some small changes

app.py

@@ -18,7 +18,7 @@ chat_col = st.container()
 chat_col.title("SambaLATS")
 description = """This demo is an implementation of Language Agent Tree Search (LATS) (https://arxiv.org/abs/2310.04406) with Samba-1 in the backend. Thank you to the original authors of demo on which this is based from [Lapis Labs](https://lapis.rocks/)!
 
-Given Samba-1's lightning quick inference, not only can we accelerate our system's speeds but also improve our system's accuracy. Using many inference calls in this LATS style, we can solve programming questions with higher accuracy. In fact, this system reaches GPT-3.5 accuracy on HumanEval Python, 74% accuracy, with LLaMa 3 8B, taking 8 seconds on average. This is a 15.5% boost on LLaMa 3 8B alone. 
+Given Samba-1's lightning quick inference, not only can we accelerate our system's speeds but also improve our system's accuracy. Using many inference calls in this LATS style, we can solve programming questions with higher accuracy. In fact, this system reaches **GPT-3.5 accuracy on HumanEval Python**, 74% accuracy, with LLaMa 3 8B, taking 8 seconds on average. This is a 15.5% boost on LLaMa 3 8B alone. 
 
 Listed below is an example programming problem (https://leetcode.com/problems/median-of-two-sorted-arrays/description/) to get started with. 
 
@@ -81,9 +81,9 @@ def run_querry():
                 response = lats_main(args)
 
         sys.stdout = old_stdout
-        runtime_container.write("Response fetched.")
-        chat_col.markdown('<hr style="margin-top: 0.5rem; margin-bottom: 0.5rem;">', unsafe_allow_html=True)
-        chat_col.write(f"```python\n{response} \n")
+        # runtime_container.write("Response fetched.")
+        # chat_col.markdown('<hr style="margin-top: 0.5rem; margin-bottom: 0.5rem;">', unsafe_allow_html=True)
+        # chat_col.write(f"```python\n{response} \n")
 
         return response