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| """Transparency dashboard endpoint — verifies it is wired to real data.""" | |
| import pytest | |
| from animetix.models import AIFeedback, AIREvalResult, AISafetyEvent | |
| from django.urls import reverse | |
| from rest_framework.test import APIClient | |
| def api_client(): | |
| return APIClient() | |
| def test_transparency_empty_db_is_safe(api_client): | |
| """Public endpoint must never 500, even with no data, and expose the | |
| canonical benchmark list + drift keys instead of leaving them empty.""" | |
| resp = api_client.get(reverse("api_transparency")) | |
| assert resp.status_code == 200, resp.content | |
| data = resp.json() | |
| assert data["status"] == "synchronized" | |
| gm = data["global_metrics"] | |
| assert gm["total_feedbacks"] == 0 | |
| assert gm["community_satisfaction"] == 0.0 | |
| assert gm["model_version"] # declared label, always present | |
| # No evals yet → reliability/last_training unknown (front applies fallbacks). | |
| assert data["model_uptime"] is None | |
| assert gm["last_training"] is None | |
| assert data["evolution_timeline"] == [] | |
| # Benchmarks come from the canonical curated service (never empty). | |
| assert len(data["sota_benchmarks"]) > 0 | |
| assert "elo_score" in data["sota_benchmarks"][0] | |
| # Drift is a dict (real KS service; "unknown" without baselines). | |
| assert isinstance(data["embedding_drift"], dict) | |
| # The fabricated bias metric is gone. | |
| assert "bias_score" not in data["ethics_audit"] | |
| # No evaluations → rate is withheld (None), not a misleading 0%. | |
| assert data["ethics_audit"]["hallucination_rate"] is None | |
| assert data["ethics_audit"]["safety_compliance"] is None | |
| assert data["ethics_score"] is None | |
| def test_transparency_reflects_real_data(api_client): | |
| # 4 feedbacks, 3 positive → satisfaction 0.75 | |
| for is_pos in (True, True, True, False): | |
| AIFeedback.objects.create(is_positive=is_pos) | |
| # 20 recent evals (>= MIN_EVAL_SAMPLE), 4 hallucinations → rate 0.2, reliab. 80% | |
| for i in range(20): | |
| AIREvalResult.objects.create( | |
| faithfulness=0.9, | |
| relevancy=0.8, | |
| precision=0.7, | |
| hallucination_detected=(i < 4), | |
| ) | |
| # Safety: 2 blocked over 20 evaluated interactions → compliance 0.9 | |
| AISafetyEvent.objects.create(event_type="output", action="block") | |
| AISafetyEvent.objects.create(event_type="output", action="rewrite") | |
| AISafetyEvent.objects.create(event_type="input", action="none") | |
| resp = api_client.get(reverse("api_transparency")) | |
| assert resp.status_code == 200 | |
| data = resp.json() | |
| gm = data["global_metrics"] | |
| assert gm["total_feedbacks"] == 4 | |
| assert gm["community_satisfaction"] == 0.75 | |
| assert data["ethics_audit"]["hallucination_rate"] == 0.2 | |
| assert data["model_uptime"] == 80.0 | |
| assert data["ethics_audit"]["safety_compliance"] == 0.9 | |
| # ethics_score = mean(1-halluc=0.8, satisfaction=0.75, compliance=0.9) * 100 | |
| assert data["ethics_score"] == pytest.approx(81.7, abs=0.1) | |
| assert len(data["evolution_timeline"]) == 1 | |
| assert 0.0 <= data["evolution_timeline"][0]["accuracy"] <= 1.0 | |
| assert gm["last_training"] is not None | |
| def test_transparency_withholds_rates_below_sample_threshold(api_client): | |
| """A handful of evals must NOT produce a rate (would be statistical noise).""" | |
| for i in range(5): | |
| AIREvalResult.objects.create(faithfulness=0.9, hallucination_detected=(i == 0)) | |
| resp = api_client.get(reverse("api_transparency")) | |
| assert resp.status_code == 200 | |
| data = resp.json() | |
| assert data["ethics_audit"]["hallucination_rate"] is None | |
| assert data["model_uptime"] is None | |
| assert data["ethics_audit"]["safety_compliance"] is None | |