depth

README.md

@@ -4,9 +4,9 @@ emoji: ⚡
 colorFrom: blue
 colorTo: red
 sdk: gradio
-sdk_version: 5.37.0
+sdk_version: 2.8.13
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

@@ -1,17 +1,17 @@
 import gradio as gr
-from transformers import DPTImageProcessor, DPTForDepthEstimation
+from transformers import DPTFeatureExtractor, DPTForDepthEstimation
 import torch
 import numpy as np
 from PIL import Image
 
 torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
 
-processor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
+feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
 model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
 
 def process_image(image):
     # prepare image for the model
-    encoding = processor(image, return_tensors="pt")
+    encoding = feature_extractor(image, return_tensors="pt")
     
     # forward pass
     with torch.no_grad():
@@ -37,9 +37,10 @@ description = "Demo for Intel's DPT, a Dense Prediction Transformer for state-of
 examples =[['cats.jpg']]
 
 iface = gr.Interface(fn=process_image, 
-                     inputs=gr.Image(type="pil"), 
-                     outputs=gr.Image(type="pil", label="predicted depth"),
+                     inputs=gr.inputs.Image(type="pil"), 
+                     outputs=gr.outputs.Image(type="pil", label="predicted depth"),
                      title=title,
                      description=description,
-                     examples=examples)
+                     examples=examples,
+                     enable_queue=True)
 iface.launch(debug=True)

requirements.txt

@@ -1,4 +1,4 @@
 torch
-transformers
+git+https://github.com/nielsrogge/transformers.git@add_dpt_redesign#egg=transformers
 numpy
 Pillow