Update app.py

app.py

@@ -91,17 +91,36 @@ print ("IMAGE MODEL CKPT:", MODEL_NAME)
 load_network(model, MODEL_NAME, strict=True, param_key='params')
 
 
-# Footer
-st.markdown(
-    """
-    <footer>
-        By DL Titans
-    </footer>
-    """,
-    unsafe_allow_html=True
-)
 
 
+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>"
+
 #### Image,Prompts examples
 examples = [
             ['images/0801x4.png'],
@@ -129,7 +148,6 @@ css = """
     }
 """
 
-
 demo = gr.Interface(
     fn=process_img,
     inputs=[gr.Image(type="pil", label="Input", value="images/0878x4.png"),],