"""
Unit tests for AI provider JSON parsing robustness and classifier orchestration.
Uses a mock provider — no real AI calls made.
"""
import json
import pytest
from ai.openai_provider import _parse_classification, _parse_suggestions, _strip_code_fences
from ai.base import ClassificationResult


def test_parse_clean_json():
    raw = '{"assigned_topics": ["finance", "invoices"], "new_topic_suggestions": []}'
    result = _parse_classification(raw)
    assert result.topics == ["finance", "invoices"]
    assert result.suggested_new_topics == []


def test_parse_with_code_fence():
    raw = '```json\n{"assigned_topics": ["legal"], "new_topic_suggestions": ["contracts"]}\n```'
    result = _parse_classification(raw)
    assert result.topics == ["legal"]
    assert result.suggested_new_topics == ["contracts"]


def test_parse_with_preamble():
    raw = 'Here is the classification:\n{"assigned_topics": ["hr"], "new_topic_suggestions": []}\nDone.'
    result = _parse_classification(raw)
    assert result.topics == ["hr"]


def test_parse_malformed_returns_empty():
    raw = "I cannot classify this document."
    result = _parse_classification(raw)
    assert result.topics == []
    assert result.suggested_new_topics == []


def test_strip_code_fences():
    raw = "```json\n{}\n```"
    assert _strip_code_fences(raw) == "{}"


def test_parse_suggestions_clean():
    raw = '{"suggested_topics": ["Human Resources", "Onboarding"]}'
    result = _parse_suggestions(raw)
    assert "Human Resources" in result
    assert "Onboarding" in result


def test_parse_suggestions_with_fence():
    raw = "```\n{\"suggested_topics\": [\"Finance\"]}\n```"
    result = _parse_suggestions(raw)
    assert result == ["Finance"]


def test_parse_suggestions_malformed():
    raw = "No suggestions available."
    result = _parse_suggestions(raw)
    assert result == []


@pytest.mark.asyncio
async def test_classifier_with_mock_provider(isolated_data_dir):
    """Test classifier orchestration with a mock provider."""
    from unittest.mock import AsyncMock, patch
    from ai.base import ClassificationResult
    import services.storage as st

    # Create a document
    doc_id = "test-doc-1"
    st.save_metadata({
        "id": doc_id,
        "original_name": "test.txt",
        "filename": "test-doc-1.txt",
        "mime_type": "text/plain",
        "size_bytes": 50,
        "extracted_text": "Invoice for services rendered in March 2026.",
        "topics": [],
        "created_at": "2026-01-01T00:00:00Z",
        "classified_at": None,
    })

    # Create some topics
    st.create_topic("Finance")
    st.create_topic("Legal")

    mock_result = ClassificationResult(
        topics=["Finance"],
        suggested_new_topics=["Invoices"],
        reasoning="Document is about financial invoicing.",
    )

    with patch("services.classifier.get_provider") as mock_get_provider:
        mock_provider = AsyncMock()
        mock_provider.classify = AsyncMock(return_value=mock_result)
        mock_get_provider.return_value = mock_provider

        from services.classifier import classify_document
        topics = await classify_document(doc_id)

    assert "Finance" in topics
    assert "Invoices" in topics

    # Verify new topic was auto-created
    all_topics = st.load_topics()
    assert any(t["name"] == "Invoices" for t in all_topics)

    # Verify document was updated
    meta = st.get_metadata(doc_id)
    assert "Finance" in meta["topics"]