Update app.py

app.py

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+from transformers import GLPNFeatureExtractor, GLPNForDepthEstimation
+import torch
+import numpy as np
+from PIL import Image
+import requests
+import gradio as gr
+import os
+
+# url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+# image = Image.open(requests.get(url, stream=True).raw)
+
+feature_extractor = GLPNFeatureExtractor.from_pretrained("vinvino02/glpn-nyu")
+model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-nyu")
+
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+def predict(image):
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        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,
+    )
+    # visualize the prediction
+    output = prediction.squeeze().cpu().numpy()
+    formatted = (output * 255 / np.max(output)).astype("uint8")
+    depth_image = Image.fromarray(formatted)
+    return depth_image
+
+
+# Gradio App
+title="Image Segmentation GAN"
+description="This segments a Normal Image"
+
+demo=gr.Interface(fn=predict,
+                  inputs=gr.Image(type='pil'),
+                  outputs=gr.Image(type='pil'),
+                  title=title ,
+                  examples=example_list,
+                  description=description)
+demo.launch(debug=False)