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from transformers import pipeline | |
import gradio as gr | |
#reference appropriate Hugging Face model | |
model_name = 'koakande/bert-finetuned-ner' | |
# Load token classification pipeline modelfrom Hugging Face | |
model = pipeline("token-classification", model=model_name, aggregation_strategy="simple") | |
# write a prediction method for the model | |
def predict_entities(text): | |
# Use the loaded model to identify entities in the text | |
entities = model(text) | |
# Highlight identified entities in the input text | |
highlighted_text = text | |
for entity in entities: | |
entity_text = text[entity['start']:entity['end']] | |
replacement = f"<span style='border: 2px solid green;'>{entity_text}</span>" | |
highlighted_text = highlighted_text.replace(entity_text, replacement) | |
return highlighted_text | |
# gradio interface | |
iface = gr.Interface( | |
fn=predict_entities, | |
inputs=gr.Textbox(lines=5, placeholder="Enter text..."), | |
outputs=gr.HTML(), | |
title="Named Entity Identification", | |
description="Enter text to identify entities using the model.", | |
) | |
iface.launch() |