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Create app.py
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app.py
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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# Load the model and tokenizer from Hugging Face Model Hub
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tokenizer = AutoTokenizer.from_pretrained("zeyadusf/FlanT5Summarization-samsum")
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model = AutoModelForSeq2SeqLM.from_pretrained("zeyadusf/FlanT5Summarization-samsum")
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def summarize(text):
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inputs = tokenizer(text, return_tensors="pt", truncation=True)
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summary_ids = model.generate(inputs.input_ids, max_length=512, min_length=64, length_penalty=2.0, num_beams=4, early_stopping=True)
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return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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# Define the Gradio interface
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iface = gr.Interface(fn=summarize, inputs="text", outputs="text", title="Summarization with PEFT")
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iface.launch()
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