configurable app overview from stremlit/app_description

function_calling/streamlit/app.py

@@ -28,6 +28,7 @@
 
 CONFIG_PATH = os.path.join(kit_dir, 'config.yaml')
 PRESET_QUERIES_PATH = os.path.join(kit_dir, 'prompts', 'streamlit_preset_queries.yaml')
+APP_DESCRIPTION_PATH = os.path.join(kit_dir, 'streamlit', 'app_description.yaml')
 
 # tool mapping of defined tools
 TOOLS = {
@@ -51,10 +52,16 @@ def load_preset_queries() -> Any:
         return yaml.safe_load(yaml_file)
 
 
+def load_app_description() -> Any:
+    with open(APP_DESCRIPTION_PATH, 'r') as yaml_file:
+        return yaml.safe_load(yaml_file)
+
+
 config = load_config()
 prod_mode = config.get('prod_mode', False)
 st_tools = config.get('st_tools', {})
 st_preset_queries = load_preset_queries()
+st_description = load_app_description()
 
 db_path = config['tools']['query_db']['db'].get('path')
 additional_env_vars = config.get('additional_env_vars', None)
@@ -169,25 +176,7 @@ def handle_userinput(user_question: Optional[str]) -> None:
             'ai',
             avatar='https://sambanova.ai/hubfs/logotype_sambanova_orange.png',
         ):
-            st.write(
-                'This example application for function calling automates multi-step analysis by enabling language '
-                'models to use information and operations from user-defined functions. While you can use any '
-                'functions or database with the application, an example use case is implemented here: Uncovering '
-                'trends in music sales using the provided sample database and tools. By leveraging natural language '
-                'understanding, database interaction, code generation, and data visualization, the application '
-                ' provides a real-world example of how models can use function calling to automate multi-step analysis '
-                'tasks with accuracy.\n '
-                'In addition to the sample DB of music sales, the application includes several tools that are '
-                'available for the model to call as functions, some of the included tools are:\n'
-                '- **query_db**: Allows users to interact with the sample music sales database via natural queries. '
-                'You can ask questions about the data, such as "What are the top-selling albums of all time?" or "What '
-                'is the total revenue from sales in a specific region?" The function will then retrieve the relevant '
-                'data from the database and display the results.\n'
-                '- **calculator**: Provides a simple calculator interface that allows the model to perform '
-                'mathematical calculations using natural language inputs. The user can ask questions like "What is 10% '
-                'of 100?" or "What is the sum of 2+2?" and the function will return the result.\n'
-                '- **get_time**: Returns the current date and time for use in queries or calculations.'
-            )
+            st.write(st_description.get('app_overview'))
 
     # show history in chat view
     for i in range(len(st.session_state.chat_history)):

function_calling/streamlit/app_description.yaml

@@ -0,0 +1,20 @@
+app_overview: "This example application for function calling automates multi-step analysis by enabling language \ 
+  \ models to use information and operations from user-defined functions. While you can use any \ 
+  \ functions or database with the application, an example use case is implemented here: Uncovering \ 
+  \ trends in music sales using the provided sample database and tools. By leveraging natural language \ 
+  \ understanding, database interaction, code generation, and data visualization, the application \ 
+  \  provides a real-world example of how models can use function calling to automate multi-step analysis \ 
+  \ tasks with accuracy.\n \ 
+  \ In addition to the sample DB of music sales, the application includes several tools that are \ 
+  \ available for the model to call as functions, some of the included tools are:\n\ 
+  \ - **query_db**: Allows users to interact with the sample music sales database via natural queries. \ 
+  \ You can ask questions about the data, such as 'What are the top-selling albums of all time?' or 'What \ 
+  \ is the total revenue from sales in a specific region?' The function will then retrieve the relevant \ 
+  \ data from the database and display the results.\n\ 
+  \ - **calculator**: Provides a simple calculator interface that allows the model to perform \ 
+  \ mathematical calculations using natural language inputs. The user can ask questions like 'What is 10% \ 
+  \ of 100?' or 'What is the sum of 2+2?' and the function will return the result.\n\ 
+  \ - **get_time**: Returns the current date and time for use in queries or calculations.\n\
+  \ - **python repl**: Enables the model to execute Python code snippets for complex calculations or operations.\
+  \ you can input commands such as 'calculate the factorial of 9' or 'is the word rotator a palindrome'\
+  \ and the REPL will execute the code and return the results."