doc modify

Signed-off-by: Nfangzhou0329 <fangzhou12@huawei.com>

zh-cn/application-dev/ai/mindspore-guidelines-based-js.md

@@ -93,14 +93,14 @@ build() {
    ```shell
    Launching com.example.myapptfjs
    $ hdc uninstall com.example.myapptfjs
-   $ hdc install -r "D:\TVOS\JSAPI\MyAppTfjs\entry\build\default\outputs\default\entry-default-signed.hap"
+   $ hdc install -r "path/to/xxx.hap"
    $ hdc shell aa start -a EntryAbility -b com.example.myapptfjs
    ```
 
 2. 使用hdc连接设备，并将mnet.caffemodel.ms推送到设备中的沙盒目录。mnet_caffemodel_nhwc.bin在本地项目中的rawfile目录下。
 
    ```shell
-   hdc -t 7001005458323933328a00bcdf423800 file send .\mnet.caffemodel.ms /data/app/el2/100/base/com.example.myapptfjs/haps/entry/files/
+   hdc -t your_device_id file send .\mnet.caffemodel.ms /data/app/el2/100/base/com.example.myapptfjs/haps/entry/files/
    ```
 3. 在设备屏幕点击Test_MSLiteModel_predict触发用例，在HiLog打印结果中得到如下结果：
 

zh-cn/application-dev/reference/apis/js-apis-mindSporeLite.md

@@ -185,12 +185,12 @@ let modelName = '/path/to/xxx.ms';
 let syscontext = globalThis.context;
 syscontext.resourceManager.getRawFileContent(modelName).then((buffer) => {
   let modelBuffer = buffer;
-}).catch(error => {
-  console.error('Failed to get buffer, error code: ${error.code},message:${error.message}.');
-})
-mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, (result) => {
+  mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, (result) => {
   	const modelInputs = result.getInputs();
   	console.log(modelInputs[0].name);
+  })
+}).catch(error => {
+  console.error('Failed to get buffer, error code: ${error.code},message:${error.message}.');
 })
 ```
 ## mindSporeLite.loadModelFromBuffer
@@ -217,14 +217,14 @@ let modelName = '/path/to/xxx.ms';
 let syscontext = globalThis.context;
 syscontext.resourceManager.getRawFileContent(modelName).then((error,buffer) => {
   let modelBuffer = buffer;
-}).catch(error => {
-  console.error('Failed to get buffer, error code: ${error.code},message:${error.message}.');
-})
-let context: mindSporeLite.Context = {};
-context = {'target': ['cpu']};
-mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, context, (result) => {
+  let context: mindSporeLite.Context = {};
+  context = {'target': ['cpu']};
+  mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, context, (result) => {
     const modelInputs = result.getInputs();
     console.log(modelInputs[0].name);
+  })  
+}).catch(error => {
+  console.error('Failed to get buffer, error code: ${error.code},message:${error.message}.');
 })
 ```
 ## mindSporeLite.loadModelFromBuffer
@@ -256,12 +256,12 @@ let modelName = '/path/to/xxx.ms';
 let syscontext = globalThis.context;
 syscontext.resourceManager.getRawFileContent(modelName).then((buffer) => {
   let modelBuffer = buffer;
-}).catch(error => {
-  console.error('Failed to get buffer, error code: ${error.code},message:${error.message}.');
-})
-mindSporeLite.loadModelFromBuffer(modelBuffer.buffer).then((result) => {
+  mindSporeLite.loadModelFromBuffer(modelBuffer.buffer).then((result) => {
     const modelInputs = result.getInputs();
     console.log(modelInputs[0].name);
+  })  
+}).catch(error => {
+  console.error('Failed to get buffer, error code: ${error.code},message:${error.message}.');
 })
 ```
 ## mindSporeLite.loadModelFromFd
@@ -402,7 +402,7 @@ predict(inputs: MSTensor[], callback: Callback<MSTensor[]>): void
 import resourceManager from '@ohos.resourceManager'
 let inputName = 'input_data.bin';
 let syscontext = globalThis.context;
-syscontext.resourceManager.getRawFileContent(inputName).then((buffer) => {
+syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
   let inputBuffer = buffer;
   let model_file = '/path/to/xxx.ms';
   let mindSporeLiteModel = await mindSporeLite.loadModelFromFile(model_file);
@@ -442,7 +442,7 @@ predict(inputs: MSTensor[]): Promise<MSTensor[]>
 import resourceManager from '@ohos.resourceManager'
 let inputName = 'input_data.bin';
 let syscontext = globalThis.context;
-syscontext.resourceManager.getRawFileContent(inputName).then((buffer) => {
+syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
   let inputBuffer = buffer;
   let model_file = '/path/to/xxx.ms';
   let mindSporeLiteModel = await mindSporeLite.loadModelFromFile(model_file);
@@ -543,7 +543,7 @@ getData(): ArrayBuffer
 import resourceManager from '@ohos.resourceManager'
 let inputName = 'input_data.bin';
 let syscontext = globalThis.context;
-syscontext.resourceManager.getRawFileContent(inputName).then((buffer) => {
+syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
   let inputBuffer = buffer;
   let model_file = '/path/to/xxx.ms';
   let mindSporeLiteModel = await mindSporeLite.loadModelFromFile(model_file);
@@ -578,13 +578,13 @@ setData(inputArray: ArrayBuffer): void
 import resourceManager from '@ohos.resourceManager'
 let inputName = 'input_data.bin';
 let syscontext = globalThis.context;
-syscontext.resourceManager.getRawFileContent(inputName).then((buffer) => {
+syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
   inputBuffer = buffer;
+  let model_file = '/path/to/xxx.ms';
+  let mindSporeLiteModel = await mindSporeLite.loadModelFromFile(model_file);
+  const modelInputs = mindSporeLiteModel.getInputs();
+  modelInputs[0].setData(inputBuffer.buffer);
 })
-let model_file = '/path/to/xxx.ms';
-let mindSporeLiteModel = await mindSporeLite.loadModelFromFile(model_file);
-const modelInputs = mindSporeLiteModel.getInputs();
-modelInputs[0].setData(inputBuffer.buffer);
 ```
 
 ## DataType