Update app.py

app.py

@@ -10,15 +10,22 @@ def predict(inp_1, inp_2):
     return {"nubia_score": features["nubia_score"]}, labels
 
 title = "NUBIA"
-description = "NeUral Based Interchangeability Assessor. A SoTA evaluation metric for text generation."
+description = """## 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")]
+css =""".gradio-bg[theme=huggingface] .gradio-interface .output-label .confidence {
+    color: white !important;
+}
+"""
 examples = [
     ["This car is expensive! I can't buy it.", "That automobile costs a fortune! Purchasing it? Impossible!"],
     ["This car is expensive! I can't buy it.", "That automobile costs a good amount. Purchasing it? Totally feasible!"],
     ["The dinner was delicious.", "The dinner did not taste good."]
 ]
 iface = gradio.Interface(fn=predict, inputs=inputs, outputs=outputs, capture_session=True, examples=examples,
-                         title=title, description=description, allow_flagging=False)
+                         title=title, description=description, allow_flagging=False, css=css)
 
 iface.launch()