| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| import gradio as gr | |
| tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn") | |
| def text_summarize(article): | |
| inputs = tokenizer(article, return_tensors = 'pt') | |
| output = model.generate(inputs.input_ids, | |
| max_new_tokens = 200, | |
| do_sample = True, | |
| top_p = 0.9, | |
| top_k = 50) | |
| output_text = tokenizer.decode(output[0], skip_special_tokens=True) | |
| return output_text | |
| iface = gr.Interface( | |
| fn = text_summarize, | |
| inputs = gr.Textbox(label = "Article", lines = 8, placeholder = "Paste your text here.."), | |
| outputs = gr.Textbox(label = "Summarized Text", lines = 5) | |
| ) | |
| iface.launch() | |