#!/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()