uvicorn debug

src/api.py

@@ -47,5 +47,5 @@ async def prediction(request: Request, text: str = Form(None)):
 
 
 # lancer le serveur et le recharger a chaque modification sauvegardee
-if __name__ == "__main__":
-    uvicorn.run("api:app", port=8000, reload=True)
+# if __name__ == "__main__":
+#     uvicorn.run("api:app", port=8000, reload=True)

src/inference.py

@@ -7,7 +7,6 @@ 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)
@@ -52,6 +51,6 @@ def inferenceAPI(text: str) -> str:
     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."))
+# 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."))