| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-cnn-12-6") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-cnn-12-6") | |
| def summarize_text(text): | |
| inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True) | |
| summary_ids = model.generate( | |
| inputs["input_ids"], | |
| attention_mask=inputs["attention_mask"], | |
| max_length=100, | |
| min_length=20, | |
| length_penalty=2.0, | |
| num_beams=4, | |
| early_stopping=True, | |
| no_repeat_ngram_size=3 | |
| ) | |
| summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| return summary | |
| iface = gr.Interface( | |
| fn=summarize_text, | |
| inputs=gr.Textbox(lines=5, label="Input Text"), | |
| outputs=gr.Textbox(label="Summary"), | |
| title="DistilBART Summarizer", | |
| description="Summarize any input text using DistilBART fine-tuned model." | |
| ) | |
| iface.launch() | |