Update frontend.py

frontend.py

@@ -1,15 +1,9 @@
 # frontend.py
-"""
-Streamlit frontend for MCP Agent
-Provides a chat interface for interacting with the MCP backend
-"""
-
 import streamlit as st
 import asyncio
 from backend import get_agent, MCPAgent
 import time
 
-# Page configuration
 st.set_page_config(
     page_title="MCP Agent Chat",
     page_icon="🤖",
@@ -17,7 +11,6 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
-# Custom CSS for better chat appearance
 st.markdown("""
 <style>
     .stChatMessage {
@@ -50,14 +43,20 @@ if "messages" not in st.session_state:
 
 # Sidebar
 with st.sidebar:
-    st.title("🤖 MCP Agent")
+    st.title("🤖 MCP AI Algo Trade App Demo")
+    st.markdown("### ℹ️ About")
+    st.markdown("""
+    This chat interface connects to:
+    - **Math Server**: Basic arithmetic operations
+    - **Stock Server**: Mock Real-time market data
+    
+    The agent uses LangChain and MCP to intelligently route your queries to the appropriate tools.
+    """)
     st.markdown("---")
     
-    # Status indicator
     if st.session_state.agent_initialized:
         st.success("✅ Agent Connected")
         
-        # Show available tools
         st.markdown("### 🛠️ Available Tools")
         available_tools = st.session_state.get("available_tools", [])
         for tool in available_tools:
@@ -67,7 +66,6 @@ with st.sidebar:
     
     st.markdown("---")
     
-    # Example queries
     st.markdown("### 💡 Example Queries")
     example_queries = [
         "What's the price of AAPL?",
@@ -85,21 +83,12 @@ with st.sidebar:
     
     st.markdown("---")
     
-    # Clear chat button
     if st.button("🗑️ Clear Chat", type="secondary"):
         st.session_state.messages = []
         st.session_state.history = []
         st.rerun()
     
-    # Info section
-    st.markdown("### ℹ️ About")
-    st.markdown("""
-    This chat interface connects to:
-    - **Math Server**: Basic arithmetic operations
-    - **Stock Server**: Real-time market data
     
-    The agent uses LangChain and MCP to intelligently route your queries to the appropriate tools.
-    """)
 
 # Main chat interface
 st.title("💬 MCP Agent Chat")
@@ -123,96 +112,81 @@ async def initialize_agent():
                 return False
     return True
 
-# Process user message
 async def process_user_message(user_input: str):
     """Process the user's message and get response from agent"""
     agent = get_agent()
     
-    # Add user message to history
+
     st.session_state.history.append({"role": "user", "content": user_input})
     
     try:
-        # Get response from agent
+
         response = await agent.process_message(user_input, st.session_state.history)
         
-        # Add assistant response to history
+
         st.session_state.history.append({"role": "assistant", "content": response})
         
         return response
     except Exception as e:
         return f"Error: {str(e)}"
 
-# Display chat messages
+
 for message in st.session_state.messages:
     with st.chat_message(message["role"]):
         st.markdown(message["content"])
 
-# Process pending query from example buttons
+
 if st.session_state.pending_query:
     query = st.session_state.pending_query
     st.session_state.pending_query = None  # Clear it
     
-    # Add to messages
     st.session_state.messages.append({"role": "user", "content": query})
     
-    # Process the query
     async def process_example():
         if not st.session_state.agent_initialized:
             if not await initialize_agent():
                 return "Failed to initialize agent. Please check the servers."
         return await process_user_message(query)
     
-    # Get response
     with st.spinner("Processing..."):
         response = asyncio.run(process_example())
     
-    # Add response to messages
     st.session_state.messages.append({"role": "assistant", "content": response})
     
-    # Rerun to display the new messages
     st.rerun()
 
-# Chat input
 if prompt := st.chat_input("Type your message here..."):
-    # Add user message to chat
     st.session_state.messages.append({"role": "user", "content": prompt})
     
-    # Display user message
     with st.chat_message("user"):
         st.markdown(prompt)
     
-    # Get and display assistant response
     with st.chat_message("assistant"):
         message_placeholder = st.empty()
         
-        # Run async function
         async def get_response():
-            # Initialize agent if needed
             if not st.session_state.agent_initialized:
                 if not await initialize_agent():
                     return "Failed to initialize agent. Please check the servers."
             
-            # Process message
             return await process_user_message(prompt)
         
-        # Execute async function
         with st.spinner("Thinking..."):
             response = asyncio.run(get_response())
         
         message_placeholder.markdown(response)
         st.session_state.messages.append({"role": "assistant", "content": response})
 
-# Auto-initialize agent on first load
 if not st.session_state.agent_initialized:
     with st.spinner("🔧 Initializing agent..."):
         asyncio.run(initialize_agent())
 
-# Footer
 st.markdown("---")
 st.markdown(
     """
     <div style='text-align: center; color: #666;'>
-    Powered by LangChain, MCP, and Hugging Face 🤗
+    Powered by LangGraph, LangChain, MCP, and Hugging Face
+    Developed by Lorentz Yeung
     </div>
     """,
     unsafe_allow_html=True