ea4all-gradio-agents-mcp-hackathon-ui-retrofit

ea4all/ea4all_mcp.py

@@ -51,13 +51,12 @@ config = RunnableConfig(
 #ea4all-qna-agent-conversational-with-memory
 async def run_qna_agentic_system(prompt: str, chat_memory: list, request:gr.Request) -> AsyncGenerator[list, None]:
     """
-    Handles conversational Q&A for the Application Landscape using an agentic system.
-
+    description: 
+        Handles conversational Q&A for the Application Landscape using an agentic system.
     Args:
         prompt (str): The user's question or message.
         chat_memory (list): The conversation history as a list of ChatMessage objects.
         request (gr.Request): The Gradio request object for user identification.
-
     Returns:
         list: Response to user's question.
     """
@@ -165,13 +164,12 @@ async def run_qna_agentic_system(prompt: str, chat_memory: list, request:gr.Requ
 #Trigger Solution Architecture Diagram QnA
 async def run_vqa_agentic_system(message: dict, chat_memory: list, request:gr.Request) -> AsyncGenerator[list, None]:
     """
-    Handles Visual Question Answering (VQA) for uploaded architecture diagrams.
-
+    description: 
+        Handles Visual Question Answering (VQA) for uploaded architecture diagrams.
     Args:
         message (dict): Contains 'files' (list of file paths) and 'text' (user's question).
         chat_memory (list): The conversation history as a list of ChatMessage objects.
         request (gr.Request): The Gradio request object for user identification.
-
     Returns:
         list: Response to user's question.
     """
@@ -239,12 +237,11 @@ async def run_vqa_agentic_system(message: dict, chat_memory: list, request:gr.Re
 #Run Togaf Agentic System
 async def run_reference_architecture_agentic_system(business_query: str, request:gr.Request) -> AsyncGenerator[list, None]:
     """
-    Generates a reference architecture blueprint based on a business requirement using the TOGAF agentic system.
-
+    description: 
+        Generates a reference architecture blueprint based on a business requirement using the TOGAF agentic system.
     Args:
         business_query (str): The business requirement or query provided by the user.
         request (gr.Request): The Gradio request object for user identification.
-
     Returns:
         list: Response to user's question.
     """
@@ -314,13 +311,12 @@ async def run_reference_architecture_agentic_system(business_query: str, request
 
 async def run_pmo_agentic_system(prompt, chat_memory):
     """
-    Answers questions about Project Portfolio Management and Architect Demand Management.
-
+    description: 
+        Answers questions about Project Portfolio Management and Architect Demand Management.
     Args:
         prompt (str): The user's question about project portfolio or resource management.
         chat_memory (list): The conversation history as a list of ChatMessage objects.
-
-    Yields:
+    Returns:
         list: Updated chat memory after each event or response.
     """