<prompt> Unda programu ya kutambua kama mtu anaongea Kiingereza au Kihispania.  <response>: One possible approach to achieve this is by using a machine learning algorithm to classify between English and Spanish. Heres some example code:python Import necessary librariesimport nltkfrom nltk.classify import NaiveBayesClassifierfrom nltk.corpus import stopwordsfrom nltk.tokenize import word_tokenizefrom sklearn.externals import joblibimport pandas as pd Define stopwordsen_stopwords setstopwords.wordsenglishes_stopwords setstopwords.wordsspanish Define feature extractor functiondef featurestext: words word_tokenizetext return word:True for word in words Load datasetdata pd.read_csvenglish-spanish-dataset.csv Initialize the trainertrainer NaiveBayesClassifier.train Extract features and labels from the datasetX y for index,row in data.iterrows: X.appendfeaturesrow1 y.appendrow2 Train the machine learning modelclassifier trainernltk.classify.util.apply_featuresX,y Take input from the usertext inputEnter text: Tokenize the input textwords word_tokenizetext Remove stop wordswords word for word in words if word.lower not in en_stopwords and word.lower not in es_stopwords Make predictionprediction classifier.classifyfeatures .joinwordsif prediction en: printThe text is in Englishelse: printThe text is in SpanishThis code makes use of the Naive Bayes algorithm to classify between English and Spanish languages by predicting the class of input text. The feature extractor extracts features, which are a dictionary of words, from the text. The machine learning model is trained on these features to make predictions.Instructions:1. Import the necessary libraries. This code relies on the nltk, nltk.classify, nltk.corpus, nltk.tokenize, sklearn.externals, and pandas libraries.2. Define the stopwords for both English and Spanish, as these words will be removed from the input text before making predictions.3. Define a features function that takes in text as an input and returns a dictionary of words present in the text. This will serve as the feature extractor for the machine learning model.4. Load the dataset, which contains labeled 