Intent Classification using NLU

I was running a successful NLU intent classifier using an older RASA version. Following modules were used for Intent classification.

from rasa.nlu.model import Interpreter  
from rasa.shared.nlu.training_data.loading import load_data  

However, with the new Rasa version, I see the code does not work anymore. The sample code for the intent prediction was

interpreter = Interpreter.load(model_path)
data = interpreter.parse(utterance)

Can someone help me what would be the code for Rasa 3.0.5 ?

@darshanpv you have to use the Agent class to parse message now

from rasa.core.agent import Agent
agent = Agent.load(model_path)
result = await agent.parse_message(message)

Though after converting, i have seen significant memory usage(almost 40% more) and longer load times

Could you please check as well.

Thanks @souvikg10 I was able to classify the intent.

In my case, I am using the following NLU configuration, and works fine with no performance hit.

language: en
 - name: WhitespaceTokenizer
 - name: RegexFeaturizer
 - name: LexicalSyntacticFeaturizer
 - name: CountVectorsFeaturizer
 - name: CountVectorsFeaturizer
   analyzer: char_wb
   min_ngram: 1
   max_ngram: 4
 - name: DIETClassifier
   epochs: 100
 - name: EntitySynonymMapper
 - name: ResponseSelector
   epochs: 100

can you check the load time of the model?

for me it kind of doubled but i think that is since version 2.8.9