import pytest
from unittest.mock import MagicMock, patch
from langchain_core.messages import HumanMessage, AIMessage
from ea_chatbot.graph.nodes.query_analyzer import query_analyzer_node, QueryAnalysis
from ea_chatbot.graph.state import AgentState

@pytest.fixture
def mock_state_with_history():
    return {
        "messages": [
            HumanMessage(content="Show me the 2024 results for Florida"),
            AIMessage(content="Here are the results for Florida in 2024...")
        ],
        "question": "What about in New Jersey?",
        "analysis": None,
        "next_action": "",
        "summary": "The user is asking about 2024 election results."
    }

@patch("ea_chatbot.graph.nodes.query_analyzer.get_llm_model")
@patch("ea_chatbot.graph.nodes.query_analyzer.QUERY_ANALYZER_PROMPT")
def test_query_analyzer_uses_history_and_summary(mock_prompt, mock_get_llm, mock_state_with_history):
    """Test that query_analyzer_node passes history and summary to the prompt."""
    mock_llm_instance = MagicMock()
    mock_get_llm.return_value = mock_llm_instance
    mock_structured_llm = MagicMock()
    mock_llm_instance.with_structured_output.return_value = mock_structured_llm
    
    mock_structured_llm.invoke.return_value = QueryAnalysis(
        data_required=["2024 results", "New Jersey"],
        unknowns=[],
        ambiguities=[],
        conditions=[],
        next_action="plan"
    )
    
    query_analyzer_node(mock_state_with_history)
    
    # Verify that the prompt was formatted with the correct variables
    mock_prompt.format_messages.assert_called_once()
    kwargs = mock_prompt.format_messages.call_args[1]
    
    assert kwargs["question"] == "What about in New Jersey?"
    assert "summary" in kwargs
    assert kwargs["summary"] == mock_state_with_history["summary"]
    assert "history" in kwargs
    # History should contain the messages from the state
    assert len(kwargs["history"]) == 2
    assert kwargs["history"][0].content == "Show me the 2024 results for Florida"

@patch("ea_chatbot.graph.nodes.query_analyzer.get_llm_model")
def test_query_analyzer_context_window(mock_get_llm):
    """Test that query_analyzer_node only uses the last 6 messages (3 turns)."""
    messages = [HumanMessage(content=f"Msg {i}") for i in range(10)]
    state = {
        "messages": messages,
        "question": "Latest question",
        "analysis": None,
        "next_action": "",
        "summary": "Summary"
    }
    
    mock_llm_instance = MagicMock()
    mock_get_llm.return_value = mock_llm_instance
    mock_structured_llm = MagicMock()
    mock_llm_instance.with_structured_output.return_value = mock_structured_llm
    mock_structured_llm.invoke.return_value = QueryAnalysis(
        data_required=[], unknowns=[], ambiguities=[], conditions=[], next_action="plan"
    )

    with patch("ea_chatbot.graph.nodes.query_analyzer.QUERY_ANALYZER_PROMPT") as mock_prompt:
        query_analyzer_node(state)
        kwargs = mock_prompt.format_messages.call_args[1]
        # Should only have last 6 messages
        assert len(kwargs["history"]) == 6
        assert kwargs["history"][0].content == "Msg 4"