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"""Tests for CLI execution modes and argument handling.
This module tests the Mosaic CLI, including:
- Argument parsing and routing
- Single-slide processing mode
- Batch CSV processing mode
- Model download behavior
- Output file generation
"""
import pytest
from unittest.mock import Mock, patch, MagicMock, call
from pathlib import Path
import pandas as pd
class TestArgumentParsing:
"""Test CLI argument parsing and mode routing."""
@patch("mosaic.gradio_app.launch_gradio")
@patch("mosaic.gradio_app.download_and_process_models")
@patch("sys.argv", ["mosaic"])
def test_no_arguments_launches_web_interface(self, mock_download, mock_launch):
"""Test no arguments routes to web interface mode."""
mock_download.return_value = ({}, {}, [])
from mosaic.gradio_app import main
main()
# Should call launch_gradio
assert mock_launch.called
assert mock_launch.call_count == 1
@patch("mosaic.gradio_app.analyze_slide")
@patch("mosaic.gradio_app.download_and_process_models")
@patch("sys.argv", ["mosaic", "--slide-path", "test.svs", "--output-dir", "out"])
def test_slide_path_routes_to_single_mode(self, mock_download, mock_analyze):
"""Test --slide-path routes to single-slide mode."""
mock_download.return_value = ({"Unknown": "UNK"}, {"UNK": "Unknown"}, [])
mock_analyze.return_value = (None, None, None)
from mosaic.gradio_app import main
with patch("mosaic.gradio_app.Path.mkdir"):
main()
# Should call analyze_slide
assert mock_analyze.called
@patch("mosaic.gradio_app.load_all_models")
@patch("mosaic.gradio_app.load_settings")
@patch("mosaic.gradio_app.validate_settings")
@patch("mosaic.gradio_app.analyze_slide")
@patch("mosaic.gradio_app.download_and_process_models")
@patch("sys.argv", ["mosaic", "--slide-csv", "test.csv", "--output-dir", "out"])
def test_slide_csv_routes_to_batch_mode(
self,
mock_download,
mock_analyze,
mock_validate,
mock_load_settings,
mock_load_models,
):
"""Test --slide-csv routes to batch mode."""
mock_download.return_value = ({"Unknown": "UNK"}, {"UNK": "Unknown"}, [])
mock_load_settings.return_value = pd.DataFrame(
{
"Slide": ["test.svs"],
"Site Type": ["Primary"],
"Sex": ["Unknown"],
"Tissue Site": ["Unknown"],
"Cancer Subtype": ["Unknown"],
"IHC Subtype": [""],
"Segmentation Config": ["Biopsy"],
}
)
mock_validate.return_value = mock_load_settings.return_value
mock_analyze.return_value = (None, None, None)
mock_cache = Mock()
mock_cache.cleanup = Mock()
mock_load_models.return_value = mock_cache
from mosaic.gradio_app import main
with patch("mosaic.gradio_app.Path.mkdir"):
main()
# Should call load_all_models (batch mode)
assert mock_load_models.called
class TestSingleSlideMode:
"""Test single-slide processing mode."""
@patch("mosaic.gradio_app.Path.mkdir")
@patch("mosaic.gradio_app.analyze_slide")
@patch("mosaic.gradio_app.download_and_process_models")
def test_analyze_slide_called_with_correct_params(
self, mock_download, mock_analyze, mock_mkdir, cli_args_single
):
"""Test analyze_slide called with correct parameters in single mode."""
mock_download.return_value = ({"Unknown": "UNK"}, {"UNK": "Unknown"}, [])
mock_analyze.return_value = (None, None, None)
# Patch ArgumentParser to return our test args
with patch(
"mosaic.gradio_app.ArgumentParser.parse_args", return_value=cli_args_single
):
from mosaic.gradio_app import main
main()
# Verify analyze_slide was called
assert mock_analyze.called
call_args = mock_analyze.call_args[0] # Positional args
# Check key parameters (analyze_slide uses positional args)
assert call_args[0] == cli_args_single.slide_path # slide_path
assert call_args[1] == cli_args_single.segmentation_config # seg_config
assert call_args[2] == cli_args_single.site_type # site_type
@patch("PIL.Image.Image.save")
@patch("mosaic.gradio_app.Path.mkdir")
@patch("mosaic.gradio_app.analyze_slide")
@patch("mosaic.gradio_app.download_and_process_models")
def test_output_files_saved_correctly(
self,
mock_download,
mock_analyze,
mock_mkdir,
mock_save,
cli_args_single,
mock_analyze_slide_results,
):
"""Test output files are saved with correct names."""
from PIL import Image
mock_download.return_value = ({"Unknown": "UNK"}, {"UNK": "Unknown"}, [])
# Mock analyze_slide to return results
mask, aeon_results, paladin_results = mock_analyze_slide_results
mock_analyze.return_value = (mask, aeon_results, paladin_results)
# Patch ArgumentParser
with patch(
"mosaic.gradio_app.ArgumentParser.parse_args", return_value=cli_args_single
):
# Patch DataFrame.to_csv to avoid actual file writes
with patch("pandas.DataFrame.to_csv"):
from mosaic.gradio_app import main
main()
# Verify save was called for mask
assert mock_save.called
class TestBatchCsvMode:
"""Test batch CSV processing mode."""
@patch("mosaic.gradio_app.Path.mkdir")
@patch("mosaic.gradio_app.load_all_models")
@patch("mosaic.gradio_app.analyze_slide")
@patch("mosaic.gradio_app.validate_settings")
@patch("mosaic.gradio_app.load_settings")
@patch("mosaic.gradio_app.download_and_process_models")
def test_load_all_models_called_once(
self,
mock_download,
mock_load_settings,
mock_validate,
mock_analyze,
mock_load_models,
mock_mkdir,
cli_args_batch,
sample_settings_df,
mock_analyze_slide_results,
):
"""Test load_all_models called once in batch mode."""
from PIL import Image
mock_download.return_value = ({"Unknown": "UNK"}, {"UNK": "Unknown"}, [])
mock_load_settings.return_value = sample_settings_df
mock_validate.return_value = sample_settings_df
# Return fresh DataFrames on each call to avoid mutation
def mock_analyze_side_effect(*args, **kwargs):
mask = Image.new("RGB", (100, 100), color="red")
aeon_results = pd.DataFrame(
{"Cancer Subtype": ["LUAD"], "Confidence": [0.95]}
)
paladin_results = pd.DataFrame(
{
"Cancer Subtype": ["LUAD", "LUAD", "LUAD"],
"Biomarker": ["TP53", "KRAS", "EGFR"],
"Score": [0.85, 0.72, 0.63],
}
)
return (mask, aeon_results, paladin_results)
mock_analyze.side_effect = mock_analyze_side_effect
mock_cache = Mock()
mock_cache.cleanup = Mock()
mock_load_models.return_value = mock_cache
with patch(
"mosaic.gradio_app.ArgumentParser.parse_args", return_value=cli_args_batch
):
with patch("pandas.DataFrame.to_csv"):
with patch("PIL.Image.Image.save"):
from mosaic.gradio_app import main
main()
# load_all_models should be called exactly once
assert mock_load_models.call_count == 1
# analyze_slide should be called for each slide (3 times)
assert mock_analyze.call_count == 3
# All analyze_slide calls should receive the model_cache
for call in mock_analyze.call_args_list:
assert call[1]["model_cache"] == mock_cache
# cleanup should be called
assert mock_cache.cleanup.called
@patch("mosaic.gradio_app.Path.mkdir")
@patch("mosaic.gradio_app.load_all_models")
@patch("mosaic.gradio_app.analyze_slide")
@patch("mosaic.gradio_app.validate_settings")
@patch("mosaic.gradio_app.load_settings")
@patch("mosaic.gradio_app.download_and_process_models")
def test_combined_outputs_generated(
self,
mock_download,
mock_load_settings,
mock_validate,
mock_analyze,
mock_load_models,
mock_mkdir,
cli_args_batch,
sample_settings_df,
mock_analyze_slide_results,
):
"""Test combined output files are generated in batch mode."""
from PIL import Image
mock_download.return_value = (
{"Unknown": "UNK", "Lung Adenocarcinoma (LUAD)": "LUAD"},
{"UNK": "Unknown", "LUAD": "Lung Adenocarcinoma (LUAD)"},
["LUAD"],
)
mock_load_settings.return_value = sample_settings_df
mock_validate.return_value = sample_settings_df
# Return fresh DataFrames on each call
def mock_analyze_side_effect(*args, **kwargs):
mask = Image.new("RGB", (100, 100), color="red")
aeon_results = pd.DataFrame(
{"Cancer Subtype": ["LUAD"], "Confidence": [0.95]}
)
paladin_results = pd.DataFrame(
{
"Cancer Subtype": ["LUAD", "LUAD", "LUAD"],
"Biomarker": ["TP53", "KRAS", "EGFR"],
"Score": [0.85, 0.72, 0.63],
}
)
return (mask, aeon_results, paladin_results)
mock_analyze.side_effect = mock_analyze_side_effect
mock_cache = Mock()
mock_cache.cleanup = Mock()
mock_load_models.return_value = mock_cache
csv_calls = []
def track_csv_write(path, *args, **kwargs):
"""Track CSV file writes."""
csv_calls.append(str(path))
with patch(
"mosaic.gradio_app.ArgumentParser.parse_args", return_value=cli_args_batch
):
with patch("pandas.DataFrame.to_csv", side_effect=track_csv_write):
with patch("PIL.Image.Image.save"):
from mosaic.gradio_app import main
main()
# Should have combined files
combined_files = [c for c in csv_calls if "combined" in c]
assert len(combined_files) >= 2 # combined_aeon and combined_paladin
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