loggin

app.py

@@ -1,5 +1,10 @@
 import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.utils import logging
+
+logging.set_verbosity_debug()
+
+logger = logging.get_logger("transformers")
 
 # Load model directly from your Hugging Face repository
 def load_model():    
@@ -22,7 +27,9 @@ def generate_soap_note(doctor_patient_conversation):
 
 
     # Decode and extract the response part
-    return tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    decoded_response = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    logger.debug(f"Decoded response: {decoded_response}")
+    return decoded_response
     
 # Load model and tokenizer (this will run once when the app starts)
 model, tokenizer = load_model()