fix gradio sdk upgrade issues

app.py

@@ -8,65 +8,75 @@ from kornia_moons.feature import *
 from kornia_moons.viz import *
 import gradio as gr
 
-def load_torch_image(fname):
-    img: Tensor = K.io.load_image(fname, K.io.ImageLoadType.RGB32)
-    img = img[None]  # 1xCxHxW / fp32 / [0, 1]
-    img = K.geometry.resize(img, (700, 700))
-    return img
+def load_torch_image(img):
+    if isinstance(img, np.ndarray):
+        # If the input is already a numpy array, convert it to a tensor
+        img_tensor = K.image_to_tensor(img).float() / 255.0
+    else:
+        # If it's a file path, load it using kornia
+        img_tensor = K.io.load_image(img, K.io.ImageLoadType.RGB32)
+    
+    img_tensor = img_tensor.unsqueeze(0)  # Add batch dimension: 1xCxHxW
+    img_tensor = K.geometry.resize(img_tensor, (700, 700))
+    return img_tensor
  
-def inference(file1,file2):
-    fname1 = file1.name
-    fname2 = file2.name   
-    img1 = load_torch_image(fname1)
-    img2 = load_torch_image(fname2)
-
+def inference(img1, img2):
+    img1_tensor = load_torch_image(img1)
+    img2_tensor = load_torch_image(img2)
+    
     matcher = KF.LoFTR(pretrained='outdoor')
-
-    input_dict = {"image0": K.color.rgb_to_grayscale(img1), # LofTR works on grayscale images only 
-                "image1": K.color.rgb_to_grayscale(img2)}
-
+    input_dict = {
+        "image0": K.color.rgb_to_grayscale(img1_tensor),  # LoFTR works on grayscale images only 
+        "image1": K.color.rgb_to_grayscale(img2_tensor)
+    }
+    
     with torch.no_grad():
         correspondences = matcher(input_dict)
+    
     mkpts0 = correspondences['keypoints0'].cpu().numpy()
     mkpts1 = correspondences['keypoints1'].cpu().numpy()
     H, inliers = cv2.findFundamentalMat(mkpts0, mkpts1, cv2.USAC_MAGSAC, 0.5, 0.999, 100000)
     inliers = inliers > 0
+    
     fig, ax = plt.subplots()
-
     draw_LAF_matches(
         KF.laf_from_center_scale_ori(torch.from_numpy(mkpts0).view(1,-1, 2),
                                     torch.ones(mkpts0.shape[0]).view(1,-1, 1, 1),
                                     torch.ones(mkpts0.shape[0]).view(1,-1, 1)),
-
         KF.laf_from_center_scale_ori(torch.from_numpy(mkpts1).view(1,-1, 2),
                                     torch.ones(mkpts1.shape[0]).view(1,-1, 1, 1),
                                     torch.ones(mkpts1.shape[0]).view(1,-1, 1)),
         torch.arange(mkpts0.shape[0]).view(-1,1).repeat(1,2),
-        K.tensor_to_image(img1),
-        K.tensor_to_image(img2),
+        K.tensor_to_image(img1_tensor.squeeze()),
+        K.tensor_to_image(img2_tensor.squeeze()),
         inliers,
         draw_dict={'inlier_color': (0.2, 1, 0.2),
                 'tentative_color': None, 
-                'feature_color': (0.2, 0.5, 1), 'vertical': False}, ax=ax)
+                'feature_color': (0.2, 0.5, 1), 'vertical': False},
+        ax=ax
+    )
     plt.axis('off')
-    fig.savefig('example.jpg',dpi=110,bbox_inches='tight')
-    return 'example.jpg'
+    return fig
 
 
 title = "Kornia-Loftr"
 description = "Gradio demo for Kornia-Loftr: Detector-Free Local Feature Matching with Transformers. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
 article = "<p style='text-align: center'><a href='https://kornia.readthedocs.io/en/latest/' target='_blank'>Open Source Differentiable Computer Vision Library</a> | <a href='https://github.com/kornia/kornia' target='_blank'>Kornia Github Repo</a> | <a href='https://github.com/zju3dv/LoFTR' target='_blank'>LoFTR Github</a> | <a href='https://arxiv.org/abs/2104.00680' target='_blank'>LoFTR: Detector-Free Local Feature Matching with Transformers</a></p>"
 css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}"
-
 examples = [['kn_church-2.jpg','kn_church-8.jpg']]
-gr.Interface(
+
+iface = gr.Interface(
     inference, 
-    [gr.inputs.Image(type="file", label="Input1"),gr.inputs.Image(type="file", label="Input2")], 
-    gr.outputs.Image(type="file", label="Output"),
+    [
+        gr.Image(type="numpy", label="Input1"),
+        gr.Image(type="numpy", label="Input2")],
+    gr.Plot(label="Feature Matches"),
     title=title,
     description=description,
     article=article,
     enable_queue=True,
     examples=examples,
     css=css
-    ).launch(debug=True)
+    )
+
+iface.launch(debug=True)