import pytest
from unittest.mock import MagicMock, patch
from langchain_core.messages import HumanMessage, AIMessage
from ea_chatbot.graph.nodes.planner import planner_node
from ea_chatbot.graph.nodes.researcher import researcher_node
from ea_chatbot.graph.nodes.summarizer import summarizer_node
from ea_chatbot.schemas import TaskPlanResponse

@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": {"data_required": ["2024 results", "New Jersey"], "unknowns": [], "ambiguities": [], "conditions": []},
        "next_action": "plan",
        "summary": "The user is asking about 2024 election results.",
        "plan": "Plan steps...",
        "code_output": "Code output..."
    }

@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 = TaskPlanResponse(
        goal="goal",
        reflection="reflection",
        context={
            "initial_context": "context",
            "assumptions": [],
            "constraints": []
        },
        steps=["Step 1: test"]
    )
    
    planner_node(mock_state_with_history)
    
    mock_prompt.format_messages.assert_called_once()
    kwargs = mock_prompt.format_messages.call_args[1]
    assert kwargs["question"] == "What about in New Jersey?"
    assert kwargs["summary"] == mock_state_with_history["summary"]
    assert len(kwargs["history"]) == 2

@patch("ea_chatbot.graph.nodes.researcher.get_llm_model")
@patch("ea_chatbot.graph.nodes.researcher.RESEARCHER_PROMPT")
def test_researcher_uses_history_and_summary(mock_prompt, mock_get_llm, mock_state_with_history):
    mock_llm_instance = MagicMock()
    mock_get_llm.return_value = mock_llm_instance
    
    researcher_node(mock_state_with_history)
    
    mock_prompt.format_messages.assert_called_once()
    kwargs = mock_prompt.format_messages.call_args[1]
    assert kwargs["question"] == "What about in New Jersey?"
    assert kwargs["summary"] == mock_state_with_history["summary"]
    assert len(kwargs["history"]) == 2

@patch("ea_chatbot.graph.nodes.summarizer.get_llm_model")
@patch("ea_chatbot.graph.nodes.summarizer.SUMMARIZER_PROMPT")
def test_summarizer_uses_history_and_summary(mock_prompt, mock_get_llm, mock_state_with_history):
    mock_llm_instance = MagicMock()
    mock_get_llm.return_value = mock_llm_instance
    
    summarizer_node(mock_state_with_history)
    
    mock_prompt.format_messages.assert_called_once()
    kwargs = mock_prompt.format_messages.call_args[1]
    assert kwargs["question"] == "What about in New Jersey?"
    assert kwargs["summary"] == mock_state_with_history["summary"]
    assert len(kwargs["history"]) == 2