Update app.py

app.py

@@ -1,13 +1,13 @@
 import torch
-from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation, DPTFeatureExtractor, DPTForDepthEstimation
+from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation, DPTImageProcessor, DPTForDepthEstimation
 from PIL import Image, ImageFilter
 import numpy as np
 import gradio as gr
 
-# Load pre-trained models and feature extractors
-seg_feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
+# Load pre-trained models and processors
+seg_processor = SegformerImageProcessor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
 seg_model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
-depth_feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
+depth_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
 depth_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
 
 def process_image(image):
@@ -15,7 +15,7 @@ def process_image(image):
     image = image.resize((512, 512))
     
     # Perform semantic segmentation
-    seg_inputs = seg_feature_extractor(images=image, return_tensors="pt")
+    seg_inputs = seg_processor(images=image, return_tensors="pt")
     with torch.no_grad():
         seg_outputs = seg_model(**seg_inputs)
     seg_logits = seg_outputs.logits
@@ -26,7 +26,7 @@ def process_image(image):
     binary_mask = (segmentation == person_class_index).astype(np.uint8) * 255
     
     # Perform depth estimation
-    depth_inputs = depth_feature_extractor(images=image, return_tensors="pt")
+    depth_inputs = depth_processor(images=image, return_tensors="pt")
     with torch.no_grad():
         depth_outputs = depth_model(**depth_inputs)
     predicted_depth = depth_outputs.predicted_depth[0].cpu().numpy()
@@ -64,3 +64,4 @@ interface = gr.Interface(
 # Launch the interface
 if __name__ == "__main__":
     interface.launch()
+