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import pytest
from unittest.mock import MagicMock, patch, ANY
from pathlib import Path
import sys
# 1. Mock the dependencies BEFORE import
mock_llama = MagicMock()
mock_chat_handler = MagicMock()
sys.modules["llama_cpp"] = mock_llama
sys.modules["llama_cpp.llama_chat_format"] = MagicMock()
sys.modules["llama_cpp.llama_chat_format"].Llava15ChatHandler = mock_chat_handler
from src.perception.engine import Qwen2PerceptionEngine
from src.config.settings import settings
class TestPerceptionEngine:
@patch("src.perception.engine.settings")
def test_load_model_calls_llama_correctly(self, mock_settings):
"""Test that load_model initializes the Llama class with GPU settings."""
# Setup
engine = Qwen2PerceptionEngine()
fake_path = Path("/tmp/fake_model.gguf")
fake_projector = Path("/tmp/mmproj.gguf")
# Mock the glob search for the projector
mock_settings.paths.models_dir.glob.return_value = [fake_projector]
# Act
engine.load_model(fake_path)
# Assert
# 1. Check if it looked for the projector
mock_settings.paths.models_dir.glob.assert_called()
# 2. Check if ChatHandler was initialized
mock_chat_handler.assert_called_with(clip_model_path=str(fake_projector))
# 3. Check if Llama was instantiated with GPU layers
mock_llama.Llama.assert_called_with(
model_path=str(fake_path),
chat_handler=ANY, # The instance of chat handler
n_ctx=2048,
n_gpu_layers=-1, # Important: Must be -1 for full GPU
n_batch=512,
verbose=False
)
def test_analyze_frame_structure(self):
"""Test that analyze_frame constructs the correct message format for Qwen."""
engine = Qwen2PerceptionEngine()
# Mock the internal Llama model
engine._model = MagicMock()
engine._model.create_chat_completion.return_value = {
"choices": [{"message": {"content": "A dog on a bike"}}]
}
# Mock image loader to avoid file IO
with patch("src.perception.engine.open", create=True) as mock_open:
mock_open.return_value.__enter__.return_value.read.return_value = b"fake_image_bytes"
result = engine.analyze_frame("test.jpg", "Describe this")
# Assert
assert result == "A dog on a bike"
# Verify the prompt structure
calls = engine._model.create_chat_completion.call_args
messages = calls.kwargs['messages']
assert messages[0]['role'] == 'system'
assert messages[1]['role'] == 'user'
# Check content list (Image + Text)
content = messages[1]['content']
assert content[0]['type'] == 'image_url'
assert content[1]['type'] == 'text'
assert content[1]['text'] == 'Describe this' |