Machine Learning with Conversation Slot filling

We are trying to put huggingface-ner sample as machine learning model behind template - Conversational AI - Intent classification and slot filling.
We are able to do the prediction using below code. In below code we have taken text of each dialog and sent to the model. Model is returning predictions as well.

**We are now stuck at display the predictions back to UI. Please tell us what changes are needed, so that the UI accepts the returned predictions in json reply, and show them in the dialogues.**

```
if "entity_slot" in li._control_tags:
from_name = 'ParagraphLabels'; to_name='dialogue'; #TODO: not sure what is purpose of this
dialogues = [task['data']['humanMachineDialogue'] for task in tasks]
for dialogue in dialogues:
for turn in dialogue:
if 'text' in turn:
texts.append(turn['text'])
```

Start from the `entity_slot` branch and the `humanMachineDialogue` extraction shown in the issue. Trace how the model's returned predictions are serialized and consumed by the dialogue UI; done means the JSON predictions are accepted and displayed in the dialogues, but no file or test is identified here.

Written by the indexing model from the issue text.