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import spacy
import gradio as gr
from spacy import displacy
# Load your trained spaCy model
nlp = spacy.load("sr_Spacy_Serbian_Model_SrpKor4Tagging_BERTICOVO")
# Define a function to display the tags and lemmas
def display_tags_and_lemmas(text):
# First, we'll use spaCy to tag and parse the text
text = text.strip()
if not text:
return "", ""
doc = nlp(text.strip())
html = displacy.render(doc, style="ent", page=True, minify=True)
# We'll also create a custom HTML to display lemmas nicely
lemma_html = "<div class='lemma-display'><table class='table table-hover'>"
lemma_html += "<thead><tr><th>Token</th><th>Lemma</th><th>POS Tag</th></tr></thead><tbody>"
for token in doc:
lemma_html += f"<tr><td>{token.text}</td><td>{token.lemma_}</td><td>{token.tag_}</td></tr>"
lemma_html += "</tbody></table></div>"
# Return both the displaCy HTML and our custom lemma table
return html, lemma_html
# Define Gradio interface
iface = gr.Interface(
fn=display_tags_and_lemmas,
inputs=gr.Textbox(lines=5, placeholder="Unesite rečenicu ovde..."),
outputs=gr.HTML(label="Leme i POS oznake"),
title="spaCy Tagger i Lemmatizer",
description="Unesite rečenicu da biste videli njene imenovane entitete, POS oznake i leme.",
examples=["Lep je dan, danas. Sutra će biti još lepši!", "Psi su trčali svakog dana. Mačke su spavale."],
theme="compact",)
iface.launch() |