from core import ai_fetch


class _FakeMessage:
    def __init__(self, content, annotations=None):
        self.content = content
        self.annotations = annotations


class _FakeChoice:
    def __init__(self, message):
        self.message = message


class _FakeResponse:
    def __init__(self, message):
        self.choices = [_FakeChoice(message)]


def test_fetch_with_ai_returns_result_when_content_sufficient(monkeypatch):
    class _FakeClient:
        class chat:
            class completions:
                @staticmethod
                def create(**kwargs):
                    return _FakeResponse(_FakeMessage('x' * 400))

    monkeypatch.setattr(ai_fetch, 'OpenAI', lambda api_key, base_url: _FakeClient())

    result = ai_fetch.fetch_with_ai('https://intrepidtravel.com/peru')

    assert result['success'] is True
    assert result['char_count'] == 400


def test_fetch_with_ai_returns_none_when_content_too_short(monkeypatch):
    class _FakeClient:
        class chat:
            class completions:
                @staticmethod
                def create(**kwargs):
                    return _FakeResponse(_FakeMessage('too short'))

    monkeypatch.setattr(ai_fetch, 'OpenAI', lambda api_key, base_url: _FakeClient())

    assert ai_fetch.fetch_with_ai('https://intrepidtravel.com/peru') is None


def test_fetch_with_ai_returns_none_on_exception(monkeypatch):
    class _FakeClient:
        class chat:
            class completions:
                @staticmethod
                def create(**kwargs):
                    raise Exception('api down')

    monkeypatch.setattr(ai_fetch, 'OpenAI', lambda api_key, base_url: _FakeClient())

    assert ai_fetch.fetch_with_ai('https://intrepidtravel.com/peru') is None


def test_fetch_with_ai_appends_citation_content(monkeypatch):
    citation = type('Citation', (), {'content': 'extra citation text'})()
    annotation = type('Annotation', (), {'url_citation': citation})()

    class _FakeClient:
        class chat:
            class completions:
                @staticmethod
                def create(**kwargs):
                    return _FakeResponse(_FakeMessage('x' * 350, annotations=[annotation]))

    monkeypatch.setattr(ai_fetch, 'OpenAI', lambda api_key, base_url: _FakeClient())

    result = ai_fetch.fetch_with_ai('https://intrepidtravel.com/peru')
    assert 'extra citation text' in result['text']


def test_fetch_with_ai_prompt_forbids_markdown_and_scopes_to_single_page(monkeypatch):
    """
    Regression test — a real failure showed gpt-4o-mini defaulting to a
    markdown-formatted, multi-trip summary of the whole domain when the
    prompt didn't explicitly forbid that. The resulting markdown then
    confused the downstream extraction model (build_prompt()'s "Return
    ONLY a valid JSON object, no markdown" instruction) into producing
    unparseable output. The prompt sent here must explicitly rule both
    of those out.
    """
    captured = {}

    class _FakeClient:
        class chat:
            class completions:
                @staticmethod
                def create(**kwargs):
                    captured['messages'] = kwargs['messages']
                    return _FakeResponse(_FakeMessage('x' * 400))

    monkeypatch.setattr(ai_fetch, 'OpenAI', lambda api_key, base_url: _FakeClient())

    ai_fetch.fetch_with_ai('https://intrepidtravel.com/peru')

    prompt_text = captured['messages'][0]['content']
    assert 'no markdown' in prompt_text.lower()
    assert 'this exact url' in prompt_text.lower() or 'this one tour' in prompt_text.lower()