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# tests/test_language_refactor.py
"""
Verification tests for the language logic refactoring.
Tests:
1. _generate_my_listings_message returns a string
2. Language detection works for "Bonjour" (mocking the LLM response)
3. Caching is indeed removed (no import errors)
Run with: pytest tests/test_language_refactor.py -v
"""
import pytest
import asyncio
from unittest.mock import AsyncMock, patch, MagicMock
class TestCachingRemoved:
"""Test that caching has been properly disabled."""
def test_cache_functions_are_noops(self):
"""Verify cache functions exist but do nothing."""
from app.ai.agent.message_cache import (
get_cached_message,
cache_message,
clear_message_cache,
get_cache_stats
)
# get_cached_message should always return None
result = get_cached_message("test context", "en", "friendly", "short")
assert result is None, "get_cached_message should always return None (caching disabled)"
# cache_message should not raise
cache_message("test context", "en", "friendly", "short", "test message")
# clear_message_cache should not raise
clear_message_cache()
# get_cache_stats should return disabled status
stats = get_cache_stats()
assert stats.get("status") == "DISABLED" or stats.get("active_entries") == 0
def test_generate_localized_response_no_cache_import_issues(self):
"""Verify generate_localized_response can be imported without errors."""
try:
from app.ai.agent.brain import generate_localized_response
assert callable(generate_localized_response)
except ImportError as e:
pytest.fail(f"Import error: {e}")
class TestGenerateMyListingsMessage:
"""Test the _generate_my_listings_message function."""
@pytest.mark.asyncio
async def test_returns_string_for_empty_listings(self):
"""Test that empty listings returns a helpful message."""
from app.ai.agent.brain import _generate_my_listings_message
# Mock the LLM call
with patch('app.ai.agent.brain.brain_llm') as mock_llm:
mock_response = MagicMock()
mock_response.content = "You don't have any listings yet! 🏠 Would you like me to help you create your first one?"
mock_llm.ainvoke = AsyncMock(return_value=mock_response)
result = await _generate_my_listings_message(
listings=[],
language="en",
user_name="John"
)
assert isinstance(result, str), "_generate_my_listings_message should return a string"
assert len(result) > 0, "Result should not be empty"
@pytest.mark.asyncio
async def test_returns_string_for_listings_with_data(self):
"""Test that listings with data returns a personalized message."""
from app.ai.agent.brain import _generate_my_listings_message
# Mock the LLM call
with patch('app.ai.agent.brain.brain_llm') as mock_llm:
mock_response = MagicMock()
mock_response.content = "Hey John! 🏠 Here are your 2 listings (1 for rent, 1 for sale). 💡 Tip: Long-press any listing to edit or delete it!"
mock_llm.ainvoke = AsyncMock(return_value=mock_response)
test_listings = [
{"_id": "123", "title": "Apartment", "listing_type": "rent"},
{"_id": "456", "title": "House", "listing_type": "sale"},
]
result = await _generate_my_listings_message(
listings=test_listings,
language="en",
user_name="John Doe"
)
assert isinstance(result, str), "_generate_my_listings_message should return a string"
assert len(result) > 0, "Result should not be empty"
class TestLanguageDetection:
"""Test language detection in classify_intent."""
@pytest.mark.asyncio
async def test_french_detection_bonjour(self):
"""Test that 'Bonjour' is detected as French."""
from app.ai.agent.state import AgentState
# Mock the LLM response for classification
with patch('app.ai.agent.nodes.classify_intent.llm') as mock_llm:
mock_response = MagicMock()
mock_response.content = '''
{
"type": "greeting",
"confidence": 0.95,
"reasoning": "User greeted in French",
"language": "fr",
"requires_auth": false,
"next_action": "greet"
}
'''
mock_llm.ainvoke = AsyncMock(return_value=mock_response)
from app.ai.agent.nodes.classify_intent import classify_intent
# Create minimal state with required fields
state = AgentState(
user_id="test_user",
session_id="test_session",
user_role="renter" # Required field
)
state.last_user_message = "Bonjour"
result_state = await classify_intent(state)
# Verify language was detected
assert result_state.language_detected == "fr", \
f"Expected language_detected='fr', got '{result_state.language_detected}'"
@pytest.mark.asyncio
async def test_spanish_detection_hola(self):
"""Test that 'Hola' is detected as Spanish."""
from app.ai.agent.state import AgentState
# Mock the LLM response for classification
with patch('app.ai.agent.nodes.classify_intent.llm') as mock_llm:
mock_response = MagicMock()
mock_response.content = '''
{
"type": "greeting",
"confidence": 0.95,
"reasoning": "User greeted in Spanish",
"language": "es",
"requires_auth": false,
"next_action": "greet"
}
'''
mock_llm.ainvoke = AsyncMock(return_value=mock_response)
from app.ai.agent.nodes.classify_intent import classify_intent
# Create minimal state with required fields
state = AgentState(
user_id="test_user",
session_id="test_session",
user_role="renter" # Required field
)
state.last_user_message = "Hola, busco un apartamento"
result_state = await classify_intent(state)
# Verify language was detected
assert result_state.language_detected == "es", \
f"Expected language_detected='es', got '{result_state.language_detected}'"
class TestGenerateLocalizedResponse:
"""Test the generate_localized_response function."""
@pytest.mark.asyncio
async def test_returns_string_on_success(self):
"""Test that a successful LLM call returns a string."""
from app.ai.agent.brain import generate_localized_response
# Mock the LLM call
with patch('app.ai.agent.brain.brain_llm') as mock_llm:
mock_response = MagicMock()
mock_response.content = "Bienvenue! Comment puis-je vous aider?"
mock_llm.ainvoke = AsyncMock(return_value=mock_response)
result = await generate_localized_response(
context="Greet user warmly",
language="fr",
tone="friendly",
max_length="short"
)
assert isinstance(result, str)
assert len(result) > 0
@pytest.mark.asyncio
async def test_fallback_on_failure(self):
"""Test that LLM failure returns generic English fallback."""
from app.ai.agent.brain import generate_localized_response
# Mock the LLM call to fail
with patch('app.ai.agent.brain.brain_llm') as mock_llm:
mock_llm.ainvoke = AsyncMock(side_effect=Exception("LLM Error"))
result = await generate_localized_response(
context="Greet user warmly",
language="fr",
tone="friendly",
max_length="short",
max_retries=1 # Reduce retries for faster test
)
# Should return the fallback message
assert result == "Service temporarily unavailable. Please try again."
if __name__ == "__main__":
pytest.main([__file__, "-v"])
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