src/inference.py

"""
 Allows to predict the summary for a given entry text
"""
import pickle

import torch

import dataloader
from model import Decoder, Encoder, EncoderDecoderModel
from transformers import AutoModel

with open("model/vocab.pkl", "rb") as vocab:
    words = pickle.load(vocab)
vectoriser = dataloader.Vectoriser(words)


def inferenceAPI(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
    """
    text = text.split()
    # On défini les paramètres d'entrée pour le modèle
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    encoder = Encoder(len(vectoriser.idx_to_token) + 1, 256, 512, 0.5, device)
    encoder.to(device)
    decoder = Decoder(len(vectoriser.idx_to_token) + 1, 256, 512, 0.5, device)
    decoder.to(device)

    # On instancie le modèle
    model = EncoderDecoderModel(encoder, decoder, vectoriser, device)

    # model.load_state_dict(torch.load("model/model.pt", map_location=device))
    # model.eval()
    # model.to(device)

    # On vectorise le texte
    source = vectoriser.encode(text)
    source = source.to(device)

    # On fait passer le texte dans le modèle
    with torch.no_grad():
        output = model(source).to(device)
        output.to(device)
        output = output.argmax(dim=-1)
    return vectoriser.decode(output)


if __name__ == "__main__":
    # inference()
    print(inferenceAPI("If you choose to use these attributes in logged messages, you need to exercise some care. In the above example, for instance, the Formatter has been set up with a format string which expects ‘clientip’ and ‘user’ in the attribute dictionary of the LogRecord. If these are missing, the message will not be logged because a string formatting exception will occur. So in this case, you always need to pass the extra dictionary with these keys."))