Update app.py

app.py

@@ -4,11 +4,12 @@ from kornia.core import Tensor
 from kornia.contrib import ImageStitcher
 import kornia.feature as KF
 import torch
+import numpy as np
 
-def inference(file_1, file_2): 
-    img_1: Tensor = K.io.load_image(file_1.name, K.io.ImageLoadType.RGB32)
+def inference(file_1, file_2):
+    img_1: Tensor = K.io.load_image(file_1, K.io.ImageLoadType.RGB32)
     img_1 = img_1[None]  # 1xCxHxW / fp32 / [0, 1]
-    img_2: Tensor = K.io.load_image(file_2.name, K.io.ImageLoadType.RGB32)
+    img_2: Tensor = K.io.load_image(file_2, K.io.ImageLoadType.RGB32)
     img_2 = img_2[None]  # 1xCxHxW / fp32 / [0, 1]
     
     IS = ImageStitcher(KF.LoFTR(pretrained='outdoor'), estimator='ransac')
@@ -16,34 +17,20 @@ def inference(file_1, file_2):
         result = IS(img_1, img_2)
         
     return K.tensor_to_image(result[0])
-    
 
 examples = [
-    ['examples/foto1B.jpg',
-    'examples/foto1A.jpg'],
-]
-
-
-inputs = [
-    gr.inputs.Image(type='file', label='Input Image'),
-    gr.inputs.Image(type='file', label='Input Image'),
+    ['examples/foto1B.jpg', 'examples/foto1A.jpg'],
 ]
 
-outputs = [
-    gr.outputs.Image(type='file', label='Output Image'),
-
-]
-
-title = "Image Stitching using Kornia and LoFTR"
-
-demo_app = gr.Interface(
-    fn=inference,
-    inputs=inputs,
-    outputs=outputs,
-    title=title,
-    examples=examples,
-    cache_examples=True,
-    live=True,
-    theme='huggingface',
-)
-demo_app.launch()
+with gr.Blocks(theme='huggingface') as demo_app:
+    gr.Markdown("# Image Stitching using Kornia and LoFTR")
+    with gr.Row():
+        input_image1 = gr.Image(label="Input Image 1")
+        input_image2 = gr.Image(label="Input Image 2")
+    output_image = gr.Image(label="Output Image")
+    stitch_button = gr.Button("Stitch Images")
+    stitch_button.click(fn=inference, inputs=[input_image1, input_image2], outputs=output_image)
+    gr.Examples(examples=examples, inputs=[input_image1, input_image2])
+
+if __name__ == "__main__":
+    demo_app.launch()