init

app.py

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+import numpy
+from sentence_transformers import SentenceTransformer, util
+from PIL import Image
+
+
+model = SentenceTransformer('clip-ViT-B-32')
+
+def image_classifier(im1: numpy.ndarray, im2: numpy.ndarray):
+    encoded_image = model.encode([Image.fromarray(im1), Image.fromarray(im2)], batch_size=128,
+                                 convert_to_tensor=True, show_progress_bar=True)
+    processed_images = util.paraphrase_mining_embeddings(encoded_image)
+    return processed_images[0][0]
+
+
+
+with gr.Blocks() as b:
+    with gr.Row():
+        with gr.Column():
+            image1 = gr.Image(label="image 1")
+            image2 = gr.Image(label="image 2")
+            with gr.Row():
+                compare = gr.Button("Compare")
+        output = gr.Label(label="output")
+    compare.click(fn=image_classifier, inputs=[image1, image2], outputs=output)
+
+
+b.launch()