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| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| tokenizer = AutoTokenizer.from_pretrained("mmcquade11/autonlp-reuters-summarization-34018133") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("mmcquade11/autonlp-reuters-summarization-34018133") | |
| def summarize(text): | |
| input_ids = torch.tensor(tokenizer.encode(text, add_special_tokens=True)).unsqueeze(0) | |
| summary_ids = model.generate(input_ids, num_beams=4, max_length=100, early_stopping=True) | |
| return tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| def summarize_text(text): | |
| return summarize(text) | |
| iface = gr.Interface(summarize_text, "textbox", "label") | |
| if __name__ == "__main__": | |
| iface.launch() | |