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import gradio as gr
from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
# Define the model name
MODEL_NAME = "impresso-project/ner-stacked-bert-multilingual"
# Load the tokenizer and model using the pipeline
ner_tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
ner_pipeline = pipeline(
"generic-ner",
model=MODEL_NAME,
tokenizer=ner_tokenizer,
trust_remote_code=True,
device="cpu",
)
# Helper function to flatten entities and prepare them for HighlightedText
def prepare_entities_for_highlight(text, results):
entities = []
for category, entity_list in results.items():
for entity in entity_list:
# Appending each entity's word, start and end for highlighting, including the entity label and score
entities.append(
{
"start": entity["start"],
"end": entity["end"],
"label": f"{entity['entity']} ({entity['score']:.2f}%)",
}
)
return {"text": text, "entities": entities}
# Function to process the sentence and extract entities
def extract_entities(sentence):
results = ner_pipeline(sentence)
# Format the results for HighlightedText
return prepare_entities_for_highlight(sentence, results)
# Create Gradio interface
def ner_app_interface():
input_sentence = gr.Textbox(
lines=5, label="Input Sentence", placeholder="Enter a sentence for NER..."
)
output_entities = gr.HighlightedText(label="Extracted Entities")
# Interface definition
interface = gr.Interface(
fn=extract_entities,
inputs=input_sentence,
outputs=output_entities,
title="Named Entity Recognition",
description="Enter a sentence to extract named entities using the NER model from the Impresso project.",
examples=[
[
"In the year 1789, King Louis XVI, ruler of France, convened the Estates-General at the Palace of Versailles."
]
],
live=False,
)
interface.launch(share=True)
# Run the app
if __name__ == "__main__":
ner_app_interface()
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