text: from copy import deepcopy
  from typing import List, Dict
  
  from overrides import overrides
  import numpy
  import json
  from nltk.tree import Tree
  
  from allennlp.common.util import JsonDict
  from allennlp.data import Instance
  from allennlp.predictors.predictor import Predictor
  from allennlp.data.fields import LabelField
  
  
  @Predictor.register("jsonl_predictor")
  class TextClassifierPredictor(Predictor):
      """
      Predictor for any model that takes in a sentence and returns
      a single class for it.  In particular, it can be used with
      the :class:`~allennlp.models.basic_classifier.BasicClassifier` model
      """
  
      def predict(self, sentence: str) -> JsonDict:
          return self.predict_json(json.loads(sentence))
  
      @overrides
      def load_line(self, line: str) -> JsonDict:
          """
          If your inputs are not in JSON-lines format (e.g. you have a CSV)
          you can override this function to parse them correctly.
          """
          return json.loads(line)
  
      @overrides
      def _json_to_instance(self, json_dict: JsonDict) -> Instance:
          """
          Expects JSON that looks like ``{"sentence": "..."}``.
          Runs the underlying model, and adds the ``"label"`` to the output.
          """
          sentence = json_dict["text"]
  
          return self._dataset_reader.text_to_instance(sentence)
  
      @overrides
      def predictions_to_labeled_instances(
          self, instance: Instance, outputs: Dict[str, numpy.ndarray]
      ) -> List[Instance]:
          new_instance = deepcopy(instance)
          label = numpy.argmax(outputs["probs"])
  
          label_vocab = self.model.vocab.get_token_to_index_vocabulary("labels")
          label_vocab = {v: k for k, v in label_vocab.items()}
  
          new_instance.add_field("label", LabelField(label_vocab[label]))
          return [new_instance]
  