Update handler.py

handler.py

@@ -131,15 +131,12 @@ class EndpointHandler:
         self.opts = ScriptOptions
         repo_id = MODEL_REPO_MAP.get(self.opts.model)
 
-        print(f"Loading model '{self.opts.model}' from '{repo_id}'...")
         self.model: nn.Module = timm.create_model("hf-hub:" + repo_id).eval()
         state_dict = timm.models.load_state_dict_from_hf(repo_id)
         self.model.load_state_dict(state_dict)
 
-        print("Loading tag list...")
         self.labels: LabelData = load_labels_hf(repo_id=repo_id)
 
-        print("Creating data transform...")
         self.transform = create_transform(**resolve_data_config(self.model.pretrained_cfg, model=self.model))
 
         # move model to GPU, if available
@@ -173,7 +170,6 @@ class EndpointHandler:
         start_time=time.time()
         for document in data:
                 image=load_image(document.get('createdImage', 'https://nomorecopyright.com/default.jpg'))
-                print("Loading image and preprocessing...")
                 # get image
                 # ensure image is RGB
                 img_input = pil_ensure_rgb(image)
@@ -187,7 +183,6 @@ class EndpointHandler:
                     # move model to GPU, if available
                     if torch_device.type != "cpu":
                         inputs = inputs.to(torch_device)
-                    print("Running inference...")
                     outputs = self.model.forward(inputs)
                     # apply the final activation function (timm doesn't support doing this internally)
                     outputs = F.sigmoid(outputs)
@@ -195,7 +190,6 @@ class EndpointHandler:
                     if torch_device.type != "cpu":
                         inputs = inputs.to("cpu")
                         outputs = outputs.to("cpu")
-                print("Processing results...")
                 caption, taglist, ratings, character, general = get_tags(
                     probs=outputs.squeeze(0),
                     labels=self.labels,