write a function in python using nlp and/or string matching to handle these situations and return a string based intent variable Here is an example function in

write a function in python using nlp and/or string matching to handle these situations and return a string based intent variable Here is an example function in Python that uses natural language processing (NLP) and string matching to handle the scenarios you provided and return a string-based intent variable: python Copy code import re from nltk import word_tokenize from nltk.stem import PorterStemmer from sklearn.feature_extraction.text import TfidfVectorizer def get_intent(message): # List of keywords for each intent refund_keywords = ['refund', 'lost', 'ticket'] soldout_keywords = ['sold out', 'no tickets', 'soldout'] nonexistent_keywords = ['non-existent', 'fake', 'nonexistent'] human_keywords = ['human', 'representative', 'speak'] unclear_keywords = ['unclear', 'confusing', 'conflict'] country_keywords = ['country', 'abroad'] payment_keywords = ['payment', 'form', 'credit'] past_keywords = ['past', 'over', 'finished'] age_keywords = ['child', 'age', 'limit'] # Create a stemmer stemmer = PorterStemmer() # Stem each keyword refund_keywords = [stemmer.stem(word) for word in refund_keywords] soldout_keywords = [stemmer.stem(word) for word in soldout_keywords] nonexistent_keywords = [stemmer.stem(word) for word in nonexistent_keywords] human_keywords = [stemmer.stem(word) for word in human_keywords] unclear_keywords = [stemmer.stem(word) for word in unclear_keywords] country_keywords = [stemmer.stem(word) for word in country_keywords] payment_keywords = [stemmer.stem(word) for word in payment_keywords] past_keywords = [stemmer.stem(word) for word in past_keywords] age_keywords = [stemmer.stem(word) for word in age_keywords] # Create a list of all keywords all_keywords = refund_keywords + soldout_keywords + nonexistent_keywords + human_keywords + unclear_keywords + country_keywords + payment_keywords + past_keywords + age_keywords # Tokenize the message and stem each word message_tokens = [stemmer.stem(word) for word in word_tokenize(message)] # Create a Tf-idf vectorizer and fit it to the message tfidf = TfidfVectorizer(vocabulary=all_keywords) message_tfidf = tfidf.fit_transform([message]) # Get the index of the highest-scoring keyword highest_scoring_index = message_tfidf.argmax() # Use the index to determine the intent if highest_scoring_index < len(refund_keywords): intent = "refund" elif highest_scoring_index < len(refund_keywords) + len(soldout_keywords): intent = "soldout" elif highest_scoring_index < len(refund_keywords) + len(soldout_key