Update app.py

app.py

@@ -17,6 +17,9 @@ transform = T.Compose([
     T.ToTensor(),
 ])
 
+# Larger output display
+image_output_size = 512
+
 def predict_depth(image):
     img = transform(image).unsqueeze(0).to(device)
     with torch.no_grad():
@@ -26,14 +29,14 @@ def predict_depth(image):
     return [image, pred_image]
 
 # Gradio UI
-examples = [["example.png"]]
+examples = [["img_000.png"],["img_001.png"],["img_002.png"],["img_003.png"],["img_004.png"],["img_005.png"],]
 
 demo = gr.Interface(
     fn=predict_depth,
-    inputs=gr.Image(type="pil", label="Input RGB Image"),
+    inputs=gr.Image(type="pil", label="Input RGB Image", tool="editor", height=image_output_size),
     outputs=[
-        gr.Image(type="pil", label="Original Image"),
-        gr.Image(type="pil", label="Predicted Depth Map"),
+        gr.Image(type="pil", label="Original Image", height=image_output_size),
+        gr.Image(type="pil", label="Predicted Depth Map", height=image_output_size),
     ],
     title="🔭 DepthStar: Light-weight Depth Estimation",
     description="Upload an RGB image and get the depth map predicted by our tiny DepthStar model.",