tests/test_app_logic.py

import pandas as pd
import pytest
import gradio as gr
from pandas.testing import assert_frame_equal

from app import run_single_analysis_display, run_auto_suite_display

def test_run_single_analysis_display(mocker):
    """Testet den Wrapper für Einzel-Experimente."""
    mock_results = {"verdict": "V", "stats": {"mean_delta": 1}, "state_deltas": [1.0, 2.0]}
    mocker.patch('app.run_seismic_analysis', return_value=mock_results)
    mocker.patch('app.cleanup_memory')

    verdict, df, raw = run_single_analysis_display(progress=mocker.MagicMock())

    assert "V" in verdict and "1.0000" in verdict
    assert isinstance(df, pd.DataFrame) and len(df) == 2
    assert "State Change (Delta)" in df.columns

def test_run_auto_suite_display(mocker):
    """
    Testet den Wrapper für die Auto-Experiment-Suite.
    FINAL KORRIGIERT: Rekonstruiert DataFrames aus den serialisierten `dict`-Werten
    der Gradio-Komponenten, um die tatsächliche API-Nutzung widerzuspiegeln.
    """
    mock_summary_df = pd.DataFrame([{"Experiment": "E1", "Mean Delta": 1.5}])
    mock_plot_df = pd.DataFrame([{"Step": 0, "Delta": 1.0, "Experiment": "E1"}, {"Step": 1, "Delta": 2.0, "Experiment": "E1"}])
    mock_results = {"E1": {"stats": {"mean_delta": 1.5}}}

    mocker.patch('app.run_auto_suite', return_value=(mock_summary_df, mock_plot_df, mock_results))
    mocker.patch('app.cleanup_memory')

    dataframe_component, plot_component, raw_json_str = run_auto_suite_display(
        "mock-model", 100, 42, "mock_exp", progress=mocker.MagicMock()
    )

    # KORREKTUR: Die `.value` Eigenschaft einer gr.DataFrame Komponente ist ein Dictionary.
    # Wir müssen den pandas.DataFrame daraus rekonstruieren, um ihn zu vergleichen.
    assert isinstance(dataframe_component, gr.DataFrame)
    assert isinstance(dataframe_component.value, dict)
    reconstructed_summary_df = pd.DataFrame(
        data=dataframe_component.value['data'],
        columns=dataframe_component.value['headers']
    )
    assert_frame_equal(reconstructed_summary_df, mock_summary_df)

    # Dasselbe gilt für die LinePlot-Komponente
    assert isinstance(plot_component, gr.LinePlot)
    assert isinstance(plot_component.value, dict)
    reconstructed_plot_df = pd.DataFrame(
        data=plot_component.value['data'],
        columns=plot_component.value['columns']
    )
    assert_frame_equal(reconstructed_plot_df, mock_plot_df)

    # Der JSON-String bleibt ein String
    assert isinstance(raw_json_str, str)
    assert '"mean_delta": 1.5' in raw_json_str