first successful run of photo llm (#11)

Reviewed-on: #11
Co-authored-by: Mathias Scheider <mathias2784@web.de>

test/conftest.py

@@ -0,0 +1,20 @@
+import pytest
+
+from photools3.llm_session import LlmSession
+from photools3.photo import Photo
+from test.test_photo import PATHS_DNG_EXAMPLES
+
+
+@pytest.fixture
+def test_photo():
+    return Photo(PATHS_DNG_EXAMPLES[0])
+
+
+@pytest.fixture
+def llm_session():
+    return LlmSession(
+        system_prompt=(
+            "You are a helpful assistent to photographer. "
+            'Answer in JSON format: `{"caption": "...", "tags": ["tag1", "tag2", ...]}`'
+        )
+    )

test/test_llm_session_integration.py

@@ -0,0 +1,13 @@
+import pytest
+
+from photools3.llm_session import LlmSession
+
+
+@pytest.mark.skipif(
+    not LlmSession(system_prompt="").is_api_reachable(), reason="API not available"
+)
+def test_llm_session(llm_session, test_photo):
+    result = llm_session.call(
+        prompt="keywords for the attached picture", photos=[test_photo]
+    )
+    assert result

test/test_llm_session_mocked.py

@@ -0,0 +1,47 @@
+from unittest.mock import MagicMock, patch
+
+from photools3.llm_session import LlmSession
+
+
+def test_llm_session_mocked(test_photo):
+    # We want to mock OpenAI client's chat.completions.create
+    with patch("photools3.llm_session.OpenAI") as mocked_openai:
+        # Setup the mock response structure
+        mock_client = MagicMock()
+        mocked_openai.return_value = mock_client
+
+        # Mock the models list to return a proper object with an ID string
+        # This prevents Pydantic ValidationError when LlmSession tries to use model_id
+        mock_model = MagicMock()
+        mock_model.id = "gpt-4o"
+        mock_client.models.list.return_value.data = [mock_model]
+
+        # Create a response object: response.choices[0].message.content
+        mock_response = MagicMock()
+        mock_choice = MagicMock()
+        mock_message = MagicMock()
+
+        mock_message.content = (
+            '{"caption": "A beautiful sunset", "tags": ["sunset", "nature"]}'
+        )
+        mock_choice.message = mock_message
+        mock_response.choices = [mock_choice]
+
+        # Configure the client to return our mock response
+        mock_client.chat.completions.create.return_value = mock_response
+
+        # Create a new LlmSession instance that will use the mocked OpenAI client
+        mocked_session = LlmSession(system_prompt="You are a helper.")
+
+        # Run the test
+        result = mocked_session.call(
+            prompt="keywords for the attached picture", photos=[test_photo]
+        )
+
+        # Verify the results
+        assert result["caption"] == "A beautiful sunset"
+        assert "sunset" in result["tags"]
+        assert "nature" in result["tags"]
+
+        # Verify that the mock was called
+        assert mock_client.chat.completions.create.called