from agents.planning_agent import planning_agent
from agents.reasoning_agent import reasoning_agent

from src.helper import convert_messages


def run_conversation_pipeline(messages):

    """
    Main orchestration pipeline.

    Flow:
    messages
        ↓
    planner
        ↓
    retrieval routing
        ↓
    reasoning
        ↓
    final response assembly
    """

    # ==========================================
    # Convert FastAPI Messages → LangChain Messages
    # ==========================================

    lc_messages = convert_messages(messages)

    # ==========================================
    # PLANNER LAYER
    # ==========================================

    planner_response = planning_agent(lc_messages)
    print("Planner Layer")
    planner_type = planner_response.get("type")

    # ==========================================
    # CASE 1 — PRE-RETRIEVAL CLARIFICATION
    # ==========================================

    if planner_type == "clarification":
        print("Pre Retrieval Clarification")
        return {
            "reply": planner_response["message"],
            "recommendations": [],
            "end_of_conversation": False
        }

    # ==========================================
    # CASE 2 — REFUSAL / INJECTION
    # ==========================================

    elif planner_type == "refusal":
        print("Refusal/Injection")
        return {
            "reply": planner_response["message"],
            "recommendations": [],
            "end_of_conversation": False
        }

    # ==========================================
    # CASE 3 — RETRIEVAL
    # ==========================================

    elif planner_type == "retrieval":
        print("Retrieval -> Moving to Reasoning Layer")
        retrieved_docs = planner_response["docs"]

        # ==========================================
        # REASONING LAYER
        # ==========================================

        reasoning_response = reasoning_agent(
            messages=lc_messages,
            docs=retrieved_docs
        )
        print("Reasoning Layer")
        reasoning_type = reasoning_response.get("type")

        # ==========================================
        # CASE 3A — POST-RETRIEVAL CLARIFICATION
        # ==========================================

        if reasoning_type == "clarification":
            print("Post Retrieval Clarification")
            return {
                "reply": reasoning_response["message"],
                "recommendations": [],
                "end_of_conversation": False
            }

        # ==========================================
        # CASE 3B — COMPARISON
        # ==========================================

        elif reasoning_type == "comparison":
            print("Comparison")
            return {
                "reply": reasoning_response["message"],
                "recommendations": [],
                "end_of_conversation": False
            }

        # ==========================================
        # CASE 3C — FINAL RECOMMENDATIONS
        # ==========================================

        elif reasoning_type == "recommendation":
            print("Final Recommendations" if reasoning_response["end_of_conversation"] else "Recommendations")
            return {
                "reply": reasoning_response["message"],
                "recommendations": reasoning_response["recommendations"],
                "end_of_conversation": reasoning_response["end_of_conversation"]
            }

        # ==========================================
        # FALLBACK
        # ==========================================

        else:
            print("Reasoning Layer Fallback")
            return {
                "reply": reasoning_response.get(
                    "message",
                    "Unable to process reasoning response."
                ),
                "recommendations": [],
                "end_of_conversation": False
            }

    # ==========================================
    # GLOBAL FALLBACK
    # ==========================================
    print("Planning Layer Fallback")
    return {
        "reply": planner_response.get(
            "message",
            "Unable to process request."
        ),
        "recommendations": [],
        "end_of_conversation": False
    }