Update app.py


app.py

@@ -33,7 +33,9 @@ model_topic.resize_token_embeddings(len(tokenizer_topic))
 
 
 def sentiment(sent: str):
-    print("\n\nNew inference at: ", datetime.utcnow(), "\n\n")
+    print("\n--------------------------------------------------------------------------------------------------------------------------\n")
+    print("New review inference at: ", datetime.utcnow())
+    print("\n--------------------------------------------------------------------------------------------------------------------------\n")
     sent_ = normalize(text=sent) 
     input_sent = torch.tensor([tokenizer_sent.encode(sent_)]).to(device)
     with torch.no_grad():
@@ -71,7 +73,9 @@ 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")
+    print("\n--------------------------------------------------------------------------------------------------------------------------\n")
+    print("New resume inference at: ", datetime.utcnow())
+    print("\n--------------------------------------------------------------------------------------------------------------------------\n")
     global key_list, device
     result = {}
     for i in key_list: