import pytest
from unittest.mock import MagicMock, patch
from ea_chatbot.graph.nodes.planner import planner_node
from ea_chatbot.schemas import ChecklistResponse, ChecklistTask
from langchain_core.messages import HumanMessage, AIMessage

@pytest.fixture
def mock_state_with_history():
    return {
        "messages": [
            HumanMessage(content="What about NJ?"),
            AIMessage(content="NJ has 9 million voters.")
        ],
        "question": "Show me the breakdown by county for 2024",
        "analysis": {
            "data_required": ["2024 results", "New Jersey"],
            "unknowns": [],
            "ambiguities": [],
            "conditions": []
        },
        "next_action": "plan",
        "summary": "The user is asking about NJ 2024 results.",
        "checklist": [],
        "current_step": 0
    }

@patch("ea_chatbot.graph.nodes.planner.get_llm_model")
@patch("ea_chatbot.utils.database_inspection.get_data_summary")
@patch("ea_chatbot.graph.nodes.planner.PLANNER_PROMPT")
def test_planner_uses_history_and_summary(mock_prompt, mock_get_summary, mock_get_llm, mock_state_with_history):
    mock_get_summary.return_value = "Data 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 = ChecklistResponse(
        goal="goal",
        reflection="reflection",
        checklist=[ChecklistTask(task="Step 1: test", worker="data_analyst")]
    )
    
    planner_node(mock_state_with_history)
    
    # Verify history and summary were passed to prompt format
    # We check the arguments passed to the mock_prompt.format_messages
    call_args = mock_prompt.format_messages.call_args[1]
    assert call_args["summary"] == "The user is asking about NJ 2024 results."
    assert len(call_args["history"]) == 2
    assert "breakdown by county" in call_args["question"]