| import gradio as gr |
| from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM |
|
|
| model_name = "t5-base" |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
| summarization = pipeline("summarization", model=model, tokenizer=tokenizer) |
|
|
| def generate_summary(text): |
| summary = summarization(text, max_length=100, min_length=25, do_sample=False) |
| return summary[0]["summary_text"] |
|
|
| input_text = gr.inputs.Textbox(lines=5, placeholder="Enter your text here...") |
| output_text = gr.outputs.Textbox(label="Summary") |
|
|
| iface = gr.Interface( |
| fn=generate_summary, |
| inputs=input_text, |
| outputs=output_text, |
| title="Text Summarization", |
| description="Enter your text and get a summary using the Hugging Face T5 model.", |
| ) |
|
|
| iface.launch(share=True) |
|
|
