hugging_face_integration_app dependency cleanup

Signed-off-by: bluna301 <[email protected]>

Author: bluna301

examples/apps/hugging_face_integration_app/med_image_generation/app.py

@@ -1,39 +1,35 @@
-import logging 
-from pathlib import Path
-import torch 
-from diffusers import StableDiffusionPipeline
-from monai.deploy.core import AppContext, Application
-from PIL import Image
-import numpy as np
 import argparse
+import logging
+
+import torch
+from diffusers import StableDiffusionPipeline
 
+from monai.deploy.core import Application
 
 
 class App(Application):
-	name = "Diffusion Image App"
-	description = "Simple application showing diffusion to generate Images"
-	def compose(self):
-		model_id = "Nihirc/Prompt2MedImage"
-		device = "cuda"
-		parser = argparse.ArgumentParser()
-		parser.add_argument("--input_prompt", type=str, default="Generate a X-ray")
-		parser.add_argument("--output", type=str, default="./out.jpg")
-		args = parser.parse_args()
-
-		input_prompt = args.input_prompt
-		output_path = args.output
-		print("Input Prompt: ", input_prompt)
-		pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-		pipe = pipe.to(device)
-		prompt = "Show me an X ray pevic fracture"
-		image = pipe(prompt).images[0]
-		image.save(output_path)
+    name = "Diffusion Image App"
+    description = "Simple application showing diffusion to generate Images"
+
+    def compose(self):
+        model_id = "Nihirc/Prompt2MedImage"
+        device = "cuda"
+        parser = argparse.ArgumentParser()
+        parser.add_argument("--input_prompt", type=str, default="Generate a X-ray")
+        parser.add_argument("--output", type=str, default="./out.jpg")
+        args = parser.parse_args()
+
+        input_prompt = args.input_prompt
+        output_path = args.output
+        print("Input Prompt: ", input_prompt)
+        pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+        pipe = pipe.to(device)
+        prompt = "Show me an X ray pevic fracture"
+        image = pipe(prompt).images[0]
+        image.save(output_path)
 
 
 if __name__ == "__main__":
-	logging.info(f"Begin {__name__}")
-	App().run()
-	logging.info(f"End {__name__}")
-
-	
-
+    logging.info(f"Begin {__name__}")
+    App().run()
+    logging.info(f"End {__name__}")