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#!/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()
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