Update compare_streamlit.md

Co-authored-by: Philipp Rudiger <prudiger@anaconda.com>

doc/explanation/comparisons/compare_streamlit.md

@@ -2,7 +2,7 @@
 
 ![Panel Layout Example](https://user-images.githubusercontent.com/42288570/243362603-45ba78a4-d67b-43bc-b3c2-386105fe6ed8.png)
 
-**Streamlit is an an alternative** to Panel, Jupyter, Bokeh, and Dash. Like Jupyter, Streamlit provides an interactive, incremental way to build apps. Streamlit works with Python text files written in a separate editor, while Jupyter uses a web-based notebook cell editor. Although a web-based editor simplifies working locally on remote files, using a local Python text file enables users to maximize productivity by choosing their preferred editor. Dash, Panel, and Bokeh also support bare Python files developed in a local editor. Similarly, like Streamlit, they all watch that file and automatically re-run it when changes occur in the editor (e.g., for Panel, launch `panel serve --show --autoreload file.py` to watch the Python file and re-launch the served app on any changes).
+**Streamlit is an alternative** to Panel, Jupyter, Bokeh, and Dash. Like Jupyter, Streamlit provides an interactive, incremental way to build apps. Streamlit works with Python text files written in a separate editor, while Jupyter uses a web-based notebook cell editor. Although a web-based editor simplifies working locally on remote files, using a local Python text file enables users to maximize productivity by choosing their preferred editor. Dash, Panel, and Bokeh also support bare Python files developed in a local editor. Similarly, like Streamlit, they all watch that file and automatically re-run it when changes occur in the editor (e.g., for Panel, launch `panel serve --show --autoreload file.py` to watch the Python file and re-launch the served app on any changes).
 
 A key difference with Streamlit is that **the entire Python source file is effectively re-run *every time a widget changes value***, preventing confusing out-of-order execution of notebook cells and simplifying reasoning about state. However, this approach necessitates making all lengthy computations cacheable, which is not always straightforward and can introduce complexities in state management. Moreover, similar to Dash's lack of server-side state, Streamlit's approach can make it challenging to generate responsive apps for complex situations requiring a precise mapping between a widget event and specific Python code. In contrast, Panel offers better support for fully reactive applications, where each widget or plot component is explicitly tied to computation, re-running only the necessary code for that action. Consequently, Panel can support larger, more complex applications, allowing specific behaviors to be implemented and delivered independently.