| from app.utils.climate_intelligence import ( |
| build_climate_intelligence_context, |
| extract_nigeria_location, |
| infer_crop_and_growth_stage, |
| ) |
|
|
|
|
| def test_build_climate_intelligence_context_contains_climate_risk_and_recommendations(): |
| context = build_climate_intelligence_context( |
| latitude=10.0, |
| longitude=7.0, |
| crop="maize", |
| growth_stage="pre-planting", |
| days=7, |
| ) |
|
|
| assert isinstance(context, dict) |
| assert "location" in context |
| assert "current_weather" in context |
| assert "forecast" in context |
| assert "climate_risk" in context |
| assert "recommendations" in context |
| assert context["location"]["country"] == "Nigeria" |
| assert context["climate_risk"] in {"low", "medium", "high"} |
| assert isinstance(context["recommendations"], list) |
| assert context["recommendations"] |
|
|
|
|
| def test_extract_nigeria_location_handles_lagos_and_ikeja(): |
| assert extract_nigeria_location("What is the weather like in Lagos?")[0] == "Lagos" |
| assert extract_nigeria_location("Ikeja weather forecast")[0] == "Ikeja" |
|
|
|
|
| def test_infer_crop_and_growth_stage_handles_farm_questions(): |
| crop, stage = infer_crop_and_growth_stage("Should I plant maize in Kaduna before the rains?") |
| assert crop == "maize" |
| assert stage in {"pre-planting", "planting"} |
|
|
|
|
| def test_recommendations_are_deduped_within_risk_branch(): |
| """Recommendations coming out of build_climate_intelligence_context must not |
| contain near-duplicate bullets, regardless of which branch produced them.""" |
| context = build_climate_intelligence_context( |
| latitude=12.0, |
| longitude=8.0, |
| crop="maize", |
| growth_stage="pre-planting", |
| days=7, |
| ) |
| recs = context["recommendations"] |
| assert isinstance(recs, list) |
| assert 1 <= len(recs) <= 3, f"expected 1–3 recs, got {len(recs)}" |
|
|
| normalized = [set(rec.lower().split()) for rec in recs] |
| for i, words_i in enumerate(normalized): |
| for j, words_j in enumerate(normalized): |
| if i >= j: |
| continue |
| overlap = len(words_i & words_j) / max(len(words_i), 1) |
| assert overlap <= 0.5, ( |
| f"near-duplicate recommendations detected: " |
| f"{recs[i]!r} vs {recs[j]!r} (overlap={overlap:.2f})" |
| ) |
|
|
|
|
| def test_risk_reason_is_a_single_readable_sentence(): |
| """risk_reason must be a single readable summary — no '|' separators and |
| no obvious self-repetition.""" |
| context = build_climate_intelligence_context( |
| latitude=12.0, |
| longitude=8.0, |
| crop="maize", |
| growth_stage="pre-planting", |
| days=7, |
| ) |
| risk_reason = context["risk_reason"] |
| assert isinstance(risk_reason, str) |
| assert "|" not in risk_reason, f"unexpected pipe in risk_reason: {risk_reason!r}" |
| |
| |
| if len(risk_reason) > 40: |
| half = len(risk_reason) // 2 |
| first = risk_reason[:half].strip().lower() |
| second = risk_reason[half:].strip().lower() |
| |
| seen = {first[i : i + 25] for i in range(0, max(len(first) - 25, 1), 10)} |
| seen.discard("") |
| for chunk in seen: |
| assert chunk not in second, ( |
| f"risk_reason appears to repeat itself: {risk_reason!r}" |
| ) |
|
|
