How to set a slot value through custom component?

Rasa Open Source
1

Hello everyone,

This is my custom sentiment analysis component:

from rasa.nlu.components import Component
from rasa.nlu import utils
from rasa.nlu.model import Metadata
from rasa_sdk import Tracker
from rasa_sdk.events import SlotSet

import requests

import os

class SentimentAnalyzer(Component):
    """A pre-trained sentiment component"""

    name = "sentiment"
    provides = ["entities"]
    requires = []
    defaults = {}
    language_list = ["en", "el"]

    def __init__(self, component_config=None):
        super(SentimentAnalyzer, self).__init__(component_config)

    def train(self, training_data, cfg, **kwargs):
        """Not needed, because the the model is pretrained"""
        pass

    def convert_to_rasa(self, value, confidence):
        """Convert model output into the Rasa NLU compatible output format."""

        entity = {
            "value": value,
            "confidence": confidence,
            "entity": "sentiment",
            "extractor": "sentiment_extractor",
        }

        return entity

    def process(self, message, **kwargs):
        """Retrieve the text message, pass it to the classifier
            and append the prediction results to the message class."""

        try:
            str = message.data['text']

            data = {"text": str}
            response = requests.post(
                "http://url/classes", json=data
            )   
            resp = response.json()  # This returns {"sentiment_classes":[{"sentiment_class":"positive","sentiment_score":<score>}, {"sentiment_class":"neutral","sentiment_score":<score>}, {"sentiment_class":"negative","sentiment_score":<score>}]}

            classes = resp.get("sentiment_classes")
            score = 0
            sentiment = None
            for i in range(3):
                if classes[i].get("sentiment_score") > score:
                    score = classes[i].get("sentiment_score")
                    sentiment = classes[i].get("sentiment_class")

            if sentiment == "positive":
                sentiment = "pos"
            elif sentiment == "negative":
                sentiment = "neg"
            else:
                sentiment = "neu"

            entity = self.convert_to_rasa(sentiment, score)
            message.set("entities", [entity], add_to_output=True)     
        except KeyError:
            pass


    def persist(self, file_name, dir_name):
        """Pass because a pre-trained model is already persisted"""

        pass

Besides the sentiment extraction I want to store the POST’s response to a slot like below:

resp = response.json()  # This returns {"sentiment_classes":[{"sentiment_class":"positive","sentiment_score":<score>}, {"sentiment_class":"neutral","sentiment_score":<score>}, {"sentiment_class":"negative","sentiment_score":<score>}]}

SlotSet(key=classes, value=str(resp))

“Classes” is a slot defined on my domain file. But this doesn’t work. Is there any way to make it work? I have read two similar posts but I do not know how to implement these solutions