Update handler.py

handler.py

@@ -7,7 +7,12 @@ import io
 from transformers import BlipForConditionalGeneration, BlipProcessor
 import logging
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
+# Configure logging
+logging.basicConfig(level=logging.DEBUG)
+# Configure logging
+logging.basicConfig(level=logging.ERROR)
+# Configure logging
+logging.basicConfig(level=logging.WARNING)
 class EndpointHandler():
     def __init__(self, path=""):
         self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
@@ -15,19 +20,16 @@ class EndpointHandler():
         self.model.eval()
 
     def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
-        logging.debug('------------This is a debug message')
-        logging.info('----------------------This is an info message')
-        logging.warning('--------This is a warning message')
-        logging.error('----------This is an error message')
-        logging.critical('-------------------This is a critical message')
+        logging.error(f"----------This is an error message")
+        #logging.critical('-------------------This is a critical message')
         print("000000--",type(data))
-        logging.warning('--------This is a warning message')
+        #logging.warning('--------This is a warning message')
         print("input data is here------------",data)
         input_data = data.get("inputs", {})
         logging.warning('------input_data--This is a warning message', input_data)
         print("input data is here-2-----------",type(input_data))
         encoded_images = input_data.get("images")
-        logging.warning('---encoded_images-----This is a warning message',encoded_images)
+        logging.warning(f"---encoded_images-----This is a warning message {str(encoded_images)}")
         print("input encoded_images is here------------",type(encoded_images))
         if not encoded_images:
             return {"captions": [], "error": "No images provided"} 
@@ -46,7 +48,7 @@ class EndpointHandler():
             # Non test code
             dataBytesIO = io.BytesIO(byteImg)
             raw_images =[Image.open(dataBytesIO)]
-            logging.warning('----raw_images----This is a warning message',raw_images)
+            logging.warning(f"----raw_images----This is a warning message {str(raw_images)}")
             # Check if any images were successfully decoded
             if not raw_images:
                 print("No valid images found.")
@@ -57,12 +59,11 @@ class EndpointHandler():
                 "pixel_values": torch.cat([inp["pixel_values"] for inp in processed_inputs], dim=0).to(device),
                 "max_new_tokens":40
             }
-            logging.warning('---processed_inputs-----This is a warning message', processed_inputs)
             with torch.no_grad():
                 out = self.model.generate(**processed_inputs)
 
             captions = self.processor.batch_decode(out, skip_special_tokens=True)
-            logging.warning('----captions----This is a warning message',captions)
+            logging.warning(f"----captions----This is a warning message {str(captions)}")
             print("caption is here-------",captions)
             return {"captions": captions}
         except Exception as e: