Deprecation issue is fixed

app.py

@@ -1,78 +1,78 @@
-# Importing the requirements
-import numpy as np
-import gradio as gr
-import torch
-from PIL import Image
-from transformers import DPTFeatureExtractor, DPTForDepthEstimation
-
-
-# Load the model and feature extractor
-feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-beit-large-512")
-model = DPTForDepthEstimation.from_pretrained("Intel/dpt-beit-large-512")
-
-
-def process_image(image):
-    """
-    Preprocesses an image, passes it through a model, and returns the formatted depth map as an image.
-
-    Args:
-        image (PIL.Image.Image): The input image.
-
-    Returns:
-        PIL.Image.Image: The formatted depth map as an image.
-    """
-
-    # Preprocess the image for the model
-    encoding = feature_extractor(image, return_tensors="pt")
-
-    # Forward pass through the model
-    with torch.no_grad():
-        outputs = model(**encoding)
-        predicted_depth = outputs.predicted_depth
-
-    # Interpolate the predicted depth map to the original image 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")
-
-    # Return the formatted depth map as an image
-    return Image.fromarray(formatted)
-
-
-# Image input for the interface
-image = gr.Image(type="pil", label="Image")
-
-# Output for the interface
-answer = gr.Image(type="pil", label="Depth Map")
-
-# Examples for the interface
-examples = [
-    ["cat.jpg"],
-    ["dog.jpg"],
-    ["bird.jpg"],
-]
-
-# Title, description, and article for the interface
-title = "Zero Shot Depth Estimation"
-description = "Gradio Demo for the Intel/DPT Beit-Large-512 Depth Estimation model. This model can estimate the depth of objects in images. To use it, simply upload your image and click 'submit', or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://arxiv.org/pdf/2307.14460' target='_blank'>MiDaS v3.1 – A Model Zoo for Robust Monocular Relative Depth Estimation</a> | <a href='https://huggingface.co/Intel/dpt-beit-large-512' target='_blank'>Model Page</a></p>"
-
-
-# Launch the interface
-interface = gr.Interface(
-    fn=process_image,
-    inputs=[image],
-    outputs=answer,
-    examples=examples,
-    title=title,
-    description=description,
-    article=article,
-    theme="Soft",
-    allow_flagging="never",
-)
-interface.launch(debug=False)
+# Importing the requirements
+import numpy as np
+import gradio as gr
+import torch
+from PIL import Image
+from transformers import DPTImageProcessor, DPTForDepthEstimation
+
+
+# Load the model and feature extractor
+feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-beit-large-512")
+model = DPTForDepthEstimation.from_pretrained("Intel/dpt-beit-large-512")
+
+
+def process_image(image):
+    """
+    Preprocesses an image, passes it through a model, and returns the formatted depth map as an image.
+
+    Args:
+        image (PIL.Image.Image): The input image.
+
+    Returns:
+        PIL.Image.Image: The formatted depth map as an image.
+    """
+
+    # Preprocess the image for the model
+    encoding = feature_extractor(image, return_tensors="pt")
+
+    # Forward pass through the model
+    with torch.no_grad():
+        outputs = model(**encoding)
+        predicted_depth = outputs.predicted_depth
+
+    # Interpolate the predicted depth map to the original image 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")
+
+    # Return the formatted depth map as an image
+    return Image.fromarray(formatted)
+
+
+# Image input for the interface
+image = gr.Image(type="pil", label="Image")
+
+# Output for the interface
+answer = gr.Image(type="pil", label="Depth Map")
+
+# Examples for the interface
+examples = [
+    ["cat.jpg"],
+    ["dog.jpg"],
+    ["bird.jpg"],
+]
+
+# Title, description, and article for the interface
+title = "Zero Shot Depth Estimation"
+description = "Gradio Demo for the Intel/DPT Beit-Large-512 Depth Estimation model. This model can estimate the depth of objects in images. To use it, upload your photo and click 'submit', or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/pdf/2307.14460' target='_blank'>MiDaS v3.1 – A Model Zoo for Robust Monocular Relative Depth Estimation</a> | <a href='https://huggingface.co/Intel/dpt-beit-large-512' target='_blank'>Model Page</a></p>"
+
+
+# Launch the interface
+interface = gr.Interface(
+    fn=process_image,
+    inputs=[image],
+    outputs=answer,
+    examples=examples,
+    title=title,
+    description=description,
+    article=article,
+    theme="Soft",
+    allow_flagging="never",
+)
+interface.launch(debug=False)