Update app.py

app.py

@@ -35,6 +35,7 @@ model_topic.resize_token_embeddings(len(tokenizer_topic))
 def sentiment(sent: str):
     print("\n--------------------------------------------------------------------------------------------------------------------------\n")
     print("New review inference at: ", datetime.utcnow())
+    print("review: ", sent)
     print("\n--------------------------------------------------------------------------------------------------------------------------\n")
     sent_ = normalize(text=sent) 
     input_sent = torch.tensor([tokenizer_sent.encode(sent_)]).to(device)
@@ -75,6 +76,7 @@ label2id = {v: k for k, v in id2label.items()}
 def pred_resume(pdf_path) -> dict:
     print("\n--------------------------------------------------------------------------------------------------------------------------\n")
     print("New resume inference at: ", datetime.utcnow())
+    print("Pdf name: ", pdf_path.name)
     print("\n--------------------------------------------------------------------------------------------------------------------------\n")
     global key_list, device
     result = {}