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import gradio as gr |
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from transformers import PegasusForConditionalGeneration |
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from transformers import PegasusTokenizer |
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from transformers import pipeline |
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model_name = "google/pegasus-xsum" |
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pegasus_tokenizer = PegasusTokenizer.from_pretrained(model_name) |
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def summarize(input_text): |
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nwords=len(input_text.split(" ")) |
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summarizer = pipeline("summarization", model=model_name, tokenizer=pegasus_tokenizer,min_length=int(nwords/10)+20, max_length=int(nwords/5+20), framework="pt") |
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summary=summarizer(input_text)[0]['summary_text'] |
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return(summary) |
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gr.Interface(fn=summarize,inputs="textbox",outputs="textbox").launch(share="True"); |
