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| from fastapi.testclient import TestClient | |
| from expense_ml.api.main import app | |
| client = TestClient(app) | |
| def test_health_contract(): | |
| response = client.get("/health") | |
| assert response.status_code == 200 | |
| assert response.json()["status"] in {"ok", "degraded"} | |
| def test_classify_rejects_empty_description(): | |
| response = client.post("/api/v1/classify", json={"description": ""}) | |
| assert response.status_code == 422 | |
| def test_classify_accepts_top_n_range_validation(): | |
| response = client.post("/api/v1/classify", json={"description": "coffee", "top_n": 0}) | |
| assert response.status_code == 422 | |
| def test_merchants_match_endpoint(): | |
| response = client.post("/api/v1/merchants/match", json={"query": "swiggy delivery", "top_k": 3}) | |
| assert response.status_code == 200 | |
| data = response.json() | |
| assert data["query"] == "swiggy delivery" | |
| assert "matches" in data | |
| assert isinstance(data["matches"], list) | |
| def test_duplicates_check_endpoint(): | |
| response = client.post( | |
| "/api/v1/duplicates/check", | |
| json={ | |
| "query": "Uber airport ride", | |
| "candidates": ["UBER TRIP AIRPORT", "Amazon purchase", "Starbucks coffee"], | |
| "threshold": 0.5, | |
| "top_k": 3, | |
| }, | |
| ) | |
| assert response.status_code == 200 | |
| data = response.json() | |
| assert len(data["matches"]) >= 1 | |
| assert data["matches"][0]["text"] == "UBER TRIP AIRPORT" | |
| def test_anomalies_score_endpoint(): | |
| response = client.post( | |
| "/api/v1/anomalies/score", | |
| json={ | |
| "transactions": [ | |
| {"amount": 15.0, "hour": 12, "day_of_week": 2, "balance": 2000.0}, | |
| {"amount": 999999.0, "hour": 3, "day_of_week": 0, "balance": 10.0}, | |
| ] | |
| }, | |
| ) | |
| assert response.status_code == 200 | |
| data = response.json() | |
| assert len(data["results"]) == 2 | |
| assert "is_anomaly" in data["results"][0] | |
| assert "anomaly_score" in data["results"][0] | |
| def test_forecast_spending_endpoint(): | |
| import pandas as pd | |
| dates = pd.date_range("2026-01-01", periods=30, freq="D").strftime("%Y-%m-%d").tolist() | |
| history = [{"date": d, "amount": 50.0 + (i % 7) * 5.0} for i, d in enumerate(dates)] | |
| response = client.post( | |
| "/api/v1/forecast/spending", | |
| json={"history": history, "horizon_days": 5}, | |
| ) | |
| assert response.status_code == 200 | |
| data = response.json() | |
| assert data["horizon_days"] == 5 | |
| assert len(data["forecast"]) == 5 | |
| assert "predicted_amount" in data["forecast"][0] | |