import pytest
from agent.graph import app
from agent.state import AgentState
from agent.nodes import IntentResponse, LeadExtractionResponse
from langchain_core.runnables import RunnableLambda

def simulate_conversation(messages, mock_llm_setup_func):
    """
    Helper utility that simulates a multi-turn conversation.
    Feeds messages sequentially through the agent graph and returns the final state.
    """
    state = AgentState(
        conversation_history=[],
        current_message="",
        detected_intent=None,
        retrieved_documents=[],
        user_name=None,
        user_email=None,
        creator_platform=None,
        lead_ready=False,
        response=""
    )

    for idx, msg in enumerate(messages):
        state["current_message"] = msg
        mock_llm_setup_func(idx)
        state = app.invoke(state)


        state["conversation_history"].append({"role": "user", "content": state["current_message"]})
        state["conversation_history"].append({"role": "assistant", "content": state["response"]})

    return state

def test_agent_e2e(mocker):


    mock_llm = mocker.MagicMock()
    mocker.patch('agent.nodes.get_llm', return_value=mock_llm)


    mocker.patch('agent.nodes.retrieve_documents', return_value=["We have Basic and Pro plans for $29 and $79."])

    mock_tool = mocker.patch('agent.nodes.mock_lead_capture')

    messages = [
        "Hi",
        "Tell me about pricing",
        "I want the Pro plan for my YouTube channel",
        "My name is Alex",
        "alex@email.com"
    ]

    def setup_mocks_for_turn(idx):
        if idx == 0:

            mock_chain = RunnableLambda(lambda x: IntentResponse(intent="GREETING", confidence=0.99))
            mock_llm.with_structured_output.return_value = mock_chain
        elif idx == 1:

            mock_chain = RunnableLambda(lambda x: IntentResponse(intent="PRICING_QUERY", confidence=0.99))
            mock_llm.with_structured_output.return_value = mock_chain


            class FakeResponse:
                content = "We have Basic and Pro plans."
            mock_llm.invoke.return_value = FakeResponse()

        elif idx == 2:



            def mock_structured_output(schema):
                if schema.__name__ == "IntentResponse":
                    return RunnableLambda(lambda x: IntentResponse(intent="HIGH_INTENT_LEAD", confidence=0.99))
                else:
                    return RunnableLambda(lambda x: LeadExtractionResponse(user_name=None, user_email=None, creator_platform="YouTube"))
            mock_llm.with_structured_output.side_effect = mock_structured_output

        elif idx == 3:

            def mock_structured_output(schema):
                if schema.__name__ == "IntentResponse":
                    return RunnableLambda(lambda x: IntentResponse(intent="HIGH_INTENT_LEAD", confidence=0.99))
                else:
                    return RunnableLambda(lambda x: LeadExtractionResponse(user_name="Alex", user_email=None, creator_platform=None))
            mock_llm.with_structured_output.side_effect = mock_structured_output

        elif idx == 4:

            def mock_structured_output(schema):
                if schema.__name__ == "IntentResponse":
                    return RunnableLambda(lambda x: IntentResponse(intent="HIGH_INTENT_LEAD", confidence=0.99))
                else:
                    return RunnableLambda(lambda x: LeadExtractionResponse(user_name=None, user_email="alex@email.com", creator_platform=None))
            mock_llm.with_structured_output.side_effect = mock_structured_output

    final_state = simulate_conversation(messages, setup_mocks_for_turn)

    assert final_state.get("user_name") == "Alex"
    assert final_state.get("user_email") == "alex@email.com"
    assert final_state.get("creator_platform") == "YouTube"
    assert final_state.get("lead_ready") is True

    mock_tool.assert_called_once_with("Alex", "alex@email.com", "YouTube")