How can I create a new entity and use it to find that entity in my test data? How do I get my tokenization to work?

I want to create a new entity: let's call it "medicine", and train it using my corpus. From there, identify all entities of "medicine". My code doesn't work, can someone help me?

import nltk


test= input("Please enter your file name")
test1= input("Please enter your second file name")

with open(test, "r") as file:  
    new = file.read().splitlines()


with open(test1, "r") as file2:
    new1= file2.read().splitlines()


for s in new:
    for x in new1:
        sample = s.replace('value', x)

        sample1 = ''.join(str(v) for v in sample)

        print(sample1)


        sentences = nltk.sent_tokenize(sample1)
        tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
        tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
        chunked_sentences = nltk.ne_chunk_sents(tagged_sentences, binary=True)


        print(sentences)

def extract_entity_names(t):
    entity_names = []

    if hasattr(t, 'label') and t.label:
        if t.label() == 'NE':
            entity_names.append(' '.join([child[0] for child in t]))
        else:
            for child in t:
                entity_names.extend(extract_entity_names(child))

    return entity_names