Upload handler.py

handler.py

@@ -3,13 +3,17 @@ from transformers import BlipProcessor, BlipForConditionalGeneration
 from PIL import Image
 from io import BytesIO
 import torch
-import base64
+import logging
 
+
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 
 class EndpointHandler():
     def __init__(self, path=""):
+        logger.debug("Initializing model and processor.")
         self.model = BlipForConditionalGeneration.from_pretrained(
             "quadranttechnologies/qhub-blip-image-captioning-finetuned").to(device)
         self.processor = BlipProcessor.from_pretrained("quadranttechnologies/qhub-blip-image-captioning-finetuned")
@@ -25,6 +29,7 @@ class EndpointHandler():
             A :obj:`dict`:. The object returned should be a dict of one list like {"descriptions": ["Description of the image"]} containing :
                 - "description": A string corresponding to the generated description.
         """
+        logger.debug(f"Received data keys: {data.keys()}")
 
         images = data.pop("inputs", data)
         text = data.get("text", "")