Update app.py

app.py

@@ -25,13 +25,10 @@ def predict(image, labels):
 description = """
 <div class="container" style="display:flex;">
   <div class="image">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/132_vit_align/align.png" alt="ALIGN performance" />
+    <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/7/73/Rafale_-_RIAT_2012_%287852045796%29.jpg/1200px-Rafale_-_RIAT_2012_%287852045796%29.jpg" alt="rafale" />
   </div>
   <div class="text">
-  <p>Gradio demo for <a href="https://huggingface.co/docs/transformers/main/en/model_doc/align">ALIGN</a>, 
-    as introduced in <a href="https://arxiv.org/abs/2102.05918"></a><i>"Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision"</i>. ALIGN features a dual-encoder architecture with EfficientNet and BERT as its text and vision encoders, and learns to align visual and text representations with contrastive learning. 
-    Unlike previous work, ALIGN leverages a massive noisy dataset and shows that the scale of the corpus can be used to achieve SOTA representations with a simple recipe.
-    \n\nALIGN is not open-sourced and the `kakaobrain/align-base` model used for this demo is based on the Kakao Brain implementation that follows the original paper. The model is trained on the open source [COYO](https://github.com/kakaobrain/coyo-dataset) dataset by the Kakao Brain team. To perform zero-shot image classification with ALIGN, upload an image and enter your candidate labels as free-form text separated by a comma followed by a space.</p>
+  <p>Compare anything</p>
   </div>
 </div>
 """