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A names entity Recognition using gradio.
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API_URL = "https://api-inference.huggingface.co/models/dslim/bert-base-NER"
# Helper function
import requests, json
#Summarization endpoint
def get_completion(inputs,ENDPOINT_URL, parameters=None):
hf_api_key = "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"
headers = {
"Content-Type": "application/json"
}
data = { "inputs": inputs }
if parameters is not None:
data.update({"parameters": parameters})
response = requests.request("POST",
ENDPOINT_URL, headers=headers,
data=json.dumps(data)
)
return json.loads(response.content.decode("utf-8"))
import gradio as gr
def merge_tokens(tokens):
merged_tokens = []
for token in tokens:
if merged_tokens and token['entity_group'].startswith('I-') and merged_tokens[-1]['entity_group'].endswith(token['entity'][2:]):
# If 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, parameters=None, ENDPOINT_URL=API_URL)
merged_tokens = merge_tokens(output)
return {"text": input, "entities": merged_tokens}
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",
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(inline= False)