Update app.py

app.py

@@ -10,7 +10,12 @@ def predict(inp_1, inp_2):
     return {"nubia_score": features["nubia_score"]}, labels
 
 title = "NUBIA"
-description = """## A **N**e**u**ral **B**ased **I**nterchangeability **A**ssessor. 
+description = """
+<p>
+<center>
+## A **N**e**u**ral **B**ased **I**nterchangeability **A**ssessor. 
+</center>
+</p>
 This is a demo of NUBIA: a SoTA evaluation metric for text generation. Simply input your texts (or click one of the examples to load them) and the model will compute score for how interchangeable they are. 
 Check out the [paper](https://arxiv.org/abs/2004.14667), [blog post](https://wl-research.github.io/blog/), [FAQ](https://github.com/wl-research/nubia/blob/master/FAQ.md) and [demo colab notebook](https://colab.research.google.com/drive/1_K8pOB8fRRnkBPwlcmvUNHgCr4ur8rFg).
 """
@@ -19,6 +24,9 @@ outputs = [gradio.outputs.Label(label="Interchangeability Score"), gradio.output
 css =""".gradio-bg[theme=huggingface] .gradio-interface .output-label .confidence {
     color: white !important;
 }
+.description a {
+    text-decoration: underline !important;
+}
 """
 examples = [
     ["This car is expensive! I can't buy it.", "That automobile costs a fortune! Purchasing it? Impossible!"],