import sys
import os
import pytest
from fastapi.testclient import TestClient

# Add project root to sys.path
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))

from main import app

client = TestClient(app)

@pytest.fixture(scope="module")
def client():
    with TestClient(app) as c:
        yield c

def test_read_root(client):
    response = client.get("/")
    assert response.status_code == 200
    assert response.url.path == "/docs"

def test_need_level_guardrail_high(client):
    """Test rule: Income < 1000 -> High Need"""
    payload = {
        "family_size": 4,
        "income_monthly": 900,
        "monthly_expenses": 3000,
        "debts": 0,
        "number_of_children": 2,
        "age": 35,
        "case_type": "Divorced",
        "housing_type": "Rental",
        "health_status": "Good",
        "city": "Cairo",
        "gender": "Female"
    }
    response = client.post("/api/ai/need-level", json=payload)
    assert response.status_code == 200
    data = response.json()
    assert data["need_level"] == "High"
    assert data["method"] == "rule_based_guardrail"
    assert "score" in data
    assert data["score"] == 100.0

def test_need_level_guardrail_low(client):
    """Test rule: Income > 15000 & Debts < 1000 -> Low Need"""
    payload = {
        "family_size": 2,
        "income_monthly": 16000,
        "monthly_expenses": 5000,
        "debts": 500,
        "number_of_children": 0,
        "age": 40,
        "case_type": "Married",
        "housing_type": "Owned",
        "health_status": "Good",
        "city": "Cairo",
        "gender": "Male"
    }
    response = client.post("/api/ai/need-level", json=payload)
    assert response.status_code == 200
    data = response.json()
    assert data["need_level"] == "Low"
    assert data["method"] == "rule_based_guardrail"
    assert data["score"] == 0.0

def test_assistance_type_medical(client):
    """Test rule: Health Status = Chronic Disease -> Medical"""
    payload = {
        "family_size": 4,
        "income_monthly": 2000,
        "monthly_expenses": 3000,
        "debts": 0,
        "number_of_children": 2,
        "age": 35,
        "case_type": "Divorced",
        "housing_type": "Rental",
        "health_status": "Chronic Disease",
        "city": "Cairo",
        "gender": "Female"
    }
    response = client.post("/api/ai/assistance-type", json=payload)
    assert response.status_code == 200
    data = response.json()
    assert data["assistance_type"] == "Medical"

def test_ml_inference_path(client):
    """Test case that bypasses guardrails to hit the ML model"""
    payload = {
        "family_size": 4,
        "income_monthly": 5000, # Between 1000 and 15000
        "monthly_expenses": 3000,
        "debts": 0,
        "number_of_children": 2,
        "age": 35,
        "case_type": "Married",
        "housing_type": "Rental",
        "health_status": "Good",
        "city": "Cairo",
        "gender": "Male"
    }
    response = client.post("/api/ai/need-level", json=payload)
    assert response.status_code == 200
    data = response.json()
    # We should get a valid score and a method indicating ML model or at least not guardrail
    # Note: method might be "ml_model_rf_v1" or "fallback_mock" depending on if model loaded
    # but strictly it shouldn't represent an error.
    assert "score" in data
    assert data["method"] != "rule_based_guardrail"