[spark] Update README (#2596)

extensions/spark/README.md

@@ -45,32 +45,28 @@ Using the DJL Spark Extension is simple and straightforward. Here is an example
 ### Scala
 
 ```scala
-import ai.djl.spark.SparkTransformer
-import ai.djl.spark.translator.SparkImageClassificationTranslator
+import ai.djl.spark.task.vision.ImageClassifier
 
-val transformer = new SparkTransformer[Classifications]()
-  .setInputCols(Array("input_col1", "input_col2"))
-  .setOutputCols(Array("value"))
+val classifier = new ImageClassifier()
+  .setInputCols(Array("origin", "height", "width", "nChannels", "mode", "data"))
+  .setOutputCol("prediction")
   .setEngine("PyTorch")
-  .setModelUrl("model_url")
-  .setOutputClass(classOf[Classifications])
-  .setTranslator(new SparkImageClassificationTranslator())
-val outputDf = transformer.transform(df)
+  .setModelUrl("djl://ai.djl.pytorch/resnet")
+  .setTopK(2)
+var outputDf = classifier.classify(df)
 ```
 
 ### Python
 
 ```python
-from djl_spark.transformer import SparkTransformer
-from djl_spark.translator import SparkImageClassificationTranslator
-
-transformer = SparkTransformer(input_cols=["input_col1", "input_col2"],
-                               output_cols=["value"],
-                               engine="PyTorch",
-                               model_url="model_url",
-                               output_class="ai.djl.modality.Classifications",
-                               translator=SparkImageClassificationTranslator())
-outputDf = transformer.transform(df)
+from djl_spark.task.vision import ImageClassifier
+
+classifier = ImageClassifier(input_cols=["origin", "height", "width", "nChannels", "mode", "data"],
+                             output_col="prediction",
+                             engine="PyTorch",
+                             model_url="djl://ai.djl.pytorch/resnet",
+                             top_k=2)
+outputDf = classifier.classify(df)
 ```
 
 See [examples](https://github.com/deepjavalibrary/djl-demo/tree/master/apache-spark/spark3.0) for more details.