arcanaflow/processor

1from transformers import AutoModelForSeq2SeqLM, AutoTokenizer2import re3 4translation_tokenizer = AutoTokenizer.from_pretrained("translation_model_saved", use_auth_token=True)5translation_model = AutoModelForSeq2SeqLM.from_pretrained("translation_model_saved", use_auth_token=True)6 7summarizer_tokenizer = AutoTokenizer.from_pretrained("google-pegasus-xsum", use_auth_token=True)8summarizer_model = AutoModelForSeq2SeqLM.from_pretrained("google-pegasus-xsum", use_auth_token=True)9 10def summarize(message):11    inputs = summarizer_tokenizer(message,return_tensors="pt")12    outputs = summarizer_model.generate(inputs["input_ids"])13    processed_text = summarizer_tokenizer.decode(outputs[0])14    summary = re.sub('</s>','',processed_text)15    return summary16 17def translate(message):18    inputs = translation_tokenizer(message,return_tensors="pt")19    outputs = translation_model.generate(inputs["input_ids"])20    processed_text = translation_tokenizer.decode(outputs[0])21    translation = re.sub('</s>','',processed_text)22    return translation