Update main_dl.py

main_dl.py

@@ -37,7 +37,7 @@ def load_vocab(file_path):
         return file.read().split('\n')[:-1]
 
 
-async def decode_sequence_rnn(input_sentence, src, tgt):
+def decode_sequence_rnn(input_sentence, src, tgt):
     global translation_model
 
     vocab_size = 15000
@@ -73,7 +73,7 @@ async def decode_sequence_rnn(input_sentence, src, tgt):
         decoded_sentence += " " + sampled_token
         if sampled_token == "[end]":
             break
-    return await decoded_sentence[8:-6]
+    return decoded_sentence[8:-6]
 
 # ===== Enf of Keras ====
 
@@ -174,7 +174,7 @@ class PositionalEmbedding(layers.Layer):
         })
         return config
     
-async def decode_sequence_tranf(input_sentence, src, tgt):
+def decode_sequence_tranf(input_sentence, src, tgt):
     global translation_model
 
     vocab_size = 15000
@@ -211,7 +211,7 @@ async def decode_sequence_tranf(input_sentence, src, tgt):
         decoded_sentence += " " + sampled_token
         if sampled_token == "[end]":
             break
-    return await decoded_sentence[8:-6]
+    return decoded_sentence[8:-6]
 
 # ==== End Transforformer section ====
 
@@ -248,28 +248,28 @@ def check_api():
     return {'message': "L'API fonctionne"}
 
 @api.get('/small_vocab/rnn', name="Traduction par RNN")
-def trad_rnn(lang_tgt:str,
+async def trad_rnn(lang_tgt:str,
               texte: str):
     global translation_model
     
     if (lang_tgt=='en'):
         translation_model = rnn_fr_en
-        return decode_sequence_rnn(texte, "fr", "en")
+        return await decode_sequence_rnn(texte, "fr", "en")
     else:
         translation_model = rnn_en_fr
-        return decode_sequence_rnn(texte, "en", "fr")
+        return await decode_sequence_rnn(texte, "en", "fr")
     
 @api.get('/small_vocab/transformer', name="Traduction par Transformer")
-def trad_transformer(lang_tgt:str,
+async def trad_transformer(lang_tgt:str,
               texte: str):
     global translation_model
     
     if (lang_tgt=='en'):
         translation_model = transformer_fr_en
-        return decode_sequence_tranf(texte, "fr", "en")
+        return await decode_sequence_tranf(texte, "fr", "en")
     else:
         translation_model = transformer_en_fr
-        return decode_sequence_tranf(texte, "en", "fr")
+        return await decode_sequence_tranf(texte, "en", "fr")
 
 @api.get('/small_vocab/plot_model', name="Affiche le modèle")
 def affiche_modele(lang_tgt:str,