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f6ab87f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | import gradio as gr
import transformers
model_name = "t5-small"
tokenizer = transformers.T5Tokenizer.from_pretrained(model_name)
model = transformers.T5ForCausalLM.from_pretrained(model_name)
def summarize_text(text, max_length):
input_ids = tokenizer.encode(text, return_tensors='pt', max_length=512)
summary_ids = model.generate(input_ids,
max_length=max_length,
num_beams=4,
early_stopping=True)
return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
iface = gr.Interface(
fn=summarize_text,
inputs=gr.inputs.Textbox(lines=5, default="Enter your text here"),
outputs=gr.outputs.Textbox(lines=3, default="Summary will appear here"),
parameters={
"max_length": gr.inputs.Slider(default=50, min_value=20, max_value=200, step=10, label="Summary Length")
},
title="Text Summarization with T5",
description="Generate a brief summary of the input text using the T5 model."
)
iface.launch()
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