Update app.py


app.py

@@ -33,14 +33,15 @@ model_topic.resize_token_embeddings(len(tokenizer_topic))
 
 
 def sentiment(sent: str):
-    sent_ = normalize(text=sent) # segment input sentence, maybe raise ConnectionError: HTTPConnectionPool())
+    print("\n\nNew inference at: ", datetime.utcnow(), "\n\n")
+    sent_ = normalize(text=sent) 
     input_sent = torch.tensor([tokenizer_sent.encode(sent_)]).to(device)
     with torch.no_grad():
         out_sent = model_sent(input_sent)
         logits_sent = out_sent.logits.softmax(dim=-1).tolist()[0]
         pred_sent = dict_[np.argmax(logits_sent)]
 
-    sent = replace_all(text=sent) # segment input sentence, maybe raise ConnectionError: HTTPConnectionPool())
+    sent = replace_all(text=sent) 
     sent_segment = sent.split(".")
     for i, s in enumerate(sent_segment):
         s = s.strip()
@@ -70,6 +71,7 @@ key_list = ["person_name","dob_value","gender_value","phonenumber_value","email_
                 "designation_value","degree_value","skill_value"]
 label2id = {v: k for k, v in id2label.items()}
 def pred_resume(pdf_path) -> dict:
+    print("\n\nNew inference at: ", datetime.utcnow(), "\n\n")
     global key_list, device
     result = {}
     for i in key_list: