Create A Custom Pipeline To Detect Email In Text Using Spacy

import spacyfrom spacy.language import Languageimport re# Step 1: Create a blank English modelnlp = spacy.blank("en")  # blank pipeline# Step 2: Create a custom component to detect emails@Language.component("email_component")def email_component(doc):    # Regex pattern for emails    email_pattern = r"[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+"        entities = []    for match in re.finditer(email_pattern, doc.text):        start_char, end_char = match.span()        span = doc.char_span(start_char, end_char, label="EMAIL")        if span is not None:            entities.append(span)        # Set detected emails as entities    doc.ents = entities    return doc# Step 3: Add the custom component to the pipelinenlp.add_pipe("email_component")# Step 4: Test the pipelinetext = "Contact us at support@example.com or sales@example.org for more info."doc = nlp(text)# Print detected emailsprint("\nEmails detected:")for ent in doc.ents:    print(ent.text, ent.label_)

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