Add result

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>

README.md

@@ -13,7 +13,7 @@ The pre-trained model used here is from the paper **PANNs: Large-Scale Pretraine
 
 ## Code Example
 
-Predict labels and generate embeddings given the audio path "test.wav". 
+Predict labels and generate embeddings given the audio path "test.wav".
 
  *Write the pipeline in simplified style*:
 
@@ -25,7 +25,7 @@ import towhee
           .audio_decode.ffmpeg()
           .runas_op(func=lambda x:[y[0] for y in x])
           .audio_classification.panns()
-          .show() 
+          .show()
 )
 ```
 
@@ -39,9 +39,11 @@ import towhee
           .audio_decode.ffmpeg['path', 'frames']()
           .runas_op['frames', 'frames'](func=lambda x:[y[0] for y in x])
           .audio_classification.panns['frames', ('labels', 'scores', 'vec')]()
-          .show() 
+          .select['path', 'labels', 'scores', 'vec']()
+          .show()
 )
 ```
+<img src="./result.png" width="800px"/>
 
 <br />
 
@@ -93,4 +95,3 @@ The input data should represent for an audio longer than 2s.
 - labels: a list of topk predicted labels by model.
 - scores: a list of scores corresponding to labels, representing for possibility.
 - vec: a audio embedding generated by model, shape of which is (2048,)
-