im using rasa 3 and i’m trying to implement a custom graph component for sentiment analysis. I’m using a pretrained model with vadersentiment.

My sentiment.py code :

from __future__ import annotations

from rasa.engine.graph import GraphComponent, ExecutionContext

from rasa.shared.nlu.training_data.training_data import TrainingData

from rasa.engine.recipes.default_recipe import DefaultV1Recipe

from typing import List, Type, Dict, Text, Any, Optional

from rasa.engine.graph import ExecutionContext

from rasa.engine.storage.resource import Resource

from rasa.engine.storage.storage import ModelStorage

from rasa.shared.nlu.training_data.message import Message

from rasa.shared.nlu.constants import TEXT

from rasa.nlu.extractors.extractor import EntityExtractorMixin

from nltk.sentiment.vader import SentimentIntensityAnalyzer


@DefaultV1Recipe.register(
    DefaultV1Recipe.ComponentType.ENTITY_EXTRACTOR, is_trainable=False
)

class SentimentAnalyzer(GraphComponent, EntityExtractorMixin):
   
    """A pre-trained sentiment component"""

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

    def __init__(self, component_config: Dict[Text, Any]) -> None:

        self.component_config = component_config

    @classmethod
    def create(
            cls,
            config: Dict[Text, Any],
            model_storage: ModelStorage,
            resource: Resource,
            execution_context: ExecutionContext,
    ) -> GraphComponent:
        return cls(config)

    def train(self, training_data: TrainingData) -> Resource:

        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, messages: List[Message]) -> List[Message]:
        """Retrieve the text message, pass it to the classifier and append the prediction results 
        to the message class."""
        sid = SentimentIntensityAnalyzer()
        for message in messages:
            res = sid.polarity_scores(message.get(TEXT))
            key, value = max(res.items(), key=lambda x: x[1])
            entity = self.convert_to_rasa(key, value)
            message.set("entities", [entity], add_to_output=True)

        return messages

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

I have this errors :

2022-01-14 15:05:34 ERROR rasa.server - An unexpected error occurred. Error: Error running graph component for node run_sentiment.SentimentAnalyzer9.

2022-01-14 15:05:34 ERROR rasa.core.training.interactive - An exception occurred while recording messages.

Traceback (most recent call last):

File "c:\users\hp\pycharmprojects\newpr\venv\lib\site-packages\rasa\core\training\interactive.py", line 1498, in record_messages await _enter_user_message(conversation_id, endpoint)

File "c:\users\hp\pycharmprojects\newpr\venv\lib\site-packages\rasa\core\training\interactive.py", line 1341, in _enter_user_message await send_message(endpoint, conversation_id, message)

File "c:\users\hp\pycharmprojects\newpr\venv\lib\site-packages\rasa\core\training\interactive.py", line 161, in send_message return await endpoint.request(

File "c:\users\hp\pycharmprojects\newpr\venv\lib\site-packages\rasa\utils\endpoints.py", line 173, in request raise ClientResponseError(

rasa.utils.endpoints.ClientResponseError: 500, Internal Server Error, body='b'{"version":"3.0.4","status":"failure","message":"An unexpected error occurred. Error: Error running graph component for node run_sentiment.SentimentAnalyzer9.","reason":"ConversationError","details":{},"help":null,"code":500}'' ror running graph component for node run_sentiment.SentimentAnalyzer9.","reason":"ConversationError","details {},"help":null,"code":500}''