app.py

import os
import io
import base64

from transformers import pipeline
import gradio as gr


hf_api_key = os.environ['HF_API_KEY']

get_completion = pipeline("ner", model="dslim/bert-base-NER")

def merge_tokens(tokens):
    merged_tokens = []

    for token in tokens:
        if merged_tokens and token['entity'].startswith('I-') and merged_tokens[-1]['entity'].endswith(token['entity'][2:]):
            # If the 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):
    output = get_completion(input)
    merged_tokens = merge_tokens(output)
    return {"text": input, "entities": merged_tokens}

# Create 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!",
                    flagging_mode="never",  # Updated from allow_flagging
                    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(
    share=True, 
    # server_port=int(os.environ.get('PORT2', 7860))  # Uncomment if needed
)