fix(doc): add tutorial for tfLite plugin

Signed-off-by: Jianxiang Ran <rxan_embedded@163.com>

docs/en_US/guide/ai/tensorflow_lite.md

@@ -0,0 +1,162 @@
+# Run TensorFlow Lite model with eKuiper function plugin
+
+[LF Edge eKuiper](https://www.lfedge.org/projects/ekuiper/) is an edge lightweight IoT data analytics / streaming
+software which can be run at all kinds of resource constrained IoT devices.
+
+[TensorFlow Lite](https://www.tensorflow.org/lite/guide) is a set of tools to help developers run TensorFlow models on
+mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary
+size.
+
+
+By integrating eKuiper and TensorFlow Lite, users only need to upload a pre-built TensorFlow model, which can be used in rules to analyze data in the flow. In this tutorial, we will demonstrate how to quickly call a pre-trained TensorFlow model through ekuiper.
+
+
+## Prerequisite
+
+### Model Download
+
+To run the TensorFlow Lite interpreter, we need a trained model. In this tutorial, we will not describe how to train and cover this model, you can see how to do it by looking at [tflite converter](https://www.tensorflow.org/lite/convert). We can either train a new model or select a trained model online.
+In this tutorial we will use the [sin](https://github.com/mattn/go-tflite/tree/master/_example/sin) model and [mobilenet_v1_1.0_224](https://tfhub.dev/tensorflow/lite-model/mobilenet_v1_1.0_224/1/default/1) demo.
+
+### eKuiper Start up
+
+This tutorial uses the eKuiper Docker image `lfedge/ekuiper:1.8.0-slim` and the eKuiper manager Docker image `emqx/ekuiper-manager:1.8.0` released by the team to demonstrate. Please refer to [here]( https://hub.docker.com/r/emqx/ekuiper-manager) on how to use them.
+
+### TensorFlow Lite Plugin Download
+
+
+TensorFlow Lite is provided as a precompiled plug-in, and users need to download and install it themselves.
+
+![download plugin](../../resources/tflite_install.png)
+![symbol register](../../resources/tflite_register.png)
+
+## Sin Mode Set up
+
+Please download the [sin model](https://github.com/mattn/go-tflite/blob/master/_example/sin/sin_model.tflite), which returns inference results based on input values. For example, if the user enters π/2, let us take 1.57, the calculation result of sin 1.57 is about 1.
+Users need to prepare MQTT Broker and create an MQTT source to send data to be processed to eKuiper rule and send inference results back to MQTT Broker.
+
+### MQTT Source
+
+Note that the model input data format must be a byte array, and json does not support the byte type, so the data type needs to be specified in the data source, and the source will preprocess it into a byte array.
+![stream set up](../../resources/stream_byte.png)
+
+### Model Upload
+
+Users can upload model files to eKuiper through eKuiper manager. As shown below.
+![model upload](../../resources/sin_upload.png)
+
+
+### Call Model in TensorFlow Lite
+
+After users install the TensorFlow Lite plugin, they can call the model in SQL as normal built-in functions. The first parameter is the model name, and the second parameter is the data to be processed.
+![call model](../../resources/tflite_sin_rule.png)
+
+### Validation results
+
+The result is shown in the figure below, when the input is 1.57, the derivation result is about 1.
+![result check](../../resources/mqttx_sin.png)
+
+
+## MobileNet V1 Model Set up
+
+Please download the [MobileNet V1 model](https://tfhub.dev/tensorflow/lite-model/mobilenet_v1_1.0_224/1/default/1), this model inputs image information of 224 * 224 pixels, and returns a size of 1001 float array.
+In order to obtain image information, we use another precompiled video plug-in to regularly extract images from the live stream as the input of the rules, and send the inference results to the MQTT Broker.
+
+### video source install and configure
+
+The video source periodically pulls data from the live source and extracts image data from it. [This link](https://gcwbcdks.v.kcdnvip.com/gcwbcd/cdrmipanda_1/index.m3u8) can be used as the live broadcast source. The figure below shows the video plug-in download and configuration respectively.
+
+![video install](../../resources/video_install.png)
+![video config](../../resources/video_config.png)
+
+*Note*: The source data format part should be selected as binary type.
+
+### image function plugin install
+
+Since the precompiled model requires 224 * 224 pixel image data, another precompiled plugin image needs to be installed to resize the image.
+
+![image install](../../resources/image_install.png)
+![resize register](../../resources/image_register.png)
+
+
+### Model Upload
+
+Users can upload model files to eKuiper through eKuiper manager. As shown below.
+![model upload](../../resources/mobilenet_upload.png)
+
+
+### Call Model in TensorFlow Lite
+
+After users install the TensorFlow Lite plugin, they can call the model in SQL as normal built-in functions. The first parameter is the model name, and the second parameter is the return result of calling the resize function. Where `self` is the key corresponding to the binary data.
+![call model](../../resources/tflite_resize_rule.png)
+
+### Validation results
+
+The result is shown in the figure below. After the image data is inferred, the returned result is a byte array (encoded by base64).
+![check result](../../resources/mqttx_mobilenet.png)
+
+The following is the byte array obtained after base64 decoding, with a total of 1001 elements. Its significance needs to be interpreted in conjunction with the model being tested.
+In this example, the test model is an image recognition model, which supports a total of 1001 item classifications, so the 1001 elements in the derivation result correspond to the 1001 items in sequence. For example, the first array element matches the first item, and the value of the element represents the matching degree, and the larger the value, the higher the matching degree.
+A list of items is available [here](https://github.com/lf-edge/ekuiper/blob/master/extensions/functions/labelImage/etc/labels.txt).
+![result](../../resources/tflite_image_result.png)
+
+Users can write code to filter out the item tags with the highest matching degree, the following is the sample code
+
+```go
+package demo
+
+import (
+	"bufio"
+	"os"
+	"sort"
+)
+
+func loadLabels() ([]string, error) {
+	labels := []string{}
+	f, err := os.Open("./labels.txt")
+	if err != nil {
+		return nil, err
+	}
+	defer f.Close()
+	scanner := bufio.NewScanner(f)
+	for scanner.Scan() {
+		labels = append(labels, scanner.Text())
+	}
+	return labels, nil
+}
+
+type result struct {
+	score float64
+	index int
+}
+
+func bestMatchLabel(keyValue map[string]interface{}) (string, bool) {
+	labels, _ := loadLabels()
+	resultArray := keyValue["tfLite"].([]interface{})
+	outputArray := resultArray[0].([]byte)
+	outputSize := len(outputArray)
+	
+	var results []result
+	for i := 0; i < outputSize; i++ {
+		score := float64(outputArray[i]) / 255.0
+		if score < 0.2 {
+			continue
+		}
+		results = append(results, result{score: score, index: i})
+	}
+	sort.Slice(results, func(i, j int) bool {
+		return results[i].score > results[j].score
+	})
+	// output is the biggest score labelImage
+	if len(results) > 0 {
+		return labels[results[0].index], true
+	} else {
+		return "", true
+	}
+
+}
+```
+
+## in conclusion
+
+In this tutorial, we use the pre-compiled TensorFlow Lite plugin to directly call the pre-trained TensorFlow Lite model in ekuiper, which avoids writing code and simplifies the inference steps.

docs/zh_CN/guide/ai/tensorflow_lite.md

@@ -48,7 +48,7 @@ TensorFlow Lite 以预编译插件的形式提供，用户需自行下载安装
 
 ### 验证结果
 
-结果如下图所示，当输入为 1.57 时，推倒结果约为 1。
+结果如下图所示，当输入为 1.57 时，推导结果约为 1。
 ![结果查询](../../resources/mqttx_sin.png)
 
 
@@ -88,6 +88,68 @@ video 源定期从直播源中拉取数据并从中抽取图片数据。直播
 结果如下图所示，图片数据经过推理后，返回结果为字节数组(经过 base64 编码)。
 ![结果查询](../../resources/mqttx_mobilenet.png)
 
+以下为经过 base64 译码后得到的字节数组，共有 1001 个元素。需要结合所测试模型来解释其意义。 
+在本例中，测试模型为图像识别模型，此模型共支持 1001 种物品分类，因此推导结果中的 1001 个元素与 1001 种物品按照顺序一一对应。例如第一个数组元素匹配第一个物品，其中元素的值代表匹配程度，值越大匹配度越高。
+物品的列表可从[此处](https://github.com/lf-edge/ekuiper/blob/master/extensions/functions/labelImage/etc/labels.txt)获得。
+![结果解释](../../resources/tflite_image_result.png)
+
+用户可编写代码从中筛选出匹配度最高的物品标签,以下为示例代码
+
+```go
+package demo
+
+import (
+	"bufio"
+	"os"
+	"sort"
+)
+
+func loadLabels() ([]string, error) {
+	labels := []string{}
+	f, err := os.Open("./labels.txt")
+	if err != nil {
+		return nil, err
+	}
+	defer f.Close()
+	scanner := bufio.NewScanner(f)
+	for scanner.Scan() {
+		labels = append(labels, scanner.Text())
+	}
+	return labels, nil
+}
+
+type result struct {
+	score float64
+	index int
+}
+
+func bestMatchLabel(keyValue map[string]interface{}) (string, bool) {
+	labels, _ := loadLabels()
+	resultArray := keyValue["tfLite"].([]interface{})
+	outputArray := resultArray[0].([]byte)
+	outputSize := len(outputArray)
+	
+	var results []result
+	for i := 0; i < outputSize; i++ {
+		score := float64(outputArray[i]) / 255.0
+		if score < 0.2 {
+			continue
+		}
+		results = append(results, result{score: score, index: i})
+	}
+	sort.Slice(results, func(i, j int) bool {
+		return results[i].score > results[j].score
+	})
+	// output is the biggest score labelImage
+	if len(results) > 0 {
+		return labels[results[0].index], true
+	} else {
+		return "", true
+	}
+
+}
+```
+
 
 ## 结论