test_llm_with_history.py

"""
Test script for LLM interface with conversation history.
"""

from utils import SocialGraphManager
from llm_interface import LLMInterface

# Initialize the social graph manager
graph_manager = SocialGraphManager("social_graph.json")

# Get a person with conversation history
person_id = "emma"  # Emma has conversation history
person_context = graph_manager.get_person_context(person_id)

# Initialize the LLM interface
llm_interface = LLMInterface()

# Store the original generate_suggestion method
original_method = llm_interface.generate_suggestion

# Create a mock method to print the prompt
def mock_generate_suggestion(*args, **kwargs):
    """Mock method to print the prompt instead of sending it to the LLM."""
    # Extract the prompt that would be sent to the LLM
    person_context = args[0]
    user_input = args[1] if len(args) > 1 else kwargs.get("user_input")
    
    # Build the prompt as the original method would
    # Extract context information
    name = person_context.get("name", "")
    role = person_context.get("role", "")
    topics = person_context.get("topics", [])
    context = person_context.get("context", "")
    selected_topic = person_context.get("selected_topic", "")
    frequency = person_context.get("frequency", "")
    mood = person_context.get("mood", 3)
    
    # Get mood description
    mood_descriptions = {
        1: "I'm feeling quite down and sad today. My responses might be more subdued.",
        2: "I'm feeling a bit low today. I might be less enthusiastic than usual.",
        3: "I'm feeling okay today - neither particularly happy nor sad.",
        4: "I'm feeling pretty good today. I'm in a positive mood.",
        5: "I'm feeling really happy and upbeat today! I'm in a great mood.",
    }
    mood_description = mood_descriptions.get(mood, mood_descriptions[3])
    
    # Get current date and time
    import datetime
    current_datetime = datetime.datetime.now()
    current_time = current_datetime.strftime("%I:%M %p")
    current_day = current_datetime.strftime("%A")
    current_date = current_datetime.strftime("%B %d, %Y")
    
    # Build enhanced prompt
    prompt = f"""I am Will, a 38-year-old with MND (Motor Neuron Disease) from Manchester.
I am talking to {name}, who is my {role}.
About {name}: {context}
We typically talk about: {', '.join(topics)}
We communicate {frequency}.

Current time: {current_time}
Current day: {current_day}
Current date: {current_date}

My current mood: {mood_description}
"""
    
    # Add communication style based on relationship
    if role in ["wife", "son", "daughter", "mother", "father"]:
        prompt += "I communicate with my family in a warm, loving way, sometimes using inside jokes.\n"
    elif role in ["doctor", "therapist", "nurse"]:
        prompt += "I communicate with healthcare providers in a direct, informative way.\n"
    elif role in ["best mate", "friend"]:
        prompt += "I communicate with friends casually, often with humor and sometimes swearing.\n"
    elif role in ["work colleague", "boss"]:
        prompt += "I communicate with colleagues professionally but still friendly.\n"
    
    # Add topic information if provided
    if selected_topic:
        prompt += f"\nWe are currently discussing {selected_topic}.\n"
    
    # Add conversation history if available
    conversation_history = person_context.get("conversation_history", [])
    if conversation_history:
        # Get the two most recent conversations
        recent_conversations = sorted(
            conversation_history, 
            key=lambda x: x.get("timestamp", ""), 
            reverse=True
        )[:2]
        
        if recent_conversations:
            prompt += "\nOur recent conversations:\n"
            
            for conversation in recent_conversations:
                # Format the timestamp
                timestamp = conversation.get("timestamp", "")
                try:
                    dt = datetime.datetime.fromisoformat(timestamp)
                    formatted_date = dt.strftime("%B %d at %I:%M %p")
                except (ValueError, TypeError):
                    formatted_date = timestamp
                
                prompt += f"\nConversation on {formatted_date}:\n"
                
                # Add the messages
                messages = conversation.get("messages", [])
                for message in messages:
                    speaker = message.get("speaker", "Unknown")
                    text = message.get("text", "")
                    prompt += f'{speaker}: "{text}"\n'
    
    # Add the user's message if provided, or set up for conversation initiation
    if user_input:
        # If user input is provided, we're responding to something
        prompt += f'\n{name} just said to me: "{user_input}"\n'
        prompt += f"I want to respond directly to what {name} just said.\n"
    else:
        # No user input means we're initiating a conversation
        if selected_topic:
            # If a topic is selected, initiate conversation about that topic
            prompt += f"\nI'm about to start a conversation with {name} about {selected_topic}.\n"
            prompt += f"I want to initiate a conversation about {selected_topic} in a natural way.\n"
        else:
            # Generic conversation starter
            prompt += f"\nI'm about to start a conversation with {name}.\n"
            prompt += "I want to initiate a conversation in a natural way based on our relationship.\n"
    
    # Print the prompt
    print("\n=== PROMPT WITH CONVERSATION HISTORY ===")
    print(prompt)
    print("=======================================\n")
    
    # Return a mock response
    return "This is a mock response to test conversation history inclusion in the prompt."

# Replace the original method with our mock method
llm_interface.generate_suggestion = mock_generate_suggestion

# Test with a user input
user_input = "Do you think you'll be up for dinner with the kids tonight?"
llm_interface.generate_suggestion(person_context, user_input=user_input)

# Restore the original method
llm_interface.generate_suggestion = original_method