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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) | |