Remove print statments

app.py

@@ -16,28 +16,13 @@ from XML_to_HTML import NER_XML_to_HTML
 from NER_Distiller import distill_entities
 
 app = FastAPI()
-print("Version 2...")
-
-# Download files from model repo
-# tag_vocab_path = hf_hub_download(
-    # repo_id="SinaLab/Nested",
-    # filename="tag_vocab.pkl"
-# )
-
 
 pretrained_path = "aubmindlab/bert-base-arabertv2"  # must match training
 tokenizer = AutoTokenizer.from_pretrained(pretrained_path)
 encoder = AutoModel.from_pretrained(pretrained_path).eval()
 
-# checkpoint_path = hf_hub_download(
-#     repo_id="SinaLab/Nested",
-#     filename="checkpoints/checkpoint_2.pt"
-# )
-
-
 
 checkpoint_path = snapshot_download(repo_id="SinaLab/Nested", allow_patterns="checkpoints/")
-print("checkpoint_path : ", checkpoint_path)
 
 args_path = hf_hub_download(
     repo_id="SinaLab/Nested",
@@ -47,8 +32,6 @@ args_path = hf_hub_download(
 with open(args_path, 'r') as f:
     args_data = json.load(f)
     
-print("data : ", args_data)
-
 # Load model
 with open("Nested/utils/tag_vocab.pkl", "rb") as f:
     label_vocab = pickle.load(f)
@@ -156,36 +139,26 @@ def extract(sentence):
 
 
 def NER(sentence, mode):
-    print("within NER, and mode is: ", mode)
     output_list = []
     xml = ""
     if mode.strip() == "1":
         output_list = jsons_to_list_of_lists(extract(sentence))
         return output_list
     elif mode.strip() == "2":
-        print("outputlist : ", output_list)
         if output_list != []:
             xml = IBO_to_XML(output_list)
-            print("xml is: ", xml)
             return xml
         else:
             output_list = jsons_to_list_of_lists(extract(sentence))
-            print("output_list .....: ", output_list)
             xml = IBO_to_XML(output_list)
-            print("else xml is: ", xml)
             return xml
                     
     elif mode.strip() == "3":
-        print("mode is 3")
         if xml != "":
-            #print("in if")
             html = NER_XML_to_HTML(xml)
             return html
         else:
-            print("in else : ")
-            print("extract : ", extract(sentence))
             output_list = jsons_to_list_of_lists(extract(sentence))
-            #print("output list : ", output_list)
             xml = IBO_to_XML(output_list)
             html = NER_XML_to_HTML(xml)
             return html
@@ -228,57 +201,8 @@ def predict(request: NERRequest):
         "statusCode": 0,
     }
 
-    print("content: ", content)
     return JSONResponse(
         content=content,
         media_type="application/json",
         status_code=200,
     )
-
-
-# sentence = "ذهب احمد إلى جامعة"
-# Load tagger
-# tagger, tag_vocab, train_config = load_checkpoint(checkpoint_path)
-
-# Convert text to a tagger dataset and index the tokens in args.text
-# dataset, token_vocab = text2segments(sentence)
-
-# vocabs = namedtuple("Vocab", ["tags", "tokens"])
-# vocab = vocabs(tokens=token_vocab, tags=tag_vocab)
-
-# From the datasets generate the dataloaders
-# dataloader = get_dataloaders(
-#     (dataset,),
-#     vocab,
-#     args_data,
-#     batch_size=32,
-#     shuffle=(False,),
-# )[0]
-
-# Perform inference on the text and get back the tagged segments
-# segments = tagger.infer(dataloader)
-# segments_lists = []
-
-
-## Print results
-## for segment in segments:
-##     s = [
-##         f"{token.text} ({'|'.join([t['tag'] for t in token.pred_tag])})"
-##         for token in segment
-##     ]
-##     print(" ".join(s))
-
-
-# for segment in segments:
-#     for token in segment:
-#         segments_list = {}
-#         segments_list["token"] = token.text
-#         list_of_tags = [t['tag'] for t in token.pred_tag]
-#         list_of_tags = [i for i in list_of_tags if i not in('O',' ','')]
-#         if list_of_tags == []:
-#             segments_list["tags"] = ' '.join(['O'])
-#         else:
-#             segments_list["tags"] = ' '.join(list_of_tags)
-#         segments_lists.append(segments_list)  
-
-# print(segments_lists)