Upload app.py

app.py

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+import gradio as gr
+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')
+
+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 = feature_extractor(image, return_tensors="pt")
+    
+    # forward pass
+    with torch.no_grad():
+       outputs = model(**encoding)
+       predicted_depth = outputs.predicted_depth
+    
+    # interpolate to original size
+    prediction = torch.nn.functional.interpolate(
+                        predicted_depth.unsqueeze(1),
+                        size=image.size[::-1],
+                        mode="bicubic",
+                        align_corners=False,
+                 ).squeeze()
+    output = prediction.cpu().numpy()
+    formatted = (output * 255 / np.max(output)).astype('uint8')
+    img = Image.fromarray(formatted)
+    return img
+    
+    return result
+    
+title = "Demo: zero-shot depth estimation with DPT"
+description = "Demo for Intel's DPT, a Dense Prediction Transformer for state-of-the-art dense prediction tasks such as semantic segmentation and depth estimation."
+examples =[['cats.jpg']]
+
+iface = gr.Interface(fn=process_image, 
+                     inputs=gr.inputs.Image(type="pil"), 
+                     outputs=gr.outputs.Image(type="pil", label="predicted depth"),
+                     title=title,
+                     description=description,
+                     examples=examples,
+                     enable_queue=True)
+iface.launch(debug=True)