refactoring de requirements.txt

src/inference_lstm.py

@@ -0,0 +1,59 @@
+"""
+ Allows to predict the summary for a given entry text
+ using LSTM model
+"""
+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 = AutoModel.from_pretrained("EveSa/SummaryProject-LSTM")
+
+    # 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."))