Update app.py

app.py

@@ -1,38 +1,6 @@
-import sagemaker
-import boto3
-from sagemaker.huggingface import HuggingFaceModel
+from diffusers import DiffusionPipeline
 
-try:
-	role = sagemaker.get_execution_role()
-except ValueError:
-	iam = boto3.client('iam')
-	role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']
+pipe = DiffusionPipeline.from_pretrained("cloudqi/cqi_text_to_image_pt_v0")
 
-# Hub Model configuration. https://huggingface.co/models
-hub = {
-	'HF_MODEL_ID':'cloudqi/cqi_text_to_image_pt_v0',
-	'HF_TASK':'text-to-image'
-}
-
-# create Hugging Face Model Class
-huggingface_model = HuggingFaceModel(
-	transformers_version='4.37.0',
-	pytorch_version='2.1.0',
-	py_version='py310',
-	env=hub,
-	role=role, 
-)
-
-# deploy model to SageMaker Inference
-predictor = huggingface_model.deploy(
-	initial_instance_count=1, # number of instances
-	instance_type='ml.m5.xlarge' # ec2 instance type
-)
-
-image_bytes = predictor.predict({
-	"inputs": "Astronaut riding a horse",
-})
-# You can access the image with PIL.Image for example
-import io
-from PIL import Image
-image = Image.open(io.BytesIO(image_bytes))
+prompt = "Gato em alta qualidade na neve\n"
+image = pipe(prompt).images[0]