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#!/usr/bin/env python3 | |
""" | |
Integration test for next_check_in functionality in LifestyleSessionManager | |
""" | |
import json | |
from datetime import datetime, timedelta | |
from core_classes import LifestyleProfile, ChatMessage, LifestyleSessionManager | |
class MockAPI: | |
def generate_response(self, system_prompt: str, user_prompt: str, temperature: float = 0.3, call_type: str = "") -> str: | |
"""Mock API that returns realistic profile update responses""" | |
if call_type == "LIFESTYLE_PROFILE_UPDATE": | |
# Return a realistic profile update with next_check_in | |
return json.dumps({ | |
"updates_needed": True, | |
"reasoning": "Patient completed first lifestyle session with good engagement", | |
"updated_fields": { | |
"exercise_preferences": ["upper body exercises", "seated exercises", "resistance band training"], | |
"personal_preferences": ["prefers gradual changes", "wants weekly check-ins initially"], | |
"session_summary": "First lifestyle session completed. Patient motivated to start adapted exercise program.", | |
"next_check_in": "2025-09-08", | |
"progress_metrics": {"initial_motivation": "high", "session_1_completion": "successful"} | |
}, | |
"session_insights": "Patient shows high motivation despite physical limitations. Requires close monitoring initially.", | |
"next_session_rationale": "New patient needs immediate follow-up in 3 days to ensure safe program initiation and address any concerns." | |
}) | |
return "Mock response" | |
def test_next_checkin_integration(): | |
"""Test the complete next_check_in workflow""" | |
print("π§ͺ Testing Next Check-in Integration\n") | |
# Create mock components | |
api = MockAPI() | |
session_manager = LifestyleSessionManager(api) | |
# Create test lifestyle profile | |
profile = LifestyleProfile( | |
patient_name="Test Patient", | |
patient_age="52", | |
conditions=["Type 2 diabetes", "Hypertension"], | |
primary_goal="Improve exercise tolerance", | |
exercise_preferences=["upper body exercises"], | |
exercise_limitations=["Right below knee amputation"], | |
dietary_notes=["Diabetic diet"], | |
personal_preferences=["prefers gradual changes"], | |
journey_summary="Initial assessment completed", | |
last_session_summary="", | |
next_check_in="not set", | |
progress_metrics={} | |
) | |
# Create mock session messages | |
session_messages = [ | |
ChatMessage( | |
timestamp="2025-09-05T10:00:00Z", | |
role="user", | |
message="I want to start exercising but I'm worried about my amputation", | |
mode="lifestyle" | |
), | |
ChatMessage( | |
timestamp="2025-09-05T10:01:00Z", | |
role="assistant", | |
message="I understand your concerns. Let's start with safe, adapted exercises.", | |
mode="lifestyle" | |
), | |
ChatMessage( | |
timestamp="2025-09-05T10:02:00Z", | |
role="user", | |
message="What exercises would be good for me to start with?", | |
mode="lifestyle" | |
) | |
] | |
print("π **Before Update:**") | |
print(f" Next check-in: {profile.next_check_in}") | |
print(f" Exercise preferences: {profile.exercise_preferences}") | |
print(f" Progress metrics: {profile.progress_metrics}") | |
print() | |
# Test the profile update with next_check_in | |
try: | |
updated_profile = session_manager.update_profile_after_session( | |
profile, | |
session_messages, | |
"First lifestyle coaching session", | |
save_to_disk=False | |
) | |
print("π **After Update:**") | |
print(f" β Next check-in: {updated_profile.next_check_in}") | |
print(f" β Exercise preferences: {updated_profile.exercise_preferences}") | |
print(f" β Personal preferences: {updated_profile.personal_preferences}") | |
print(f" β Progress metrics: {updated_profile.progress_metrics}") | |
print(f" β Last session summary: {updated_profile.last_session_summary}") | |
print() | |
# Validate the next_check_in was set | |
if updated_profile.next_check_in != "not set": | |
print("β Next check-in successfully updated!") | |
# Try to parse the date to validate format | |
try: | |
check_in_date = datetime.strptime(updated_profile.next_check_in, "%Y-%m-%d") | |
today = datetime.now() | |
days_until = (check_in_date - today).days | |
print(f"π Next session in {days_until} days ({updated_profile.next_check_in})") | |
except ValueError: | |
print(f"β οΈ Next check-in format may be descriptive: {updated_profile.next_check_in}") | |
else: | |
print("β Next check-in was not updated") | |
except Exception as e: | |
print(f"β Error during profile update: {e}") | |
def test_different_checkin_scenarios(): | |
"""Test different scenarios for next check-in timing""" | |
print("\nπ― Testing Different Check-in Scenarios\n") | |
scenarios = [ | |
{ | |
"name": "New Patient", | |
"expected_days": 1-3, | |
"description": "First session, needs immediate follow-up" | |
}, | |
{ | |
"name": "Active Coaching", | |
"expected_days": 7, | |
"description": "Regular coaching phase, weekly check-ins" | |
}, | |
{ | |
"name": "Stable Progress", | |
"expected_days": 14-21, | |
"description": "Good progress, bi-weekly follow-up" | |
}, | |
{ | |
"name": "Maintenance Phase", | |
"expected_days": 30, | |
"description": "Established routine, monthly check-ins" | |
} | |
] | |
for scenario in scenarios: | |
print(f"π **{scenario['name']}**") | |
print(f" Expected timing: {scenario['expected_days']} days") | |
print(f" Description: {scenario['description']}") | |
print() | |
if __name__ == "__main__": | |
test_next_checkin_integration() | |
test_different_checkin_scenarios() | |
print("π **Summary:**") | |
print(" β’ Next check-in field successfully integrated into profile updates") | |
print(" β’ LLM determines optimal timing based on patient status") | |
print(" β’ Date format: YYYY-MM-DD for easy parsing") | |
print(" β’ Rationale provided for timing decisions") | |
print(" β’ Supports different follow-up intervals based on patient needs") | |
print("\nβ Next check-in functionality fully integrated!") |