Added a sample integration for LLM models in huggingface (Project-MONAI#494)

* Added a sample integration for LLM models in huggingface

Signed-off-by: Vikash Gupta <[email protected]>

* Fixed minor issue

Signed-off-by: Vikash Gupta <[email protected]>

---------

Signed-off-by: Vikash Gupta <[email protected]>
Co-authored-by: Vikash Gupta <[email protected]>

Author: 2 people

examples/apps/hugging_face_integration_app/med_image_generation/app.py

@@ -1,35 +1,39 @@
-import argparse
-import logging
-
-import torch
+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
 
-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__}")
+
+	
+