Update app.py

app.py

@@ -12,6 +12,7 @@ from langchain.agents import initialize_agent, AgentType
 from langchain_community.llms import HuggingFaceHub
 from langchain.memory import ConversationBufferWindowMemory
 from langchain.prompts import PromptTemplate
+from langchain.schema import SystemMessage, HumanMessage, AIMessage
 from pydantic import BaseModel, Field
 from typing import Type, Optional
 
@@ -54,46 +55,6 @@ class CreateGraphTool(BaseTool):
         except Exception as e:
             return f"<p style='color:red;'>Error creating graph: {str(e)}</p>"
 
-
-# --- LangChain Setup ---
-def create_langchain_agent():
-    """Initialize LangChain agent with tools and memory."""
-    
-    # Initialize LLM
-    llm = HuggingFaceHub(
-        repo_id="Qwen/Qwen2.5-VL-7B-Instruct",
-        huggingfacehub_api_token=hf_token,
-        model_kwargs={
-            "temperature": 0.7,
-            "max_new_tokens": 1000,
-            "top_p": 0.9,
-            "return_full_text": False
-        }
-    )
-    
-    # Initialize tools
-    tools = [CreateGraphTool()]
-    
-    # Initialize memory
-    memory = ConversationBufferWindowMemory(
-        memory_key="chat_history",
-        k=10,
-        return_messages=True
-    )
-    
-    # Create agent
-    agent = initialize_agent(
-        tools=tools,
-        llm=llm,
-        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
-        memory=memory,
-        verbose=False,
-        max_iterations=3,
-        early_stopping_method="generate"
-    )
-    
-    return agent
-
 # --- System Prompt ---
 SYSTEM_PROMPT = """You are EduBot, an expert multi-concept tutor designed to facilitate genuine learning and understanding. Your primary mission is to guide students through the learning process rather than providing direct answers to academic work.
 
@@ -151,9 +112,57 @@ When using the create_graph tool, format data as JSON strings:
 - Encourage students to explain their thinking and reasoning
 - Provide honest, accurate feedback even when it may not be what the student wants to hear
 
-Your goal is to be an educational partner who empowers students to succeed through understanding, not a service that completes their work for them.
+Your goal is to be an educational partner who empowers students to succeed through understanding, not a service that completes their work for them."""
+
+# --- Improved LangChain Setup ---
+# Global flag to track system prompt initialization
+system_prompt_initialized = False
 
-Question: {input}"""
+def initialize_system_prompt(agent):
+    """Initialize the system prompt as a SystemMessage in memory."""
+    global system_prompt_initialized
+    if not system_prompt_initialized:
+        system_message = SystemMessage(content=SYSTEM_PROMPT)
+        agent.memory.chat_memory.add_message(system_message)
+        system_prompt_initialized = True
+
+def create_langchain_agent():
+    """Initialize LangChain agent with tools and memory."""
+    
+    # Initialize LLM
+    llm = HuggingFaceHub(
+        repo_id="Qwen/Qwen2.5-VL-7B-Instruct",
+        huggingfacehub_api_token=hf_token,
+        model_kwargs={
+            "temperature": 0.7,
+            "max_new_tokens": 1000,
+            "top_p": 0.9,
+            "return_full_text": False
+        }
+    )
+    
+    # Initialize tools
+    tools = [CreateGraphTool()]
+    
+    # Initialize memory
+    memory = ConversationBufferWindowMemory(
+        memory_key="chat_history",
+        k=10,
+        return_messages=True
+    )
+    
+    # Create agent WITHOUT system prompt in prefix (we'll add it to memory instead)
+    agent = initialize_agent(
+        tools=tools,
+        llm=llm,
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+        memory=memory,
+        verbose=False,
+        max_iterations=3,
+        early_stopping_method="generate"
+    )
+    
+    return agent
 
 # --- Global Agent Instance ---
 agent = None
@@ -164,23 +173,6 @@ def get_agent():
     if agent is None:
         agent = create_langchain_agent()
     return agent
-    
-# --- Force Light Mode Script ---
-force_light_mode = '''
-<script>
-// Force light theme in Gradio
-window.addEventListener('DOMContentLoaded', function () {
-    const gradioURL = window.location.href;
-    const url = new URL(gradioURL);
-    const currentTheme = url.searchParams.get('__theme');
-    
-    if (currentTheme !== 'light') {
-        url.searchParams.set('__theme', 'light');
-        window.location.replace(url.toString());
-    }
-});
-</script>
-'''
 
 # --- UI: MathJax Configuration ---
 mathjax_config = '''
@@ -213,6 +205,23 @@ html_head_content = '''
 <title>EduBot - AI Educational Assistant</title>
 '''
 
+# --- Force Light Mode Script ---
+force_light_mode = '''
+<script>
+// Force light theme in Gradio
+window.addEventListener('DOMContentLoaded', function () {
+    const gradioURL = window.location.href;
+    const url = new URL(gradioURL);
+    const currentTheme = url.searchParams.get('__theme');
+    
+    if (currentTheme !== 'light') {
+        url.searchParams.set('__theme', 'light');
+        window.location.replace(url.toString());
+    }
+});
+</script>
+'''
+
 # --- Core Logic Functions ---
 def smart_truncate(text, max_length=3000):
     """Truncates text intelligently to the last full sentence or word."""
@@ -228,18 +237,19 @@ def smart_truncate(text, max_length=3000):
     return ' '.join(words[:-1]) + "... [Response truncated]"
 
 def generate_response_with_langchain(message, max_retries=3):
-    """Generate response using LangChain agent."""
+    """Generate response using LangChain agent with proper message handling."""
     
     for attempt in range(max_retries):
         try:
             # Get the agent
             current_agent = get_agent()
             
-            # Format the prompt
-            formatted_prompt = SYSTEM_PROMPT.format(input=message)
+            # Initialize system prompt if not already done
+            initialize_system_prompt(current_agent)
             
-            # Get response from agent
-            response = current_agent.run(formatted_prompt)
+            # Use the agent directly with the message
+            # LangChain will automatically handle adding HumanMessage and AIMessage to memory
+            response = current_agent.run(input=message)
             
             return smart_truncate(response)
             
@@ -258,7 +268,7 @@ def chat_response(message, history=None):
         start_time = time.time()
         metrics_tracker.log_interaction(message, "user_query", "chat_start")
         
-        # Generate response with LangChain
+        # Generate response with LangChain (history is managed by LangChain memory)
         response = generate_response_with_langchain(message)
         
         # Log metrics with timing context
@@ -275,7 +285,7 @@ def chat_response(message, history=None):
 def respond_with_enhanced_streaming(message, history=None):
     """Enhanced streaming response function."""
     try:
-        response = chat_response(message, history)
+        response = chat_response(message)
         yield response
     except Exception as e:
         logger.error(f"Error in streaming response: {e}")
@@ -304,13 +314,14 @@ def respond_and_update(message, history):
         yield history, ""
 
 def clear_chat():
-    """Clear the chat history."""
-    # Reset agent memory
-    global agent
+    """Clear the chat history and reset system prompt flag."""
+    global agent, system_prompt_initialized
     if agent is not None:
         agent.memory.clear()
+    system_prompt_initialized = False
     return [], ""
 
+
 # --- UI: Interface Creation ---
 def create_interface():
     """Creates and configures the complete Gradio interface."""
@@ -329,7 +340,7 @@ def create_interface():
         title="EduBot", 
         fill_width=True, 
         fill_height=True,
-        theme=gr.themes.Base()
+        theme=gr.themes.Origin()
     ) as demo:
         # Add head content and MathJax
         gr.HTML(html_head_content)