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"""
Test script to demonstrate the conversation history functionality.
"""
import json
import datetime
from utils import SocialGraphManager
from llm_interface import LLMInterface
def test_conversation_history():
"""Test the conversation history functionality."""
print("\n=== Testing Conversation History ===")
# 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)
# Print the person's conversation history
print(f"\nConversation history for {person_context.get('name')}:")
conversation_history = person_context.get("conversation_history", [])
if not conversation_history:
print("No conversation history found.")
else:
# Sort by timestamp (most recent first)
sorted_history = sorted(
conversation_history,
key=lambda x: x.get("timestamp", ""),
reverse=True
)
for i, conversation in enumerate(sorted_history):
# Format the timestamp
timestamp = conversation.get("timestamp", "")
try:
dt = datetime.datetime.fromisoformat(timestamp)
formatted_date = dt.strftime("%B %d, %Y at %I:%M %p")
except (ValueError, TypeError):
formatted_date = timestamp
print(f"\nConversation {i+1} on {formatted_date}:")
# Print the messages
messages = conversation.get("messages", [])
for message in messages:
speaker = message.get("speaker", "Unknown")
text = message.get("text", "")
print(f" {speaker}: \"{text}\"")
# Test adding a new conversation
print("\nAdding a new conversation...")
new_messages = [
{"speaker": "Emma", "text": "How are you feeling this afternoon?"},
{"speaker": "Will", "text": "A bit tired, but the new medication seems to be helping with the muscle stiffness."},
{"speaker": "Emma", "text": "That's good to hear. Do you want me to bring you anything?"},
{"speaker": "Will", "text": "A cup of tea would be lovely, thanks."}
]
success = graph_manager.add_conversation(person_id, new_messages)
if success:
print("New conversation added successfully.")
else:
print("Failed to add new conversation.")
# Get the updated person context
updated_person_context = graph_manager.get_person_context(person_id)
updated_conversation_history = updated_person_context.get("conversation_history", [])
# Print the updated conversation history
print("\nUpdated conversation history:")
if not updated_conversation_history:
print("No conversation history found.")
else:
# Get the most recent conversation
most_recent = sorted(
updated_conversation_history,
key=lambda x: x.get("timestamp", ""),
reverse=True
)[0]
# Format the timestamp
timestamp = most_recent.get("timestamp", "")
try:
dt = datetime.datetime.fromisoformat(timestamp)
formatted_date = dt.strftime("%B %d, %Y at %I:%M %p")
except (ValueError, TypeError):
formatted_date = timestamp
print(f"\nMost recent conversation on {formatted_date}:")
# Print the messages
messages = most_recent.get("messages", [])
for message in messages:
speaker = message.get("speaker", "Unknown")
text = message.get("text", "")
print(f" {speaker}: \"{text}\"")
# Test generating a suggestion with conversation history
print("\nGenerating a suggestion with conversation history...")
llm_interface = LLMInterface()
if llm_interface.model_loaded:
# 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."""
# Call the original method up to the point where it builds the prompt
person_context = args[0]
user_input = args[1] if len(args) > 1 else kwargs.get("user_input")
# 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
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'
# 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(updated_person_context, user_input=user_input)
# Restore the original method
llm_interface.generate_suggestion = original_method
else:
print("LLM model not loaded, skipping prompt generation test.")
print("\nTest completed.")
if __name__ == "__main__":
test_conversation_history()
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