app.py

import gradio as gr
from transformers import pipeline


# Load the model locally
model_name = "dslim/bert-base-NER"
ner_pipeline = pipeline("ner", model=model_name, tokenizer=model_name, aggregation_strategy="simple")

def merge_tokens(tokens):
    merged_tokens = []
    for token in tokens:
        if merged_tokens and token['entity_group'].startswith('I-') and merged_tokens[-1]['entity_group'].endswith(token['entity_group'][2:]):
            # If current token continues the entity of the last one, merge them
            last_token = merged_tokens[-1]
            last_token['word'] += token['word'].replace('##', '')
            last_token['end'] = token['end']
            last_token['score'] = (last_token['score'] + token['score']) / 2
        else:
            # Otherwise, add the token to the list
            merged_tokens.append(token)

    return merged_tokens

def ner(input_text):
    # Use the pipeline to get entities
    output = ner_pipeline(input_text)
    merged_tokens = merge_tokens(output)
    return {"text": input_text, "entities": merged_tokens}

# Gradio interface
demo = gr.Interface(fn=ner,
                    inputs=[gr.Textbox(label="Text to find entities", lines=2)],
                    outputs=[gr.HighlightedText(label="Text with entities")],
                    title="NER with dslim/bert-base-NER",
                    description="Find entities using the `dslim/bert-base-NER` model under the hood!",
                    allow_flagging="never",
                    examples=["My name is Andrew, I'm building DeeplearningAI and I live in California", "My name is Poli, I live in Vienna and work at HuggingFace"])

demo.launch()