Update handler.py

handler.py

@@ -7,17 +7,17 @@ from diffusers.pipelines import FluxControlNetPipeline
 from io import BytesIO
 
 class EndpointHandler:
-    def __init__(self, model_dir="huy Ai123/Flux.1dev-Image-Upscaler"):
+    def __init__(self, model_dir="huyai123/Flux.1-dev-Image-Upscaler"):
         # Access the environment variable
         HUGGINGFACE_API_TOKEN = os.getenv('HUGGINGFACE_API_TOKEN')
         if not HUGGINGFACE_API_TOKEN:
-            raise ValueError("HUGGINGFACE_API_TOKEN")
+            raise ValueError("HUGGINGFACE_API_TOKEN environment variable is not set")
 
         # Load model and pipeline
-        self.controlnet = FluxControlNetModel.From_Pretrained(
+        self.controlnet = FluxControlNetModel.from_pretrained(
             model_dir, torch_dtype=torch.bfloat16, use_auth_token=HUGGINGFACE_API_TOKEN
         )
-        self.pipe = FluxControlNetPipeline.From_Pretrained(
+        self.pipe = FluxControlNetPipeline.from_pretrained(
             "black-forest-labs/FLUX.1-dev",
             controlnet=self.controlnet,
             torch_dtype=torch.bfloat16,
@@ -35,7 +35,7 @@ class EndpointHandler:
         # Upscale x4
         return image.resize((w * 4, h * 4))
 
-    def post_process(self, output):
+    def postprocess(self, output):
         # Save output image to a file-like object
         buffer = BytesIO()
         output.save(buffer, format="PNG")
@@ -49,9 +49,9 @@ class EndpointHandler:
         output_image = self.pipe(
             prompt=data.get("prompt", ""),
             control_image=control_image,
-            controlnet Conditioning_scale=0.6,
+            controlnet_conditioning_scale=0.6,
             num_inference_steps=28,
-            height=control_image.Size[1],
+            height=control_image.size[1],
             width=control_image.size[0],
         ).images[0]
         # Postprocess output