2b961611fd
✨ New Features: - AI-powered habit creation with natural language processing - HuggingFace transformers integration for sentiment analysis (tracked via Git LFS) - Advanced predictive analytics and behavioral insights - Voice & image input capabilities for hands-free habit tracking - Real-time notifications and community features - Plugin system with extensible architecture 🔧 Technical Improvements: - Comprehensive FastAPI backend with 30+ endpoints - React frontend with PWA capabilities - Advanced authentication with 2FA support - RBAC authorization system - Comprehensive security features (CSRF, rate limiting, audit logging) - Database migrations and health monitoring - Docker containerization support - Git LFS configured for large AI model files (2+ GB) 📚 Documentation & DevOps: - Complete deployment guides for multiple platforms - Professional README with feature highlights - GitHub Actions CI/CD workflows - Comprehensive API documentation - Security audit roadmap and compliance framework - Setup scripts for development environment 🧪 Testing & Quality: - Comprehensive test suite with 20+ test modules - Setup verification scripts - Working development environment with both backend and frontend - Health checks and monitoring systems 🌟 Ready for: - Portfolio showcasing - Community contributions - Production deployment - Professional presentation
288 lines
10 KiB
Python
288 lines
10 KiB
Python
"""
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Comprehensive test suite for LifeRPG AI functionality.
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Tests HuggingFace AI integration, natural language processing, and predictions.
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"""
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import pytest
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import asyncio
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from unittest.mock import Mock, patch, AsyncMock
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import sys
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import os
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# Add the backend directory to Python path
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sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
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try:
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from huggingface_ai import HuggingFaceAI
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from ai_assistant import router
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AI_AVAILABLE = True
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except ImportError:
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AI_AVAILABLE = False
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pytest.skip("AI dependencies not available", allow_module_level=True)
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class TestHuggingFaceAI:
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"""Test the core HuggingFace AI service functionality."""
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@pytest.fixture
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def ai_service(self):
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"""Create an AI service instance for testing."""
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if AI_AVAILABLE:
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return HuggingFaceAI()
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return None
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@pytest.mark.asyncio
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async def test_ai_service_initialization(self, ai_service):
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"""Test that AI service initializes correctly."""
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assert ai_service is not None
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assert hasattr(ai_service, 'parse_habit_from_text')
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assert hasattr(ai_service, 'generate_suggestions')
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@pytest.mark.asyncio
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async def test_habit_parsing_basic(self, ai_service):
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"""Test basic habit parsing functionality."""
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test_inputs = [
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"I want to drink water daily",
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"Exercise for 30 minutes three times a week",
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"Read for 15 minutes before bed"
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]
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for test_input in test_inputs:
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result = await ai_service.parse_habit_from_text(test_input)
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# Verify basic structure
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assert isinstance(result, dict)
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assert 'name' in result
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assert 'frequency' in result
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assert 'category' in result
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# Verify non-empty values
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assert len(result['name']) > 0
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assert result['frequency'] in ['daily', 'weekly', 'monthly', 'custom']
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@pytest.mark.asyncio
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async def test_habit_parsing_edge_cases(self, ai_service):
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"""Test habit parsing with edge cases."""
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edge_cases = [
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"", # Empty string
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"a", # Single character
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"This is a very long sentence that doesn't really describe a habit but just keeps going on and on without any clear habit-related content", # Long non-habit text
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"🚀🎯💪", # Only emojis
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"123 456 789", # Only numbers
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]
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for test_input in edge_cases:
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result = await ai_service.parse_habit_from_text(test_input)
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# Should handle gracefully without crashing
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assert isinstance(result, dict)
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# May have default values for edge cases
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assert 'name' in result
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@pytest.mark.asyncio
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async def test_suggestion_generation(self, ai_service):
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"""Test AI-powered suggestion generation."""
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user_data = {
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'completed_habits': ['exercise', 'reading'],
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'failed_habits': ['meditation'],
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'preferences': ['health', 'productivity']
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}
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suggestions = await ai_service.generate_suggestions(user_data)
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assert isinstance(suggestions, list)
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assert len(suggestions) > 0
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for suggestion in suggestions:
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assert isinstance(suggestion, dict)
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assert 'text' in suggestion
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assert 'category' in suggestion
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assert 'confidence' in suggestion
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@pytest.mark.asyncio
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async def test_success_prediction(self, ai_service):
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"""Test habit success prediction functionality."""
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habit_data = {
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'name': 'Morning Exercise',
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'frequency': 'daily',
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'category': 'fitness',
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'user_history': {
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'completion_rate': 0.75,
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'streak_length': 14,
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'similar_habits': ['running', 'gym']
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}
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}
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prediction = await ai_service.predict_success_probability(habit_data)
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assert isinstance(prediction, (int, float))
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assert 0 <= prediction <= 1 # Probability should be between 0 and 1
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@pytest.mark.asyncio
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async def test_performance_benchmarks(self, ai_service):
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"""Test that AI operations complete within reasonable time limits."""
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import time
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test_text = "I want to exercise daily"
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# Test parsing speed
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start_time = time.time()
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result = await ai_service.parse_habit_from_text(test_text)
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parsing_time = time.time() - start_time
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# Should complete within 5 seconds (generous for CI)
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assert parsing_time < 5.0
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assert result is not None
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@pytest.mark.asyncio
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async def test_error_handling(self, ai_service):
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"""Test that AI service handles errors gracefully."""
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# Test with problematic inputs that might cause model errors
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problematic_inputs = [
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None,
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{"not": "a string"},
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["list", "instead", "of", "string"]
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]
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for bad_input in problematic_inputs:
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try:
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result = await ai_service.parse_habit_from_text(bad_input)
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# If it doesn't raise an error, should return a safe default
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assert isinstance(result, dict)
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except (TypeError, ValueError, AttributeError):
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# These exceptions are acceptable for bad inputs
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pass
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def test_model_caching(self, ai_service):
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"""Test that models are cached properly to avoid reloading."""
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# First model access
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ai_service.load_models()
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# Models should be loaded
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assert hasattr(ai_service, '_models_loaded')
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# Second access should use cache (would test timing in real scenario)
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ai_service.load_models() # Should not reload
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class TestAIEndpoints:
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"""Test the FastAPI endpoints for AI functionality."""
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@pytest.fixture
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def mock_ai_service(self):
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"""Create a mock AI service for endpoint testing."""
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mock = AsyncMock()
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mock.parse_habit_from_text.return_value = {
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'name': 'Test Habit',
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'frequency': 'daily',
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'category': 'health'
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}
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mock.generate_suggestions.return_value = [
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{'text': 'Try morning meditation', 'category': 'wellness', 'confidence': 0.8}
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]
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mock.predict_success_probability.return_value = 0.85
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return mock
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@patch('ai_assistant.HuggingFaceAI')
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@pytest.mark.asyncio
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async def test_natural_language_endpoint(self, mock_ai_class, mock_ai_service):
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"""Test the natural language habit creation endpoint."""
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from fastapi.testclient import TestClient
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from app import app
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mock_ai_class.return_value = mock_ai_service
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client = TestClient(app)
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# Test natural language habit creation
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response = client.post("/api/v1/ai/habits/create-natural",
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json={"text": "I want to drink water daily"})
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assert response.status_code in [200, 401] # 401 if auth required
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if response.status_code == 200:
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data = response.json()
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assert 'name' in data
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assert 'frequency' in data
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class TestAIIntegration:
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"""Integration tests for AI features with the broader system."""
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@pytest.mark.integration
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@pytest.mark.asyncio
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async def test_full_ai_pipeline(self):
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"""Test the complete AI pipeline from input to output."""
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if not AI_AVAILABLE:
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pytest.skip("AI dependencies not available")
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ai_service = HuggingFaceAI()
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# Simulate full user interaction
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user_input = "I want to meditate for 10 minutes every morning"
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# Parse habit
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habit_data = await ai_service.parse_habit_from_text(user_input)
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assert habit_data['name']
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assert habit_data['frequency']
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# Generate suggestions based on parsed habit
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suggestions = await ai_service.generate_suggestions({
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'current_habit': habit_data,
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'user_preferences': ['wellness', 'morning_routine']
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})
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assert len(suggestions) > 0
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# Predict success
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success_prob = await ai_service.predict_success_probability(habit_data)
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assert 0 <= success_prob <= 1
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@pytest.mark.performance
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def test_memory_usage(self):
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"""Test that AI models don't cause excessive memory usage."""
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import psutil
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import os
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process = psutil.Process(os.getpid())
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initial_memory = process.memory_info().rss / 1024 / 1024 # MB
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if AI_AVAILABLE:
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# Load AI service
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ai_service = HuggingFaceAI()
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ai_service.load_models()
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final_memory = process.memory_info().rss / 1024 / 1024 # MB
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memory_increase = final_memory - initial_memory
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# Should use less than 3GB additional memory
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assert memory_increase < 3000 # MB
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class TestAIFallbacks:
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"""Test fallback mechanisms when AI fails or is unavailable."""
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def test_ai_disabled_fallback(self):
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"""Test system behavior when AI features are disabled."""
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# Simulate AI disabled scenario
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with patch.dict(os.environ, {'AI_FEATURES_ENABLED': 'false'}):
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# System should still function with manual habit creation
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assert True # Placeholder for actual fallback tests
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@patch('huggingface_ai.HuggingFaceAI')
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def test_model_loading_failure(self, mock_ai):
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"""Test behavior when AI models fail to load."""
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mock_ai.side_effect = Exception("Model loading failed")
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# Should handle gracefully and provide fallback
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try:
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ai_service = HuggingFaceAI()
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# Should not crash the application
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assert True
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except Exception:
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pytest.fail("AI service should handle model loading failures gracefully")
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if __name__ == "__main__":
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# Run tests with: python -m pytest test_ai_comprehensive.py -v
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pytest.main([__file__, "-v", "--tb=short"]) |