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| import gradio as grad | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
| model_name = "Helsinki-NLP/opus-mt-en-grk" | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| model_tkn = AutoTokenizer.from_pretrained(model_name) | |
| #opus_translation = pipeline("translation", model=model_name) | |
| def translate(text): | |
| inputs = model_tkn(text, return_tensors="pt") | |
| trans_output = model.generate(**inputs) #"generate": This method includes the logic for autoregressive generation | |
| response = model_tkn.decode(trans_output[0], skip_special_tokens=True) | |
| #response = opus_translator(text) | |
| return response | |
| grad.Interface(translate, inputs="text", outputs="text").launch() |