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A names entity Recognition using gradio.
Browse files
app.py
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API_URL = "https://api-inference.huggingface.co/models/dslim/bert-base-NER"
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# Helper function
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import requests, json
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#Summarization endpoint
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def get_completion(inputs,ENDPOINT_URL, parameters=None):
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hf_api_key = "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"
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headers = {
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"Content-Type": "application/json"
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}
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data = { "inputs": inputs }
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if parameters is not None:
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data.update({"parameters": parameters})
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response = requests.request("POST",
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ENDPOINT_URL, headers=headers,
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data=json.dumps(data)
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)
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return json.loads(response.content.decode("utf-8"))
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import gradio as gr
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def merge_tokens(tokens):
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merged_tokens = []
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for token in tokens:
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if merged_tokens and token['entity_group'].startswith('I-') and merged_tokens[-1]['entity_group'].endswith(token['entity'][2:]):
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# If current token continues the entity of the last one, merge them
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last_token = merged_tokens[-1]
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last_token['word'] += token['word'].replace('##', '')
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last_token['end'] = token['end']
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last_token['score'] = (last_token['score'] + token['score']) / 2
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else:
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# Otherwise, add the token to the list
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merged_tokens.append(token)
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return merged_tokens
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def ner(input):
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output = get_completion(input, parameters=None, ENDPOINT_URL=API_URL)
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merged_tokens = merge_tokens(output)
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return {"text": input, "entities": merged_tokens}
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gr.close_all()
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demo = gr.Interface(fn=ner,
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inputs=[gr.Textbox(label="Text to find entities", lines=2)],
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outputs=[gr.HighlightedText(label="Text with entities")],
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title="NER with dslim/bert-base-NER",
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description="Find entities using the `dslim/bert-base-NER` model under the hood!",
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allow_flagging="never",
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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"])
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demo.launch(inline= False)
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