from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
#Create Named Entity Recognition Pipeline
nerp = pipeline("ner", model=model, tokenizer=tokenizer)
#Build the Named Entity Recognition App
import gradio as gr
#Import Merging Tokens function from Helper to Display Output Relevant for User
from helper import merge_tokens
#Define Named Entity Recognition Function 
def ner(input):
    output = nerp(input)
    merged_tokens = merge_tokens(output)
    return {"text": input, "entities": merged_tokens}
#Set up the User Interface and Launch    
nerapp = 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` ",
                    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"])
                 
nerapp.launch()