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| import gradio as gr | |
| from transformers import pipeline | |
| def summarize(post): | |
| detector = pipeline( | |
| "text-classification", model="papluca/xlm-roberta-base-language-detection" | |
| ) | |
| lang_results = detector(post) | |
| if (detected_lang := lang_results[0]["label"]) == "en": | |
| summarizer_model_name = "sshleifer/distilbart-cnn-12-6" | |
| elif detected_lang == "es": | |
| summarizer_model_name = "ELiRF/mbart-large-cc25-dacsa-es" | |
| pipe = pipeline("summarization", model=summarizer_model_name) | |
| return pipe(post)[0]["summary_text"] | |
| iface = gr.Interface(fn=summarize, inputs="text", outputs="text") | |
| iface.launch() | |