update the examples and formatting

app.py

@@ -9,22 +9,26 @@ MSG_MAX_TURNS_EXCEEDED = f"Sorry! The CyberSolve LinAlg playground is limited to
 # MSG_CLIPPED_AT_MAX_OUT_TOKENS = "Reached maximum output tokens for DBRX Playground"
 
 EXAMPLE_PROMPTS = [
-    "How is a data lake used at Vanderbilt University Medical Center?",
-    "In a table, what are some of the greatest hurdles to healthcare in the United States?",
-    "What does EDW stand for in the context of Vanderbilt University Medical Center?",
-    "Code a sql statement that can query a database named 'VUMC'.",
-    "Write a short story about a country concert in Nashville, Tennessee.",
-    "Tell me about maximum out-of-pocket costs in healthcare.",
+    "Solve 24 = 1601c - 1605c for c.",
+    "Solve 657 = -220*t + 1086*t + 22307 for t.",
+    "Solve -11*y - 263*y + 3162 = -88*y for y.",
+    "Solve 0 = -11*b - 4148 + 4225 for b.",
+    "Solve 65*l - 361 + 881 = 0 for l.",
+    "Solve 49*l + 45*l - 125 - 63 = 0 for l.",
 ]
 
 TITLE = "CyberSolve LinAlg 1.2"
 DESCRIPTION= """Welcome to the CyberSolve LinAlg 1.2 demo! \n
 
-**Overview and Usage**: This 🤗 Space is designed to demo the abilities of the **CyberSolve LinAlg 1.2** text-to-text language model. Specifically, the **CyberSolve LinAlg 1.x** family of models
+**Overview and Usage**: This 🤗 Space is designed to demo the abilities of the **CyberSolve LinAlg 1.2** text-to-text language model. 
+
+Specifically, the **CyberSolve LinAlg 1.x** family of models
 are downstream versions of the 783M parameter FLAN-T5 text-to-text transformer, fine-tuned on the Google DeepMind Mathematics dataset for the purpose of solving linear equations of a single variable. 
 To effectively query the model for its intended task, prompt the model solve an arbitrary linear equation of a single variable with a query of the form: *"Solve 24 = 1601c - 1605c for c."*; the model 
 will return its prediciton in a simple format. The algebraic capabailites far exceed those of the base FLAN-T5 model. CyberSolve LinAlg 1.2 achieves a 90.7 percent exact match benchmark on the DeepMind Mathematics
-evaluation dataset of 10,000 unique linear equations; the FLAN-T5 base model scores 9.6 percent. On the left is a sidebar of **Examples** that can be clicked to query to model.
+evaluation dataset of 10,000 unique linear equations; the FLAN-T5 base model scores 9.6 percent. 
+
+On the left is a sidebar of **Examples** that can be clicked to query to model.
 
 **Feedback**: Feedback is welcomed, encouraged, and invaluable! To give feedback in regards to one of the model's responses, click the **Give Feedback on Last Response** button just below
 the user input bar. This allows you to provide either positive or negative feedback in regards to the model's most recent response. A **Feedback Form** will appear above the model's title.