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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline | |
| # Loading summarization model | |
| summarization_tokenizer = AutoTokenizer.from_pretrained("ieuniversity/sciencebrief_summarization") | |
| summarization_model = AutoModelForSeq2SeqLM.from_pretrained("ieuniversity/sciencebrief_summarization") | |
| def summarize(text): | |
| input_ids = summarization_tokenizer.encode(text, return_tensors="pt") | |
| output_ids = summarization_model.generate(input_ids) | |
| output_text = summarization_tokenizer.decode(output_ids[0], skip_special_tokens=True) | |
| return output_text | |
| iface_summarize = gr.Interface( | |
| fn=summarize, | |
| inputs=gr.inputs.Textbox(lines=10, label="Input Text"), | |
| outputs=gr.outputs.Textbox(label="Summary"), | |
| title="ScienceBrief Summarization model", | |
| description="Get a summary of your text using the ScienceBrief summarization model.", | |
| theme="compact" | |
| ) | |
| iface_summarize.launch() | |