Given as input: a text and a glossary files

• We compute the frequency of each glossary entry in the input text.
• We enter all glossary entries found in text in a database (DB), along with their frequencies.
• We get the machine translation into Spanish of the n most frequent glossary entries found in the input text, using the API (https://libretranslate.com/docs/).
• We add the translations to the DB.

• Read the glossary file line-by-line by iterating over the file object of the glossary file in a for loop, lowercase and strip the line then add the glossary in a dictionary as a key with a zero frequency as its value.
• In a similar fashion, read the input text file line-by-line, preprocess the line by lowercasing and removing stopwords and any non-alphabetical words (punctuations, etc), then increase the frequency of every found glossary in the preprocessed line (if found).
• Then add all found glossaries in the input text along with their frequency in a ‘frequencies’ table in the database, where all glossaries can be found in the ‘glossary’ column, and all their corresponding frequencies in the ‘frequency’ column.
• Query the ‘frequencies’ table in the DB to select the top 10 frequent glossaries. Then using the ‘libretranslate’ API, get their spanish translation. Finally, insert the n top frequent glossaries and their translations into the ‘translation’ table in the DB.