Update app.py

app.py

@@ -94,36 +94,6 @@ load_network(model, MODEL_NAME, strict=True, param_key='params')
 
 
 title = "See More Details"
-description = ''' ### See More Details: Efficient Image Super-Resolution by Experts Mining - ICML 2024, Vienna, Austria
-
-#### [Eduard Zamfir<sup>1</sup>](https://eduardzamfir.github.io), [Zongwei Wu<sup>1*</sup>](https://sites.google.com/view/zwwu/accueil), [Nancy Mehta<sup>1</sup>](https://scholar.google.com/citations?user=WwdYdlUAAAAJ&hl=en&oi=ao),  [Yulun Zhang<sup>2,3*</sup>](http://yulunzhang.com/) and [Radu Timofte<sup>1</sup>](https://www.informatik.uni-wuerzburg.de/computervision/)
-
-#### **<sup>1</sup> University of Würzburg, Germany - <sup>2</sup> Shanghai Jiao Tong University, China - <sup>3</sup> ETH Zürich, Switzerland**
-#### **<sup>*</sup> Corresponding authors**
-
-<details>
-<summary> <b> Abstract</b> (click me to read)</summary>
-<p>
-Reconstructing high-resolution (HR) images from low-resolution (LR) inputs poses a significant challenge in image super-resolution (SR). While recent approaches have demonstrated the efficacy of intricate operations customized for various objectives, the straightforward stacking of these disparate operations can result in a substantial computational burden, hampering their practical utility. In response, we introduce **S**eemo**R**e, an efficient SR model employing expert mining. Our approach strategically incorporates experts at different levels, adopting a collaborative methodology. At the macro scale, our experts address rank-wise and spatial-wise informative features, providing a holistic understanding. Subsequently, the model delves into the subtleties of rank choice by leveraging a mixture of low-rank experts. By tapping into experts specialized in distinct key factors crucial for accurate SR, our model excels in uncovering intricate intra-feature details. This collaborative approach is reminiscent of the concept of **see more**, allowing our model to achieve an optimal performance with minimal computational costs in efficient settings
-</p>
-</details>
-
-
-####  Drag the slider on the super-resolution image left and right to see the changes in the image details. SeemoRe performs x4 upscaling on the input image.
-
-<br>
-
-<code>
-@inproceedings{zamfir2024details,
-  title={See More Details: Efficient Image Super-Resolution by Experts Mining}, 
-  author={Eduard Zamfir and Zongwei Wu and Nancy Mehta and Yulun Zhang and Radu Timofte},
-  booktitle={International Conference on Machine Learning},
-  year={2024},
-  organization={PMLR}
-}
-</code>
-<br>
-'''
 
 
 article = "<p style='text-align: center'><a href='https://eduardzamfir.github.io/seemore' target='_blank'>See More Details: Efficient Image Super-Resolution by Experts Mining</a></p>"
@@ -163,10 +133,10 @@ demo = gr.Interface(
                         show_download_button=True,
                         ), #[gr.Image(type="pil", label="Ouput", min_width=500)],
     title=title,
-    description=description,
     article=article,
     examples=examples,
     css=css,
+    theme="Nymbo/Alyx_Theme"
 )
 
 if __name__ == "__main__":