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import gradio as gr
from transformers import pipeline
get_completion = pipeline("summarization",model="sshleifer/distilbart-cnn-12-6")
get_ner = pipeline("ner", model="dslim/bert-base-NER")
get_caption = pipeline("image-to-text")
def summarize_text(input):
output = get_completion(input)
return output[0]['summary_text']
def merge_tokens(tokens):
merged_tokens = []
for token in tokens:
if merged_tokens and token['entity'].startswith('I-') and merged_tokens[-1]['entity'].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 named_entity_recognition(input):
output = get_ner(input)
merged_output = merge_tokens(output)
return {"text": input, "entities": output}
interface_summarise = gr.Interface(fn=summarize_text,
inputs=[gr.Textbox(label="Text to summarise", lines=5)],
outputs=[gr.Textbox(label="Summary")],
title="Text Summarizer",
description="Summary of text via `distillBART-CNN` model!")
interface_ner = gr.Interface(fn=named_entity_recognition,
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=[
"Tim Cook is the CEO of Apple, stays in California and makes iPhones ",
"My name is Bose and I am a physicist living in Delhi"
])
demo = gr.TabbedInterface([
interface_summarise,
interface_ner],
["Text Summary ",
"Named Entity Recognition"
])
if __name__ == "__main__":
demo.launch(enable_queue=True)
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