"""
Planner Agent: Generates next plan step to answer the query.

This agent generates ONE step at a time based on:
- The original query
- Available data files
- Previously completed steps (if any)
"""

from langchain_core.messages import AIMessage

from ..utils.formatters import format_data_descriptions, format_plan, gemini_text
from ..utils.state import DSStarState, PlanStep


def planner_node(state: DSStarState) -> dict:
    """
    Planner Agent Node: Generates the next step in the plan.

    On first call: Generates initial step to start answering the query
    On subsequent calls: Generates next step based on progress so far

    Args:
        state: Current DSStarState

    Returns:
        Dictionary with updated state fields:
        - plan: Updated plan with new step appended
        - messages: Agent communication messages
        - next: Next node to visit ("coder")
    """
    print("=" * 60)
    print("PLANNER AGENT STARTING...")
    print("=" * 60)

    is_initial = len(state["plan"]) == 0
    data_context = format_data_descriptions(state["data_descriptions"])

    if is_initial:
        print("Generating INITIAL plan step...")
        prompt = f"""You are an expert data analyst.

Question to answer: {state["query"]}

Available Data Files:
{data_context}

Task: Generate list of simple, executable steps to start answering this question.
Examples of good steps:
- "Load the transactions.csv file"
- "Read and explore the sales data"

Provide ONLY the step description (one sentence) in one line in bullet points, no explanation."""
    else:
        print(f"Generating NEXT step (current plan has {len(state['plan'])} steps)...")
        plan_text = format_plan(state["plan"])

        prompt = f"""You are an expert data analyst.

Question to answer: {state["query"]}

Available Data Files:
{data_context}

Current Plan (completed steps):
{plan_text}

Last Execution Result:
{state["execution_result"][:500]}...

Task: Suggest the NEXT step to progress toward answering the question.
Make it simple and executable (one clear action).

Provide ONLY the next step description (one sentence), no explanation."""

    try:
        # Get LLM response
        response = state["llm"].invoke(prompt)

        # Handle different response formats
        if hasattr(response, "content") and isinstance(response.content, list):
            response_text = gemini_text(response)
        elif hasattr(response, "content"):
            response_text = response.content
        else:
            response_text = str(response)

        # Create new step
        new_step = PlanStep(
            step_number=len(state["plan"]), description=response_text.strip()
        )

        # Add new step to existing plan
        updated_plan = state["plan"] + [new_step]

        print(
            f"\n✓ Generated step {new_step['step_number'] + 1}: {new_step['description']}"
        )
        print("=" * 60)

        return {
            "plan": updated_plan,
            "messages": [
                AIMessage(content=f"Added step {new_step['step_number'] + 1}")
            ],
            "next": "coder",
        }

    except Exception as e:
        print(f"✗ Planner error: {str(e)}")
        return {
            "messages": [AIMessage(content=f"Planner error: {str(e)}")],
            "next": "__end__",
        }


# Standalone test function
def test_planner(llm, query: str, data_descriptions: dict, existing_plan: list = None):
    """
    Test the planner agent independently.

    Args:
        llm: LLM instance
        query: User query
        data_descriptions: Dict of filename -> description
        existing_plan: Optional existing plan steps

    Returns:
        Dictionary with planner results
    """
    # Create minimal test state
    test_state = {
        "llm": llm,
        "query": query,
        "data_descriptions": data_descriptions,
        "plan": existing_plan or [],
        "current_code": "",
        "execution_result": "",
        "is_sufficient": False,
        "router_decision": "",
        "iteration": 0,
        "max_iterations": 20,
        "messages": [],
        "next": "planner",
    }

    result = planner_node(test_state)

    print("\n" + "=" * 60)
    print("PLANNER TEST RESULTS")
    print("=" * 60)
    print(f"Updated Plan ({len(result.get('plan', []))} steps):")
    print(format_plan(result.get("plan", [])))

    return result