Update app.py

app.py

@@ -11,13 +11,7 @@ def predict(inp_1, inp_2):
 
 title = "NUBIA"
 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).
+## A **N**e**u**ral **B**ased **I**nterchangeability **A**ssessor. 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).
 """
 inputs = [gradio.inputs.Textbox(label="First Text"), gradio.inputs.Textbox(label="Second Text")]
 outputs = [gradio.outputs.Label(label="Interchangeability Score"), gradio.outputs.JSON(label="All Features")]