Update app.py

app.py

@@ -62,10 +62,11 @@ inputs = gr.inputs.Image(type='pil')
 outputs = gr.outputs.Label(type="confidences",num_top_classes=5)
 
 title = "ConvNeXt"
+
 description = "Gradio demo for ConvNeXt for image classification. 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://arxiv.org/abs/2201.03545' target='_blank'>A ConvNet for the 2020s</a> | <a href='https://github.com/facebookresearch/ConvNeXt' target='_blank'>Github Repo</a> | <a href='https://github.com/leondgarse/keras_cv_attention_models' target='_blank'>pretrained ConvNeXt model from keras_cv_attention_models</a> | <a href='https://github.com/stanislavfort/adversaries_to_convnext' target='_blank'>examples usage from adversaries_to_convnext</a></p>"
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2201.03545' target='_blank'>A ConvNet for the 2020s</a> | <a href='https://github.com/facebookresearch/ConvNeXt' target='_blank'>Github Repo</a></p>"
 
-examples = ['Tortoise-on-ground-surrounded-by-plants.jpeg']
+examples = ['test.jpeg']
 
 gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, analytics_enabled=False, examples=examples).launch(enable_queue=True)