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| """ | |
| Allows to predict the summary for a given entry text | |
| """ | |
| import torch | |
| import nltk | |
| import contractions | |
| import re | |
| import string | |
| nltk.download('stopwords') | |
| nltk.download('punkt') | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
| def clean_data(texts): | |
| texts = texts.lower() | |
| texts = contractions.fix(texts) | |
| texts = texts.translate(str.maketrans("", "", string.punctuation)) | |
| texts = re.sub(r'\n',' ',texts) | |
| return texts | |
| def inferenceAPI_t5(text: str) -> str: | |
| """ | |
| Predict the summary for an input text | |
| -------- | |
| Parameter | |
| text: str | |
| the text to sumarize | |
| Return | |
| str | |
| The summary for the input text | |
| """ | |
| # definition des parametres d'entree pour le modèle | |
| text = clean_data(text) | |
| device = torch.device("cpu" if torch.cuda.is_available() else "cpu") | |
| tokenizer= (AutoTokenizer.from_pretrained("./summarization_t5")) | |
| # chargement du modele local | |
| model = (AutoModelForSeq2SeqLM | |
| .from_pretrained("./summarization_t5") | |
| .to(device)) | |
| text_encoding = tokenizer( | |
| text, | |
| max_length=1024, | |
| padding='max_length', | |
| truncation=True, | |
| return_attention_mask=True, | |
| add_special_tokens=True, | |
| return_tensors='pt' | |
| ) | |
| generated_ids = model.generate( | |
| input_ids=text_encoding['input_ids'], | |
| attention_mask=text_encoding['attention_mask'], | |
| max_length=128, | |
| num_beams=8, | |
| length_penalty=0.8, | |
| early_stopping=True | |
| ) | |
| preds = [ | |
| tokenizer.decode(gen_id, skip_special_tokens=True, clean_up_tokenization_spaces=True) | |
| for gen_id in generated_ids | |
| ] | |
| return "".join(preds) | |
| if __name__ == "__main__": | |
| text = input('Entrez votre phrase à résumer : ') | |
| print('summary:',inferenceAPI(text)) | |