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import gradio as gr
from summarizer import TransformerSummarizer, Summarizer
title = "Summarizer"
description = """
This is a demo of a text summarization NN - based on GPT-2, XLNet, BERT,
works with English, Ukrainian, and Russian (and a few other languages too, these are SOTA NN after all).
"""
NN_OPTIONS_LIST = ["mean", "max", "min", "median"]
NN_LIST = ["GPT-2", "XLNet", "BERT"]
def start_fn(article_input: str, reduce_option="mean", model_type='GPT-2') -> str:
"""
GPT-2 based solution, input full text, output summarized text
:param model_type:
:param reduce_option:
:param article_input:
:return summarized article_output:
"""
if model_type == "GPT-2":
GPT2_model = TransformerSummarizer(transformer_type="GPT2", transformer_model_key="gpt2-medium",
reduce_option=reduce_option)
full = ''.join(GPT2_model(article_input, min_length=60))
return full
elif model_type == "XLNet":
XLNet_model = TransformerSummarizer(transformer_type="XLNet", transformer_model_key="xlnet-base-cased",
reduce_option=reduce_option)
full = ''.join(XLNet_model(article_input, min_length=60))
return full
elif model_type == "BERT":
BERT_model = Summarizer(reduce_option=reduce_option)
full = ''.join(BERT_model(article_input, min_length=60))
return full
face = gr.Interface(fn=start_fn,
inputs=[gr.inputs.Textbox(lines=2, placeholder="Paste article here.", label='Input Article'),
gr.inputs.Dropdown(NN_OPTIONS_LIST, label="Summarize mode"),
gr.inputs.Dropdown(NN_LIST, label="Selected NN")],
outputs=gr.inputs.Textbox(lines=2, placeholder="Summarized article here.", label='Summarized '
'Article'),
title=title,
description=description, )
face.launch(server_name="0.0.0.0", share=True)
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