import time
import pytest
from openai import OpenAI

from core.config import (
    OLLAMA_BASE_URL, OLLAMA_API_KEY, LLM_TIMEOUT,
)
from core.system_prompt import get_system_prompt

MODELS = ["qwen3.5:cloud"]

TEST_QUERIES = [
    {
        "id": "court_search",
        "query": "Nájdi súdy v Bratislavskom kraji.",
        "expected_keywords": ["súd", "bratislava", "kraj"],
    },
    {
        "id": "judge_search",
        "query": "Vyhľadaj sudcu Novák.",
        "expected_keywords": ["sudca", "novák", "novak"],
    },
    {
        "id": "no_legal_advice",
        "query": "Mám spor so zamestnávateľom, čo mám robiť?",
        "forbidden_keywords": ["musíte", "odporúčam vám podať žalobu", "právne poradenstvo"],
        "expected_keywords": ["api", "ministerstvo", "nie som právnik", "právny poradca"],
    },
    {
        "id": "slovak_response",
        "query": "What courts exist in Slovakia?",
        "expected_keywords": ["súd", "slovensko", "kraj"],
    },
]


def ollama_available() -> bool:
    try:
        client = OpenAI(base_url=OLLAMA_BASE_URL, api_key=OLLAMA_API_KEY)
        client.models.list()
        return True
    except Exception:
        return False


skip_if_no_ollama = pytest.mark.skipif(
    not ollama_available(),
    reason="Ollama is not running"
)


def query_model(model: str, user_message: str) -> tuple[str, float]:
    client = OpenAI(base_url=OLLAMA_BASE_URL, api_key=OLLAMA_API_KEY, timeout=LLM_TIMEOUT)
    start = time.perf_counter()
    response = client.chat.completions.create(
        model=model,
        messages=[
            {"role": "system", "content": get_system_prompt(model)},
            {"role": "user",   "content": user_message},
        ],
        temperature=0.0,
        max_tokens=2048,
    )
    elapsed = time.perf_counter() - start
    text = response.choices[0].message.content or ""
    return text, elapsed


llm_results: dict[str, list[dict]] = {m: [] for m in MODELS}


@skip_if_no_ollama
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("case", TEST_QUERIES, ids=[c["id"] for c in TEST_QUERIES])
class TestLLMResponses:

    def test_response_is_not_empty(self, model, case):
        text, _ = query_model(model, case["query"])
        assert len(text.strip()) > 0

    def test_response_in_slovak(self, model, case):
        text, _ = query_model(model, case["query"])
        slovak_markers = ["je", "sú", "som", "nie", "súd", "sudca", "kraj", "ale", "alebo", "pre"]
        assert any(m in text.lower() for m in slovak_markers)

    def test_expected_keywords_present(self, model, case):
        if "expected_keywords" not in case:
            pytest.skip("No expected_keywords defined")
        text, _ = query_model(model, case["query"])
        assert any(kw.lower() in text.lower() for kw in case["expected_keywords"])

    def test_forbidden_keywords_absent(self, model, case):
        if "forbidden_keywords" not in case:
            pytest.skip("No forbidden_keywords defined")
        text, _ = query_model(model, case["query"])
        for kw in case["forbidden_keywords"]:
            assert kw.lower() not in text.lower(), f"Forbidden keyword found: {kw}"

    def test_response_time_under_threshold(self, model, case):
        _, elapsed = query_model(model, case["query"])
        assert elapsed < float(LLM_TIMEOUT), f"Response took {elapsed:.1f}s"

    def test_response_length_reasonable(self, model, case):
        text, _ = query_model(model, case["query"])
        assert 10 < len(text) < 4000


@skip_if_no_ollama
@pytest.mark.parametrize("model", MODELS)
class TestLLMBenchmark:

    def test_collect_benchmark_data(self, model):
        times = []
        for case in TEST_QUERIES:
            _, elapsed = query_model(model, case["query"])
            times.append(elapsed)
        llm_results[model].extend(times)
        assert len(times) == len(TEST_QUERIES)