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import gradio as gr |
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from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer |
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model_name = "t5-base" |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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summarizer = pipeline('text2text-generation', model=model, tokenizer=tokenizer) |
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def summarize_text(text): |
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result = summarizer(text, max_length=100, min_length=30, do_sample=False)[0] |
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summary = result['generated_text'].strip() |
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return summary |
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iface = gr.Interface(fn=summarize_text, inputs="text", outputs="text", |
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title="Text Summarization with Hugging Face and Gradio", |
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description="Enter text to summarize.") |
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iface.launch() |
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