updates

app.py

@@ -26,22 +26,25 @@ def infer(input_text, model_choice):
     return (tokenizer.decode(generated_ids[0], skip_special_tokens=True))
 
 
+description = """The NSQL model family was published by [Numbers Station](https://www.numbersstation.ai/) and is available in three flavors:  
+- [nsql-6B](https://huggingface.co/NumbersStation/nsql-6B)
+- [nsql-2B](https://huggingface.co/NumbersStation/nsql-2B)
+- [nsql-350M]((https://huggingface.co/NumbersStation/nsql-350M))  
+
+This demo let's you choose which one you want to use and provides the three examples you can also find in their model cards.  
+
+In general you should first provide the table schemas of the tables you have questions about and then prompt it with a natural language question.  
+The model will then generate a SQL query that you can run against your database.
+"""
+
 iface = gr.Interface(
     title="Text to SQL with NSQL",
-    description="""The NSQL model family was published by [Numbers Station](https://www.numbersstation.ai/) and is available in three flavors:
-    - [nsql-6B](https://huggingface.co/NumbersStation/nsql-6B)
-    - [nsql-2B](https://huggingface.co/NumbersStation/nsql-2B)
-    - [nsql-350M]((https://huggingface.co/NumbersStation/nsql-350M))
-    This demo let's you choose which one you want to use and provides the three examples you can also find in their model cards.
-
-    In general you should first provide the table schemas of the tables you have questions about and then prompt it with a natural language question.
-    The model will then generate a SQL query that you can run against your database.
-    """,
+    description=description,
     fn=infer, 
     inputs=["text", 
             gr.Dropdown(["nsql-6B", "nsql-2B", "nsql-350M"], value="nsql-6B")], 
     outputs="text",
     examples=[[example1, "nsql-350M"],
-              [example2, "nsql-350M"],
+              [example2, "nsql-2B"],
               [example3, "nsql-350M"]])
 iface.launch()