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intent.py

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+from transformers import pipeline
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+model_name = 'qanastek/XLMRoberta-Alexa-Intents-Classification'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+intent_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
+
+
+def perform_intent_classification(text):
+    result = intent_classifier(text)
+    return {"Intent": [result]}

ner.py

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+from transformers import pipeline
+import torch
+
+ner_model = pipeline('ner')
+
+model_checkpoint = "huggingface-course/bert-finetuned-ner"
+classifier = pipeline("token-classification", model=model_checkpoint, aggregation_strategy="simple")
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+def perform_ner(text):
+    # Your NER function implementation goes here
+    # Replace this with your own checkpoint
+    result = classifier(text)
+    return {"entities": [result]}