from langgraph.graph import StateGraph, START, END
from bielik import llm
from guardian import check_input
from helpful_functions import get_last_user_message, check_situation, beliefs_check_function, introduction_talk, create_interview
from neo4j_driver import driver
from classifier import predict_raw, predict_raw1
from state import ChatState
from prompts import build_system_prompt_introduction_chapter_ellis_distortion

def detect_distortion(state: ChatState):
    if not state.get("messages"):
        print("Siema")
        state["messages"] = [{
            "role": "assistant", "content": "Cześć! Cieszę się, że jesteś. Co u ciebie, czy masz jakiś problem? Z checią ci pomogę!"
        }]
        state["awaitingUser"] = True
        state["stage"] = "detect_distortion"
        return state
    else:
        state["first_stage_iterations"] += 1
        print(state["first_stage_iterations"])
        print("Siema1")
        last_message = get_last_user_message(state)
        user_text = (last_message["content"] or "").strip()
        if state["distortion"] is None:
            result = predict_raw(user_text)
            if result != "No Distortion":
                thought = beliefs_check_function(user_text)
                if thought:
                    distortion = predict_raw1(user_text)
                    print(distortion)
                    state["distortion"] = distortion
                    state["distortion_text"] = user_text
                    print("Siema2")
        system_prompt = build_system_prompt_introduction_chapter_ellis_distortion(state["distortion"], state["situation"], state["think"], state["emotion"])
        result = introduction_talk(state["messages"], system_prompt)
        if state["situation"] == "":
            state["situation"] = result.situation
        else:
            if result.situation != "":
                state["situation"] = create_interview(result.situation, state["situation"])

        if state["emotion"] == "":
            state["emotion"] = result.emotion
        else:
            if result.emotion != "":
                state["emotion"] = create_interview(result.emotion, state["emotion"])

        if state["think"] == "":
            state["think"] = result.think
        else:
            if result.think != "":
                state["think"] = create_interview(result.think, state["think"])
        state["introduction_end_flag"] = result.chapter_end
        if state["distortion"] is not None and state["situation"] != "" and state["think"] != "" and state["emotion"] != "":
            print("Next")
            state["awaitingUser"] = False
            state["messages_detect"] = state["messages"]
            state["stage"] = "get_distortion_def"
            return state
        else:
            state["messages"].append({"role":"assistant", "content": result.model_output})
            state["awaitingUser"] = True
            state["stage"] = "detect_distortion"
            return state

def get_distortion_def(state: ChatState):
    print("Siema4")
    distortion = state["distortion"]
    query = """
            MATCH (d:Distortion {name: $name})
            RETURN d.definicja AS definicja
            """
    records, summary, keys = driver.execute_query(
        query,
        parameters_={"name": distortion},
    )
    state["distortion_def"] = records[0]["definicja"] if records else None
    state["stage"] = "talk_about_distortion"
    state["awaitingUser"] = False
    return state

def talk_about_distortion(state: ChatState):
    distortion = state["distortion"]
    distortion_def = state["distortion_def"]
    print("Siema5")
    if not state.get("distortion_explained"):
        print("Siema6")
        system_prompt_talk = f"""
        Jesteś empatycznym asystentem CBT.
        Użytkownikowi wykryto zniekształcenie poznawcze:
        Nazwa: {distortion}
        Definicja: {distortion_def}
        Przedstaw mu, że wykryłeś u niego zniekształcenie i wyjaśnij je w prosty, życzliwy sposób i zapytaj, czy chce, abyś pomógł mu to wspólnie przepracować.
        Język: polski, maksymalnie 2–3 zdania.
        """
        llm_reply = llm.invoke([
            {
                "role": "system",
                "content": system_prompt_talk,
            },
        ])
        follow_text = (
            llm_reply if isinstance(llm_reply, str)
            else getattr(llm_reply, "content", str(llm_reply))
        )
        state["messages"].append({"role": "assistant", "content": follow_text})
        state["awaitingUser"] = True
        state["stage"] = "talk_about_distortion"
        state["distortion_explained"] = True
        return state
    else:
        print("Siema7")
        last_user_msg = get_last_user_message(state)
        if not last_user_msg:
            state["awaitingUser"] = True
            return state
        classify_result = check_situation(last_user_msg["content"])
        state["classify_result"] = classify_result
        if classify_result == "understand":
            print("Siema8")
            state["messages"].append({
                "role": "assistant",
                "content": "Super! To przejdźmy teraz do kolejnego kroku"
            })
            state["stage"] = "get_intention"
            state["awaitingUser"] = False
            return state
        # elif classify_result == "low_expression":
        #     system_prompt = f"""
        #     WEJSCIE
        #     Historia wiadomości - {state["messages"]}
        #
        #     Użytkownik jest mało wylewny i odpowiada krótko.
        #     Twoim zadaniem jest napisać 2–3 empatyczne zdania po polsku, które spokojnie i nienachalnie zachęcą go do kontynuowania rozmowy.
        #     Brzmi naturalnie, bez punktów, presji ani oceniania.
        #     Na końcu zapytaj czy możemy możemy przejść do działania
        #     Twoją rolą jest tylko i wyłącznie zachęcenie do działania nie pisz nic innego
        #     """
        #     llm_reply = llm.invoke([
        #         {
        #             "role": "system",
        #             "content": system_prompt,
        #         },
        #     ])
        #     follow_text = (
        #         llm_reply if isinstance(llm_reply, str)
        #         else getattr(llm_reply, "content", str(llm_reply))
        #     )
        #     state["messages"].append({"role": "assistant", "content": follow_text})
        #     state["awaitingUser"] = True
        #     state["stage"] = "talk_about_distortion"
        else:
            print("Siema9")
            system_prompt = f"""
            WEJSCIE
            Historia wiadomości - {state["messages"]}

            Użytkownik nie zrozumiał wyjaśnienia zniekształcenia.
            Nazwa: {distortion}
            Definicja: {distortion_def}

            Język tylko polski.
            Twoje zadanie:
            - Wyjaśnij prostszymi słowami (1–2 zdania).
            - Dodaj przykład z życia (1–2 zdania).
            - Zapytaj, czy teraz jest to jasne i czy możemy przejść do działania.
            Maksymalnie 3-4 zdania
            """
            llm_reply = llm.invoke([
                {
                    "role": "system",
                    "content": system_prompt,
                },
            ])
            follow_text = (
                llm_reply if isinstance(llm_reply, str)
                else getattr(llm_reply, "content", str(llm_reply))
            )

            state["messages"].append({"role": "assistant", "content": follow_text})
            state["awaitingUser"] = True
            state["stage"] = "talk_about_distortion"
            return state

def validate_input(state: ChatState):
    stage = state.get("stage")
    if stage == "detect_distortion":
        chapter = "ETAP 1"
    elif stage == "talk_about_distortion" or stage == "get_distortion_def":
        chapter = "ETAP 2"
    elif stage == "create_socratic_question" or stage == "get_intention" or stage == "select_intention" or stage == "analyze_output":
        chapter = "ETAP 3"
    elif stage == "enter_alt_thought" or stage == "enter_alt_thought" or stage == "handle_alt_thought_input" or stage == "handle_alt_thought_input":
        chapter = "ETAP 4"
    else:
        chapter = "None"

    last_user_msg = state.get("last_user_msg_content")
    result = check_input(state["messages"], chapter, last_user_msg)
    state["last_user_msg"] = False
    if result.decision:
        state["validated"] = True
        state["awaitingUser"] = False
    else:
        state["noValidated"] = f"{chapter} - {last_user_msg}"
        state["explanation"] = result.explanation
        state["messages"].append({"role": "assistant", "content": result.message_to_user})
        state["awaitingUser"] = True
    return state

def global_router(state: ChatState) -> str:
    if state.get("awaitingUser"):
        print("[ROUTER] awaitingUser=True → __end__")
        return "__end__"

    stage = state.get("stage")
    print(f"[ROUTER] stage={stage} (fallback)")
    if not state.get("validated") and state.get("last_user_msg"):
        return "validate_input"
    if stage == "end":
        return "__end__"
    if stage == "get_distortion_def":
        return "get_distortion_def"
    if stage == "talk_about_distortion":
        return "talk_about_distortion"
    print("[ROUTER] default → detect_distortion")
    return "detect_distortion"

graph_builder = StateGraph(ChatState)
graph_builder.add_node("detect_distortion", detect_distortion)
graph_builder.add_node("get_distortion_def", get_distortion_def)
graph_builder.add_node("talk_about_distortion", talk_about_distortion)
graph_builder.add_node("validate_input", validate_input)

graph_builder.add_conditional_edges(START, global_router, {
    "detect_distortion": "detect_distortion",
    "get_distortion_def": "get_distortion_def",
    "talk_about_distortion": "talk_about_distortion",
    "validate_input": "validate_input",
    "__end__": END,
})

edge_map = {
    "detect_distortion": "detect_distortion",
    "get_distortion_def": "get_distortion_def",
    "talk_about_distortion": "talk_about_distortion",
    "validate_input": "validate_input",
    "__end__": END,
}

for node in ["detect_distortion", "get_distortion_def","talk_about_distortion", "validate_input"]:
    graph_builder.add_conditional_edges(node, global_router, edge_map)

graph = graph_builder.compile()