Update app.py

app.py

@@ -2,29 +2,10 @@ 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")
+def greet(name):
+    return "Hello " + name
 
-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
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 
-image = gr.inputs.Image(type="pil")
-label = gr.outputs.Label(num_top_classes=5)
-examples = [["cats.jpg"], ["dog.jpg"]]
-title = "Interactive demo: image classification to text and audio speech"
-description = "Demo for image classification to text and audio speech. 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)
+demo.launch()