app.py

#!/usr/bin/env python
# coding: utf-8

# # L1: NLP tasks with a simple interface 🗞️

# Load your HF API key and relevant Python libraries.

# In[1]:


import os
import io
from IPython.display import Image, display, HTML
from PIL import Image
import base64 
import gradio as gr


# Helper function
import requests, json

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


def ner(input):
    output = get_completion(input)
    return {"text": input, "entities": output}

gr.close_all()
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",
                    #Here we introduce a new tag, examples, easy to use examples for your application
                    examples=["My name is Andrew and I live in California", "My name is Poli and work at HuggingFace"])
demo.launch()