should install the [WasmEdge-TensorFlow plug-in with dependencies](../../start/install.md#wasmedge-tensorflow-plug-in).
+For using the `TFSession` struct to inference the TensorFlow models and executing in WasmEdge, users should install the [WasmEdge-TensorFlow plug-in with dependencies](../../../start/install.md#wasmedge-tensorflow-plug-in).
 
-For using the `TFLiteSession` struct and to inference the TensorFlow-Lite models executing in WasmEdge, users should install the [WasmEdge-TensorFlowLite plug-in with dependencies](../../start/install.md#wasmedge-tensorflow-lite-plug-in).
+For using the `TFLiteSession` struct and to inference the TensorFlow-Lite models executing in WasmEdge, users should install the [WasmEdge-TensorFlowLite plug-in with dependencies](../../../start/install.md#wasmedge-tensorflow-lite-plug-in).
 
 ### Create Session
 
@@ -125,7 +130,7 @@ cargo build --target=wasm32-wasi
 
 The output WASM file will be at `target/wasm32-wasi/debug/` or `target/wasm32-wasi/release`.
 
-Please refer to [WasmEdge CLI](../../start/build-and-run/cli.md) for WASM execution.
+Please refer to [WasmEdge CLI](../../../start/build-and-run/cli.md) for WASM execution.
 
 ## Examples
 

docs/embed/go/ai.md

@@ -9,28 +9,23 @@ sidebar_position: 5
 The WasmEdge extensions have been deprecated after the v0.12.1 version. We'll update to use the WasmEdge plug-in in the future.
 :::
 
-In this section, we will show you how to create a TensorFlow inference function in Rust for image classification and then embed it into a Go application. The project source code is [available here](https://github.com/second-state/WasmEdge-go-examples/tree/master/wasmedge-bindgen/go_TfliteFood).
+In this section, we will show you how to create a TensorFlow or TensorFlow-Lite inference function in Rust for image classification and then embed it into a Go application. The project source code is [available here](https://github.com/second-state/WasmEdge-go-examples/blob/master/go_TfliteFood/).
 
 ## The WASM app in Rust
 
-The Rust function for image classification is [available here](https://github.com/second-state/WasmEdge-go-examples/blob/master/wasmedge-bindgen/go_TfliteFood/rust_tflite_food/src/lib.rs). It utilizes the WasmEdge Tensorflow Lite plug-in as well as the [wasmedge_bindgen](function.md) for passing call parameters.
+The Rust function for image classification is [available here](https://github.com/second-state/WasmEdge-go-examples/blob/master/go_TfliteFood/rust_tflite_food/src/lib.rs). It utilizes the WasmEdge Tensorflow Lite plug-in as well as the [wasmedge_bindgen](function.md) for passing call parameters.
 
 ```rust
 #[wasmedge_bindgen]
 fn infer(image_data: Vec<u8>) -> Result<Vec<u8>, String> {
-  ... ...
+  let img = image::load_from_memory(&image_data).unwrap().to_rgb8();
   let flat_img = image::imageops::thumbnail(&img, 192, 192);
 
   let model_data: &[u8] = include_bytes!("lite-model_aiy_vision_classifier_food_V1_1.tflite");
   let labels = include_str!("aiy_food_V1_labelmap.txt");
 
-  let mut session = wasmedge_tensorflow_interface::Session::new(
-    model_data,
-    wasmedge_tensorflow_interface::ModelType::TensorFlowLite,
-  );
-  session
-    .add_input("input", &flat_img, &[1, 192, 192, 3])
-    .run();
+  let mut session = wasmedge_tensorflow_interface::TFLiteSession::new(model_data);
+  session.add_input("input", &flat_img).run();
   let res_vec: Vec<u8> = session.get_output("MobilenetV1/Predictions/Softmax");
   ... ...
 }
@@ -42,7 +37,7 @@ You can build the standard `Cargo` command into a WebAssembly function.
 
 ```bash
 git clone https://github.com/second-state/WasmEdge-go-examples.git
-cd rust_tflite_food
+cd go_TfliteFood/rust_tflite_food
 cargo build --target wasm32-wasi --release
 cp target/wasm32-wasi/release/rust_tflite_food_lib.wasm ../
 cd ../
@@ -56,54 +51,66 @@ wasmedge compile rust_tflite_food_lib.wasm rust_tflite_food_lib.wasm
 
 ## Go host app
 
-The [Go host app](https://github.com/second-state/WasmEdge-go-examples/blob/master/wasmedge-bindgen/go_TfliteFood/tflite_food.go) source code shows how to instantiate a WasmEdge runtime with the Tensorflow extension, and how to pass the image data to the Rust function in WasmEdge to run the inference.
+The [Go host app](https://github.com/second-state/WasmEdge-go-examples/blob/master/go_TfliteFood/tflite_food.go) source code shows how to instantiate a WasmEdge runtime with the Tensorflow extension, and how to pass the image data to the Rust function in WasmEdge to run the inference.
 
 ```go
+import (
+  "fmt"
+  "io/ioutil"
+  "os"
+
+  "github.com/second-state/WasmEdge-go/wasmedge"
+  bindgen "github.com/second-state/wasmedge-bindgen/host/go"
+)
+
 func main() {
-  // Expected Args[0]: program name (./tflite_food)
-  // Expected Args[1]: wasm file (rust_tflite_food_lib.wasm)
-  // Expected Args[2]: input image name (food.jpg)
+  fmt.Println("Go: Args:", os.Args)
+  // Expected Args[0]: program name (./mobilenet)
+  // Expected Args[1]: wasm file (rust_mobilenet_lib.wasm)
+  // Expected Args[2]: input image name (grace_hopper.jpg)
 
+  // Set not to print debug info
   wasmedge.SetLogErrorLevel()
 
   // Set Tensorflow not to print debug info
   os.Setenv("TF_CPP_MIN_LOG_LEVEL", "3")
   os.Setenv("TF_CPP_MIN_VLOG_LEVEL", "3")
 
+  // Load WasmEdge-image and WasmEdge-tensorflow from default path
+  wasmedge.LoadPluginDefaultPaths()
+
+  // Create configure
   var conf = wasmedge.NewConfigure(wasmedge.WASI)
+
+  // Create VM with configure
   var vm = wasmedge.NewVMWithConfig(conf)
+
+  // Init WASI
   var wasi = vm.GetImportModule(wasmedge.WASI)
   wasi.InitWasi(
     os.Args[1:],     // The args
     os.Environ(),    // The envs
     []string{".:."}, // The mapping preopens
   )
 
-  // Register WasmEdge-tensorflow
-  var tfmod = wasmedge.NewTensorflowModule()
-  var tflitemod = wasmedge.NewTensorflowLiteModule()
-  vm.RegisterModule(tfmod)
-  vm.RegisterModule(tflitemod)
-
   // Load and validate the wasm
   vm.LoadWasmFile(os.Args[1])
   vm.Validate()
 
   // Instantiate the bindgen and vm
-  bg := bindgen.Instantiate(vm)
+  bg := bindgen.New(vm)
+  bg.Instantiate()
 
   img, _ := ioutil.ReadFile(os.Args[2])
-  if res, err := bg.Execute("infer", img); err != nil {
+  if res, _, err := bg.Execute("infer", img); err != nil {
     fmt.Println(err)
   } else {
-    fmt.Println(string(res))
+    fmt.Println(res[0].(string))
   }
 
   bg.Release()
   vm.Release()
   conf.Release()
-  tfmod.Release()
-  tflitemod.Release()
 }
 ```
 
@@ -114,10 +121,10 @@ func main() {
 Ensure you have installed [Go, WasmEdge, and WasmEdge Go SDK with the TensorFlow extension](intro.md).
 :::
 
-The following command builds the Go host application with the WasmEdge Go SDK and its TensorFlow extension.
+Builds the Go host application with the WasmEdge Go SDK.
 
 ```bash
-go build -tags tensorflow
+go build
 ```
 
 Now you can run the Go application. It calls the WebAssembly function in WasmEdge to run inference on the input image.

docs/embed/go/intro.md

@@ -34,7 +34,7 @@ go build
 
 <!-- prettier-ignore -->
 :::note
-The WasmEdge extensions have been deprecated after the 0.12.1 version. We'll update to use the WasmEdge plug-in in the future.
+The WasmEdge extensions have been deprecated after the 0.12.1 version. Please use the corresponding plug-ins after the 0.13.0 version.
 :::
 
 By default, the `WasmEdge-go` only turns on the basic runtime.

docs/start/install.md

@@ -217,6 +217,34 @@ Then, go to [WASI-Crypto in Rust chapter](../develop/rust/wasicrypto.md) to see
 
 The wasmEdge-Image plug-in can help developers to load and decode JPEG and PNG images and convert into tensors. To install this plug-in, please use the `--plugins wasmedge_image` parameter when [running the installer command](#generic-linux-and-macos).
 
+Then, go to [TensorFlow interface (image part) in Rust chapter](../develop/rust/wasinn/tf_plugin.md#image-loading-and-conversion) to see how to run `WasmEdge-Image` functions.
+
+### WasmEdge TensorFlow Plug-in
+
+WasmEdge-TensorFlow plug-in can help developers to perform `TensorFlow` model inference as the similar API in python. To install this plug-in, please use the `--plugins wasmedge_tensorflow` parameter when [running the installer command](#generic-linux-and-macos).
+
+The WasmEdge-Tensorflow plug-in depends on the `libtensorflow_cc` shared library.
+
+<!-- prettier-ignore -->
+:::note
+If you install this plug-in WITHOUT installer, you can [refer to here to install the dependency](#tensorflow-dependencies).
+:::note
+
+Then, go to [TensorFlow interface in Rust chapter](../develop/rust/wasinn/tf_plugin.md) to see how to run `WasmEdge-TensorFlow` functions.
+
+### WasmEdge TensorFlow-Lite Plug-in
+
+The wasmEdge-TensorFlowLite plug-in can help developers to perform `TensorFlow-Lite` model inference as the similar API in python. To install this plug-in, please use the `--plugins wasmedge_tensorflowlite` parameter when [running the installer command](#generic-linux-and-macos).
+
+The WasmEdge-TensorflowLite plug-in depends on the `libtensorflowlite_c` shared library to perform AI/ML computations, and it will be installed by the installer automatically.
+
+<!-- prettier-ignore -->
+:::note
+If you install this plug-in WITHOUT installer, you can [refer to here to install the dependency](#tensorflow-lite-dependencies).
+:::note
+
+Then, go to [TensorFlow interface in Rust chapter](../develop/rust/wasinn/tf_plugin.md) to see how to run `WasmEdge-TensorFlowLite` functions.
+
 ## Install WasmEdge extensions and dependencies
 
 <!-- prettier-ignore -->

i18n/zh/docusaurus-plugin-content-docs/current/contribute/plugin/develop_plugin_cpp.md

@@ -62,7 +62,7 @@ To create a plug-in with host functions and modules, follow these steps:
   ```cpp
   #pragma once
 
-  #include "plug-in/plug-in.h"
+  #include "plugin/plugin.h"
 
   #include <cstdint>
   #include <string>
@@ -228,9 +228,6 @@ To build the plug-in shared library, developers should build in CMake with the W
   ```cmake
   wasmedge_add_library(wasmedgePluginTest
     SHARED
-    env.cpp
-    func.cpp
-    module.cpp
     testplugin.cpp
   )
 

i18n/zh/docusaurus-plugin-content-docs/current/develop/javascript/hello_world.md

@@ -19,7 +19,7 @@ Then download the pre-built WasmEdge QuickJS Runtime program, and optionally, AO
 
 ```bash
 curl -OL https://github.com/second-state/wasmedge-quickjs/releases/download/v0.5.0-alpha/wasmedge_quickjs.wasm
-wasmedgec wasmedge_quickjs.wasm wasmedge_quickjs.wasm
+wasmedge compile wasmedge_quickjs.wasm wasmedge_quickjs.wasm
 ```
 
 <!-- prettier-ignore -->

i18n/zh/docusaurus-plugin-content-docs/current/develop/javascript/networking.md

@@ -8,15 +8,16 @@ The QuickJS WasmEdge Runtime supports Node.js's `http` and `fetch` APIs via the
 
 The networking API in WasmEdge is non-blocking and hence supports asynchronous I/O-intensive applications. With this API, the JavaScript program can open multiple connections concurrently. It polls those connections or registers async callback functions to process data whenever data comes in, without waiting for any one connection to complete its data transfer. That allows the single-threaded application to handle multiple, multiple concurrent requests.
 
-- [Networking](#networking)
-  - [Prerequisites](#prerequisites)
-  - [Fetch client](#fetch-client)
-  - [HTTP server](#http-server)
-  - [TCP server and client](#tcp-server-and-client)
+- [Prerequisites](#prerequisites)
+- [Fetch client](#fetch-client)
+- [HTTP server](#http-server)
+- [TCP server and client](#tcp-server-and-client)
 
 ## Prerequisites
 
-[See here](./hello_world#prerequisites)
+[Install WasmEdge](../../start/install.md). To make HTTPS requests, install the [WasmEdge TLS plug-in](../../start/install.md#tls-plug-in).
+
+[Install WasmEdge-QuickJS](./hello_world#prerequisites). Make sure that the `modules` directory is located in your local directory where you want to execute the `wasmedge` command.
 
 ## Fetch client