Update app.py

app.py

@@ -1,7 +1,7 @@
 # app.py for Hugging Face Space: Connecting Meta Llama 3.2 Vision, Segment Anything 2, and Diffusion Model
 import gradio as gr
 import spaces  # Import the spaces module to use GPU-specific decorators
-from transformers import MllamaForConditionalGeneration, AutoProcessor
+from transformers import MllamaForConditionalGeneration, AutoProcessor, pipeline
 from diffusers import StableDiffusionPipeline
 import torch
 import os
@@ -16,9 +16,9 @@ vision_model = MllamaForConditionalGeneration.from_pretrained(
     llama_vision_model_id,
     torch_dtype=torch.bfloat16,
     device_map="auto",
-    use_auth_token=hf_token
+    token=hf_token  # Updated to use 'token' instead of 'use_auth_token'
 )
-processor = AutoProcessor.from_pretrained(llama_vision_model_id, use_auth_token=hf_token)
+processor = AutoProcessor.from_pretrained(llama_vision_model_id, token=hf_token)
 
 # Set up Meta Segment Anything 2 model (using private model with token)
 segment_model_id = "meta/segment-anything-2"
@@ -26,13 +26,13 @@ segment_pipe = pipeline(
     "image-segmentation",
     model=segment_model_id,
     device=0,  # Force usage of GPU
-    use_auth_token=hf_token,  # Use Hugging Face token for authentication
+    token=hf_token  # Updated to use 'token' instead of 'use_auth_token'
 )
 
 # Set up Stable Diffusion Lite model
 stable_diffusion_model_id = "runwayml/stable-diffusion-v1-5"
 diffusion_pipe = StableDiffusionPipeline.from_pretrained(
-    stable_diffusion_model_id, torch_dtype=torch.float16, use_auth_token=hf_token
+    stable_diffusion_model_id, torch_dtype=torch.float16, token=hf_token  # Updated to use 'token'
 )
 diffusion_pipe = diffusion_pipe.to("cuda")  # Force usage of GPU