nam194 commited on
Commit
7bfec6b
1 Parent(s): ef85f78

Update app.py

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![uncle_murphy.jpg](https://cdn-uploads.huggingface.co/production/uploads/6256774f78cab737328bd3b7/U1y_cfJRWHW81fxT5otB7.jpeg)

Files changed (1) hide show
  1. app.py +6 -2
app.py CHANGED
@@ -33,7 +33,9 @@ model_topic.resize_token_embeddings(len(tokenizer_topic))
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  def sentiment(sent: str):
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- print("\n\nNew inference at: ", datetime.utcnow(), "\n\n")
 
 
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  sent_ = normalize(text=sent)
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  input_sent = torch.tensor([tokenizer_sent.encode(sent_)]).to(device)
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  with torch.no_grad():
@@ -71,7 +73,9 @@ key_list = ["person_name","dob_value","gender_value","phonenumber_value","email_
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  "designation_value","degree_value","skill_value"]
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  label2id = {v: k for k, v in id2label.items()}
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  def pred_resume(pdf_path) -> dict:
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- print("\n\nNew inference at: ", datetime.utcnow(), "\n\n")
 
 
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  global key_list, device
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  result = {}
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  for i in key_list:
 
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  def sentiment(sent: str):
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+ print("\n--------------------------------------------------------------------------------------------------------------------------\n")
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+ print("New review inference at: ", datetime.utcnow())
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+ print("\n--------------------------------------------------------------------------------------------------------------------------\n")
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  sent_ = normalize(text=sent)
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  input_sent = torch.tensor([tokenizer_sent.encode(sent_)]).to(device)
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  with torch.no_grad():
 
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  "designation_value","degree_value","skill_value"]
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  label2id = {v: k for k, v in id2label.items()}
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  def pred_resume(pdf_path) -> dict:
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+ print("\n--------------------------------------------------------------------------------------------------------------------------\n")
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+ print("New resume inference at: ", datetime.utcnow())
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+ print("\n--------------------------------------------------------------------------------------------------------------------------\n")
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  global key_list, device
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  result = {}
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  for i in key_list: