Add A Custom Component To The NLP Pipeline Using Spacy

import spacyfrom spacy.tokens import Docfrom spacy.language import Language# Step 1: Register a custom Doc-level attributeDoc.set_extension("has_email", default=False)# Step 2: Define and register the custom pipeline component@Language.component("email_detector")def email_detector(doc):    """    Custom pipeline component that checks if a Doc contains an email address.    If found, sets doc._.has_email = True, otherwise False.    """    for token in doc:        if "@" in token.text:            doc._.has_email = True            break    else:        doc._.has_email = False    return doc# Step 3: Load a spaCy modelnlp = spacy.load("en_core_web_sm")# Step 4: Add the custom component to the pipeline using its registered namenlp.add_pipe("email_detector", last=True)# Step 5: Run the pipeline on sample textdoc1 = nlp("Contact us at support@example.com for more info.")doc2 = nlp("This text has no email address.")# Step 6: Print resultsprint(f"Text: {doc1.text}")print(f"Contains email? {doc1._.has_email}")print(f"Text: {doc2.text}")print(f"Contains email? {doc2._.has_email}")# Step 7: Inspect pipeline orderprint("\nPipeline components:", nlp.pipe_names)

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