update title of the app

app.py

@@ -5,15 +5,20 @@
 from typing import List, Literal
 from IPython.display import display, Markdown
 from transformers import AutoModelForSequenceClassification
+from funix import funix
 
 hhem = AutoModelForSequenceClassification.from_pretrained('vectara/hallucination_evaluation_model', trust_remote_code=True)
 
+print ("Loading HHEM, this may take a while.")
+
+@funix(
+    title= "GUI demo for Vectara's HHEM-2.1-Open"
+)
 def HHEM(
     LLM_Prompt: str = "The sky is blue.",
     LLM_Response: str = "The ocean is blue."
     ) -> Markdown:
-    """# GUI demo for Vectara's HHEM-2.1-Open
-
+    """
     Vectara's Hughes Hallucination Evaluation Model (HHEM)  evaluates how well an LLM's output (called the "response" or the "hypothesis") is faithful/grounded to or supported by the input given to it (called the "prompt" or the "premise"). HHEM has two versions: [HHEM-Open](https://huggingface.co/vectara/hallucination_evaluation_model) and [HHEM Commercial](https://www.vectara.com/blog/hhem-2-1-a-better-hallucination-detection-model).
 
     To use the demo, fill in the "LLM_Prompt" and "LLM_Response" fields and click the run button. A placeholder example is prefilled for you. Feel free to replace it with your own examples and evaluate them.