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app.py

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+import gradio as gr
+from transformers import ImageClassificationPipeline, PerceiverForImageClassificationConvProcessing, PerceiverFeatureExtractor
+import torch
+torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
+torch.hub.download_url_to_file('https://storage.googleapis.com/perceiver_io/dalmation.jpg', 'dog.jpg')
+ 
+feature_extractor = PerceiverFeatureExtractor.from_pretrained("deepmind/vision-perceiver-conv")
+model = PerceiverForImageClassificationConvProcessing.from_pretrained("deepmind/vision-perceiver-conv")
+image_pipe = ImageClassificationPipeline(model=model, feature_extractor=feature_extractor)
+def classify_image(image):
+  results = image_pipe(image)
+  # convert to format Gradio expects
+  output = {}
+  for prediction in results:
+    predicted_label = prediction['label']
+    score = prediction['score']
+    output[predicted_label] = score
+  return output
+image = gr.inputs.Image(type="pil")
+label = gr.outputs.Label(num_top_classes=5)
+examples = [["cats.jpg"], ["dog.jpg"]]
+title = "Interactive demo: Perceiver for image classification"
+description = "Demo for classifying images with Perceiver IO. To use it, simply upload an image or use the example images below and click 'submit' to let the model predict the 5 most probable ImageNet classes. Results will show up in a few seconds."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2107.14795'>Perceiver IO: A General Architecture for Structured Inputs & Outputs</a> | <a href='https://deepmind.com/blog/article/building-architectures-that-can-handle-the-worlds-data/'>Official blog</a></p>"
+gr.Interface(fn=classify_image, inputs=image, outputs=label, title=title, description=description, examples=examples, enable_queue=True).launch(debug=True)